Category: Covid-19

Community Notes, a crowdsourced COVID-19 vaccine misinformation countermeasure on X (formerly Twitter), generally corrected false posts accurately and pointed readers to more credible sources, according to researchers who evaluated the posts.

The University of California at San Diego (UCSD)-led team assessed the accuracy and credibility of a random sample of 205 CommunityNotes on COVID-19 vaccines from the year after the tool's December 2022 launch. The reviewers included an infectious-disease doctor and a virologist. Theresults were published last week in JAMA.

For the open-sourced Community Notes, anonymous, ideologically diverse volunteers independently flag posts containing erroneous COVID-19 and vaccine information and suggest corrections, or "notes." Notes labeled as helpful by contributors who disagreed on previous notes are shown alongside the original posts. The process is public rather than company-controlled.

"Social media can magnify health misinformation, especially about vaccination," the study authors noted. "Platform countermeasures have included censoring, shadowbanning (limiting distribution without disclosure), and adding warning labels to problematic content. Yet, evaluating these countermeasures is challenging due to restrictive public disclosures about their inner workings."

A total of 1.4% of the 45,783 notes mentioned COVID-19 vaccines. Monthly note rates rose from 22 to 186 over the study period. Of the randomly sampled notes, there was strong agreement on note topics (90%), source credibility (87%), and accuracy (96%) before disagreements were resolved.

The most common note topic was adverse events (51%), followed by conspiracy theories (37%), vaccine recommendations (7%), and vaccine effectiveness (5%). Nearly all (97%) of the notes were accurate, 2% were partially accurate, and 0.5% were inaccurate.

Of all notes, 49% cited high-credibility sources (eg, peer-reviewed studies), while 44% were of moderate credibility (eg, news stories, fact-checking sites), and 7% were of low credibility (eg, blogs, tabloids). Views of the 189 posts with view data totaled 201 million (average, 1million).

"Since the World Health Organization declared an 'infodemic' of misinformation, there have been surprisingly few achievements to celebrate," senior author John Ayers, PhD, of UCSD, said in a universitynews release. "X's Community Notes have emerged as an innovative solution, pushing back with accurate and credible health information."

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X's crowdsourced tool to counter COVID untruths mainly accurate, credible, researchers say - University of Minnesota Twin Cities

Classification task

The target classes for this task were three classes with a risk of new COVID cases. Nine single classifiers, viz., Logistic Regression (GM), Decision Tree, SVM with linear kernel, k-nearest neighbors (KNN), eXtreme Gradient Boosting (XGBoost), SVM with Radial kernel (RBF), Random Forest, Nave Bayes, and Multilayered perceptron with three hidden layers and four neurons inside of each layer (Ml (c(4, 3, 3)), were used to compare the performance of the proposed ensembles.

Table 2 lists the most important features for the new COVID-19 case classification according to Boruta, Random Forest, and Decision Tree feature selectors (for each feature description, see Table 1). The listed features can help decision-makers select factors affecting COVID-19 spread and thus optimize medical care and/or restriction policy to minimize the epidemic impact, considering all aspects of human well-being.

The classification performance metrics for 9 weak classifiers and the proposed ensembles are summarized in Table 3. As one can see from the table, the best classification results were obtained in the case of the KNN model, with Accuracy=0.816, ROC-AUC=0.797, and F1-score=0.814. Using the developed ensembles allows us to increase all the metrics substantially. Thus, in the case of Ensemble 1, Accuracy was raised to 0.895, ROC-AUC to 0.897, and F1-score to 0.897. The proposed cut-off voting improvement in Ensemble 2 further increased all the metrics compared to Ensemble 1 by approx. 2% (Accuracy, ROC-AUC, and F1-score values are 0.912, 0.916, and 0.916 correspondingly). Hence, the developed hybrid hierarchical classifiers outperform single classification algorithms by more than 10% and are well-suited for COVID-19 spread prediction in real life.

Dynamic voting based on mathematical expectation is used. In addition to the trained models themselves, the cutoff function of the classifier is trained in this algorithm. The traditional stacking is based on averaging indicators, and there is a cut-off by class with a constant coefficient of 0.5; then, the efficiency of the algorithm drops sharply to~79%. The proposed cutting method increases the overall efficiency of the ensemble by several percent. The essence of the algorithm is to choose a cut-off coefficient. In the case of this work, the voting input contains a vector of independent classifier scores, which will vote differently depending on the context. The idea of the method is to calculate the average score for each vote and add it to the list of average scores. The list of average grades is a set of independent grades on which the mathematical expectation function is applied. We got a cut-off coefficient close to the optimal class separation coefficient at the output.

We used the nested fivefold cross-validation technique to perform additional tests, as described in28. Nested cross-validation was used to validate the findings obtained using the proposed approach in addition to the usual fivefold cross-validation. Though this approach has its limitations, e.g., the assumption of the data split independence, it is widely used across the ML community. The difference between the Accuracy values across the five folds was 0.018. Next, we performed a more robust statistical test, viz. KolmogorovSmirnov normality test. The obtained p-value was 0.793.

Table 4 shows the efficiency of proposed ensembles for the whole dataset and for selected features. Selecting features allows for increasing the total analyzed metrics.

For the regression task, the following regression models were used: linear model, polynomial regression, regression tree with CART algorithm, Gradient boosted tree, random forest, l1 regularization for the linear model, and l2 regularization for the linear model. These models aimed to predict the number of confirmed COVID-19 cases and deaths. Table 5 summarizes the most important features affecting the prediction of the COVID-19 spread.

As it follows from the comparison of Tables 2 and 5, virus pressure, i.e., a measure for virus transmission from neighboring counties, defined as the weighted average of the number of confirmed cases in the adjacent counties, is the most important feature for classification and regression analysis. Besides, there is a subset of common features, which were recognized as the most important in these two studies, viz., (i) the total population of the countythe second most important common feature, (ii) distance to the nearest international airport with average daily passenger load more than ten, (iii) daily average temperature, (iv) the longitude of the county barycenter, (v) number of total COVID-19 tests performed at each day in the state of the county, and (vi) population ratio in the state. As we can see, the COVID-19 spread is affected by various factors: epidemiological, like the virus pressure; demographic, like the total population and population density; social, like the distance to the nearest international airport; climate, like daily average temperature; geographical, like the longitude of the county barycenter, and medical like the number of total COVID-19 tests performed at each day. These findings can help epidemiologists to analyze the spread and lifecycle of the virus and decision makers to select the most important restriction factors and limitations to prevent the spread of the disease.

Other factors affecting the number of COVID-19 cases and deathsas seen in Table 4are mainly social features, like social distancing, percentage of health-insured residents, median household income, and percent change in mobility trends in retail shops and recreation centers. The analysis of Table 2 reveals that while speaking on the classification, there are some additional factors affecting the chance of getting infected with coronavirus, viz., percentage of residents in the age group 2529, immigrant student ratio, intensive care unit bed ratio, and the percent change in human encounters compared to pre-COVID-19 period.

Table 6 lists the regression task performance evaluation for the six most common regression models and the proposed ensemble.

The proposed hybrid hierarchical ensemble combining both supervised and unsupervised learning allows us to increase the accuracy of the regression task by 11% in terms of MSE, 29% in terms of the area under the ROC, and 43% in terms of the MPP metric. Indeed, the ROC-AUC value increased from 0.609 for the best traditional regression model (Gradient Boosted Tree) up to 0.790 in the case of the proposed Ensemble; MSE decreased from 112.6 down to 101.3, and MPP from 18.8 to 13.1 respectively. Thus, using the proposed approach, it is possible to predict the number of COVID-19 cases and deaths based on demographic, geographic, climatic, traffic, public health, social-distancing-policy adherence, and political characteristics with sufficiently high accuracy.

Besides, we used a nested fivefold cross-validation technique28 to perform a grid search hyperparameters optimization. The tuning parameter was set to a constant value of 1. RMSE was used to select the optimal model using the smallest value. The final values used for the model were =1 and =0.211 with the MAE metrics of 9.51, RMSE of 20.11 and R2 value of 0.76.

The developed way of cutting off the classifier or regressor, which is the part of the ensemble, increases the overall efficiency of the ensemble by several percent. A vector of models with different contextual characteristics can provide reasonable generalized estimates.

Table 7 shows the efficiency of proposed ensembles for the whole dataset and for selected features. Feature selection allows for increasing all the analyzed metrics.

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A novel hybrid supervised and unsupervised hierarchical ensemble for COVID-19 cases and mortality prediction ... - Nature.com

COVID-19 Vaccine administrated to cancer patient.

Image Credits: Unsplash

Administering intranasal Interferon-alpha (IFN-a) at a daily dose of 40,000 IU via nasal spray to cancer patients has demonstrated significant efficacy in reducing the incidence of COVID-19. A rigorous study, involving 433 participants aged 62 years on average, divided them evenly between IFN-a and placebo groups. Among them, nearly half were males, with approximately 47% having hematological malignancies and 52% with solid tumors. Findings from the study were presented as late-breaking research at the ESCMID Global Congress.

The overall incidence of COVID-19 was 11.3%, with the IFN-a group experiencing a notably lower incidence at 8.3% compared to the placebo group at 14.4%. This translated to a relative risk reduction of 0.60 (95% CI: 0.33-0.97). Moreover, no significant difference was observed in the incidence of other respiratory viruses between the IFN-a and placebo groups, (5.1% vs 5.1%).

In a detailed per-protocol analysis involving 389 participants, the IFN-a group continued to exhibit a markedly lower incidence of COVID-19 at 7.7% compared to 16% in the placebo group, with a relative risk of 0.50 (95% CI: 0.26-0.84). Similar trends were observed in the incidence of other respiratory viruses, with rates of 4.6% vs 5.7%, respectively.

Subgroup analysis further underscored the efficacy of IFN-a, particularly among participants under 65 years old, females, and those vaccinated against COVID-19. However, no significant differences were observed based on underlying malignancy or active cancer treatment. In addition, endpoints including WHO severity score and hospitalization rates did not vary significantly between the 2 groups.

Previous studies investigating the efficacy of IFN-a for treating hospitalized patients with COVID-19 concluded, IFN-a does not benefit the survival of hospitalized COVID-19 patients but may increase the number of patients discharged from the hospital.2

Serious adverse events, totaling 44 cases, were unrelated to the intervention, with no adverse impact noted from IFN-a administration, including 2 deaths attributed to underlying disease. Conducted from 2020 to 2023, this randomized, double-blinded, placebo-controlled study sheds light on the potential of intranasal IFN-a as a complementary preventive measure against COVID-19, alongside vaccination and monoclonal antibodies.

References

See the original post here:

The Impact of Intranasal Interferon-alpha Treatment in Preventing COVID-19 in Cancer Patients - Contagionlive.com

Open Grid Scheduler / Flickr cc

A University of California at Los Angeles (UCLA)study late last week in JAMA Network Open finds that, despite less access to healthcare, undocumented Latino workers who visited the emergency department (ED) received COVID-19 vaccines at the same rate as US citizens.

The researchers interviewed a sample of adult non-Latino patients, legal Latino residents or citizens, and undocumented Latino patients at two California healthcare centers from September 2021 to March 2022.

The median age of the 306 participants was 51 years, 48% were women, 68% were Latino, 14% were White, 11% were Black, and 7% were of other race. Of undocumented Latinos, 25% were uninsured, and 30% usually visited the ED for healthcare.

Among all participants, 87% said they had received one or more doses of COVID-19 vaccine, and 13% reported declining the vaccine. Concern about potential adverse effects of the vaccine was the most common reason (37%) for not getting vaccinated.

Undocumented Latino workers were much more likely to report a previous COVID-19 infection than non-Latinos and legal Latino residents.

Relative to undocumented Latinos, non-Latino patients were much less likely to believe that undocumented workers could receive the COVID-19 vaccine in the United States (odds ratio [OR], 0.09). Thirteen percent of interviewees said they knew undocumented people who didn't get vaccinated because they worried about deportation. Of those who had declined the vaccine, 22% said they were interested in receiving a dose in the ED.

Undocumented Latino workers were much more likely to report a previous COVID-19 infection than non-Latinos (OR, 3.42) and legal Latino residents (OR, 2.73).

"We would have expected Latinx patients to have lower rates of vaccination, considering higher rates of infection, hospitalizations, and death," lead author Jesus Torres, MD, MPH, said in a UCLAnews release. Torres noted that EDs are one of the main healthcare access points for undocumented workers, who make up about 3% of the US population but are not often included in research.

From a public health perspective, he added, it's important to identify disadvantaged groups for research, policy work, resource allocation, and targeted vaccine campaigns.

See the rest here:

Undocumented Latinos vaccinated against COVID at same rate as US citizens, study suggests - University of Minnesota Twin Cities

LOS ANGELES On a recent afternoon, Grant Oh zigzagged across the University of Southern California campus as if he was conquering an obstacle course, coming up against police blockade after police blockade on his way to his apartment while officers arrested demonstrators protesting the Israel-Hamas war.

In many ways, the chaotic moment was the culmination of a college life that started amid the coronavirus pandemic and has been marked by continual upheaval in what has become a constant battle for normalcy. Oh already missed his prom and his high school graduation as COVID-19 surged in 2020. He started college with online classes. Now the 20-year-old will add another missed milestone to his life: USC has canceled its main commencement ceremony that was expected to be attended by 65,000 people.

A graduating senior takes photos under the University of Southern California mascot on campus Thursday in Los Angeles. The school has canceled its main graduation ceremony as protests against the Israel-Hamas war continued to intensify.

His only graduation ceremony was in middle school, and there were no caps and gowns.

"It's crazy because I remember starting freshman year with the start of the Russian invasion of Ukraine, which came after senior year of high school when the Black Lives Matter protests were happening and COVID, and xenophobia," he said "It feels definitely surreal. It still shocks me that we live in a world that is so fired up and so willing to tear itself apart."

Oh, who is getting a degree in health promotion and disease prevention, added that his loss of a memorable moment pales in comparison to what is happening: "At the end of the day, people are dying."

College campuses have always been a hotbed for protests from the civil rights era to the Vietnam war to demonstrations over apartheid in South Africa. But students today also carry additional stresses from having lived through the isolation and fear from the pandemic, and the daily influence of social media that amplifies the world's wrongs like never before, experts say.

Pro-Israeli demonstrators gather near a pro-Palestinian encampment on the UCLA campus Thursday in Los Angeles.

It's not just about missed milestones. Study after study shows Generation Z suffers from much higher rates of anxiety and depression than Millennials, said Jean Twenge, a psychologist and professor at San Diego State University, who wrote a book called "Generations." She attributes much of that to the fact that negativity spreads faster and wider on social media than positive posts.

"Gen Z, they tend to be much more pessimistic than Millennials," she said. "The question going forward is do they take this pessimism and turn it into concrete action and change, or dothey turn it into annihilation and chaos?"

Protesters have pitched tents on campuses from Harvard and MIT to Stanford and the University of Texas, Austin, raising tensions as many schools prepare for spring commencements. Hundreds of students have been arrested across the country. Inspired by demonstrations at Columbia University, students at more than a dozen U.S. colleges have formed pro-Palestinian encampments and pledged to stay put until their demands are met.

The campus will be closed for the semester at California State Polytechnic University, Humboldt, which has been negotiating with students who have been barricaded inside a campus building since Monday, rebuffing an attempt by the police to clear them out.

USC announced Thursday that it would be calling off its main graduation ceremony after protests erupted over not only the Israel-Hamas war but the school's decision earlier this month to call off the commencement speech by its valedictorian Asna Tabassum, who expressed support for Palestinians. Officials cited security concerns.

"By trying to silence Asna, it made everything way worse," Oh said, adding that he hopes there will be no violence on graduation day May 10 when smaller ceremonies will be held by different departments.

A recent graduate wears a garment with their graduation year Thursday on the University of Southern California campus in Los Angeles.

Maurielle McGarvey graduated from high school in 2019 so was able to have a ceremony but then she took a gap year when many universities held classes only online. McGarvey, who is getting a degree in screenwriting with a minor in gender and social justice studies at USC, called the cancellations "heartbreaking," and said the situation has been grossly mishandled by the university. She said police with batons came at her yelling as she held a banner while she and fellow demonstrators said a Jewish prayer.

"It's definitely been like an overall diminished experience and to take away like the last sort of like typical thing that this class was allowed after having so many weird restrictions, so many customs and traditions changed," she said. "It's such a bummer."

She said the email by the university announcing the cancellation particularly stung with its link to photos of past graduates in gowns tossing up their caps and cheering. "That's just insult to injury," she said.

Students at other universities were equally glum.

Graduating seniors take photos around the Tommy Trojan statue on the University of Southern California campus on Thursday in Los Angeles. The university canceled its main graduation ceremony.

"Our grade is cursed," said Abbie Barkan of Atlanta, 21, who is graduating from the University of Texas in two weeks with a journalism degree and who was among a group of Jewish students waving flags and chanting at a counter-protest Thursday near a pro-Palestinian demonstration on campus.

University of Minnesota senior Sarah Dawley, who participated in pro-Palestinian protests, is grateful graduation plans have not changed at her school. But she said the past weeks have left her with a mix of emotions. She's been dismayed to watch colleges call in police.

But she said she also feels hope after having gone through the pandemic and become part of a community that stands up for what they believe in.

"I think a lot of people are going to go on to do cool things because after all this, we care a lot," she said.

A University of Southern California protester is detained by USC Department of Public Safety officers during a pro-Palestinian occupation at the campus' Alumni Park on Wednesday, April 24, 2024 in Los Angeles. (AP Photo/Richard Vogel)

Tents erected at the pro-Palestinian demonstration encampment at Columbia University in New York, on Wednesday, April 24, 2024. (AP Photo/Stefan Jeremiah)

University of Texas police officers arrest a man at a pro-Palestinian protest on campus, Wednesday April 24, 2024, in Austin, Texas. (Jay Janner/Austin American-Statesman via AP)

Demonstrators chant at a pro-Palestinian protest at the University of Texas, Wednesday, April 24, 2024, in Austin, Texas. (Mikala Compton/Austin American-Statesman via AP)

Texas state troopers in riot gear try to break up a pro-Palestinian protest at the University of Texas, Wednesday, April 24, 2024, in Austin, Texas. (Jay Janner/Austin American-Statesman via AP)

A row of Palestinian flags are seen on the fence at the pro-Palestinians demonstration encampment at Columbia University in New York on Wednesday April 24, 2024. (AP Photo/Stefan Jeremiah)

State troopers try to break up a pro-Palestinian protest at the University of Texas Wednesday April 24, 2024, in Austin, Texas. Student protests over the Israel-Hamas war have popped up on an increasing number of college campuses following last week's arrest of more than 100 demonstrators at Columbia University. (Jay Janner/Austin American-Statesman via AP)

A demonstrator is restrained by police at a pro-Palestinian protest at the University of Texas, Wednesday, April 24, 2024, in Austin, Texas. (Ricardo B. Brazziell/Austin American-Statesman via AP)

State troopers on horses push back protesters during a pro-Palestinian protest at the University of Texas, Wednesday April 24, 2024, in Austin, Texas. (Mikala Compton/Austin American-Statesman via AP)

A Georgia State Patrol officer detains a protester on the campus of Emory University during a pro-Palestinian demonstration Thursday, April 25, 2024, in Atlanta. (AP Photo/Mike Stewart)

Georgia State Patrol officers detain a protester on the campus of Emory University during an pro-Palestinian demonstration Thursday, April 25, 2024, in Atlanta. (AP Photo/Mike Stewart)

Authorities detain a protester on the campus of Emory University during a pro-Palestinian demonstration, Thursday, April 25, 2024, in Atlanta. (AP Photo/Mike Stewart)

Protesters are cuffed after being detained on the campus of Emory University during a pro-Palestinian demonstration Thursday, April 25, 2024, in Atlanta. (AP Photo/Mike Stewart)

A police officer detains a protester on the campus of Emory Univeristy during an pro-Palestinian demonstration, Thursday, April 25, 2024, in Atlanta. (AP Photo/Mike Stewart)

A makeshift camp supporting the Palestinians cause is staged on the UCLA campus, Thursday, April 25, 2024, in Los Angeles. (AP Photo/Jae C. Hong)

Student protestors erected approximately 20 tents on Parrish Beach by Clothier Hall at Swarthmore College in Swarthmore, Pa. on Tuesday, April 23, 2024. (Monica Herndon/The Philadelphia Inquirer via AP)

A University of Southern California protester is detained by USC Department of Public Safety officers during a pro-Palestinian occupation at the campus' Alumni Park on Wednesday, April 24, 2024 in Los Angeles. (AP Photo/Richard Vogel)

A University of Southern California protester is detained by USC Department of Public Safety officers during a pro-Palestinian occupation at the campus' Alumni Park on Wednesday, April 24, 2024, in Los Angeles. (AP Photo/Richard Vogel)

University of Southern California protesters fight with University Public Safety officers as they try to remove tents at the campus' Alumni Park during a pro-Palestinian occupation on Wednesday, April 24, 2024 in Los Angeles. (AP Photo/Richard Vogel)

University of Southern California protesters carry a tent around Alumni Park on the University of Southern California to keep security from removing it during a pro-Palestinian occupation on Wednesday, April 24, 2024, in Los Angeles. (AP Photo/Richard Vogel)

University of Southern California protesters carry a tents around Alumni Park on the University of Southern California to keep security from removing them during a pro-Palestinian occupation on Wednesday, April 24, 2024 in Los Angeles. (AP Photo/Richard Vogel)

University of Southern California protesters carry a tents around Alumni Park on the campus of the University of Southern California to keep security from removing them during a pro-Palestinian occupation on Wednesday, April 24, 2024 in Los Angeles. (AP Photo/Richard Vogel)

University of Southern California protester raises an anti war sign in Alumni Park on the campus of the University of Southern California during a pro-Palestinian occupation on Wednesday, April 24, 2024 in Los Angeles. (AP Photo/Richard Vogel)

A University of Southern California protester, right, confronts a University Public Safety officer at the campus' Alumni Park during a pro-Palestinian occupation on Wednesday, April 24, 2024, in Los Angeles. (AP Photo/Richard Vogel)

University of Southern California protesters push and shove University Public Safety officers as tempers get heated during a pro-Palestinian occupation on the University of Southern California campus Wednesday, April 24, 2024 in Los Angeles. (AP Photo/Richard Vogel)

Texas state troopers in riot gear try to break up a pro-Palestinian protest at the University of Texas, Wednesday, April 24, 2024, in Austin, Texas. (Jay Janner/Austin American-Statesman via AP)

Student protesters hold up sheets as others pray at the pro-Palestinian demonstration encampment at Columbia University in New York on Wednesday April 24, 2024. (AP Photo/Stefan Jeremiah)

Signs are displayed on tents at the pro-Palestinian demonstration encampment at Columbia University in New York on Wednesday April 24, 2024. (AP Photo/Stefan Jeremiah)

People sit outside tents at the pro-Palestinian demonstration encampment at Columbia University in New York on Wednesday April 24, 2024. (AP Photo/Stefan Jeremiah)

A Palestinian flag is displayed at the pro-Palestinian demonstration encampment at Columbia University in New York on Wednesday April 24, 2024. (AP Photo/Stefan Jeremiah)

State troopers try to break up a pro-Palestinian protest at the University of Texas, Wednesday, April 24, 2024, in Austin. (Jay Janner/Austin American-Statesman via AP)

Pro-Palestinian protesters face off with mounted state troopers at the University of Texas, Wednesday, April 24, 2024, in Austin. (Jay Janner/Austin American-Statesman via AP)

Students participate in a pro-Palestinian protest at the University of Texas, Wednesday, April 24, 2024, in Austin. (Jay Janner/Austin American-Statesman via AP)

A pro-Palestinian protester walks past pro-Israel protesters at the University of Texas Wednesday April 24, 2024, in Austin. (Jay Janner/Austin American-Statesman via AP)

A pro-Palestinian protester faces off with state troopers at the University of Texas Wednesday April 24, 2024, in Austin. (Jay Janner/Austin American-Statesman via AP)

Cally, a former UT student, faces off with a mounted state trooper at a pro-Palestinian protest at the University of Texas, Wednesday, April 24, 2024, in Austin. (Jay Janner/Austin American-Statesman via AP)

A woman raises a fist during a pro-Palestinian protest at the University of Texas, Wednesday, April 24, 2024, in Austin. (Jay Janner/Austin American-Statesman via AP)

A woman is arrested at a pro-Palestinian protest at the University of Texas, Wednesday, April 24, 2024, in Austin. (Jay Janner/Austin American-Statesman via AP)

A woman is arrested at a pro-Palestinan protest at the University of Texas, Wednesday, April 24, 2024, in Austin, Texas. (Jay Janner/Austin American-Statesman via AP)

A woman is arrested at a pro-Palestinian protest at the University of Texas, Wednesday April 24, 2024, in Austin. (Jay Janner/Austin American-Statesman via AP)

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Class of 2024 reflects on years marked by COVID-19, protests - St. Louis Post-Dispatch

Putting the final touches on my quarterly RWM client call, I wanted to share a chart that is surprising to many people. The above shows changes in the BLS Average Hourly Earnings and Consumer Price Index since the Pandemic blew up in February 2020.

What seems to surprise people: On average, U.S. wages have outpaced inflation since 2020.1

What are the key drivers here? Two big things stand out:

Fiscal Stimulus: At nearly $2 trillion, CARES Act I was the largest fiscal spend as a percentage of GDP (about 10%) since World War Two. CARES Act II was another $800 billion; CARES Act III was nearly 1.8 trillion (I and II under Trump, III under Biden).

These dumped a ton of cash into the economy all at once in 2020 and 2021. CPI inflation peaked in June 2022, as most of the pig was through the python by then.

Shrunken Labor Force: There are many factors driving unemployment down to 3.8%, the lowest since the 1960s, but I see 3 major elements:

1. Reduced Immigration during both Trump & Biden Administrations; Under Trump, legal immigration fell around 1 million people each year; that continues under Biden during the pandemic years of 2021 and first half of 2022. It has since rebounded in 2H 22, 2023, and 2024;

2. COVID deaths in the United States were a factor, as had more than 2 million COVID-related deaths; while age was a factor, many of the people who died were in what BLS defines as Working Age (16-64);

3. Disability inlcudes a variety of elements (Illness, Injury, Long Covid, etc.) but for the last 15 years it has been gradually rising until COVID, then it spiked. For women, disability was flat until Covid, then picked up dramatically.

The basic maths is a shortage of qualified workers (or even bodies to put to work) equals rising wages.

Many other factors exist, but I suspect these two are the most significant

Previously: Which is Worse: Inflation or Unemployment? (November 21, 2022)

A Dozen Contrarian Thoughts About Inflation(July 13, 2023)

Who Is to Blame for Inflation, 1-15(June 28, 2022)

__________

1. This is the average, and there is a longer discussion to be had about how that breaks down by quartiles or deciles, but I will save that post for a future date.

Read more:

Wages & Inflation Since COVID-19 - The Big Picture - Barry Ritholtz

Ethics statement and patient consent

This study was approved by the Research Ethics Committees of Graduate School of Medicine, Chiba University (M10505). The instructions for patients include the following: Research purpose, research methods, expected effects and risks, not being disadvantaged even if you do not consent, being able to withdraw consent at any time after consent, strict management of personal information, research results being reported in academic journals, research organization and funding sources. Participant gave written informed consent, according to CARE guidelines and in compliance with the Declaration of Helsinki principles.

The patient received three courses of RDV medication. During each course, the patient received an initial dose of 200mg IV, followed by four daily doses of 100mg IV (5 days in total).

SARS-CoV-2 RNA was detected using a real-time RT-PCR kit (Ampdirect 2019-nCoV detection kit; Shimadzu, Kyoto, Japan).

Reverse transcription was performed using a LunaScript RT SuperMix Kit (New England Biolabs, MA, USA) as the following cycling condition: 25C, 2min; 55C, 10min; 95C, 1min. Then, a 2kb tiling PCR was performed using a standard protocol with Tks Gflex DNA polymerase (Takara Bio, Shiga, Japan) and four primer pools (Supplementary Table2, synthesised by Integrated DNA Technologies, IA, USA) as the following cycling condition: 94C, 1min; 40 cycles at 98C, 10s, 60C, 15s, and 68C, 1min. After amplification, a library was prepared using the xGen DNA Library EZ UNI Kit (Integrated DNA Technologies, IA, USA) and sequenced using an iSeq100 instrument (Illumina, CA, USA). Sequencing data were pre-processed using fastp (trimming 1 base in 5- and 3-ends of reads)27 and mapped onto the SARS-CoV-2 genome (NC_045512) using a BWA aligner28. Trimming of primer sequences, variant calling, and consensus sequence generation were performed using iVar with default settings (variants with 3% frequencies were called)29. Used commands are shown in Supplementary information. WGS (Whole Genome Sequencing) data are available in the NCBI Sequence Read Archive (SRA), submission SUB13521440 under BioProject number PRJNA983865 (BioSample accession numbers SAMN35736960SAMN35736967, https://www.ncbi.nlm.nih.gov/bioproject/?term=PRJNA983865).

SARS-CoV-2 lineage was assigned by both Pangolin (https://cov-lineages.org/index.html)30 and Nextclade (https://clades.nextstrain.org/)31 web applications. In addition, the phylogenetic analysis was performed using whole genome sequence data of current strains and consensus sequence of WHO designated VOC, Alpha, Beta, Gamma, Delta, and Omicron (BA.1, BA.2, BA.4, BA.5, BA.2.75, and XBB.1.5) variants. The multiple sequence alignment was performed by MAFFT32. The phylogenetic tree was inferred by IQ-TREE with 1000 bootstrap resampling33,34. The best-fit substitution model (TIM+F+I) was selected by ModelFinder plus option35. The inferred phylogenetic tree was visualised by iTOL (https://itol.embl.de/) web application36.

TMPRSS2-expressing VeroE6 cells (JCRB1819), a SARS-CoV-2-susceptive cell line11, were obtained from the JCRB (Japanese Collection of Research Bioresources Cell Bank, Tokyo, Japan), and cultured in Dulbeccos modified Eagle medium-high glucose (DMEM; Sigma-Aldrich, London, UK) supplemented with 5% (v/v) fetal bovine serum (FBS; Cytiva, Tokyo, Japan) and an antibiotic mixture containing penicillin G (100 units/ml), streptomycin (100g/ml), and amphotericin B (0.25g/ml) (Nacalai Tesque, Kyoto, Japan). Viral isolation was achieved by inoculating a portion of a saline extract of a nasopharyngeal swab from the patient (Day 22 and thereafter) onto a VeroE6/TMPRSS2 culture in a 6-well plate. The cultures were incubated at 37C/5% CO2 and monitored by daily microscopic observation.

Conspicuous CPE (usually cell rounding) spread throughout the well of the culture plate, usually 24 d.p.i. After low-speed centrifugation (800g, 6min), the culture supernatant was harvested and stored at 70C as isolated virus stock. For further experiments, a working virus solution was prepared by inoculating a portion of the stock onto fresh VeroE6/TMPRSS2 cells cultured in a 25-cm2 flask. The cells were cultured for 23 days, and the supernatants were harvested when full-blown CPE was observed and stored in the same manner as the original stock.

To examine the viral growth properties of respective isolates, we performed the following experiments in triplicate. First, VeroE6/TMPRSS2 cells were seeded in 6-well plates (4ml/well) and allowed to nearly reach confluence within 2 days. The medium was removed, and 1ml of the new medium was added. Then, 100l of virus solution adjusted to a multiplicity of infection (MOI) of 0.01 (~4000 TCID50 of each virus / (4105 cells per well)) was inoculated, and the plates were placed in a CO2 incubator for 1h, with occasional gentle shaking. This virus inoculum solution was removed and washed once with 2ml of the new medium, and 4ml of the fresh medium was added again. These were cultured further at 37C/5% CO2. Aliquots of the culture supernatants were harvested every 24h for 5 days, and their viral titres were quantified as described below.

To investigate the extent of RDV resistance in the various isolates, inhibitor assessment experiments were performed according to the method described by Stevens et al.12. First, VeroE6/TMPRSS2 cells were seeded in 24-well plates (1ml/well) and allowed to nearly reach confluence within 2 days. The medium was removed, and 500l of the new medium containing various concentrations of RDV (GS-5734; Aobious, MA, USA), ranging from 0.125M, was added. Then, 100l of the test specimen virus solution (CH-LT1 to CH-LT3m) was added into each well at a MOI of 0.001 (~100 TCID50 of each virus / (105 cells per well)), and the plates were placed in a CO2 incubator for 1h with occasional gentle shaking. This virus inoculum solution was removed and washed once with 500l of the RDV-containing medium, and 1ml of the medium containing various concentrations of RDV was added again. These were cultured further at 37C/5% CO2. Culture supernatants were harvested 72h after virus inoculation, and viral titres were quantified. Viral infectivity (%) was calculated from the viral titres (expressed as TCID50/ml values) in the harvested culture supernatants 72h after virus inoculation in the presence of various concentrations (ranging from 0.1 to 25M) of RDV divided by the titre without RDV (100). Dose-response analysis and calculation of median effect concentration (EC50) values were performed using the drc package (version 3.0-1) from the R statistical software (version 4.2.2)37.

We did not include CH-LT4 and later isolates in this assay because their growth curves were different from those of earlier isolates. We wanted to focus on the impact of mutation(s) that occurred in nsp12. Alternately, we separately performed the comparison experiments of later isolates with the original isolate CH-LT1 in the presence or absence of 5M RDV at 3 and 4 d.p.i. We used 6-well plates, and the MOI was adjusted to 0.01. Other conditions were the same as described above. All RDV resistance experiments were performed in triplicate and statistical analysis was treated.

VeroE6/TMPRSS2 cells were seeded in 96-well plates (100l/well) in a similar manner as described in the growth kinetics experiment, and allowed to nearly reach confluence within 2 days, with 100l of 10-fold serial dilutions of virus-containing culture supernatants added into each well. The presence of live virus in each well was determined based on the CPE at 4 d.p.i., and the TCID50 values were calculated using the BehrensKrber method. The viral titres are expressed as the TCID50/ml.

Antibody responses against the S and N proteins were analyzed using Anti-SARS CoV-2 S RUO and Elecsys Anti-SARS-CoV-2 RUO (Roche Diagnostics, Switzerland), respectively, on the Cobas 8000 e801 module (Roche Diagnostics). The former system allows for the quantitative detection of antibodies, predominantly IgG, that target the viral S protein receptor-binding domain. The measurement threshold is 0.4U/ml, and values of 0.8U/ml were considered positive. The latter system allows for the quantitative detection of antibodies targeting the viral N antigen, with values of 1.0 considered positive.

The three-dimensional structure of the SARS-CoV-2 RNA-dependent RNA polymerase (SARS-CoV-2 RdRp) harbouring V792I, M794I, and C799F mutations at Domain-II was constructed with MOE, version 2022.02 (CCG Inc, Montreal, Canada), based on the Brookhaven Protein Databank 6XEZ. Docking simulations of SARS-CoV-2 RdRp of V792I, M794I, and C799F mutants with RDV-TP (PubChem CID 56832906) were performed using the Amber99 force field in MOE.

All the software(s) used in our study are freely available through the following sites except Molecular Operating Environment (MOE).

fastp (version 0.23.2, https://github.com/OpenGene/fastp).

bwa (version: 0.7.17-r1188, https://github.com/lh3/bwa).

iVar (version 1.4, https://andersen-lab.github.io/ivar/html/index.html).

MAFFT (version 7, https://mafft.cbrc.jp/alignment/software/).

IQ-TREE (version 2.2.6, http://www.iqtree.org).

iTOL (version 6.8.1, https://itol.embl.de).

R (version 4.2.2, https://www.r-project.org).

drc package (version 3.0-1, https://cran.r-project.org/web/packages/drc/index.html).

Molecular Operating Environment (MOE), version 2016.08 (CCG Inc, Montreal, Canada) is commercially available (https://www.chemcomp.com/index.htm).

Further information on research design is available in theNature Portfolio Reporting Summary linked to this article.

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Study population and data

The first autochthonous case of COVID-19 in Italy was microbiologically diagnosed in the Lombardy Region on February 20, 2020. At the time, intensive testing, isolation of confirmed cases, and quarantine of case contacts were in place in the entire country [19]. Following the rapid increase of SARS-CoV-2 laboratory-confirmed infections, local and national health authorities imposed increasingly strict physical distancing measures, with a quarantine imposed on all individuals residing in 10 municipalities in the Lombardy Region and one in the Veneto Region on February 23, 2020 [2]. A regional lockdown in Lombardy and a national lockdown were imposed respectively on March 8 and March 10, 2020 [20]. Applied measures included the suspension of teaching activities and restrictions on individuals movements across different regions and culminated in the closure of all non-essential retail and shops and a stay at home order applied throughout the entire Italian territory.

Since January 2020, data on PCR-confirmed SARS-CoV-2 infections have been collected in the 19 Italian Regions and the two Autonomous Provinces and reported to National Integrated Surveillance System [19]. A central database of all infections confirmed in Italy was formally established the February 27,2020 and managed by the Italian National Institute of Health. For any confirmed infection, information was collected on the date of diagnosis, municipality of residence, and clinical severity; the date of symptom onset was also recorded for symptomatic cases. The initial line list of laboratory-confirmed cases was retrospectively consolidated, through information gathered with standardized interviews to ascertained infections and PCR testing of their close contacts.

Our analysis is based on the consolidated dataset of all ascertained cases with symptom onset between January 26 and March 7, 2020, corresponding to the 6 epidemiological weeks preceding the first regional lockdown imposed in Lombardy on March 8, 2020. We focus our analysis on this period to reduce the potential biases led by the introduction of strict restrictions to the population. Data used to perform the presented analysis were extracted in February 2021.

By adapting a method previously developed to estimate sources and sinks of malaria parasites in Madagascar [21], we investigate the likely source locations of infection of each symptomatic case retrospectively identified by public health authorities in Italy with symptom onset in the 6weeks between January 26 and March 7. For each case residing in municipality i with symptom onset on day t, we describe the risk that the case was infected T days previously because of contacts with people residing in the municipality j as:

$${{text{L}}}_{{text{i}},{text{j}}}left(t,Tright)={C}_{i,j}mathcal{G}left(Tright)frac{{Y}_{j}left(t-Tright)}{{N}_{j}}$$

where ({C}_{i,j}) represents the number of individuals daily traveling from (i) to (j), (mathcal{G}left(Tright)) is the probability distribution of the SARS-CoV-2 generation time (assumed to be equal to the distribution of the serial interval estimated in [2]), ({Y}_{j}left(t-Tright)) is the number of infected individuals residing in j who developed symptoms at time (t-T), and ({N}_{j}) is the total number of individuals residing in j.

The amount of travels across the different municipalities of Italy (({C}_{i,j})) is modeled by means of a radiation model [22], which is based on data on the size of the population residing in each municipality, the distance between their centroids, and the proportion of daily commuters recorded by Italian National Institute of Statistics in 2019 (Figure S1) [23].

We estimate the probability that a case residing in municipality i with symptom onset on dayt, was infected by a case residing in municipality j as:

$${{text{p}}}_{{text{i}},{text{j}}}left(tright)=frac{{sum }_{T=1}^{infty }{L}_{i,j}left(t,Tright)}{{sum }_{j=1}^{M}{sum }_{T=1}^{infty }{L}_{i,j}left(t,Tright)}$$

where M is the total number of municipalities in Italy in 2020 (namely, 7926).

Similarly, the probability that a case residing in municipality i and developing symptoms during the period (uppi) was infected by a case from municipality j is computed as:

$${{text{p}}}_{{text{i}},{text{j}}}left(uppi right)=frac{{sum }_{tinuppi }{p}_{i,j}left(tright){{text{Y}}}_{{text{i}}}left({text{t}}right)}{{sum }_{tinuppi }{{text{Y}}}_{{text{i}}}left({text{t}}right)}.$$

Finally, we estimate the probability that individuals developing symptoms during the period (uppi) were infected within a distance D from their residence as:

$${p}_{D}left(uppi right)=frac{{sum }_{i}{sum }_{j:{d}_{i,j}

where possible sources j run over all municipalities with a distance from i (namely, ({d}_{i,j})) lower than D.

The contribution of each municipality j in the number of infection episodes occurring at time (t) in all the other municipalities of Italy is quantified as ({sum }_{ine j}{p}_{i,j}left(tright){Y}_{i}left(tright)/{sum }_{{text{j}}=1}^{{text{M}}}{sum }_{ine j}{p}_{i,j}left(tright){Y}_{i}left(tright)).

We estimate the number of epidemic foci occurred in Italy up to March 7, 2020. To this aim, we identify for each week (w) those municipalities characterized by a non-negligible number of ascertained symptomatic cases (({sum }_{tin w}{{text{Y}}}_{{text{i}}}left({text{t}}right)>10)) and incidence (({sum }_{tin w}{{text{Y}}}_{{text{i}}}left({text{t}}right)/{{text{N}}}_{{text{i}}}>0.001)), and by the majority of transmission episodes estimated as occurring between individuals residing in the municipality (({p}_{i,i}left(wright)>0.5)).

In the probabilistic approach, we assume that the mobility fluxes among municipalities can be modeled through a radiation model. Although the radiation model has been effectively employed to describe the spatial spread of infectious diseases in high-income countries [22, 24], following the approach already used in Gatto et al. [13], we show that the flows of individuals obtained through the radiation model are in good agreement with mobility data across the 12 provinces of the Lombardy region, based on 2016 census data adjusted with the population projections for 2020 [25] (see Figures S2 and S3). Furthermore, we use a dynamic metapopulation transmission model based on a susceptible-infectious-recovered (SIR) schema to test if the radiation model is reasonably able to capture the observed spatial spread of COVID-19 in Italy and the overall temporal increase of COVID-19 patients across regions from February 1 up to March 7, 2020. To compare model simulations with data, we assume that 3% of all infections were ascertained by public health authorities, either in real time or retrospectively through contact tracing operations and epidemiological investigations [26]. In the dynamic model, infected individuals residing in the municipality j are assumed to exert a time dependent force of infection ({lambda }_{i,j}left(tright)) on individuals residing in municipality (i) defined as ({lambda }_{i,j}left(tright)=beta {C}_{i,j}{I}_{j}left(tright)/{{text{N}}}_{j}), where (beta) is the SARS-CoV-2 transmission rate, ({C}_{i,j}) is the amount of individuals daily traveling from (i) to (j) as obtained by using the radiation model, ({I}_{j}(t)) and ({N}_{j}) are, respectively, the overall number of infectious individuals and the population size in municipality (j). Based on the simulation results, we compute the probability that an individual residing in municipality i and infected at day t was infected by a case from municipality j as ({{text{p}}}_{{text{i}},{text{j}}}left(tright)={uplambda }_{i,j}left(tright)/{sum }_{j=1}^{M}{lambda }_{i,j}left(tright)), with M representing the overall number of municipalities of Italy in 2020; ({{text{p}}}_{{text{i}},{text{j}}}left(uppi right)) is computed as in the probabilistic approach, but using the overall number of infections estimated by the dynamic model instead of the symptomatic cases ascertained in the data. Given the large uncertainty surrounding the ability of the public health system in identifying (either in real time or retrospectively) cases that occurred in the early pandemic phase, we repeat the analysis and estimate the risk of SARS-CoV-2 transmission at different distances by assuming also a 10% ascertainment ratio.

The SIR model is parametrized to reproduce at the national level an epidemic curve associated with an exponential growth rate (r) corresponding to a basic reproduction number ({R}_{0}=2.8), representing the transmissibility potential of SARS-CoV-2, estimated for the Lombardy Region between February 12 and March 9, 2020 [2, 20]. The average duration of the infectivity period is assumed to be equal to the mean serial interval (G) [2]. The ({R}_{0}) associated with the simulated epidemic curve is computed by considering the growth rate (r) associated with the number of new cases simulated by the model at the national level and using the standard equation ({R}_{0}=1+rG). The model is initialized on February 1 (at ({t}_{0}=0)) with a number of infected individuals ({I}_{0}) that is consistent with the ascertainment ratio in Italy during the early pandemic phase (3% by March 8, 2020 [26]; 10% was considered for sensitivity analysis), and the consolidated number of ascertained cases developing symptoms before strict restrictions were imposed on the general population (namely, 517 individuals on February 23, 2020). The dynamic model considered in this work is deterministic. However, initial infections are distributed over the national territory by random sampling from a multinomial distribution with probabilities proportional to the cumulative number of symptomatic cases retrospectively identified in Italy across the different municipalities as of February 15, 2020. To explore the uncertainty characterizing the initial spatial dispersal of SARS-CoV-2 infections, model simulations are repeated 100 times by randomly sampling the municipalities of residence of infectious individuals at the start of simulations. Results are presented both in terms of model mean estimates and 95% Prediction Intervals (PI) associated with different initial conditions, and in terms of model estimates associated with initial conditions minimizing the root mean square error between the time series of cases retrospectively identified at the regional level and those estimated by simulating the dynamic SIR model.

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Spatial spread of COVID-19 during the early pandemic phase in Italy - BMC Infectious Diseases - BMC Infectious Diseases

Key differences in how the nasal cells of young and elderly people respond to the SARS-CoV-2 virus may explain why children typically experience milder symptoms of COVID-19, say researchers.

They say that understanding how the type of nasal cells people have change with age and how this affects the ability to combat SARS-CoV-2 infection could be crucial in developing effective antiviral treatments, especially for older people who are at higher risk of severeCOVID-19.

A study published inNature Microbiologyfocused on the early effects of SARS-CoV-2 infection on the cells first targeted by the viruses, called human nasal epithelial cells (NECs).

The study was funded by UK Research and Innovation, the National Institute for Health and Care Research, Great Ormond Street Hospital Biomedical Research Centre, Wellcome, and the Chan Zuckerberg Foundation.

By carrying out SARS-CoV-2infections of epithelial cellsin vitroand studying the responses with single cell sequencing, we get a much more detailed understanding of the viral infection kinetics and see big differences in the innate immune response between cell types, said co-senior author, Dr Kerstin Meyer from the Wellcome Sanger Institute.

Children infected with SARS-CoV-2 rarely progress to respiratory failure, but the risk of mortality in infected people over the age of 85 remains high, despite vaccination and improved treatment options.

Researchers say their findings underscore the importance of considering age as a critical factor in both research and treatment of infectious diseases.

Participants were recruited for the study from five large hospital sites in London.Cells were donated by healthy participants including children under the age of 11, adults ages 30 to 50, and, for the first time, adults over 70.

The cells were then cultured using specialized techniques, allowing them to regrow into the different types of cells originally found in the nose. Using single-cell RNA sequencing to identify the unique genetic networks and functions of thousands of individual cells, researchers identified 24 distinct epithelial cell types. Cultures from each age group were then either mock-infected or infected with SARS-CoV-2.

After three days the NECs of children responded quickly to SARS-CoV-2 by increasing interferon, the bodys first line of antiviral defense, restricting viral replication. However, this early antiviral effect became less pronounced with age.

In the study, NECs from elderly individuals not only produced more infectious virus particles but also experienced increased cell shedding and damage.

Researchers say the strong antiviral response in the NECs of children could explain why younger people typically experience milder symptoms. In contrast, the increased damage and higher viral replication found in NECs from elderly individuals could be linked to the greater severity of the disease observed in older adults.

These findings provide insights into age-relatedCOVID-19pathogenesis and demonstrate how impaired repair processes enhance SARS-CoV-2 infection in older individuals, the authors wrote.

Co-senior author Dr. Marko Nikolic, of University College Londons Division of Medicine, said, It is fascinating that when we take away immune cells from nasal samples, and are only left with nasal epithelial cells grown in a dish, we are still able to identify age-specific differences in our bodys response to the SARS-CoV-2 between the young and elderly to explain why children are generally protected from severeCOVID-19.

Researchers now hope to investigate the long-term implications of the cellular changes and test therapeutic interventions using their cell culture model.

They suggest that future research should consider how aging impacts the bodys response to other viral infections.

Read more:

Study: Age-related differences in nasal cells offer COVID-19 protection - LabPulse

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