Category: Covid-19

An aerial view of floodwaters flowing from the Tittabawassee River into the lower part of downtown Midland, Mich., on May 20. Thousands of residents were ordered to evacuate after two dams collapsed, causing flooding. Gregory Shamus/Getty Images hide caption

An aerial view of floodwaters flowing from the Tittabawassee River into the lower part of downtown Midland, Mich., on May 20. Thousands of residents were ordered to evacuate after two dams collapsed, causing flooding.

Two crises collided this spring in Michigan. The state was already under a coronavirus lockdown when a catastrophic storm hit and a pair of dams failed, flooding the city of Midland.

The local hospital, MidMichigan Medical Center Midland, hired a disaster recovery company to clean up the mess, including a water-logged basement and morgue. More than 100 workers many of them recent immigrants were brought from as far away as Texas and Florida. Bellaliz Gonzalez was one of them.

"There were cracks in the safety protocols," Gonzalez, an asylum-seeker from Venezuela, said in Spanish through an interpreter. "We would start working without masks and then the supervisors would say, 'We're going to go look for masks,' when we were already working inside!"

"It smelled like something rotten, decomposed," she added. "Like something putrid, pungent. It was horrible."

Bellaliz Gonzalez recorded a video where she said many recovery workers had tested positive for the coronavirus and many were feeling sick as they were packed into vans and transported home from Midland, Mich. Bellaliz Gonzalez hide caption

Bellaliz Gonzalez recorded a video where she said many recovery workers had tested positive for the coronavirus and many were feeling sick as they were packed into vans and transported home from Midland, Mich.

Michigan had strict rules in place for essential workers during the pandemic, but Gonzalez and other workers interviewed by NPR said those rules weren't followed. The workers said they were put up in cramped hotel rooms and weren't given enough protective equipment.

Many of the disaster recovery workers who came to Midland did get sick. A cluster of roughly 20 confirmed cases of the coronavirus drew the attention of local health officials. It also shined a light on a multibillion-dollar industry that's growing fast as climate-driven disasters become more frequent and more expensive.

"These workers are essential, but no one behaves like it," said Saket Soni, the founder and director of a nonprofit group called Resilience Force, which advocates for recovery workers.

Like workers in other industries hit hard by the coronavirus, Soni said recovery workers risk getting and spreading the virus not just to each other and to their families but to the communities where they live and work.

"In a sense, they're like the farmworkers and meat packers ... with one difference," Soni said. "This is a workforce on the go ... that spends most of the year traveling from place to place, fixing up towns, cities, homes and buildings. And that's an additional vulnerability."

Soni said the pandemic has revealed longstanding problems in how the disaster recovery industry treats a workforce that cleans up and rebuilds after hurricanes, wildfires and flooding. Many of the workers are asylum-seekers or undocumented immigrants who don't speak much English and are afraid to complain about working conditions.

Gonzalez, who is 54, worked as an environmental engineer in her native Venezuela before fleeing to the U.S. to seek asylum two years ago. She said she got sick with a high fever in Michigan but tested negative for the coronavirus.

Gonzalez said she was appalled at the working conditions. The first day on the job, she said she asked if there were going to be temperature checks and was told there were no thermometers.

"We were treated worse than animals," Gonzalez said. "They didn't care about our well-being and our lives, they didn't care that we are in the middle of a pandemic."

After the outbreak in Michigan, the finger pointing began.

"We had some people from out of state come in to help, and we are grateful for help, but they brought COVID-19 with them," Gov. Gretchen Whitmer said in an interview with member station WDET in Detroit last month.

No one knows for sure whether the workers brought the coronavirus to Michigan, or caught it there. But we do know what happened next: The workers left town, taking the virus with them.

"We would have preferred if they would have quarantined here in Michigan, but they traveled home," Whitmer said.

Public health workers there say they weren't able to communicate directly with the recovery workers because no one on the contact-tracing team speaks Spanish.

"You really shouldn't cram four and five people into a hotel room that aren't necessarily family members, or put them in a situation where they're thousands of miles from home where they can be exposed to the virus," said Joel Strasz, a public health officer in Bay County, Mich., where the workers were staying.

"All of those conditions are going to really exacerbate the situation, spread the virus," he said.

The MidMichigan Medical Center thought the cleanup company it hired was taking steps to ensure worker safety, according to Julie Newton, an infection prevention nurse at the hospital.

"I was told that they checked for symptoms and temperatures every day," Newton said. "And if someone had symptoms or temperature, they were sent to be tested and were not allowed to work."

Newton said the workers she saw were wearing masks and gloves, and that she didn't talk to the workers directly because she doesn't speak Spanish, either.

"It didn't occur to me to go through and ask a bunch of questions" about whether workers were wearing masks, or how many people were staying in a hotel room, Newton said. "I just expected that they were making sure that those things were happening."

Servpro is a disaster recovery company with franchises around the country, including the one in Michigan hired by the hospital for the cleanup effort. Neither Servpro nor the local franchise responded to requests for comment.(Editor's note: Servpro is an NPR underwriter.)

Servpro's website says its workers always adhere to "cleaning and decontamination standards set by the Centers for Disease Control and Prevention and local authorities."

BTN Services, a Houston-based company that provides cleaning and staffing services, was a subcontractor on the hospital job. CEO Alejandro Fernandez told NPR that there was "a whole bunch of misinformation going around" but declined to elaborate and did not respond to interview requests.

The structure of the industry, with multiple layers of contractors and subcontractors, makes it easier for employers to avoid accountability, said Soni from Resilience Force.

"That's a huge problem," Soni said. "It means no one owns and pays for the standards to be enforced. No one is ultimately accountable."

Recovery worker Armando Negron stands outside a hospital in Michigan. Negron tested positive for the coronavirus while in Michigan and headed home to Florida, where he landed in the hospital for six days. Armando Negron hide caption

Recovery worker Armando Negron stands outside a hospital in Michigan. Negron tested positive for the coronavirus while in Michigan and headed home to Florida, where he landed in the hospital for six days.

The workers on the hospital job say they asked for the work site's COVID-19 preparedness plan, as required by the state under a series of executive orders signed by the governor. But the workers say they never saw one.

Once workers in Michigan began testing positive for the coronavirus, they say they were put on vans that drove them back to Florida and Texas. Several workers said they asked to be quarantined in Michigan but were told they'd have to pay for their own housing if they stayed.

Bellaliz Gonzalez recorded a video on her phone in Michigan just before the workers packed into vans to go home. "We are all sick, some have tested positive, others have not been tested but have symptoms," she said in the video.

One of the workers who got sick was Armando Negron. He said he worked in the hospital morgue without a mask before testing positive for the coronavirus. He headed home to Florida, where he landed in the hospital for six days. Negron, who was born in Puerto Rico, is 56 and has survived two heart attacks.

"I was coughing so hard for 10 to 15 minutes nonstop, I felt that my chest was going to explode," Negron said in Spanish through an interpreter.

"This virus feels like a fire that gets inside your body. You don't feel good sitting down, standing or lying down. It's debilitating and I feel very tired, I don't feel normal," he said.

Meanwhile, the demand for disaster cleanup continues in spite of the coronavirus. Negron said that half a dozen people he worked with in the morgue went directly from Midland to another job site in the Midwest. Two of them, he said, got sick and have been hospitalized.

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'We Were Treated Worse Than Animals': Disaster Recovery Workers Confront COVID-19 - NPR

Abstract

Although most SARS-CoV-2-infected individuals experience mild coronavirus disease 2019 (COVID-19), some patients suffer from severe COVID-19, which is accompanied by acute respiratory distress syndrome and systemic inflammation. To identify factors driving severe progression of COVID-19, we performed single-cell RNA-seq using peripheral blood mononuclear cells (PBMCs) obtained from healthy donors, patients with mild or severe COVID-19, and patients with severe influenza. Patients with COVID-19 exhibited hyper-inflammatory signatures across all types of cells among PBMCs, particularly up-regulation of the TNF/IL-1-driven inflammatory response as compared to severe influenza. In classical monocytes from patients with severe COVID-19, type I IFN response co-existed with the TNF/IL-1-driven inflammation, and this was not seen in patients with milder COVID-19. Interestingly, we documented type I IFN-driven inflammatory features in patients with severe influenza as well. Based on this, we propose that the type I IFN response plays a pivotal role in exacerbating inflammation in severe COVID-19.

Currently, severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2), which causes coronavirus disease 2019 (COVID-19), is spreading globally (1, 2), and the World Health Organization (WHO) has declared it a pandemic. As of June 2, 2020, more than 6.1 million confirmed cases and more than 376,000 deaths have been reported worldwide (3).

SARS-CoV-2 infection usually results in a mild disease course with spontaneous resolution in the majority of infected individuals (4). However, some patients, particularly elderly patients develop severe COVID-19 infection that requires intensive care with mechanical ventilation (4, 5). The mortality rate for COVID-19 in Wuhan, China, is estimated to be 1.4% (5). Although this rate is lower than that of severe acute respiratory syndrome (SARS) and Middle East respiratory syndrome (MERS), which are caused by other human pathogenic coronaviruses (6), it is much higher than that of influenza, a common respiratory viral disease requiring hospitalization and intensive care in severe cases.

In severe cases of COVID-19, a hyper-inflammatory response, also called a cytokine storm, has been observed and is suspected of causing the detrimental progression of COVID-19 (7). Circulating levels of pro-inflammatory cytokines, including TNF and IL-6, are increased in severe cases (8). Gene expression analyses have also shown that IL-1-related pro-inflammatory pathways are highly up-regulated in severe cases (9). In a murine model of SARS-CoV infection, a delayed, but considerable type I IFN (IFN-I) response promotes the accumulation of monocytes-macrophages and the production of pro-inflammatory cytokines, resulting in lethal pneumonia with vascular leakage and impaired virus-specific T-cell responses (10).

Immune dysfunction is also observed in patients with COVID-19. In severe cases, the absolute number of T cells is reduced (8, 11), and the T cells exhibit functional exhaustion with the expression of inhibitory receptors (12, 13). However, hyper-activation of T cells as reflected in the up-regulation of CD38, HLA-DR, and cytotoxic molecules was also reported in a lethal case of COVID-19 (14). Immune dysfunction in patients with severe COVID-19 has been attributed to pro-inflammatory cytokines (15).

In the present study, we performed single-cell RNA-seq (scRNA-seq) using peripheral blood mononuclear cells (PBMCs) to identify factors associated with the development of severe COVID-19 infection. By comparing COVID-19 and severe influenza, we report that the TNF/IL-1-driven inflammatory response was dominant in COVID-19 across all types of cells among PBMCs, whereas the up-regulation of various interferon-stimulated genes (ISGs) was prominent in severe influenza. When we compared the immune responses from patients with mild and severe COVID-19 infections, we found that classical monocytes from severe COVID-19 exhibit IFN-I-driven signatures in addition to TNF/IL-1-driven inflammation.

PBMCs were collected from healthy donors (n=4), hospitalized patients with severe influenza (n=5), and patients with COVID-19 of varying clinical severity, including severe, mild, and asymptomatic (n=8). PBMCs were obtained twice from three (the subject C3, C6, and C7) of the eight COVID-19 patients at different time points during hospitalization. PBMC specimens from COVID-19 patients were assigned to severe or mild COVID-19 groups according to the National Early Warning Score (NEWS; mild < 5, severe 5) evaluated on the day of whole blood sampling (16). In NEWS scoring, respiratory rate, oxygen saturation, oxygen supplement, body temperature, systolic blood pressure, heart rate, and consciousness were evaluated (16). Severe influenza was defined when hospitalization was required irrespective of NEWS score. Patients with severe influenza were enrolled from December 2015 to April 2016, prior to the emergence of COVID-19. The severe COVID-19 group was characterized by significantly lower lymphocyte count and higher serum level of C-reactive protein than the mild COVID-19 group on the day of blood sampling (Fig. S1A). Multiplex real-time PCR for N, RdRP, and E genes of SARS-CoV-2 was performed, and there was no statistical difference in Ct values for all three genes between two groups (Fig. S1B). Demographic information is provided with experimental batch of scRNA-seq in Table S1 and clinical data in Table S2 and S3.

Employing the 10X Genomics scRNA-seq platform, we analyzed a total of 59,572 cells in all patients after filtering the data with stringent high quality, yielding a mean of 6,900 UMIs per cell and detecting 1,900 genes per cell on average (Table S4). The transcriptome profiles of biological replicates (PBMC specimens in the same group) were highly reproducible (Fig. S1C), ensuring the high quality of the scRNA-seq data generated in this study.

To examine the host immune responses in a cell type-specific manner, we subjected 59,572 cells to t-distributed stochastic neighbor embedding (tSNE) based on highly variable genes using the Seurat package (17) and identified 22 different clusters unbiased by patients or experimental batches of scRNA-seq (Fig. 1A, Fig. S1D). These clusters were assigned to 13 different cell types based on well-known marker genes and two uncategorized clusters (Fig. 1B and C, and Table S5). In downstream analysis, we only focused on 11 different immune cell types, including IgG- B cell, IgG+ B cell, effector memory (EM)-like CD4+ T cell, non-EM-like CD4+ T cell, EM-like CD8+ T cell, non-EM-like CD8+ T cell, natural killer (NK) cell, classical monocyte, intermediate monocyte, non-classical monocyte, and dendritic cell (DC) after excluding platelets, red blood cells (RBCs), and two uncategorized clusters. The subject C8 (asymptomatic case) was also excluded due to a lack of replicates. In hierarchical clustering, most transcriptome profiles from the same cell type tended to cluster together, followed by disease groups, suggesting that both immune cell type and disease biology, rather than technical artifacts, are the main drivers of the variable immune transcriptome (Fig. S1E).

(A) tSNE projections of 59,572 PBMCs from healthy donors (HDs) (4 samples, 17,590 cells), severe influenza (FLU) patients (5 samples, 10,519 cells), COVID-19 patients (asymptomatic: 1 sample, 4,425 cells; mild COVID-19: 4 samples, 16,742 cells; severe COVID-19: 6 samples, 10,296 cells) colored by group information. (B) Normalized expression of known marker genes on a tSNE plot. (C) tSNE plot colored by annotated cell types. EM: effector memory, NK cell: natural killer cell, DC: dendritic cell, RBC: red blood cell. (D) Proportion of cell types in each group excluding Uncategorized 1, Uncategorized 2, RBC, and Platelet. The colors indicate cell type information. (E) Boxplots showing the fold enrichment in cell type proportions from mild COVID-19 (n=4), severe COVID-19 (n=6), and FLU (n=5) patients compared to the HD group (mild COVID-19 vs. HD: n=16, severe COVID-19 vs. HD: n=24, FLU vs. HD: n=20). For the boxplots, the box represents the interquartile range (IQR) and the whiskers correspond to the highest and lowest points within 1.5IQR. Uncategorized 1 (relatively high UMIs per cells and presence of multiple marker genes), Uncategorized 2 (B cell-like and high expression of ribosomal protein genes), RBC, and Platelet were excluded. Two-sided KolmogorovSmirnov (KS) tests were conducted for each cell type between the disease and HD group. *p<0.05, **p<0.01, and ***p<0.001.

As a feature of immunological changes, we investigated the relative proportions of immune cells among PBMCs in the disease groups compared to the healthy donor group (Fig. 1D and E, and Fig. S1F). Unlike the limited changes in mild COVID-19, significant changes were observed in both influenza and severe COVID-19 across multiple cell types among PBMCs. In severe COVID-19, the proportion of classical monocytes significantly increased whereas those of DCs, non-classical monocytes, intermediate monocytes, NK cells, EM-like CD8+ T cells, and EM-like CD4+ T cells significantly decreased (Fig. 1E). In severe influenza, the proportion of classical monocytes significantly increased whereas those of DCs, non-EM-like CD4+ T cells, EM-like CD4+ T cells, IgG+ B cells, and IgG- B cells significantly decreased. We validated the proportions of immune cell subsets from scRNA-seq by flow cytometry analysis. The relative proportions of total lymphocytes, B cells, CD4+ T cells, CD8+ T cells, NK cells, and total monocytes from scRNA-seq significantly correlated with those from flow cytometry analysis (Fig. S1G).

In order to compare the effect of infection between diseases, we performed hierarchical clustering based on relative gene expression changes against the healthy donor group. Unexpectedly, all types of cells among PBMCs were clustered together according to the disease groups instead of cell-types (Fig. 2A). Further investigation of the variable genes based on K-means clustering supported COVID-19-specific up- or down-regulated gene expression patterns across all types of cells among PBMCs (Fig. S2A). These results indicate that, in COVID-19, peripheral blood immune cells may be influenced by common inflammatory mediators regardless of cell type. Despite distinct transcriptional signatures between COVID-19 and influenza, severe COVID-19 and influenza shared transcriptional signatures in all types of monocytes and DCs (black boxed region in Fig. 2A), possibly reflecting common mechanisms underlying the innate immune responses in severe influenza and severe COVID-19.

(A) Hierarchical clustering using the Pearson correlation coefficient (PCC) of a normalized transcriptome between diseases in cell type resolution (n = 33). The color intensity of the heat map indicates the PCC values. The color bars above the heat map indicate the cell type and disease group. The black box indicates the cell types that highly correlate between the severe COVID-19 and FLU groups. (B) Illustration of the enrichment p-values for the select GO biological pathways (n = 49) of differentially expressed genes (DEGs) in COVID-19 and FLU patients (left 6 columns: DEGs for COVID-19 and FLU groups compared to HD, right 2 columns: DEGs between COVID-19 and FLU groups). (C) tSNE plot of representative gene expression patterns for GBP1 (FLU specific), CREM (COVID-19 specific), and CCL3 (COVID-19/FLU common). (D) Top, dendrogram from WGCNA analysis performed using relative normalized gene expression between the COVID-19 and FLU groups for the genes belonging to the select biological pathways in (B) (n=316). Bottom, heat map of relative normalized gene expression between the COVID-19 and FLU groups. The color bar (left) indicates cell type information clustered by hierarchical clustering based on the PCC for relative normalized gene expression. Modularized gene expression patterns by WGCNA are shown together (G1, n=10; G2, n=147; G3, n=27; G4, n=17; G5, n=12; G6, n=64; G7, n=34; G8, n=5).

Next, we sought to identify relevant biological functions in disease-specific up- or down-regulated genes in terms of the GO biological pathways. First, we combined both mild and severe COVID-19 as a COVID-19 group and identified disease-specific changes in genes for each cell type compared to the healthy donor group using model-based analysis of single cell transcriptomics (MAST) (18). NFKB1, NFKB2, IRF1, and CXCR3 were specifically up-regulated in COVID-19, and CXCL10, STAT1, TLR4, and genes for class II HLA and immunoproteasome subunits were specifically up-regulated in influenza (Table S6). TNF, TGFB1, IL1B, and IFNG were commonly up-regulated. When we directly compared COVID-19 and influenza, NFKB1, NFKB2, and TNF were up-regulated in COVID-19, whereas STAT1, TLR4, and genes for immunoproteasome subunits were up-regulated in influenza. For each group of differentially expressed genes (DEGs), we identified the top 10 enriched GO biological pathways and collected them to demonstrate p-value enrichment in each group of DEGs (Fig. 2B). Both distinct and common biological functions were identified as illustrated by inflammatory response genes being highly active in both COVID-19 and influenza, but genes for transcription factors, including inflammatory factors (i.e., NFKB1/2, and STAT4) were up-regulated in COVID-19. In contrast, a limited response in genes associated with the IFN-I and -II signaling pathways, T-cell receptor pathways, and adaptive immune response was observed in COVID-19 compared to influenza. Such disease-specific gene expression patterns were exemplified at single cell resolution by GBP1 (IFN--mediated signaling pathway) being specifically up-regulated in influenza, CREM (positive regulation of transcription) being specifically up-regulated in COVID-19, and CCL3 (inflammatory response) being commonly up-regulated (Fig. 2C and Table S7).

We expanded our analysis in a cell type specific manner by conducting weighted gene correlation network analysis (WGCNA) (19) for the collected genes associated with Fig. 2B. We identified several modular expression patterns (Fig. 2D and Table S8). In the COVID-19 group, NFKB1/2, JUN, and TNF were modularized in CD8+ T and NK cells (G6 and G7 in Fig. 2D), and IL1B, NFKBID, and OSM were modularized in all types of monocytes and DCs (G3 in Fig. 2D). In the influenza group, GBP1, TAP1, STAT1, IFITM3, OAS1, IRF3, and IFNG were modularized in all types of T cells and NK cells (G2 in Fig. 2D), and CXCL10 and TLR4 were modularized in all types of monocytes and DCs (G5 and part of G6 in Fig. 2D). Consistently, the DEGs between COVID-19 and influenza were dominant in CD8+ T cells and all types of monocytes (Fig. S2B).

To uncover disease-specific transcriptional signatures in CD8+ T cells, we performed sub-clustering analysis from EM-like and non-EM-like CD8+ T cell clusters using Seurat (17). Each disease group-specifically enriched sub-clusters compared to the two other groups were identified in the non-EM-like CD8+ T cell cluster (Fig. 3A). Of the six sub-clusters from the non-EM-like CD8+ T cell cluster, cluster 1 and cluster 3 were significantly enriched in the influenza and COVID-19 groups, respectively (Fig. 3B and C, and S3A). Clusters with the high expression of PPBP, a marker of platelets, were excluded in following analysis (e.g., cluster 6 in Fig. S3A). Intriguingly, up-regulated genes in cluster 1 and cluster 3 were associated with previously defined gene sets for influenza A virus infection and SARS-CoV infection, respectively (Fig. S3B) (20). We also found that the cluster 3-specific up-regulated genes reflect activation of immune response, including CD27, RGS1, CCL5, SELL, and RGS10 (Fig. S3C and Table S9). Protein interaction network analysis of selected top 30 up-regulated genes in each cluster based on STRING v11 (21) revealed the up-regulation of PRF1, GNLY, GZMB, and GZMH in cluster 1 and the up-regulation of GZMK, GZMA, CXCR3, and CCL5 in cluster 3 (Fig. 3D, green). STAT1, TAP1, PSMB9, and PSME2, which are up-regulated preferentially by IFN-, were overexpressed only in influenza-specific cluster 1 (Fig. 3D, blue). We validated these data by intracellular staining for granzyme B and PMA/ionomycin-stimulated intracellular cytokine staining for IFN-. The percentages of granzyme B+ and IFN-+ cells among CD8+ T cells were significantly higher in the influenza group than in the COVID-19 group (Fig. S3D). Of the seven representative GO biological pathways for the pro-inflammatory and IFN responses, pathways for responses to IFN-I and -II were more associated with influenza-specific cluster 1, whereas pathways for the response to TNF or IL-1 were more prominent in COVID-19-specific cluster 3 (Fig. 3E).

(A) tSNE plot of the non-EM-like CD8+ T cell subpopulations in all groups (left, n=6,253), COVID-19 (top right, n=2,653), FLU (middle right, n=1,452), and HD (bottom right, n=2,148) colored by cluster information. (B, C) Boxplots showing the proportion of individual sub-clusters from the non-EM-like CD8+ T cell cluster within each group (COVID-19, n=10; FLU, n=5; HD, n=4). The proportions follow normal distribution as tested by the Shapiro-Wilk normality test except the proportion of cluster 3 in the COVID-19 group (p=0.04). Cluster 1 and cluster 3 were highly enriched in the FLU and COVID-19 group, respectively. Two-sided Welchs t test p-values were 4.4E-03 between COVID-19 and FLU in cluster 1, 3.5E-02 between FLU and HD donor in cluster 1, 8.6E-03 between COVID-19 and FLU in cluster 3, and 5.8E-3 between COVID-19 and HD in cluster 3. *p<0.05, **p<0.01. (D) STRING analysis using the top 30 up-regulated genes in cluster 1 (left) and cluster 3 (right). (E) Bar plots showing enrichment p-values of eight representative GO biological pathways for pro-inflammation and interferon in cluster 1 or cluster 3-specific up-regulated genes (cluster 1, n=66; cluster 3, n=183).

We performed sub-clustering analysis from all three types of monocyte clusters to find COVID-19-specific sub-clusters. However, there was no COVID-19-specifically enriched sub-cluster (Fig. S4A and B). Next, we further focused on classical monocytes considering their crucial roles for inflammatory responses. We investigated DEGs between influenza and COVID-19 to seek COVID-19-specific transcriptional signatures in classical monocytes (Fig. 4A). Interestingly, TNF and IL1B, major genes in the inflammatory response, were identified as COVID-19-specific and commonly up-regulated genes, respectively. To better characterize the transcriptional signatures in classical monocytes, we performed K-means clustering of up-regulated genes in at least one disease group compared to the healthy donor group. We identified five different clusters of up-regulation (Fig. 4B and Table S10): genes in cluster 1 are commonly up-regulated in all disease groups, cluster 2 is influenza-specific, cluster 3 is associated with mild/severe COVID-19, cluster 4 is associated with influenza and severe COVID-19, and cluster 5 is severe COVID-19-specific.

(A) Venn diagram of differentially expressed genes (DEGs) in COVID-19 and FLU compared to HD. The representative genes are shown together. (B) K-means clustering of DEGs between all pairs of FLU, mild COVID-19, and severe COVID-19 (n=499). The color indicates the relative gene expression between the diseases and HD. The representative genes are shown together. (C) Bar plots showing the average log10(p-value) values in enrichment analysis using the perturbed genes of four different cell lines listed in L1000 LINCS for up-regulated genes in cluster 2 (C2, left) and cluster 3 (C3, right). Error bars indicate standard deviation. (D) Combined enrichment scores were compared between C2 and C3 for the gene sets of the type I IFN response (left; GSE26104) and TNF response (right; GSE2638, GSE2639). **p<0.01. Each dot indicates an individual subject. (E) Bar plots showing the average log10(p-value) values in the enrichment analysis using the perturbed genes listed of four different cell lines in L1000 LINCS for up-regulated genes in cluster 4 (C4, left) and cluster 5 (C5, right). Error bars indicate standard deviation (C and E).

We examined each cluster-specific genes by gene set enrichment analysis (GSEA) using cytokine-responsive gene sets originated from each cytokine-treated cells (LINCS L1000 ligand perturbation analysis in Enrichr) (22). COVID-19-specific cluster 3 genes were enriched by TNF/IL-1-responsive genes whereas influenza-specific cluster 2 genes were enriched by IFN-I-responsive genes in addition to TNF/IL-1-responsive genes (Fig. 4C), indicating that the IFN-I response is dominant in influenza compared to COVID-19. We confirmed this result by analyzing cluster-specific genes with cytokine-responsive gene sets originated from other sources (Fig. 4D). Unexpectedly, cluster 4 and 5 exhibited strong associations with IFN-I-responsive genes, in addition to TNF/IL-1-responsive genes (Fig. 4E), indicating that severe COVID-19 acquires IFN-I-responsive features in addition to TNF/IL-1-inflammatory features.

Next, we directly compared classical monocytes between mild and severe COVID-19. When we analyzed DEGs, severe COVID-19 was characterized by up-regulation of various ISGs, including ISG15, IFITM1/2/3, and ISG20 (Fig. 5A). Both TNF/IL-1-responsive genes and IFN-I-responsive genes were enriched in severe COVID-19-specific up-regulated genes (Fig. 5B). We measured plasma concentrations of TNF, IL-1, IL-6, IFN-, IFN-, and IL-18 in a larger cohort of COVID-19 patients. Among these cytokines, IL-6 and IL-18 were significantly increased in severe COVID-19 compared to mild COVID-19 whereas there was no difference in plasma concentrations of the other cytokines between the two groups (Fig. S5A). These results indicate that cytokine-responsive gene signatures cannot be simply explained by a few cytokines because of overlapped effects of cytokines.

(A) Volcano plot showing DEGs between mild and severe COVID-19 groups. Each dot indicates individual gene, colored by red when a gene is significant DEG. (B) Bar plot showing the average log10(p-value) values in enrichment analysis using the perturbed genes of four different cell lines listed in L1000 LINCS for up-regulated genes in the severe COVID-19 group. Error bars indicate standard deviation. (C) Trajectory analysis of classical monocytes from specimens obtained at two different time points in a single COVID-19 patient (mild: C7-2, 1,197 cells; severe: C7-1, 631 cells). The color indicates cluster information (left) or the severity of COVID-19 (right). (D) Relative expression patterns of representative genes in the trajectory analysis are plotted along the Pseudotime. The color indicates the relative gene expression calculated by Monocle 2. (E) Bar plots showing the average log10(p-value) values in the enrichment analysis using the perturbed genes of four different cell lines in L1000 LINCS for up-regulated genes in cluster 3 (left) and cluster 1 (right). Error bars indicate standard deviation. (F) Comparison of combined enrichment scores between cluster 3 and cluster 1 for the gene sets from systemic lupus erythematosus (SLE) (n=16) and rheumatoid arthritis (RA) (n=5). ***p<0.001; ns, not significant. (G) GSEA of up-regulated genes in cluster 3 (left) and cluster 1 (right) to the class 1 gene module of monocyte-derived macrophages by Park et al. (2017). NES: normalized enrichment score, FDR: false discovery rate.

To further investigate the characteristics of severe COVID-19, we performed a trajectory analysis with Monocle 2 (23) using two internally well-controlled specimens (one severe and one mild) in which both PBMC samples were collected from a single patient (the subject C7) with COVID-19. Trajectory analysis aligned classical monocytes along the disease severity with cluster 1 and cluster 3 corresponding to later and earlier Pseudotime, respectively (Fig. 5C). Representative genes in cluster 1 was enriched in the severe stage and highly associated with the both IFN-I and TNF/IL-1-associated inflammatory response (Fig. 5D, Fig. S5B, and Table S11). GSEA confirmed that both the IFN-I response and TNF/IL-1 inflammatory response were prominent in cluster 1, but not in cluster 3 (Fig. 5E). Cluster 1 exhibited a significantly higher association with a gene set from systemic lupus erythematosus, which is a representative inflammatory disease with IFN-I features, than cluster 3 (Fig. 5F, left), but was not significantly associated with a gene set from rheumatoid arthritis (Fig. 5F, right).

We obtained additional evidence of the IFN-I-potentiated TNF inflammatory response in severe COVID-19 by analyzing a gene module that is not responsive to IFN-I, but associated with TNF-induced tolerance to TLR stimulation. Park et al. previously demonstrated that TNF tolerizes TLR-induced gene expression in monocytes, though TNF itself is an inflammatory cytokine (24). They also showed that IFN-I induces a hyper-inflammatory response by abolishing the tolerance effects of TNF, and defined a gene module responsible for the IFN-I-potentiated TNF-NF-B inflammatory response as class 1 (24). This gene module was significantly enriched in cluster 1, but not in cluster 3 (Fig. 5G), which suggests that the IFN-I response may exacerbate hyper-inflammation by abolishing a negative feedback mechanism.

Finally, we validated IFN-I response and inflammatory features using bulk RNA-seq data obtained using post-mortem lung tissues from patients with lethal COVID-19 (25). Although the analysis was limited to only two patients without individual cell-type resolution, in genome browser, up-regulation of IFITM1, ISG15, and JAK3 and down-regulation of RPS18 were observed commonly in post-mortem COVID-19 lung tissues and classical monocytes of severe COVID-19 (Fig. 6A). In the analysis with cytokine-responsive gene sets, both the IFN-I response and TNF/IL-1-inflammatory response were prominent in the lung tissues (Fig. 6B). DEGs in the lung tissues were significantly associated with cluster 4, which is commonly up-regulated in both influenza and severe COVID-19, and cluster 5, which is specific to severe COVID-19 in Fig. 4B (Fig. 6C). These genes were also significantly associated with the cluster 1 identified in the trajectory analysis, but not with cluster 3 (Fig. 6D). When gene sets were defined by DEGs between mild and severe COVID-19, the DEGs in post-mortem lung tissues were significantly associated with genes up-regulated specifically in severe COVID-19 (Fig. 6E).

(A) UCSC Genome Browser snapshots of representative genes. (B) Bar plot showing the average log10(p-value) values from the enrichment analysis using the perturbed genes of four different cell lines in L1000 LINCS for up-regulated genes (n= 386) in post-mortem lung tissues compared to biopsied healthy lung tissue. Error bars indicate standard deviation. (C) GSEA of significantly up- and down-regulated genes in post-mortem lung tissues for gene sets originated from up-regulated genes in C2 (n=96), C3 (n=143), C4 (n=218), and C5 (n=30) of Fig. 4B. (D and E) GSEA of significantly up- and down-regulated genes in post-mortem lung tissues for gene sets originated from the top 200 up-regulated genes in cluster 3 (left) and cluster 1 (right) from the trajectory analysis in Fig. 5C (D), and from gene sets originated from the top 200 up-regulated genes in classical monocytes of mild (left) and severe (right) COVID-19 (E).

Severe COVID-19 has been shown to be caused by a hyper-inflammatory response (7). Particularly, inflammatory cytokines secreted by classical monocytes and macrophages are considered to play a crucial role in severe progression of COVID-19 (26). In the current study, we confirmed the results from previous studies by showing that the TNF/IL-1 inflammatory response is dominant in COVID-19 although a small number of patients were enrolled. However, we also found that severe COVID-19 is accompanied by the IFN-I response in addition to the TNF/IL-1 response. These results indicate that the IFN-I response might contribute to the hyper-inflammatory response by potentiating TNF/IL-1-driven inflammation in severe progression of COVID-19.

In the current study, we carried out scRNA-seq using PBMCs instead of specimens from the site of infection, e.g., lung tissues or bronchoalveolar lavage (BAL) fluids. However, hierarchical clustering based on relative changes to the healthy donor group showed that all types of cells among PBMCs were clustered together according to the disease groups as shown in Fig. 2A, indicating that there is disease-specific global impact across all types of cells among PBMCs. This finding suggests that peripheral blood immune cells are influenced by common inflammatory mediators regardless of cell type. However, we could not examine granulocytes in the current study because we used PBMCs, not whole blood samples for scRNA-seq.

In transcriptome studies for cytokine responses, we often analyze cytokine-responsive genes rather than cytokine genes themselves. However, we cannot exactly specify responsible cytokine(s) from the list of up-regulated genes because of overlapped effects of cytokines. For example, up-regulation of NF-B-regulated genes can be driven by TNF, IL-1 or other cytokines, and up-regulation of IFN-responsive genes can be driven by IFN-I or other interferons. In the current study, we designated the IFN-I response because many up-regulated IFN-responsive genes were typical ISGs.

Recently, Wilk et al. also performed scRNA-seq using PBMCs from COVID-19 patients and healthy controls (27). Similar to our study, they found IFN-I-driven inflammatory signatures in monocytes from COVID-19 patients. However, they did not find substantial expression of pro-inflammatory cytokine genes such as TNF, IL6, IL1B, CCL3, CCL4 and CXCL2 in peripheral monocytes from COVID-19 patients whereas we detected the up-regulation of TNF, IL1B, CCL3, CCL4 and CXCL2 in the current study. Moreover, they found a developing neutrophil population in COVID-19 patients that was not detected in our study. These discrepant results might be due to different platforms for scRNA-seq. Wilk et al. used the Seq-Well platform whereas we used the 10X Genomics platform that is more generally used. We also note that recent scRNA-seq analyses of COVID-19 sometimes lead to unrelated or contradictory conclusions to each other despite the same platform (28, 29). Although it often occurs in unsupervised analysis of highly multi-dimensional data, more caution will be required in designing scRNA-seq analysis of COVID-19, including definition of the severity and sampling time points.

Recently, Blanco-Melo et al. examined the transcriptional response to SARS-CoV-2 in in vitro infected cells, infected ferrets, and post-mortem lung samples from lethal COVID-19 patients and reported that IFN-I and -III responses are attenuated (25). However, we noted that IFN-I signaling pathway and innate immune response genes were relatively up-regulated in post-mortem lung samples from lethal COVID-19 patients compared to SARS-CoV-2-infected ferrets in their paper. Given that SARS-CoV-2 induces only mild disease without severe progression in ferrets (30), we interpret that IFN-I response is up-regulated in severe COVID-19 (e.g., post-mortem lung samples from lethal COVID-19 patients), but not in mild COVID-19 (e.g., SARS-CoV-2-infected ferrets). Indeed, severe COVID-19-specific signatures discovered in our current study were significantly enriched in the publically available data of post mortem lung tissues from the Blanco-Melo et al.s study although the analysis was limited to only two patients without individual cell-type resolution (Fig. 6). In a recent study, Zhou et al. also found a robust IFN-I response in addition to pro-inflammatory response in BAL fluid of COVID-19 patients (31). Moreover, up-regulation of IFN-I-responsive genes has been demonstrated in SARS-CoV-2-infected intestinal organoids (32).

Although IFN-I has direct antiviral activity, their immunopathological role was also reported previously (33). In particular, the detrimental role of the IFN-I response was elegantly demonstrated in a murine model of SARS (10). In SARS-CoV-infected BALB/c mice, the IFN-I response induced the accumulation of pathogenic inflammatory monocytes-macrophages and vascular leakage, leading to death. It was proposed that a delayed, but considerable IFN-I response is critical for the development of acute respiratory distress syndrome and increased lethality during pathogenic coronavirus infection (6, 34).

Currently, the management of patients with severe COVID-19 relies on intensive care and mechanical ventilation without a specific treatment because the pathogenic mechanisms of severe COVID-19 have not yet been clearly elucidated. In the current study, we demonstrated that severe COVID-19 is characterized by TNF/IL-1-inflammatory features combined with the IFN-I response. In a murine model of SARS-CoV infection, timing of the IFN-I response is a critical factor determining outcomes of infection (6, 10). Delayed IFN-I response contributes to pathological inflammation whereas early IFN-I response controls viral replication. Therefore, we propose that anti-inflammatory strategies targeting not only inflammatory cytokines, including TNF, IL-1, and IL-6, but also pathological IFN-I response needs to be investigated for the treatment of patients with severe COVID-19.

Patients diagnosed with COVID-19 were enrolled from Asan Medical Center, Severance Hospital, and Chungbuk National University Hospital. SARS-CoV-2 RNA was detected in patients nasopharyngeal swab and sputum specimens by multiplex real-time reverse-transcriptase PCR using the Allplex 2019-nCoV Assay kit (Seegene, Seoul, Republic of Korea). In this assay, N, RdRP, and E genes of SARS-CoV-2 were amplified, and Ct values were obtained for each gene. SARS-CoV-2-specific antibodies were examined using the SARS-CoV-2 Neutralization Antibody Detection kit (GenScript, Piscataway, NJ) and were positive in all COVID-19 patients in convalescent plasma samples or the last plasma sample in a lethal case. Hospitalized patients diagnosed with influenza A virus infection by a rapid antigen test of a nasopharyngeal swab were also enrolled from Asan Medical Center and Chungbuk National University Hospital from December 2015 to April 2016, prior to the emergence of COVID-19. Patients clinical features, laboratory findings, and chest radiographs were collected from their electronic medical records at each hospital. This study protocol was reviewed and approved by the institutional review boards of all participating institutions. Written informed consent was obtained from all patients.

Peripheral blood mononuclear cells (PBMCs) were isolated from peripheral venous blood via standard Ficoll-Paque (GE Healthcare, Uppsala, Sweden) density gradient centrifugation, frozen in freezing media, and stored in liquid nitrogen until use. All samples showed a high viability of about 90% on average after thawing. Single-cell RNA-seq libraries were generated using the Chromium Single Cell 3 Library & Gel Bead Kit v3 (10X genomics, Pleasanton, CA) following the manufacturers instructions. Briefly, thousands of cells were separated into nanoliter-scale droplets. In each droplet, cDNA was generated through reverse transcription. As a result, a cell barcoding sequence and Unique Molecular Identifier (UMI) were added to each cDNA molecule. Libraries were constructed and sequenced as a depth of approximately 50,000 reads per cell using the Nextseq 550 or Novaseq 6000 platform (Illumina, San Diego, CA).

The sequenced data were de-multiplexed using mkfastq (cellranger 10X genomics, v3.0.2) to generate fastq files. After de-multiplexing, the reads were aligned to the human reference genome (GRCh38; 10x cellranger reference GRCh38 v3.0.0), feature-barcode matrices generated using the cellranger count, and then aggregated by cellranger aggr using default parameters. The following analysis was performed using Seurat R package v3.1.5 (17). After generating the feature-barcode matrix, we discarded cells that expressed <200 genes and genes not expressed in any cells. To exclude low-quality cells from our data, we filtered out the cells that express mitochondrial genes in >15% of their total gene expression as described in previous studies (29, 35, 36). Doublets were also excluded, which were dominant in the cluster Uncategorized 1. Although there was a high variability in the number of UMIs detected per cell, majority of cells (90.5%) were enriched in a reasonable range of the UMIs (1,000 - 25,000), and 59% of cells with less than 1,000 UMIs were platelet or RBC excluded in downstream analysis. In each cell, the gene expression was normalized based on the total read count and log-transformed. To align the cells originating from different samples, 2000 highly variable genes from each sample were identified by the vst method in Seurat R package v3.1.5 (17). Using the canonical correlation analysis (CCA), we found anchors and aligned the samples based on the top 15 canonical correlation vectors. The aligned samples were scaled and principal component analysis (PCA) conducted. Finally, the cells were clustered by unsupervised clustering (0.5 resolution) and visualized by tSNE using the top 15 principal components.

To identify marker genes, up-regulated genes in each cluster relative to the other clusters were selected based on the Wilcoxon rank sum test in Seurats implementation with >0.25 log fold change compared to the other clusters and a Bonferroni-adjusted p < 0.05 (Table S4). By manual inspection, among the 22 different clusters, 20 were assigned to 11 known immune cell types, RBCs which are characterized by HBA1, HBA2, and HBB, and platelets. The clusters characterized by similar marker genes were manually combined as one cell type. The two remaining clusters were assigned to Uncategorized 1 and Uncategorized 2 because they had no distinct features of known cell types. Based on the distribution of UMI counts, the cluster Uncategorized 1 was featured by relatively high UMIs per cell compared to other clusters and presence of higher expression of multiple cell type marker genes. The cluster Uncategorized 2 was featured by a B cell-like signatures and high expression of ribosomal protein genes, not recommended to be further analyzed according to the 10X platform guideline. In these aspects, RBCs, platelets, Uncategorized 1, and Uncategorized 2 were excluded in downstream analysis.

To check the reproducibility of biological replicates (individuals within a same group), we calculated the Spearmans rank correlation coefficient for UMI counts that were merged according to each individual. The correlation coefficients of all individual pairs within the same group were visualized by a boxplot (COVID-19, n=45; FLU, n=10; HD, n=6).

In Fig. S1E, to investigate the similarity of the transcriptomes between cell types across diseases, we merged the UMI counts of each cell type according to healthy donor, influenza, mild COVID-19, and severe COVID-19. Next, the UMI counts for each gene were divided by the total UMI count in each cell type and multiplied by 100,000 as the normalized gene expression. Based on a median expression value >0.5, we calculated the relative changes in gene expression divided by the median value for each gene. Hierarchical clustering analysis was performed based on the PCC of the relative change in gene expression.

In Fig. 2A and Fig. S2A, to compare the highly variable gene expression among mild and severe COVID-19 and influenza relative to healthy donors, the normalized gene expression used in Fig. S1E was divided by the values in the healthy donor group. We selected the highly variable genes in terms of the top 25% standard deviation followed by log2-transformation (pseudo-count =1). In Fig. 2A, hierarchical clustering analysis was performed based on the PCCs of the selected highly variable genes. For Fig. S2A, to investigate the expression patterns of the selected highly variable genes (n=6,052), K-means clustering (k=50) was performed based on Euclidean distance. We manually ordered the clusters and visualized them as a heat map, revealing four distinct patterns: influenza-specific (n=1,046), COVID-19 specific (n=1,215), influenza/COVID-19 common (n=1,483), and cell type-specific (n=2,308).

To investigate the dynamic changes in cell type composition, we calculated the proportion of cell types in each individual. As a control, we calculated the relative variation in each cell type composition between all pairs of healthy donors. Similarly, for each disease group, we calculated the relative variation in each cell type by dividing the fraction of the cell type in individual patient by that of individual healthy donor. After log2-transformation, we conducted statistical analysis using the relative variation in composition between the control and disease groups using a two-sided KolmogorovSmirnov test.

For any two transcriptome profiles, to identify DEGs, we utilized the model-based analysis of single cell transcriptomics (MAST) algorithm in Seurats implementation based on a Bonferroni-adjusted p<0.05 and a log2 fold change > 0.25.

In Fig. 2B, the DEGs in COVID-19 and influenza compared to healthy donors or COVID-19 compared to influenza were identified at cell type resolution. All DEGs were combined according to the disease groups for further analysis. The overlapping up or down DEGs between COVID-19 and influenza compared to healthy donors were defined as Common up or Common down. The specific DEGs in COVID-19 or influenza were assigned as COVID-19 up/down or FLU up/down, respectively. In addition, COVID-19-specific up- or down-regulated genes compared to influenza were assigned as COVID-19>FLU or FLU>COVID-19, respectively. The Gene Ontology analysis was performed by DAVID. For each group of DEGs, the top 10 enriched GO biological pathways were selected, resulting in 49 unique GO biological pathways across all groups. The -log10(p-values) are shown as a heat map in Fig. 2B.

The weighted gene correlation network analysis (WGCNA) was conducted with the genes listed in the top 10 GO biological pathways of COVID-19 up, FLU up, and Common up defined in Fig. 2B. The normalized gene expression values of the genes in COVID-19 were divided by the values in influenza and log2-transformed (pseudo-count =1). We used default parameters with the exception of soft threshold =10 and networkType = signed when we constructed a topological overlap matrix. The modular gene expression patterns were defined using cutreeDynamic with a minClusterSize of 5. We visualized the modular gene expression pattern as a heat map in which the cell types were ordered according to hierarchical clustering with the default parameters of hcluster in R.

To find disease-specific subpopulations, each immune cell type was subjected to the subclustering analysis using Seurat. Briefly, the highly variable genes (n=1000) were selected based on vst and then scaled by ScaleData in Seurat with the vars.to.regress option to eliminate variation between individuals. The subpopulations were identified by FindClusters with default parameters, except resolution (non-EM-like CD8+ T cells, 0.3; classical monocytes, 0.2); the inputs were the top eight principal components (PCs) obtained from PCA of the scaled expression of the highly variable genes. The subpopulations were visualized by tSNE using the top eight PCs.

The trajectory analysis was performed with 2000 highly variable genes in classical monocytes across mild (C7-2) and severe (C7-1) COVID-19 as defined by the vst method in Seurat. The following analysis was performed using Monocle2. Briefly, the input was created from the UMI count matrix of the highly variable genes using the newCellDataSet function with default parameters, except expressionFamily = negbinomial.size. The size factors and dispersion of gene expression were estimated. The dimension of the normalized data was reduced based on DDRTree using reduceDimension with default parameters, except scaling = FALSE, which aligned the cells to the trajectory with three distinct clusters.

To determine genes that gradually changed along the trajectory, we identified the DEGs using MAST between clusters 1 and 3, which represent the severe stage and mild stage, respectively. The expression patterns of representative DEGs were visualized along the Pseudotime after correction with estimated size factors and dispersion for all genes.

In Fig. 4B, we performed K-means clustering of DEGs among all pairs of mild COVID-19, severe COVID-19, and influenza. The log2-transformed relative gene expression of DEGs compared to healthy donors was subjected to K-means clustering (k=10). Here, we used up-regulated DEGs in at least one disease group compared to the healthy donor group. We manually assigned five clusters based on gene expression patterns.

The transcriptome profiles of post-mortem lung tissues from two lethal cases of COVID-19 and biopsied heathy lung tissues from two donors were downloaded from a public database (GSE147507). The DEGs were identified using DESeq2 based on a Bonferroni-adjusted p < 0.05 and a log2 fold change > 1.

Enrichr, the web-based software for gene set enrichment analysis (GSEA) was used for LINCS L1000 ligand perturbation analysis (22), virus perturbation analysis, and disease perturbation analysis from the GEO database. Combined score was calculated as a parameter of enrichment as the log(p-value) multiplied by the z-score from the Fisher exact test. GSEA 4.0.3 software was used to conduct the GSEA when a ranked list of genes was available (Fig. 5G, Fig. 6C-E) (37). Results for IFN--responsive genes were not presented because those were considerably overlapped with IFN--responsive genes, which are typical ISGs. The normalized enrichment score and FDR-q value were calculated to present the degree and significance of enrichment.

Cryopreserved PBMCs were thawed, and dead cells were stained using the Live/Dead Fixable Cell Stain kit (Invitrogen, Carlsbad, CA). Cells were stained with fluorochrome-conjugated antibodies, including anti-CD3 (BV605; BD Biosciences), anti-CD4 (BV510; BD Biosciences), anti-CD8 (BV421; BD Biosciences), anti-CD14 (PE-Cy7; BD Biosciences), anti-CD19 (Alexa Fluor 700; BD Biosciences), and anti-CD56 (VioBright FITC; Miltenyi Biotec). For staining with anti-granzyme B (BD Biosciences), cells were permeabilized using a Foxp3 staining buffer kit (eBioscience).

For intracellular cytokine staining of IFN-, PBMCs were stimulated with phorbol 12-myristate 13-acetate (PMA, 50 ng/ml) (Sigma Aldrich) and ionomycin (1 g/ml) (Sigma Aldrich). Brefeldin A (GolgiPlug, BD Biosciences) and monesin (GolgiStop, BD Biosciences) were added 1 hour later. After another 5 hours of incubation, cells were harvested for staining with the Live/Dead Fixable Cell Stain kit, anti-CD3, anti-CD4, and anti-CD8. Following cell permeabilization, cells were further stained with anti-IFN- (Alexa Fluor 488; eBioscience).

Flow cytometry was performed on an LSR II instrument using FACSDiva software (BD Biosciences) and the data analyzed using FlowJo software (Treestar, San Carlos, CA).

Cytokines were measured in plasma samples, including IFN-, IL-18 (ELISA, R&D Systems, Minneapolis, MN), IL-1 (Cytometric bead array flex kit, BD Biosciences, San Jose, CA), TNF, IL-6, and IFN- (LEGENDplex bead-based immunoassay kit, BioLegend, San Diego, CA).

We performed the KS test to compare the distributions of two groups without assuming that the distributions follow normality. Welchs t test was conducted to compare the two distributions after confirming the normality of the distributions using the Shapiro-Wilk normality test. A Wilcoxon signed rank test was conducted to compare the differences between two groups with paired subjects. The Mann-Whitney test was performed to compare the means of two groups. Statistical analyses were performed using Prism software version 5.0 (GraphPad, La Jolla, CA). p<0.05 was considered significant.

immunology.sciencemag.org/cgi/content/full/5/49/eabd1554/DC1

Fig. S1. Clinical characteristics and assessment of the quality of scRNA-seq results.

Fig. S2. Transcriptome features of highly variable genes.

Fig. S3. Characterization of disease-specific CD8+ T-cell subpopulations.

Fig. S4. Subpopulation analysis of classical monocytes.

Fig. S5. STRING analysis of up-regulated genes in cluster 1 obtained from the trajectory analysis of classical monocytes.

Table S1. Experimental batches of scRNA-seq.

Table S2. Clinical characteristics of severe influenza patients.

Table S3. Clinical characteristics of COVID-19 patients.

Table S4. The scRNA-seq results.

Table S5. A list of marker genes for each cluster.

Table S6. A list of DEGs and associated biological pathways in Fig. 2B.

Table S7. Cell types in which the GBP1, CREM, and CCL3 were upregulated in Fig. 2C.

Table S8. A list of genes in each module obtained from WGCNA in Fig. 2D.

Table S9. A list of up-regulated genes in non-EM-like CD8+ T-cell subpopulations.

Table S10. A list of genes included in each cluster defined by K-mean clustering of classical monocytes.

Table S11. A list of genes up-regulated in early and late Pseudotime.

This is an open-access article distributed under the terms of the Creative Commons Attribution license, which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited.

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Immunophenotyping of COVID-19 and influenza highlights the role of type I interferons in development of severe COVID-19 - Science

More patients are in the hospital due to COVID-19 than ever.

This, along with a recent surge in positive cases, has hospital officials on high alert.

Officials at Blessing Hospital said there's currently eight patients in the hospital due to COVID-19.

They said they're ready if that number were to continue to increase.

As hospitalizations at Blessing Hospital rise, so do efforts to keep everything under control.

"We're ready," Blessing Health System CEO and President Maureen Kahn said. "We've got the equipment, we've got the staff who are trained and ready to take care of the patients as they come into the organization."

Kahn said while each case is different, the hospital is equipped to handle it.

"We're managing these patients in the medical surgical units in the hospital in our negative pressure rooms, which give them the added protection of containing and giving them special airflow in those rooms," Kahn said.

She said they have enough beds and PPE for severe cases.

"We have plenty of ventilators, should a patient need to be put on a ventilator," Kahn said. "But now, we have none of our ventilators in use on any of these patients."

Kahn said they also have enough medication for ways to treat symptoms.

"Remember, these medications are not cures, but they help minimize the symptoms these patients may experience," Kahn said.

Health department officials said they want residents to take this more serious in order to help limit cases and hospitalizations.

"Everybody in our community, despite how they feel about masking, despite what they think about gathering together in this environment, need to be conscientious about your individual behaviors right now for a lot of reasons," Adams County Public Health Administrator Jerrod Welch said.

Should they have the need, Kahn said they are prepared to admit more patients.

"Eight is a large number, but we have plenty of capacity," Kahn said. "We probably have like 30 available beds right now."

Kahn said two of the patients are not from Adams County, but from surrounding counties.

She said they are still allowing visitors here at the hospital and still have a number of guidelines in place for them.

Kahn said if you have any symptoms or think you may have been exposed, you should get tested.

To do so, you can call the COVID-19 Community Hotline.

The rest is here:

COVID-19 hospitalizations on the rise at Blessing Hospital - WGEM

Will I get COVID-19 doing this? Here's how risky normal activities are  KOMO News

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Will I get COVID-19 doing this? Here's how risky normal activities are - KOMO News

People who know Maria Van Kerkhove describe her as someone who has worked her whole life to be in this place, at this moment.

This place is at the core of the World Health Organizations coronavirus team, this moment is when the WHO is trying to steer the globes response to the Covid-19 pandemic. No one would expect such a job to be anything less than highly stressful, but lately, the ride has been a rocky one.

Van Kerkhove, who for months has joined Director-General Tedros Adhanom Ghebreyesus during regular press briefings, found herself in a firestorm last month after saying that people with Covid-19 who are asymptomatic very rarely transmit the infection.

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The agencys pandemic response team, which Van Kerkhove helps lead as the head of the WHOs emerging diseases unit, came under fire again this week when more than 200 scientists accused the WHO in an open letter of resisting evidence that virus-laced aerosols emitted by people infected with Covid-19 are fueling spread of the disease.

While the latter critique was aimed broadly at the WHO, Van Kerkhove was personally in the hot seat in the case of the earlier controversy. At the time, she was speaking about people who never develop symptoms. But asymptomatic is also a term sometimes used to describe people who are infected and who havent yet developed symptoms. Its been established those people can and do transmit the infection.

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Covid-19 Twitter erupted after Van Kerkhoves remarks. The following day, she and Mike Ryan, the head of the WHOs health emergencies program, turned to Facebook Live to clarify the comments.

She should have recognized thats how it would get interpreted, Ashish Jha, director of Harvards Global Health Institute, said a couple of days later. Even the next days walk-back I just dont think they clarified it well enough.

Van Kerkhove, who strives for precision in her Covid-19 messaging, is still bruised by the episode.

I was talking with my husband [recently] and I was saying Im struggling at the moment with the pushback and the second-guessing and the challenging, she told STAT in an interview. Im trying to get information out to help people. Im trying to get the information out to clarify WHOs position, which is to help people, which is to suppress transmission and to save lives.

Theres no other motivation. So, the idea that, you know, were doing things that could potentially be harming people and hurting people, its very difficult for me to rectify, Van Kerkhove said.

To that end, Van Kerkhove and others at the WHO have written hundreds of guidance documents on Covid-19 on breastfeeding while infected (encouraged when possible), on preparing hospitals for surges in patients, on controlling spread of the virus at border crossings, to name but a few.

Ryan described the incident as a storm in a teacup. When Van Kerkhove wanted to clarify her statement the following day, he joined her online. Through that whole thing she showed just immense courage and character, he said.

Ryan, of course, is hardly unbiased. Certain professionals, you almost feel like theyve spent a lifetime preparing for a role a role they didnt know they were going to have, he said in a recent interview. She feels like someone like that to me. Someone whos been subconsciously preparing for the big one.

Indeed, Van Kerkhoves awe of science germinated early on. Her twin sister, Alisa DeJoseph, with whom she grew up in upstate New York, recalls being the right brain, excelling in the arts, while Maria was the left brain, excelling in math and science.

Peter Goodfriend, who taught Van Kerkhove advanced placement biology her senior year of high school, said he once videotaped and brought into class a TV segment about The Hot Zone, Richard Prestons just-released bestseller on Ebola. He also bought and read the book, then lent it to Van Kerkhove one of those students who just stand out, as he put it.

It made an impression. Van Kerkhove, now 43, said she remembers trying to figure out the careers of the characters Preston wrote about. Some were virologists, she knew. But there was another set of professionals, doing a job she hadnt previously heard of: epidemiologists.

I thought the idea of understanding why certain people get sick, why others dont, what were those differences? That was quite fascinating. Almost detective-like, she said.

Pursuit of this newfound career path took Van Kerkhove to some of the best universities around. They were also places where degrees dont come cheap Cornell, Stanford, and the London School of Hygiene and Tropical Medicine, where she got a bachelor of biological sciences, a masters in epidemiology, and a Ph.D. in infectious disease epidemiology, respectively. I had a lot of student loans, she said.

During summer breaks at Cornell, she did field work on projects run by her professors traveling to Mexico, Venezuela, and Costa Rica. Sometimes the research involved studying the plants indigenous peoples used for medicinal purposes; one summer she was studying leaves and fruits capuchin monkeys rub on their fur. Decades later she recalls how dark the nights were, the constant chorus of frogs, the tang of freshly squeezed juice in the mornings.

After Cornell, Van Kerkhove was accepted to Stanford to do a masters degree in epidemiology, a one- or two-year program that she completed in one. She then pressed pause on her studies, moving to New York City to take a job as an epidemiologist for Exponent Health Services Practice, a consulting firm. Much of her time was spent on the issue of power line expansions and the fears of communities that electromagnetic fields emitted by them could cause cancers.

In what people who know her well would probably describe as classic Van Kerkhove behavior, she dug in, trying to learn everything she could about the subject. The experience taught her how to weigh evidence, she said, and the critical importance of risk communications one of the skills shes leaning heavily into in the Covid-19 pandemic.

What I tried to do was link the science to the concern and tried to explain, you know, what I could and alleviate some fears, she said.

With some of her student loans paid off, Van Kerkhove was ready to pursue a Ph.D. She wanted to study at an institution that focused on global health. Enter the London School of Hygiene and Tropical Medicine.

This was the mid-2000s, when bird flu the H5N1 virus was racing through Asia and beyond, decimating poultry flocks. It rarely infected people, but when it did, the outcome was more often than not fatal. About 60% of people known to have been infected with that virus died.

Van Kerkhove spent the better part of two years shuttling between London and Cambodia, where she worked with scientists at the Pasteur Institute in Phnom Penh, trying to chart the movement of poultry in a country where commercial-scale poultry production didnt exist.

The study Van Kerkhove and her Cambodian colleagues produced showed that infected poultry entering the country from China made its way through Vietnam to Cambodia through a series of middlemen. It became the subject of Van Kerkhoves Ph.D. thesis. That was a great piece of work, said Malik Peiris, a world-renowned virologist at Hong Kong University who was one of the thesis reviewers and was later a colleague on Van Kerkhoves work on MERS, a camel coronavirus.

A number of the Cambodian scientists Van Kerkhove collaborated with remain at the Pasteur Institute. Sowath Ly, who is now deputy head of the institute, said they marvel to see the scientist with whom they quizzed Cambodian villagers about bird flu sitting beside the director general of the WHO informing the world about Covid-19.

We are very proud of her, said Ly, who described Van Kerkhove as a good mentor.

Others are more reserved about the WHOs handling of the pandemic response. Jha, the Harvard expert, described the agencys communications efforts as good but not great. (Still, he credited the agency for communicating at all, noting that the Centers for Disease Control and Prevention barely briefs at all these days.)

Multiple people who have worked with Van Kerkhove talk about her laser focus and her prodigious capacity for work.

While doing postdoctoral work at Londons Imperial College under prominent mathematical modeler Neil Ferguson, she became a liaison between Fergusons group and the WHOs influenza team. Effectively, Ferguson lent Van Kerkhove to WHO; for a number of years she traveled weekly from London to Geneva to lend a badly needed hand.

She worked under Tony Mounts, a CDC infectious diseases epidemiologist who was at the time seconded to the WHO. Van Kerkhoves productivity intimidated some of his other staff, Mounts recalled, because she was so efficient that she tended to run circles around people at times.

When the 2009 influenza pandemic began, his unit tapped into that capacity, producing with her help important papers assessing the risk factors for severe H1N1 infection that is the flu strain that triggered the pandemic and estimating global mortality.

Its really stuff we couldnt have gotten done without her. We just didnt have the time or the people or the expertise on our team without her to do that, says Mounts, who is now on assignment to USAID. She just kind of buckles down and gets work done.

In 2015 she was hired by the Institut Pasteur in Paris to create a network of rapid outbreak response teams throughout the famed organizations 33 branches worldwide. Van Kerkhove speaks well of the experience, but friends say she didnt get the support she needed to make the goal a reality. Two years later the WHO was looking for someone to head its coronavirus work. It was a job she wanted, and back to Geneva she went.

Around Christmastime last year, Van Kerkhove was in North Carolina with her husband, Neil, and their two children. They were visiting family when she got a phone call that changed the tenor of the vacation. A mysterious virus spreading in China, she was told. A couple of days later, she was en route to Geneva again.

The work has been nonstop since.

Van Kerkhove was part of the WHOs nine-day mission to China in February to study the new disease and Chinas response to it. After her return to Geneva, some staff at WHO headquarters contracted Covid-19. Fearful shed bring the virus home to her family, Van Kerkhove decided to quarantine herself when she was at home.

For at least two months, she didnt touch her children: Cole, now 9 , and Miro, who is 18 months old.

She often left for work before they were up, arriving home after they were in bed. When she was home, she sequestered herself in a room a technique many frontline health workers have used in this pandemic. She would talk to her children through windows. It was awful. Awful! she shuddered.

Cole, who had initially been excited his mother was trying to help the world respond to a crisis, became convinced shed die from the new disease when she went to China. Miro thought his mother was playing a game of hide and seek, and would run after her whenever he saw her.

I would laugh in front of him and then come into the bedroom and cry because it was just a horrible, horrible thing, she said. Eventually the rate of new infections in Geneva started dropping, schools reopened, and there were no recent cases among WHO staff.

There was one day that I came home and I was on front lawn, and the baby just ran up to me and I just grabbed him. I just couldnt do it anymore, Van Kerkhove said.

She credits her husband for being incredibly supportive, but acknowledges 2020 has been a slog.

Its difficult for all of us. I havent been home a lot in six months, she said.

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A disease detective on the frontlines of WHO's Covid-19 response - STAT

UPCOMING FREE COMMUNITY TESTING: MARSHALL, MERCER, MONONGALIA, PRESTON, UPSHUR, AND WAYNE COUNTIES Additionally during his briefing, Gov. Justice offered a reminder that the next round offree community COVID-19 testingwill be provided later this week Friday, July 10 and Saturday, July 11 in Marshall, Mercer, Monongalia, Preston, Upshur, and Wayne counties.

The effort is part of a plan to provide free optional testing to all residents in several counties that are experiencing higher rates of COVID-19 transmission. It targets residents who have struggled to be seen by a physician or do not have insurance to pay for testing. However, other residents, including those who are asymptomatic are welcome to be tested.

Testing is scheduled as follows:

Marshall County Friday, July 10 9 a.m. 4 p.m. McMechen City Hall: 325 Logan Street, McMechen, WV 26040 Saturday, July 11 9 a.m. 4 p.m. Marshall County Health Department: 513 6th Street, Moundsville, WV 26041

Mercer County Saturday, July 11 9:30 a.m. 4 p.m. Mercer County Health Department: 978 Blue Prince Road, Bluefield, WV 24701

Monongalia County Friday, July 10 9 a.m. 4 p.m. Morgantown Farmers Market (Downtown): 400 Spruce Street, Morgantown, WV 26505 Saturday, July 11 9 a.m. 4 p.m. Mountainview Elementary School: 661 Green Bag Road, Morgantown, WV 26508

Preston County Friday, July 10 & Saturday, July 11 9 a.m. 4 p.m. Kingwood Elementary School: 207 South Price Street, Kingwood, WV 26537

Upshur County Friday, July 10 & Saturday, July 11 10 a.m. 7 p.m. Buckhannon-Upshur High School: 270 B-U Drive, Buckhannon, WV 26201 Friday, July 10 & Saturday, July 11 10 a.m. 4 p.m. 78 Queens Alley, Rock Cave, WV 26234

Wayne County Friday, July 10 10 a.m. 4 p.m. Dunlow Community Center: 1475 Left Fork Dunlow Bypass Road, Dunlow, WV 25511 Saturday, July 11 10 a.m. 4 p.m. Wayne Elementary School: 80 McGinnis Drive, Wayne, WV 25570

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COVID-19 UPDATE: Gov. Justice announces reopening date for West Virginia schools - West Virginia Department of Health and Human Resources

In early March, most doctors in the United States had never seen a person sick with COVID-19. Four months later, nearly every emergency room and intensive care physician in the country is intimately familiar with the disease. In that time, theyve learned a lot about how best to treat patients. But in some cases, theyre still taking the same approach they did in the spring.

Theres so much thats different, and so much thats the same, says Megan Ranney, an emergency physician and associate professor at the Brown University Department of Emergency Medicine.

For the first few months of the pandemic, recommendations for every incremental decision made in a hospital were changing faster than they ever have before. You almost couldnt keep up from one day to the next, your practice would change and your protocols would change. It was really disorienting for doctors and nurses, Ranney says.

Information spread between colleagues, through medical education blogs and podcasts, and on social media. Doctors talked about new research on Twitter and shared new strategies in Facebook groups and on WhatsApp. If a suggestion that floated by a doctor in a Facebook group was low-risk and seemed like it might be helpful, it could be put into practice immediately. If its a small change, they could start using it the next day, she says.

Thats how the now-common practice of asking patients with COVID-19 to flip onto their stomachs spread: through word-of-mouth and on social media. When someone is on their back, their organs squish their lungs and make it harder for their airways to fully expand. When someone is on their stomach, their lungs have more room to fill up with air. The advice started circulating through the medical community before there was a formal, published study on the practice.

Testing it out wouldnt have many downsides (it wasnt dangerous to patients), and it was easy to do. Theres this possibility that it could be positive, and there were a lot of stories about it having a positive effect, Ranney says. So, it spread in a much more organic and quick way, because it was something that we could do, but we werent worried it would hurt patients.

Doctors like Seth Trueger, an assistant professor of emergency medicine at Northwestern University, saw the position help patients get enough oxygen to avoid needing a ventilator. I started jokingly call it tummy time, he says. Studies are starting to validate those observations, finding that patients who spent time on their stomachs were, in fact, better off.

Since March, physicians have also figured out other ways to help severely ill patients avoid ventilation. We appreciate that its probably not a great thing for these patients, and weve developed other ways to get people high levels of oxygen, says James Hudspeth, the COVID response inpatient floor lead at Boston Medical Center. For example, doctors are turning to nasal cannulas, which are noninvasive prongs that blow oxygen into the nose, before a ventilator.

They have better medications for hospitalized patients now, too. Since March, doctors have cycled through a few different options like hydroxychloroquine, which turned out not to be effective. Now, theyre primarily using remdesivir, and antiviral drug that appears to help COVID-19 patients recover more quickly, and the steroid dexamethasone, which helps improve the survival rate for patients on ventilators. Many intensive care units and many hospitals have created their own standard order sets, or standard therapies, for people with COVID-19, Ranney says. Those shift as new evidence comes out around different medications.

Thats not unusual, Ranney says. Hospitals regularly change the drugs they use for conditions like flu and pneumonia as new data comes out. Whats unusual is to change practice so quickly, she says. Thats just the reality of a global pandemic, with a disease weve never seen before.

Most of the changes in doctors strategies over the past few months have been in patients who are severely ill. If someone is sick enough to be hospitalized with COVID-19 but doesnt need to be in intensive care, there still isnt much doctors can do for them. Theyll get fluids to make sure they stay hydrated and are given oxygen if they need it. Doctors will try to keep their fever down and monitor them to see if they get sicker, but thats about it.

Its just those basic things, Ranney says. Doctors now are more vigilant to the threat from blood clots, which have appeared in many COVID-19 patients over the past months. Because testing is more available in hospitals than it was earlier this year, theyll also confirm that a moderately ill patient actually does have COVID-19 and avoid giving them unnecessary treatments. But active interventions for patients with less severe symptoms are still around the same as they were back in March. Were still kind of in this watchful waiting, she says.

One lingering question, Hudspeth says, is figuring out how to keep those moderately ill patients from becoming severely ill. Steroids may be helpful earlier on, he says, as could artificial antibody treatments that block the virus, though those strategies are still under investigation. Part the challenge we face at the present moment is that the moderate patients are often where we would want to intervene, he says.

Changes to treatment strategies for patients who are not severely sick have been harder to come by in part because its riskier to try something new in that group. If someone isnt dangerously sick, there isnt as much to gain from using an experimental treatment that may have a chance of causing harm, so doctors are less likely to take risks. Were more likely to try stuff with sicker patients, Ranney says. And their families are more likely to consent to a clinical trial.

Despite the open issues around COVID-19 treatments, the rate of new information is slowing down. Doctors arent shifting their practices as quickly as they were back in March and April, and Trueger says he thinks the next few months may be relatively stable. Doctors might get new information about which medications are more or less helpful, but other common best practices might be more entrenched. I dont think things are going to change as rapidly as the changes we had up front, when we were really flying half blind, he says.

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Doctors are better at treating COVID-19 patients now than they were in March - The Verge

Now, as nurses are hailed as health care heroes during the pandemic, we're faced with what to do about these psychological injuries, not only for the 4 million nurses in the US the largest health care workforce in America but for the rest of us who depend on them.For the past five years, I've examined the types of psychological trauma that nurses experience. Along with Dr. John Thompson, my co-author, I've described them in our 2019 book, prophetic as it was published six months before Covid-19 first appeared in China.

Prior to the pandemic, nurses faced ethical and personal safety dilemmas during disasters and other emergencies. They saw patients suffer, not only from illness itself, but because of health care interventions, otherwise known as medically induced trauma (think of a patient on a ventilator).

Demands for resources largely ignored for decades

More recently, there has been a shortage of PPE (personal protective equipment) throughout US hospitals. But I know nurses who were told by employers to take care of Covid-19 patients regardless of whether or not adequate PPE was available. Clearly this was a danger to both nurses and patients; surely this qualifies as a traumatizing experience.

Other nurses some new, some working previously in non-acute care have been deployed to critical care units. Understanding the technology of these complex environments requires a steep learning curve. The knowledge, then, to competently care for these patients may be considered an insufficient resource.

The toll on patients and nurses

Nurses, more than anything, strive to deliver high-quality care and connect with patients during their most vulnerable times. But often there isn't the time. The inability to achieve that goal causes stress. Imagine being forced to choose between giving morning meds and sitting down with a patient newly diagnosed with cancer or spending time with the family of a patient with Covid-19. Choices like that leave nurses focused on tasks and morally injured.

Some traumas may be unavoidable. That happens when the nurse fully engages with the patient and co-experiences suffering. This is called secondary or vicarious trauma. That's why we need to offer trauma-informed care to both nurse and patient. Meaningful connections with others is critical, but so is psychological safety.

Compassion helps to heal

While I haven't been at the bedside for a number of years, I still remember how it felt to report to the acute care center at the hospital, hoping no one had called in sick. When that happened, I was assigned an extra patient or two. I knew I couldn't give the care I wanted to the paraplegic young man. I knew I couldn't spend more time communicating with the older adult who had a sudden right-sided stroke. I couldn't give them the things so important to healing -- the physical care, the nursing presence and the compassion they needed and deserved.

Those experiences stay with you. It's a heck of a feeling.

Nursing care is both an art and a science; it is a distinct profession that wields enormous influence on those who need the most help. They do not merely follow instructions from other providers. It's a beautiful profession, unlike any other, founded on intellect, judgment and a caring spirit. It pushes a person to examine values such as social justice and the ethics of life, and it becomes a part of who a nurse is.

Until all of us see nursing this way and until organizations provide sufficient resources to prevent avoidable trauma, which will allow nurses to provide safe, quality care nurses will continue to suffer. More will choose to leave the profession. Particularly now, that's a loss society can't afford.

Karen J. Foli is an associate professor at the School of Nursing, Purdue University. Disclosure: Foli received funding for her research from the National Council of State Boards of Nursing: Center for Regulatory Excellence. She receives royalties from her book, "The Influence of Psychological Trauma in Nursing."

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Nurses are struggling with trauma. But they were suffering long before Covid-19 hit - CNN

The news: A group of 239 scientists from 32 countries have written an open letter to the World Health Organization arguing that covid-19 can be transmitted through the air. You might think we know that already, but most current guidance is based on the idea that covid-19 is transmitted via droplets expelled from an infected persons nose or mouth. The thought is that these larger respiratory droplets quickly fall to the floor. That's the position the WHO has taken from early on in the pandemic, and thats why we have been keeping at a distance from one other. However, the signatories of the open letter say the organization is underestimating the role of airborne transmission, where much smaller droplets (called aerosols) stay suspended in the air. These aerosols can travel farther than droplets and linger in an area even when an infected person has left.

Whats the evidence? The letter says multiple studies have demonstrated beyond any reasonable doubt that viruses are released during exhalation, talking, and coughing in microdroplets small enough to remain aloft in air. It says these microdroplets pose a risk of exposure at distances beyond 1 to 2 m from an infected individual. An early laboratory study carried out by the US National Institutes of Healthfound that the coronavirus can linger in the air for up to four hours in aerosol form. The coronavirus was also detected in aerosols collected at two hospitals in Wuhan, China, according to a study published in Nature in April. And superspreading events add to the weight of evidence: for example, after a choir practice in the US nearly 50 people were infected even though they kept a safe distance apart.

The implications: If airborne transmission is a route for the spread of the virus, it could lead to changes in the current advice. It would suggest that social distancing may be insufficient, especially indoors. This may place yet more importance on mask-wearing around people who are not part of your household if you meet them indoors, even if you are distancing, and increasing ventilation in enclosed areas. It could make air-filtering systems more important to try to cut down on the recirculation of air. And it might mean health-care workers caring for coronavirus patients need the highest grade of maskN95to filter out the smallest droplets.

See the rest here:

A group of 239 scientists says theres growing evidence covid-19 is airborne - MIT Technology Review

The US is struggling with a resurgence of the coronavirus in the South and West. But the severity of Arizonas Covid-19 outbreak is in a league of its own.

Over the week of June 30, Arizona reported 55 new coronavirus cases per 100,000 people per day. Thats 34 percent more than the second-worst state, Florida. Its more than double Texas, another hard-hit state. Its more than triple the US average.

Arizona also maintained the highest rate of positive tests of any state at more than 25 percent over the week of June 30 meaning more than a quarter of people who were tested for the coronavirus ultimately had it. Thats more than five times the recommended maximum of 5 percent. Such a high positive rate indicates Arizona doesnt have enough testing to match its big Covid-19 outbreak.

To put it another way: As bad as Arizonas coronavirus outbreak seems right now, the state is very likely still undercounting a lot of cases since it doesnt have enough testing to pick up all the new infections.

The state also leads the country in coronavirus-related hospitalizations. According to the Centers for Disease Control and Prevention, more than one in five inpatient beds in Arizona are occupied by Covid-19 patients about 42 percent more than Texas and 65 percent more than Florida, the states with the next-highest share of Covid-19 patient-occupied beds. With hospitalizations rapidly climbing, Arizona became the first in the country to trigger crisis care standards to help doctors and nurses decide who gets treatment as the system deals with a surge of patients. Around 90 percent of the states intensive care unit beds are occupied, based on Arizona Department of Health Services data.

While reported deaths typically lag new coronavirus cases, the state has also seen its Covid-19 death toll increase over the past several weeks.

This is the result, experts say, of Arizonas missteps at three crucial points in the pandemic. The state reacted too slowly to the coronavirus pandemic in March. As cases began to level off nationwide, officials moved too quickly to reopen in early and mid-May. As cases rose in the state in late May and then June, its leaders once again moved too slowly.

What youre seeing is not only a premature opening, but one done so rapidly there was no way to ensure the health care and public health systems didnt get stressed in this process, Saskia Popescu, an infectious disease epidemiologist based in Arizona, told me.

At the same time, recommended precautions against the coronavirus werent always taken seriously by the general public with experts saying that, anecdotally, mask use in the state can be spotty. That could be partly a result of Republican Gov. Doug Ducey downplaying the threat of the virus: While he eventually told people to wear masks in mid-June, as of late May he claimed that its safe out there, adding, I want to encourage people to get out and about, to take a loved one to dinner, to go retail shopping.

Duceys actions and comments gave the impression we were past Covid-19 and it was no longer an issue, Popescu said, which I believe encouraged people to become lax in their masking [and] social distancing.

After weeks of increases in coronavirus cases and hospitalizations, Ducey pulled back Arizonas reopening on June 29, closing downs bars, theaters, and gyms.

Experts say the move is a positive step forward, but also one that came too late: With coronavirus symptoms taking up to two weeks to develop, there are already infections out there that arent yet showing up in the data. The state can expect cases, hospitalizations, and, probably, deaths to continue to climb over the next few weeks.

Ducey acknowledged the sad reality: It will take several weeks for the mitigations that we have put in place and are putting in place to take effect, he said. But they will take effect.

Duceys office argued it took the action as was necessary at the time, based on the data it collected and its experts recommendations. Our steps are in line with our facts on the ground that weve been tracking closely, Patrick Ptak, a spokesperson for the governors office, told me.

Arizona now offers a warning to the rest of the world. The states caseload was for months far below the totals in New York, Michigan, and Louisiana, among the states that suffered the brunt of the virus in the US in the early months. But by letting its guard down, Arizona became a global hot spot for Covid-19 a testament to the need for continued vigilance against the coronavirus until a vaccine or similarly effective treatment is developed.

Arizona was initially slow to close down. While neighboring California instituted a stay-at-home order on March 19, Ducey didnt issue a similar order for Arizona until March 31 12 days later.

That might not seem like too much time, but experts say it really is: When the number of Covid-19 cases statewide can double within just 24 to 72 hours, days and weeks matter.

Arizona was also quick to reopen its economy. After states started to close down, experts and the White House recommended that states see a decline in coronavirus cases for two weeks before they reopen. Arizona never saw such a decline. In fact, it arguably never even saw a real plateau. The number of daily new cases rose slowly and steadily through April and into May, and then the exponential spike took off.

So its not quite right to say that Arizona is experiencing a second wave of the coronavirus. It arguably never controlled the first wave, and the current rise of cases is a result of continued inaction as the initial wave of the virus continued spreading across the state. (The Navajo Nation, which is partly in Arizona, was an initial coronavirus hot spot. But its case count has declined since May, in part because it took strong measures against the virus.)

Arizona and other states experiencing a surge in Covid-19 now never got to flat, Pia MacDonald, an epidemiologist at the research institute RTI International, told me. That means the states didnt get to very good compliance with the public health interventions that we all need to take to make sure the outbreak doesnt continue to grow.

Despite no sustained decline in Covid-19 cases, Arizona moved forward with reopening anyway. Ptak, the governors spokesperson, acknowledged that the state didnt meet the two-week decline in cases, but he said the state had met another federal gating criteria for reopening by seeing a decline in the test positivity rate week after week throughout May.

Once the state started to reopen, it moved quickly. Within weeks, Arizona not only let hospitals do elective surgeries but started to allow dining-in at restaurants and bars, and gyms and salons, among other high-risk indoor spaces, to reopen. The short time frame prevented the state from seeing the full impact of each step of its reopening, even as it moved forward with additional steps.

Will Humble, executive director of the Arizona Public Health Association, argued it was this rate of reopening that really caused problems for the state. It was a free-for-all by May 15, Humble told me. Referencing federal guidelines for reopening in phases, he added, Arizona effectively went from phase 0 to phase 3.

Its not just that Ducey aggressively reopened the state, but that he also prevented local governments from imposing their own stricter measures. That included requirements for masks, which Ducey didnt allow municipalities to impose until mid-June weeks after Covid-19 cases started to rapidly rise. (Ptak claimed the governor acted once he received requests from mayors along the southern border to do so.)

Some of that is likely political. As recommendations and requirements for masks have expanded, some conservatives have suggested wearing a mask is emblematic of an overreaction to the coronavirus pandemic that has eroded civil liberties. President Donald Trump has by and large refused to wear a mask in public, even saying that people wear masks to spite him and suggesting, contrary to the evidence, that masks do more harm than good. While some Republicans are breaking from Trump on this issue, his comments and actions have helped politicize mask-wearing and other measures.

For example, there was an anti-mask rally in Scottsdale, Arizona, on June 24. There, a local council member, Republican Guy Phillips, shouted George Floyds dying words I cant breathe! before ripping his own mask off, according to the Washington Post. (Phillips later apologized to anyone who became offended.)

Evidence supports the use of masks: Several recent studies found masks reduce transmission. Some experts hypothesize and early research suggests that masks played a significant role in containing outbreaks in several Asian countries where their use is widespread, like South Korea and Japan.

But for a Republican governor like Ducey, the politicization of the issue means a large chunk of his political base is resistant to the kind of measures needed to get the coronavirus under control. And those same constituents are likelier to reject taking precautions against the coronavirus, even if theyre recommended by government officials or experts.

Ducey himself seemed to play into the politics: One day before Trump visited a plant in the state, and as the president urged states to reopen, Ducey announced an acceleration of the states reopening plans.

Other factors, beyond policy, likely played a role as well in the rise in cases. While summer in other parts of the country lets people go outside more often where the coronavirus is less likely to spread triple-digit temperatures in Arizona can actually push people inside, where poor ventilation and close contact is more likely to lead to transmission.

Some officials have argued Black Lives Matter protests played a role in the new outbreak. But the research and data so far suggest the demonstrations didnt lead to a significant increase in Covid-19 cases, thanks to protests mostly taking place outside and participants embracing steps, such as wearing masks, that mitigate the risk of transmission. In Arizona, the surge in coronavirus cases also began before the protests took off in the state.

Arizona saw its coronavirus cases start to increase by Memorial Day on May 25. The increase came hard with the test positivity rate rising too, indicating early on that the increase was not merely the result of more testing in Arizona. Hospitalizations and deaths soon followed.

Yet Ducey didnt begin to scale back the states reopening until more than a month later on June 29. This left weeks for the coronavirus to spread throughout the community.

The sad reality is Arizona will suffer the consequences of the governors slow action for weeks. Because people can spread the virus without showing symptoms, can take up to weeks to show symptoms or get seriously ill, and theres a delay in when new cases and deaths are reported, Arizona is bound to see weeks of new infections and deaths even after Duceys renewed restrictions.

Even if I put in 100 percent face mask use and everybody complied with it in Arizona right now, there would still be weeks of pain, Cyrus Shahpar, a director at the global health advocacy group Resolve to Save Lives, told me. There are people out there spreading disease, and it takes time [to pick them up as cases], from exposure to symptom onset to testing to getting the testing results.

Experts argue the state still needs to go even further. Humble advocated for more hospital staffing, a statewide mask requirement, more rigorous rules and better enforcement of the rules for reopening businesses, and improved testing capacity and contact tracing. He also pointed to the lack of timely testing in prisons as one area that hasnt gotten enough attention and could lead to a blind spot for future Covid-19 outbreaks.

One potentially mitigating factor is the states infected have trended younger than they did in initial bouts of the USs coronavirus outbreak, with people aged 20 to 44 making up roughly half of cases. That could keep the death toll down a bit though Covid-19 deaths in Arizona have already risen, and experts warn of the risks of long-term complications from the coronavirus, including severe lung scarring, among young people as well.

Above all, experts say that the rise in cases was preventable and predictable.

The research suggests the lockdowns worked. One study in Health Affairs concluded:

Adoption of government-imposed social distancing measures reduced the daily growth rate by 5.4 percentage points after 15 days, 6.8 after 610 days, 8.2 after 1115 days, and 9.1 after 1620 days. Holding the amount of voluntary social distancing constant, these results imply 10 times greater spread by April 27 without SIPOs (10 million cases) and more than 35 times greater spread without any of the four measures (35 million).

The flipside, then, is likely true: Easing lockdowns likely led to more virus transmission.

This is what researchers saw in previous disease outbreaks.

Several studies of the 1918 flu pandemic found that quicker and more aggressive steps to enforce social distancing saved lives in those areas. But this research also shows the consequences of pulling back restrictions too early: A 2007 study in JAMA found that when St. Louis widely praised for its response to the 1918 pandemic eased its school closures, bans on public gatherings, and other restrictions, it saw a rise in deaths.

Heres how that looks in chart form, with the dotted line representing excess flu deaths and the black and gray bars showing when social distancing measures were in place. The peak came after those measures were lifted, and the death rate fell only after they were reinstated.

This did not happen only in St. Louis. Analyzing data from 43 cities, the JAMA study found this pattern repeatedly across the country. Howard Markel, a co-author of the study and the director of the University of Michigans Center for the History of Medicine, described the results as a bunch of double-humped epi curves officials instituted social distancing measures, saw flu cases fall, then pulled back the measures and saw flu cases rise again.

Arizona is now seeing that in real time: Social distancing worked at first. But as the state relaxed social distancing, it saw cases quickly rise.

This is why experts consistently cautioned not just Arizona but other states against reopening too quickly. Its why they asked for some time two weeks of falling cases before states could start to reopen. Its why they asked for states to take the reopening process slowly, ensuring that each relaxation didnt lead to a surge in new Covid-19 cases.

Because Arizona and its leaders didnt heed such warnings, its now suffering a predictable, preventable crisis making it the state with the worst coronavirus epidemic in the country thats suffered the most widespread coronavirus outbreak in the world.

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Why Arizonas Covid-19 epidemic became the worst in the US - Vox.com