Category: Covid-19

This is part of our Coronavirus Update series in which Harvard specialists in epidemiology, infectious disease, economics, politics, and other disciplines offer insights into what the latest developments in the COVID-19 outbreak may bring.

Drawing on insights from another deadly airborne disease, tuberculosis, a Harvard infectious disease expert suggested Friday that air conditioning use across the southern U.S. may be a factor in spiking COVID-19 cases and that ultraviolet lights long used to sterilize the air of TB bacteria could do the same for SARS-CoV-2.

Edward Nardell, professor of medicine and of global health and social medicine at Harvard Medical School (HMS) and professor of environmental health and of immunology and infectious diseases at the Harvard T.H. Chan School of Public Health, said that hot summer temperatures can create situations similar to those in winter, when respiratory ailments tend to surge, driving people indoors to breathe and rebreathe air that typically is little refreshed from outside.

The states that, in June, are already using a lot of air conditioning because of high temperatures are also the places where theres been greater increases in spread of COVID-19, suggesting more time indoors as temperatures rise, Nardell said. The same [thing] happens in wintertime, with more time indoors.

Though transmission of the SARS-CoV-2 virus has been understood to transmit mainly through large droplets expelled during coughing, sneezing, or talking, Nardell said that evidence has risen that at least some cases of COVID-19 occur via airborne transmission. That happens when virus particles contained in smaller droplets dont settle out within six feet and instead hang in the air and drift on currents. Airborne transmission is thought to have been a factor in the coronavirus spread among members of a Washington choir, through an apartment building in Hong Kong, and in a restaurant in Wuhan, China, Nardell said.

As people go indoors in hot weather and the rebreathed air fraction goes up, the risk of infection is quite dramatic.

Edward Nardell, Harvard Medical School

Airborne transmission would make people even more vulnerable to the virus in a closed room. Nardell said that in an office occupied by five people, as windows are closed and air conditioners turned on, CO2 levels rise steeply, a sign that occupants are rebreathing air in the room and from each other.

As people go indoors in hot weather and the rebreathed air fraction goes up, the risk of infection is quite dramatic, Nardell said, adding that the data, while gathered related to tuberculosis, would apply to any infection with airborne potential.

Nardell outlined the work Friday morning during an online presentation sponsored by the Massachusetts Consortium on Pathogen Readiness (MassCPR), an HMS-led collaboration of researchers from 15 Massachusetts institutions and the Guangzhou Institute for Respiratory Health in China. MassCPRs aim is to foster research that will rapidly translate to the front lines of the COVID-19 pandemic.

The 90-minute public briefing, focused on issues raised by reopening efforts, was hosted by HMS Dean George Daley and included presentations on Americans mobility during the pandemic, contact-tracing efforts, development of personal protective equipment, and of viral and antibody testing as ways to detect new cases and better understand the pandemics course through society.

We are united in our common goal to leverage our collective biomedical expertise to confront the immediate challenges of the COVID-19 pandemic, said Daley, who serves on MassCPRs steering committee. But we are also committed to building a scientific community that is better-prepared for the next emerging pathogen.

In his presentation, Nardell, whose past work has focused on ways to combat drug-resistant tuberculosis, said a dynamic similar to that in the U.S. South is being replayed elsewhere in the world. He cited a rise in air conditioner sales in India, where the systems are designed to bring in little outside air, again increasing chances of transmission. India, with nearly 500,000 COVID-19 cases, reported 17,296 new cases and 407 deaths on Friday, according to the World Health Organization.

Nardell said that being outside or increasing ventilation inside can be effective in slowing transmission, though the ventilation systems in many corporate settings limit how much fresh air can be brought in. Portable room air cleaners also can be used, though they can have limited air flow, he said. Germicidal lamps, a technology that Nardell said is almost 100 years old, have been proven effective in protecting against tuberculosis infection and are already in use in some settings to fight SARS-CoV-2. Compared with mechanical ventilation and portable room air cleaners, the lights, according to one study, have been shown to be up to 10 times more effective, Nardell said.

The lamps are set up to shine horizontally, high in the room where sterilization is needed. Air currents, stirred in part by warmth from human bodies, circulate up to the ceiling, where the ultraviolet light kills floating pathogens, and then back down again. This technology, Nardell said, is not only proven, it can be deployed cheaply and easily in a number of settings as society reopens.

The lights are not a panacea, however, and the predominant route of transmission needs to be considered in determining whether they are appropriate. Despite the need for disinfection in nursing homes, for example, transmission there may be mainly through close contact between staff and patients, making them less-than-ideal sites for the germicidal lights, Nardell said.

Where [the lights] should be considered in the upcoming resurgence would be, obviously, in a health care setting, but also in public buildings such as stores, restaurants, banks, and schools, Nardell said. We need to know where transmission is occurring to know where they should go.

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Air conditioning may be factor in COVID-19 spread in the South - Harvard Gazette

Who and what next?

The coronavirus 2019 (COVID-19) pandemic has brought tighter restrictions on the daily lives of millions of people, but we do not yet understand what measures are the most effective. Zhang et al. modeled virus transmission in Wuhan, China, in February 2020, investigating the effects of interventions ranging from patient management to social isolation. Age-mixing patterns were estimated by contact surveys conducted in Wuhan and Shanghai at the beginning of February 2020. Once people reduced their average daily contacts from 14 to 20 down to 2, transmission rapidly fell below the epidemic threshold. The model also showed that preemptive school closures helped to reduce transmission, although alone they would not prevent a COVID-19 outbreak. Limiting human mixing to within households appeared to be the most effective measure.

Science, this issue p. 1481

Intense nonpharmaceutical interventions were put in place in China to stop transmission of the novel coronavirus disease 2019 (COVID-19). As transmission intensifies in other countries, the interplay between age, contact patterns, social distancing, susceptibility to infection, and COVID-19 dynamics remains unclear. To answer these questions, we analyze contact survey data for Wuhan and Shanghai before and during the outbreak and contact-tracing information from Hunan province. Daily contacts were reduced seven- to eightfold during the COVID-19 social distancing period, with most interactions restricted to the household. We find that children 0 to 14 years of age are less susceptible to severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) infection than adults 15 to 64 years of age (odds ratio 0.34, 95% confidence interval 0.24 to 0.49), whereas individuals more than 65 years of age are more susceptible to infection (odds ratio 1.47, 95% confidence interval 1.12 to 1.92). Based on these data, we built a transmission model to study the impact of social distancing and school closure on transmission. We find that social distancing alone, as implemented in China during the outbreak, is sufficient to control COVID-19. Although proactive school closures cannot interrupt transmission on their own, they can reduce peak incidence by 40 to 60% and delay the epidemic.

The novel coronavirus disease 2019 (COVID-19) epidemic caused by severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) began in Wuhan City, China, in December 2019 and quickly spread globally, with 2,063,161 cases reported in 185 countries or regions as of 16 April 2020 (1). A total of 82,692 cases of COVID-19, including 4632 deaths, have been reported in mainland China, including 50,333 cases in Wuhan City and 628 cases in Shanghai City (2). The epidemic in Wuhan and in the rest of China subsided after implementation of strict containment measures and movement restrictions, with recent cases originating from travel (3). However, key questions remain about the age profile of susceptibility to infection, how social distancing alters age-specific contact patterns, and how these factors interact to affect transmission. These questions are relevant to the choice of control policies for governments and policy-makers around the world. In this study, we evaluate changes in mixing patterns linked to social distancing by collecting contact data in the midst of the epidemic in Wuhan and Shanghai. We also estimate age differences in susceptibility to infection based on contact-tracing data gathered by the Hunan Provincial Center for Disease Control and Prevention (CDC), China. Based on these empirical data, we developed a mathematical disease transmission model to disentangle how transmission is affected by age differences in the biology of COVID-19 infection and altered mixing patterns owing to social distancing. Additionally, we project the impact of social distancing and school closure on COVID-19 transmission.

To estimate changes in age-mixing patterns associated with COVID-19 interventions, we performed contact surveys in two cities: Wuhan, the epicenter of the outbreak, and Shanghai, one of the largest and most densely populated cities in southeast China. Shanghai experienced extensive importation of COVID-19 cases from Wuhan as well as local transmission (4). The surveys were conducted from 1 February 2020 to 10 February 2020, as transmission of COVID-19 peaked across China and stringent interventions were put in place. Participants in Wuhan were asked to complete a questionnaire describing their contact behavior (5, 6) on two different days: (i) a regular weekday between 24 December 2019 and 30 December 2019, before the COVID-19 outbreak was officially recognized by the Wuhan Municipal Health Commission (used as baseline); and (ii) the day before the interview (outbreak period). Participants in Shanghai were asked to complete the same questionnaire used for Wuhan but only report contacts for the outbreak period. For the baseline period in Shanghai, we relied on a survey conducted in 20172018 that followed the same design (7). In these surveys, a contact was defined as either a two-way conversation involving three or more words in the physical presence of another person or a direct physical contact (e.g., a handshake). Details are given in the supplementary materials (SM, sections 1 and 2).

We analyzed a total of 1245 contacts reported by 636 study participants in Wuhan and 1296 contacts reported by 557 participants in Shanghai. In Wuhan, the average daily number of contacts per participant was significantly reduced, from 14.6 for the baseline period (mean contacts weighted by age structure: 14.0) to 2.0 for the outbreak period (mean contacts weighted by age structure: 1.9) (p < 0.001). The reduction in contacts was significant for all stratifications by sex, age group, type of profession, and household size (Table 1). A larger reduction was observed in Shanghai, where the average daily number of contacts decreased from 18.8 (mean contacts weighted by age structure: 19.8) to 2.3 (mean contacts weighted by age structure: 2.1). Although an average individual in Shanghai reported more contacts than one in Wuhan on a regular weekday, this difference essentially disappeared during the COVID-19 outbreak period. A similar decrease in the number of contacts was found in the United Kingdom during the COVID-19 lockdown period (8).

N is the number of participants who provided non-missing contact data.

The typical features of age-mixing patterns (6, 7) emerge in Wuhan and Shanghai when we consider the baseline period (Fig. 1, A and D). These features can be illustrated in the form of age-stratified contact matrices (provided as ready-to-use tables in the SM, section 3.6), where each cell represents the average number of contacts that an individual has with other individuals, stratified by age groups. The bottom left corner of the matrix, corresponding to contacts between school-age children, is where the largest number of contacts is recorded. The contribution of contacts in the workplace is visible in the central part of the matrix, and the three diagonals (from bottom left to top right) represent contacts between household members. By contrast, for the outbreak period when strict social distancing policies were in place, many of the above-mentioned features disappear, essentially leaving the sole contribution of household mixing (Fig. 1, B and E). In particular, assortative contacts between school-age individuals are fully removed, as illustrated by differencing baseline and outbreak matrices (Fig. 1, C and F). Overall, contacts during the outbreak mostly occurred at home with household members (94.1% in Wuhan and 78.5% in Shanghai). Thus, the outbreak contact matrix nearly coincides with the within-household contact matrix in both study sites, and the pattern of assortativity by age observed for regular days almost entirely disappears (SM, section 3.6). These findings are consistent with trends in within-city mobility data, which indicate an 86.9% drop in Wuhan and 74.5% drop in Shanghai between early January and early February (see SM, section 4). Such a large decrease in internal mobility is consistent with most of the contacts occurring in the household during the outbreak period. Of note, the strict social distancing measures implemented in Wuhan and Shanghai did not entirely zero out contacts in the workplace, because essential workers continued to perform their activities (as observed in our data; see SM, section 3.5).

(A) Baseline period contact matrix for Wuhan (regular weekday only). Each cell of the matrix represents the mean number of contacts that an individual in a given age group has with other individuals, stratified by age groups. The color intensity represents the number of contacts. To construct the matrix, we performed bootstrap sampling with replacement of survey participants weighted by the age distribution of the actual population of Wuhan. Every cell of the matrix represents an average over 100 bootstrapped realizations. (B) Same as (A), but for the outbreak contact matrix for Wuhan. (C) Difference between the baseline period contact matrix and the outbreak contact matrix in Wuhan. (D) Same as (A), but for Shanghai. (E and F) Same as (B) and (C), but for Shanghai.

The estimated mixing patterns are based on self-reported contacts that can thus be affected by various biases. In particular, reported contacts for the baseline period in Wuhan may be prone to recall bias because contacts were assessed retrospectively. Further, because of the retrospective nature of the baseline survey in Wuhan, we were unable to account for the lower number of contacts during weekends. The more complete data from Shanghai did not suffer recall bias and allowed us to weight contacts for weekdays and weekends; sensitivity analyses suggest that this has little impact on results (SM, section 8.3). Another possible bias is that survey participants may have felt pressure to minimize reported contacts that occurred during the outbreak, given that social distancing was in place and strictly enforced by the government, even if the anonymity and confidentiality of the survey were emphasized. However, results are robust to inflating reported contacts outside of the home severalfold, suggesting that these compliance and social acceptability biases linked to the outbreak period do not affect our main findings (SM, section 8.2). Another caveat is that in parallel to population-level social distancing measures, case-based interventions were implemented and could have affected contacts, including rapid isolation of confirmed and suspected cases and quarantine of close contacts for 14 days. However, only a small portion of the population in the two study sites was affected by contact tracing and quarantine, thus having little to no effect on average contact patterns in the general population.

Next, to understand the interplay between social distancing interventions, changes in human mixing patterns, and outbreak dynamics, we need to consider potential age differences in susceptibility to infection. This is currently a topic of debate, because little information on the age profile of asymptomatic cases is available (9, 10). To this aim, we analyzed COVID-19 contact-tracing information gleaned from detailed epidemiological field investigations conducted by the Hunan CDC (SM, section 5). Briefly, all close contacts of COVID-19 cases reported in Hunan province were placed under medical observation for 14 days and were tested using real-time reverse transcription polymerase chain reaction (RT-PCR). Those who tested positive were considered as SARS-CoV-2 infections. We estimated the odds ratios (ORs) for a contact of a certain age group to be infected, relative to a reference age group. We performed generalized linear mixed model regression to account for clustering and potential correlation structure of contacts exposed to the same index case (e.g., in the household). We included the age group and gender of a contact, type of contact, and whether the contact traveled to Hubei or Wuhan as regression covariates (SM, section 5). We found that susceptibility to SARS-CoV-2 infection increased with age. Young individuals (aged 0 to 14 years) had a lower risk of infection than individuals aged 15 to 64 years {OR = 0.34 [95% confidence interval (CI): 0.24 to 0.49], p < 0.0001}. By contrast, older individuals aged 65 years and older had a higher risk of infection than adults aged 15 to 64 years [OR = 1.47 (95% CI: 1.12 to 1.92), p = 0.005]. These findings are in contrast with a previous study in Shenzhen, where susceptibility to infection did not change with age (9).

Next, we explore how our data can inform control strategies for COVID-19. A key parameter regulating the dynamics of an epidemic is the basic reproduction number (R0), which corresponds to the average number of secondary cases generated by an index case in a fully susceptible population. We estimated the impact of interventions on R0, relying on our age-specific estimates of susceptibility to infection and contact patterns before and during interventions. We used the next-generation matrix approach to quantify changes in R0 (11) (SM, section 6). Additionally, to illustrate the impact of age-mixing patterns on the dynamics of the epidemic, we developed a simple SIR model of SARS-CoV-2 transmission (SM, section 6). In the model, the population is divided into three epidemiological categories: susceptible, infectious, and removed (either recovered or deceased individuals), stratified by 14 age groups. Susceptible individuals can become infectious after contact with an infectious individual according to the estimated age-specific susceptibility to infection. The rate at which contacts occur is determined by the estimated mixing patterns of each age group. The mean time interval between two consecutive generations of cases was taken to be 5.1 days, assuming it aligns with the mean of the serial interval reported by Zhang et al. (3).

In the early phases of COVID-19 spread in Wuhan, before interventions were put in place, R0 values were estimated to range between 2.0 and 3.5 (1218). In this analysis, we extended this range from 1 to 4 for the baseline period (i.e., before interventions). We find that the considerable changes of mixing patterns observed in Wuhan and Shanghai during the social distancing period led to a drastic decrease in R0 (Fig. 2). When we consider contact matrices representing the outbreak period, keeping the same baseline disease transmissibility as in the preintervention period, the reproductive number drops well below the epidemic threshold in Wuhan (Fig. 2A) and Shanghai (Fig. 2B). This finding is robust to relaxing assumptions about age differences in susceptibility to infection; the epidemic is still well controlled if SARS-CoV-2 infection is assumed to be equally likely in all age groups (Fig. 2, A and B). We also performed sensitivity analyses regarding possible recall and compliance biases of self-reported contacts as well as the definition of contact (i.e., considering only contacts lasting more than 5 min). The results are consistent with those reported here (SM, section 8).

(A) Estimated R0 during the outbreak (mean and 95% CI), as a function of baseline R0 (i.e., that derived by using the contact matrix estimated for the baseline period). The figure refers to Wuhan and includes both the scenario accounting for the estimated susceptibility to infection by age and the scenario where we assume that all individuals are equally susceptible to infection. The distribution of the transmission rate is estimated through the next-generation matrix approach by using 100 bootstrapped contact matrices for the baseline period to obtain the desired R0 values. We then use the estimated distribution of the transmission rate and the bootstrapped outbreak contact matrices to estimate R0 for the outbreak period. The 95% CIs account for the uncertainty on the distribution of the transmission rate, mixing patterns, and susceptibility to infection by age. (B) Same as (A), but for Shanghai. (C) Infection attack rate 1 year after the initial case of COVID-19 (mean and 95% CI) as a function of the baseline R0. The estimates are made by simulating the SIR transmission model (see SM) using the contact matrix for the baseline period and considering the estimated susceptibility to infection by age and assuming that all individuals are equally susceptible to infection. The 95% CIs account for the uncertainty on the mixing patterns and susceptibility to infection by age. (D) Same as (C), but for Shanghai.

In an uncontrolled epidemic (without intervention measures, travel restrictions, or spontaneous behavioral responses of the population) and for R0 in the range of 2 to 3, we estimate the mean infection attack rate to be in the range 53 to 92% after a year of SARS-CoV-2 circulation, with slight variation between Wuhan (Fig. 2C) and Shanghai (Fig. 2D). These estimates should be considered as an upper bound of the infection attack rate because they are based on a compartmental model that does not account for high clustering of contacts (e.g., repeated contacts among household members). If we consider a scenario in which social distancing measures are implemented early on, as the new virus emerges, the estimated R0 remains under the epidemic threshold and thus the epidemic cannot take off in either location. Furthermore, we estimate that the magnitude of interventions implemented in Wuhan and Shanghai would have been enough to block transmission for an R0 before the interventions of up to ~6 in Wuhan and ~7.8 in Shanghai.

Next, we use the model to estimate the impact of preemptive mass school closure. We considered two different contact pattern scenarios, based on data from Shanghai: contacts estimated during vacation periods (7) and contacts estimated during regular weekdays, after all contacts occurring in school settings have been removed (7). Both scenarios represent a simplification of a school closure strategy. Indeed, school closures in response to the COVID-19 pandemic in China have entailed interruption of all educational on-site services. However, mixing patterns measured during school vacations indicate that a fraction of children still attend additional educational activities, as is typical in Chinese cities. On the other hand, when removing all contacts in the school setting, we do not consider potential trickle-down effects on the mixing patterns of other age groups; for instance, parents may need to leave work to take care of school-age children. Our modeling approach indicates that limiting contact patterns to those observed during vacations would interrupt transmission for baseline R0 up to 1.5 (Fig. 3, A and C). Removing all school contacts would do the same for baseline R0 up to 1.2. If we apply these interventions to a COVID-19 scenario, assuming a baseline R0 of 2 to 3.5, we can achieve a noticeable decrease in infection attack rate and peak incidence and a delay in the epidemic, but transmission is not interrupted (Fig. 3, B and D). For instance, for a baseline R0 of 2.5 and assuming a vacation mixing pattern, the mean peak daily incidence is reduced by about 64%. In the corresponding scenario where school contacts are removed, we estimate a reduction of about 42%. Overall, school-based closure policies are not sufficient to entirely prevent a COVID-19 outbreak, but they can affect disease dynamics and hence hospital surge capacity. It is important to stress that individuals aged 5 to 19 years in Shanghai represent 9.5% of the population (19), markedly lower than the mean in China [16.8% (19)] and other countries [including Western countries; e.g., 19.7% in the United States (20)].

(A) Estimated R0 during the outbreak (mean and 95% CI), as a function of baseline R0 (i.e., that derived by using the contact matrix estimated for the baseline period). The figure refers to Shanghai and the scenario accounting for the estimated susceptibility to infection by age. Three contact patterns are considered: (i) as estimated during the COVID-19 outbreak, (ii) as estimated during school vacations (7), and (iii) as estimated for the baseline period, but suppressing all contacts at school. (B) Daily incidence of new SARS-CoV-2 infections (mean and 95% CI), as estimated by the SIR model, assuming age-specific susceptibility to infection (see SM). Three mixing patterns are considered: (i) as estimated for the baseline period, (ii) as estimated during school vacations (7), and (iii) as estimated for the baseline period, but suppressing all contacts at school. The inset shows the infection attack rate 1 year after the introduction of the first COVID-19 case (mean and 95% CI). (C) Same as (A), but assuming equal susceptibility to infection by age. (D) Same as (B), but assuming equal susceptibility to infection by age.

The results of this study should be considered in light of the following limitations. In our simulation model, we estimated the effect of social distancing alone; combining social distancing with other interventions would have a synergistic effect to even further reduce transmission. It is likely that population-wide social distancing, case-based strategies, and decontamination efforts all contributed to achieve control in Wuhan and Shanghai, and their effect is difficult to separate out in retrospective observational studies. Our estimates of age differences in susceptibility to infection are based on active testing of 7375 contacts of 136 confirmed index cases. These data suffer from the usual difficulties inherent to the reconstruction of epidemiological links and detection of index cases. Contact data are useful, but seroepidemiology studies will be essential to fully resolve population susceptibility profiles to SARS-CoV-2 infection and disease. Although the age patterns of contacts were similar in the two study locations during the COVID-19 outbreak period, these patterns may not be fully representative of other locations in China and abroad, where social distancing measures may differ. Because reliable estimates of the contribution of asymptomatic SARS-CoV-2 infections to transmission are still lacking, we did not explicitly model differences between symptomatic and asymptomatic individuals. We considered a serial interval of 5.1 days (3), based on a prior estimate from China, at a time when case-based and contact-tracing intervention measures were in place, which tends to shorten the interval between successive cases. However, this choice does not affect the estimated changes in reproduction number between the baseline and outbreak periods. Modeling results may underestimate the effect of social distancing interventions because our results concentrate on the number of contacts and ignore the type of social interactions (e.g., increased distance between individuals while in contact or use of a face mask), which may have changed owing to increased awareness of the population (21, 22). Finally, it is worth noting that our school closure simulations are not meant to formulate a full intervention strategy, which would require identification of epidemic triggers to initiate closures and evaluation of different durations of intervention (6). Nonetheless, our modeling exercise provides an indication of the possible impact of a nationwide preemptive strategy on the infection attack rate and peak incidence. To generalize these findings to other contexts, location-specific age-mixing patterns and population structures should be considered. Perhaps most importantly, strict lockdown strategies of the kind implemented in Wuhan, Shanghai, and other regions of the world are extremely disruptive economically and mentally, and more targeted approaches to block transmission are preferable in the long run. We do not necessarily endorse blunt lockdown policies here; we merely describe their impact on COVID-19 transmission based on the Chinese experience.

Our study provides evidence that the interventions put in place in Wuhan and Shanghai, and the resulting changes in human behavior, drastically decreased daily contacts, essentially reducing them to household interactions. This led to a dramatic reduction of SARS-CoV-2 transmission. As lockdown measures are put in place in other locations, human mixing patterns in the outbreak period could be captured by data on within-household contacts, which are available for several countries around the world (57, 2325). Moving forward, it will be particularly important to design targeted strategies for long-term control of COVID-19, including school- and work-based control strategies, along with large-scale testing and contact tracing (2628). Research should concentrate on refining age-specific estimates of susceptibility to infection, disease, and infectiousness, which are instrumental to evaluating the impact of these strategies.

J. Zhang, M. Litvinova, Y. Liang, Y. Wang, W. Wang, S. Zhao, Q. Wu, S. Merler, C. Viboud, A. Vespignani, M. Ajelli, H. Yu, Data and code for changes in contact patterns shape the dynamics of the novel coronavirus disease 2019 outbreak in China. Zenodo (2020);.doi:10.5281/zenodo.3775672

M. J. Keeling, P. Rohani, Modeling Infectious Diseases in Humans and Animals (Princeton Univ. Press, 2011), chap. 3.

A. Agresti, An Introduction to Categorical Data Analysis (Wiley, 2018).

R. Anderson, R. May, Infectious Diseases of Humans: Dynamics and Control (Oxford Univ. Press, 1991).

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Changes in contact patterns shape the dynamics of the COVID-19 outbreak in China - Science Magazine

An updated map of state hot spots is available based on our analysis of more recent data.

There is growing concern about rising COVID-19 cases and other troubling trends in a subset of states that have reopened, and a few Governors and Mayors have either paused reopening or signaled their intention to do. Understanding in which states the pandemic is moving in the wrong, or right, direction, is critical but complex, as no single metric can tell the full story. For example, an increasing number of cases could be the result of more testing or the result of increasing transmission, or a combination of both.

A few existing resources examine state-level data to make assessments about current risk levels (see, in particular, https://www.covidexitstrategy.org/, https://covidactnow.org/, and https://www.aei.org/covid-2019-action-tracker/). We similarly sought to examine current trends across the U.S. and identify state hotspots for COVID-19. Specifically, we looked at two metrics that are readily available for all states and, when taken together, signal concern:

For each metric, we examined data from the most recent 14-day period to account for the lag between transmission and the incubation period of the virus, as well as the time at which an individual seeks and receives testing and testing results are reported to health officials; even so, it is important to note that cases from the most recent two-week period still reflect prior transmission patterns. We calculated the percent change based on a 7-day rolling average, which helps to account for fluctuations in reporting throughout each week and other noise in the data. We excluded states for which the percent change in at least one of the 14-day metrics was below 5%.

In addition to cases and positivity rates, we also include data on the percent change in the number of tests conducted, hospitalizations, and deaths, to provide additional context for interpreting trends. For example, hospitalization data provide information on severity of illness and strain on the health care system, though not all states report this information and it is a lagging measure that reflects transmissions from even longer ago. Increasing cases in the most recent period is likely predictive of future hospitalizations (and deaths), though depends on the characteristics or people being infected in any given area (since older people and those with pre-existing conditions are more likely to get severely ill once infected).

Looking at the period from June 8 to June 22, 20 states are classified as hotspots (i.e., have increasing cases and increasing positivity rates over the most recent 14-day period). See Figure and Table 1 below. These states are primarily in the West (9) and South (8); three are in the Midwest. Most are states that were not hit hard earlier in the pandemic. While four of the states are reporting fewer than 40 daily cases, nine are reporting 400 or more new cases per day, and 13 states have positivity rates above the recommended WHO 5% threshold. Outside of these hotspots, an additional four states have either increasing cases or positivity rates, coupled with increasing hospitalizations and/or deaths, which could be a cause for concern.

There are likely multiple policy, epidemiologic, and other factors driving these increases, including: when stay-at-home orders were lifted (and how long they were in place); the pace of reopening; the use of other social distancing measures (such as face mask requirements); increased population movement due to warming weather; outbreaks in congregate settings; the Memorial Day Holiday period; and, potentially, protests. For example, while 16 of the 20 states had lifted their stay-at-home orders by the end of May (California, Hawaii, and Oregon have maintained their stay-at-home orders as they have begun phased reopening), they did so at different paces (see Table 1). Many moved quickly, reopening businesses and lifting other restrictions within a two-week period. All states, except California, have lifted or eased bans on large gatherings and many have not required face masks, or have only recently done so after concerns were raised about rising cases. However, teasing out the role of these various factors and policy changes, many of which occurred simultaneously, will require further analysis.

Using these two metrics increasing COVID-19 cases and positivity rates over the most recent 14-day period, we find that almost 40% (20 states) are moving in the wrong direction. These hotspot states are primarily in the South and West, and most were not hit hard in the earliest days of the U.S. outbreak. Rising cases seen in these states now likely reflect an increase in transmission that began before the most recent two-week period and suggest that increasing hospitalizations could follow. In addition, their rising positivity rates indicate that the growth in cases is not due to increased testing and likely reflects an actual increase in transmission.

While these trends are concerning, and beginning to drive the national trend, most states (27) are moving in the right direction. Many of these states were among the last to reopen. Some phased in reopening over a longer time period and many have required the widespread use of face masks.

The factors determining which states are hotspots and which are not are difficult to tease out, but the inescapable reality is that the epidemic is getting worse, not better, in a significant portion of the country.

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Where are the COVID-19 Hotspots? Tracking State Outbreaks - Kaiser Family Foundation

The reports seemed to take doctors by surprise: The respiratory virus that causes Covid-19 made some patients nauseous. It left others unable to smell. In some, it caused acute kidney injury.

As the pandemic grew from an outbreak affecting thousands in Wuhan, China, to some 10 million cases and 500,000 deaths globally as of late June, the list of symptoms has also exploded. The Centers for Disease Control and Prevention constantly scrambled to update its list in an effort to help clinicians identify likely cases, a crucial diagnostic aid at a time when swab tests were in short supply and typically took (and still take) days to return results. The loss of a sense of smell made the list only in late April.

For many diseases, it can take years before we fully characterize the different ways that it affects people, said nephrologist Dan Negoianu of Penn Medicine. Even now, we are still very early in the process of understanding this disease.

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What they are understanding is that this coronavirus has such a diversity of effects on so many different organs, it keeps us up at night, said Thomas McGinn, deputy physician in chief at Northwell Health and director of the Feinstein Institutes for Medical Research. Its amazing how many different ways it affects the body.

One early hint that that would be the case came in late January, when scientists in China identified one of the two receptors by which the coronavirus, SARS-CoV-2, enters cells. It was the same gateway, called the ACE2 receptor, that the original SARS virus used. Studies going back some two decades had mapped the bodys ACE2 receptors, showing that theyre in cells that line the insides of blood vessels in what are called vascular endothelial cells in cells of the kidneys tubules, in the gastrointestinal tract, and even in the testes.

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Given that, its not clear why the new coronavirus ability to wreak havoc from head to toe came as a surprise to clinicians. Since ACE2 is also the receptor for SARS, its expression in other organs and cell types has been well-known, said Anirban Maitra of MD Anderson Cancer Center, who led a study mapping the receptor in cells of the GI tract. (Maitra is an expert in pancreatic cancer and, like many scientists this year, added Covid-19 to his research.)

Infecting cells is only the first way SARS-CoV-2 wreaks havoc. Patients with severe Covid-19 also suffer a runaway inflammatory response and, often, clot formation, said infectious disease physician Rochelle Walensky of Massachusetts General Hospital. That can cause symptoms as different as a lack of blood flow to the intestines and the red, inflamed Covid toe.

Weve had five cases of patients whove had to have their gut removed, Walensky said. You see these cases and you say, wait a minute; the virus is doing this, too? It has definitely been keeping us on our toes.

Venky Soundararajan had a hunch that the extent of ACE2 distribution throughout the body was lying in plain sight. The co-founder and chief scientific officer of nference, which uses artificial intelligence to mine existing knowledge, he and his colleagues turned their system into a hunt for ACE2 knowledge. Combing 100 million biomedical documents from published papers to genomic and other -omics databases, they uncovered multiple tissues and cell types with ACE2 receptors, they reported last month in the journal eLife.

They also calculated what percent of each cell type expresses reasonable amounts of ACE2, Soundararajan said. On average, about 40% of kidney tubule cells do, and in a surprise for a respiratory virus, cells in the GI tract were the strongest expressors of ACE2 receptors, he said.

The data mining found that ACE2 is also expressed in the noses olfactory cells. Thats not a new finding per se the nference system found it in existing databases, after all but it hadnt been appreciated by scientists or clinicians. It explains the loss or altered sense of smell that Covid-19 patients experience. Its importance became clear earlier this month, when scientists at the Mayo Clinic and nference reported that loss of a sense of smell is the earliest signature of Covid-19, appearing days before a positive swab test.

That study, using health records of 77,167 people tested for Covid-19, showed how the assumption that infection would first and foremost cause respiratory symptoms was misplaced. In the week before they were diagnosed, Covid-19 patients were 27 times more likely than people who tested negative for the virus to have lost their sense of smell. They were only 2.6 times more likely to have fever or chills, 2.2 times more likely to have trouble breathing or to be coughing, and twice as likely to have muscle aches. For months, government guidelines kept people not experiencing such typical signs of a respiratory infection from getting tested.

Faced with a disease the world had never seen before, physicians are learning as they go. By following the trail of ACE2 receptors, they are more and more prepared to look for, and treat, consequences of SARS-CoV-2 infection well beyond the obvious:

Gut: The coronavirus infects cells that line the inside of the large and small intestine, called gut enterocytes. That likely accounts for the diarrhea, nausea, and abdominal pain that about one-third of Covid-19 patients experience, said MD Andersons Maitra: The GI symptoms reflect physiological [dysfunction] of cells of the lower GI tract.

Why dont all patients have GI symptoms or indeed, the whole panoply of symptoms suggested by the near ubiquity of ACE2 receptors? For those with mild to moderate Covid-19, the infectious load in the GI tract may simply not be sufficient to cause symptoms, Maitra said.

Kidney: The cells lining the tubules that filter out toxic compounds from the blood are rife with ACE2 receptors. Last month, scientists studying 1,000 Covid-19 patients at a New York City hospital reported that 78% of those in intensive care developed acute kidney injury.

Smell: An analysis of 24 studies with data from 8,438 Covid-19 patients from 13 countries found this month that 41% had lost their sense of taste or smell, or both. That shouldnt be surprising, said Fabio Ferreli of Humanitas University in Milan: Perhaps the highest levels of ACE2 receptors are expressed in cells in the nasal epithelium. The sensory loss isnt due to nasal inflammation, swelling, or congestion, he said, but to direct damage to these epithelial cells. Loss of smell also impacts taste, but the virus may also have a direct effect on taste: The nference analysis found high levels of the ACE2 gene in tongue cells called keratinocytes, which contribute to the sense of taste.

There is another implication of the high expression of ACE2 in olfactory epithelium cells, scientists at Johns Hopkins concluded in a paper posted to the preprint site bioRxiv last month: ACE2 levels in the olfactory epithelium of the upper airways that are 200 to 700 times higher than in the lower airways might explain the viruss high transmissibility. It was weeks before experts recognized that the virus could spread from person to person.

Lungs: This is where a respiratory virus should strike, and SARS-CoV-2 does. The lungs type II alveolar cells among other jobs, they release a compound that allows the lungs to pass oxygen to the blood and take carbon dioxide from it are studded with ACE2 receptors. Once infected with the coronavirus, they become dysfunctional or die, and are so swarmed by immune cells that this inflammatory response can explode into the acute respiratory distress syndrome (ARDS) that strikes many patients with severe Covid-19, Walensky said.

There is new evidence that the virus also attacks platelet-producing cells, called megakaryocytes, in the lungs. In a study published on Thursday, pathologist Amy Rapkiewicz of NYU Winthrop Hospital found something she had never seen before: extensive clotting in the veins and other small blood vessels of patients hearts, kidneys, liver, and lungs. She suspects that the platelets produced by infected megakaryocytes travel through the bloodstream to multiple organs, damaging their vasculature and producing potentially fatal clots. You see that and you say, wow, this is not just a respiratory virus,' Rapkiewicz said.

Pancreas: In April, scientists in China reported that there was higher expression of the gene for ACE2 in the pancreas than in the lungs. Genetic data are an indirect measure of ACE2 receptors themselves, but could have been a tip-off to physicians to monitor patients for symptoms there. As it happens, the Chinese researchers also found blood markers for pancreas damage in Covid-19 patients, including in about 17% of those with severe disease.

Heart: Patients with severe Covid-19 have a high incidence of cardiac arrests and arrhythmias, scientists at the Perelman School of Medicine at the University of Pennsylvania recently found. Thats likely due to an extreme inflammatory response, but there might be more direct effects of the coronavirus, too. A large team of European researchers reported in April that arrhythmia (including atrial fibrillation), heart injury, and even heart failure and pulmonary embolism might reflect the fact that ACE2 receptors are highly expressed in cells along the inside walls of capillaries. When these vascular endothelial cells become infected, the resulting damage can cause clots, MGHs Walensky said, which in turn can cause Covid toe, strokes, and ischemic bowel (too little blood flow to the gut). Studies from around the world suggest that 7% to 31% of Covid-19 patients experience some sort of cardiac injury.

Gallbladder: Specialized cells in this organ, too, have high levelsof ACE2 receptors. Damage to the gallbladder (like the pancreas) can cause digestive symptoms.

With the number of Covid-19 patients closing in on 10 million, physicians fervently hope the virus has no more surprises in store. But theyre not counting on it.

Ive seen patients every day during this crisis, said Northwells McGinn. There have been times when Ive said, wait, the virus cant do anything new and then theres a young woman with a stroke or an older man with myocarditis, inflammation of the heart muscle. I keep thinking Im going to run out of material for the teaching videos he does on Covid-19, but it hasnt happened.

Originally posted here:

Not just the lungs: Covid-19 attacks like no other 'respiratory' virus - STAT

The true number of Americans who've been infected with COVID-19 may top 20 million, according to new estimates from the Centers for Disease Control and Prevention.

"Our best estimate right now is that for every case that's reported, there actually are 10 other infections," Dr. Robert Redfield, director of the CDC, said on a call with reporters Thursday.

Full coverage of the coronavirus outbreak

The assessment comes from looking at blood samples across the country for the presence of antibodies to the virus. For every confirmed case of COVID-19, 10 more people had antibodies, Redfield said, referring to proteins in the blood that indicate whether a person's immune system has previously fought off the coronavirus.

Those samples aren't just from people who have had antibody testing. They also come from testing performed on donated blood at blood banks or from other laboratory testing of blood.

Currently, there are 2.3 million COVID-19 cases reported in the U.S. The CDC's new estimate pushes the actual number of coronavirus cases up to at least 23 million.

"This virus causes so much asymptomatic infection," Redfield said. "The traditional approach of looking for symptomatic illness and diagnosing it obviously underestimates the total amount of infections."

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The estimation comes amid rises in cases across the Southeast and Western U.S., particularly among younger adults in their 20s, 30s and 40s.

Download the NBC News app for full coverage of the coronavirus outbreak

Also Thursday, the CDC expanded its list of who is at greatest risk for COVID-19 complications, removing the age cutoff of 65.

"There's not an exact cutoff of age at which people should or should not be concerned," Dr. Jay Butler, head of the COVID-19 response at the CDC, said. Rather, a person's risk increases with age, but that doesn't preclude younger adults from complications.

Indeed, people of any age with certain underlying health conditions have a higher risk, though the likelihood of having these conditions increases with age. At risk are those with heart disease, chronic kidney disease, chronic obstructive pulmonary disease, obesity, type 2 diabetes, sickle cell disease and anyone with a compromised immune system.

CDC also clarified the list of other conditions that might increase a persons risk of severe illness, including asthma, high blood pressure, neurologic conditions such as dementia, cerebrovascular disease such as stroke, and pregnancy.

Research published on Thursday from the CDC specifically addressed the risk in pregnant women. When compared to nonpregnant women with the virus, pregnant women with COVID-19 were more likely to be hospitalized, admitted to the intensive care unit and put on a ventilator.

Death rates between the two groups of women, however, were similar.

Redfield also urged Americans to be vigilant about behavior measures known to minimize spread of the coronavirus, particularly as the country heads into the July Fourth holiday.

The coronavirus spreads mainly from person to person through respiratory droplets from coughing, sneezing, talking and singing.

"The most powerful tool that we have is social distancing," he said. That means maintaining a physical distance of at least 6 feet in public, wearing face coverings and regular hand-washing.

"If you must go out into the community, being in contact with fewer people is better than many," Redfield added.

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Erika Edwards is a health and medical news writer and reporter for NBC News and "TODAY."

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CDC says COVID-19 cases in U.S. may be 10 times higher than reported - NBC News

Minks at a mink farm. Yuri Tutov /AFP via Getty Images hide caption

Minks at a mink farm.

Minks on two fur farms in the Netherlands began getting sick in late April. Some were coughing, with runny noses; others had signs of severe respiratory disease. Soon, they started dying.

Researchers took swabs from the animals and dissected the ones that had died.

The culprit: SARS-COV-2, the novel coronavirus causing a global pandemic.

It's part of an emerging pattern of animals getting infected with the novel coronavirus with a new concern: The minks are thought to have passed the disease back to humans. Since the discovery, more than 500,000 minks have been culled on fur farms in the Netherlands over worries that their mink populations could spread the virus among humans.

The minks were first exposed to the coronavirus by infected farm workers, according to Wim van der Poel, a veterinarian who studies viruses at Wageningen University & Research in the Netherlands. Then the virus spread among the animals in the farms like wildfire.

"The animals are in cages with wire tops and closed walls between them," says Van der Poel, who co-authored a Eurosurveillance paper investigating the mink farm infections that was published this month. "So it probably spread through droplet or aerosol transmission, from the top of one cage to another, when an animal is coughing or heavily breathing."

The Netherlands is one of the world's top exporters of mink fur for coats and trim. The outbreak was first reported on two of its approximately 125 farms and has now been found in at least 17. Van der Poel says the virus was likely spread to more farms either from infected workers who traveled between locations or from virus-contaminated products that moved from one farm to another.

Minks are the latest addition to the list of animals that we know can be infected with the novel coronavirus, says Linfa Wang, director of the emerging infectious diseases program at Duke-NUS Medical School in Singapore. "The first one we noticed was cats," he says. "Then it was followed by dogs, which are susceptible but not as much as cats. And then the tigers in the New York zoo. And now the minks." Laboratory experiments have also confirmed that hamsters and some monkeys can also get sick from the virus. And the virus is believed to have originated in Chinese horseshoe bats.

The findings from the mink farm adds an concerning layer to our understanding of how the virus spreads because infected minks are thought to have passed the virus back to people, according to Dutch government reports. At least two farm workers are believed to have caught the novel coronavirus from handling the minks or breathing virus-contaminated clouds of dust.

The situation confirms a longstanding concern among researchers which has, until now, been hypothetical: In some animal-to-human interactions, the virus can transmit both ways. "It's another route of transmission that we have to worry about," says Kevin Olival, an ecologist at EcoHealth Alliance, a nonprofit that monitors for emerging diseases. "And if the virus somehow establishes itself in animal populations or in other wildlife populations around the world, it'll be really hard to eradicate it if it keeps spilling back into the human population."

Those concerns prompted Dutch authorities to cull all the minks on farms where the novel coronavirus has been found. While many of the animals on affected farms were found to have survived the virus and developed antibodies, it's unclear how long they might be protected. And many new pups, presumed to be susceptible to the coronavirus, are born in the late spring, van der Poel says. So hundreds of thousands of animals are being gassed to ensure that they do not become reservoirs for the virus.

From a disease control perspective, Wang says it's helpful that the outbreaks were contained to farms and caused symptoms in the infected animals, "so at least you notice the animal is sick." A less controllable scenario, he says, is if the virus takes root in wandering wildlife populations for instance, if it were to become established among North American bats, a group that could carry the disease without developing symptoms and spread it widely.

Wang sees this ability of the novel coronavirus to jump easily between different species as troubling especially since the virus has spread so rapidly among humans. Many common viruses, such as measles and hepatitis, mostly infect humans. On the other hand, some emerging viruses such as Ebola, Nipah and SARS infect a broad spectrum of species. But outbreaks among humans have been contained.

When it comes to eradicating a disease, "the worst is an animal virus that jumps over and becomes established in humans and has a wide host range," Wang says, meaning it can infect many different animal species that might pass it back to humans. "And that's SARS-COV-2."

Currently, the novel coronavirus is mainly spreading through close contact between people, says Maria Van Kerkhove, technical lead for the World Health Organization's health emergencies program.

Still, researchers think the potential for animal reservoirs is something public health officials should be thinking about now. The lesson from the mink farms, says van der Poel, is that we should be testing all kinds of animals for the novel coronavirus as well as keeping a lookout for other novel diseases they may be carrying.

"We have to have an open mind and open eyes for any new virus that pops up," he says.

Jerome Socolovsky contributed to this story.

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Dutch Minks Contract COVID-19 And Appear To Infect Humans - NPR

By: Anna Abram, Deputy Commissioner for Policy, Legislation, and International Affairs and Mark Abdoo, Associate Commissioner for Global Policy and Strategy

The European Union (EU) is one of the U.S. Food and Drug Administrations most important collaborators in tackling public heath challenges. The FDA and the European Commission (EC) and its European Medicines Agency (EMA) have long leveraged each others expertise and experience to promote the safety, effectiveness, and quality of medical products to advance the health of our citizens. Now, our work, built together over more than a decade, has paved the way for a multitude of critical collaborations on many scientific and regulatory fronts as part of our COVID-19 response.

What has helped drive this collaboration are the 30 technical expert groups, or clusters that the FDA and the EMA have established since 2003. These clusters meet regularly for regulatory discussions held under confidentiality commitments and were well positioned to pivot to focus on COVID-19 because of our ongoing work together. Since the start of the pandemic, many of these groups which often also include regulatory authorities from Japan, Canada, Australia, and other countries have shifted their work priorities to focus on COVID-19. For example, our expert group on vaccines has expanded into a multilateral forum to discuss regulatory issues related to the development of SARS-CoV-2 vaccines, whereas the pharmacovigilance group has begun to identify opportunities for collaboration on observational studies related to COVID-19 natural history and interventions. Similarly, our standing meetings on blood products are now focusing on developments related to COVID-19 convalescent plasma. The drug shortages discussions revolve around information sharing on strategies for COVID-19 related shortages and supply disruptions of medicines.

In these expert group discussions, the FDA and the EMA have been exchanging information on the rapidly evolving scientific landscape of products and clinical trials and, as possible, discussing the interpretation of data supporting regulatory decisions. For example, two days after the release of results from a clinical trial that the National Institute of Allergies and Infectious Diseases (NIAID-ACTT study) conducted, the FDA authorized the emergency use of the investigational antiviral drug remdesivir for the treatment of suspected or laboratory-confirmed COVID-19 in adults and children hospitalized with severe disease. The FDA subsequently hosted a virtual meeting of key regulatory partners, including from Europe, Canada, and Japan, to discuss the safety and efficacy of remdesivir as a treatment for COVID-19. At the time, Japan had announced their version of early access to the drug and most of the other participating authorities were about to begin their own reviews for making such a decision, with EMA issuing a recommendation to expand remdesivir compassionate use shortly thereafter.

Against this backdrop of robust collaboration, on June 18-19, the FDA hosted virtual bilateral meetings with the EC and EMA to review progress on ongoing activities and share horizon scanning across high-priority areas. For example, public and private sector entities are proactively exploring strategic partnerships to address the anticipated challenges related to manufacturing and rapid scale-up of potential COVID-19 vaccines or therapeutics. As two-prominent international regulatory bodies, the FDA and the EC/EMA can help inform global regulatory strategies to accelerate production and global access of products. In addition, the application of real-world data to understand the natural history of COVID-19, treatment patterns, and the performance of diagnostics is of keen interest for both the FDA and the EU.

The FDA and the EU are also promoting engagement with global regulators, under the International Coalition of Medicines Regulatory Authorities (ICMRA) forum, which is comprised of 28 regulatory authorities from around the globe. For example, in March, as vaccine candidates began to be identified, the FDA and the EMA jointly chaired the first global regulators meeting to discuss regulatory strategies to facilitate the development of SARS-CoV-2 vaccines. Subsequent ICMRA workshops that are focused on COVID-19 therapeutic development, observational studies, and real-world data as well as policy approaches are helping to support mutual awareness and consideration of potential guidance alignment. Such vigorous efforts are reflected in the ICMRA joint statement, which was issued in April 2020, with global regulatory authorities expressing their collective support in countering COVID-19.

To maintain strong oversight of foreign-manufactured medical products, the FDA is leveraging inspection reports completed by the EU and United Kingdom under the Pharmaceutical Annex to the US-EU Mutual Recognition Agreement (MRA). The MRA creates an environment in which FDA and the EU may rely on inspections performed by each others regulatory authorities to inform our regulatory decisions, such as drug approvals or addressing drug shortages.

We are also leveraging our international collaborations in our medical device work. The FDA and global partners (including the EC and other European partners) regularly exchange information on medical device safety issues and regulatory developments. These international relationships have never been more important as we work to maintain critical supplies of medical devices such as personal protective equipment, ventilators and ventilator accessories, as well as diagnostic testing supplies and test kits for COVID-19. Our interactions have included working through our embassies and European contacts to address supply chain disruptions, medical device shortages, and removal of fraudulent and poor-quality products from the market. Moving forward, shared learnings about antibody tests, otherwise known as serology tests their validation, results of ongoing epidemiological studies, and potential use in broader testing programs will inform our continued efforts to confront this pandemic.

Our work in these endeavors is, as always, rooted in the FDAs unwavering commitment to helping to foster the development of safe and effective medical products. We recognize the shared challenges of public health authorities across the world in fighting this pandemic as well as the tremendous opportunities to accelerate our mission critical work through robust scientific collaboration. We will continue to collaborate with our global regulatory counterparts as we seek to bring safe and effective COVID-19 vaccines and treatments to our citizens as quickly as possible and as part of advancing our vital mission to protect and promote public health.

Read the rest here:

Partnering with the European Union and Global Regulators on COVID-19 - FDA.gov

Media Advisory

Tuesday, June 23, 2020

NIH workshop attendees review capabilities, limits of SARS-CoV-2 serology testing.

More than 300 scientists and clinicians from the federal government, industry and academia published a report of their conclusions and recommendations on COVID-19 serology studies online in Immunity. The group gathered for an online workshop in May to discuss the role of serology testing in understanding and responding to the COVID-19 public health crisis and to explore strategies to address key scientific knowledge opportunities and gaps in the emerging field. Serology tests for COVID-19 are designed to detect antibodies against SARS-CoV-2, the virus that causes COVID-19. While such tests do not diagnose active infection, they can indicate prior infection with SARS-CoV-2 that may have been missed because a person did not experience significant symptoms or access testing while infected.

The COVID-19 Serology Studies workshop was convened by an interagency working group comprised of experts from the U.S. Department of Health and Human Services including scientists at the National Institute of Allergy and Infectious Diseases (NIAID), the National Cancer Institute (NCI), and the National Heart, Lung and Blood Institute (NHLBI), parts of the National Institutes of Health, as well as the Centers for Disease Control and Prevention and the Biomedical Advanced Research and Development Authority and the Department of Defense. Attendees assessed efforts to better understand the implications of serology test results, to produce and validate test kits, and to quantify undetected cases of SARS-CoV-2 infection.

Attendees recommended that additional research is needed to determine if and to what extent a positive antibody test means a person may be protected from reinfection with SARS-CoV-2. Attendees emphasized that until such data is available, serology tests should not be used as a stand-alone tool to make decisions about personal safety related to SARS-CoV-2 exposure. Researchers are now pursuing studies in humans and in animal models to better understand SARS-CoV-2 immunity. Attendees noted that such understanding could help identify optimal donors of convalescent plasma that potentially could be used to help treat those with severe COVID-19.

Researchers from NCI reviewed progress in their effort to independently validate SARS-CoV-2 serology tests on behalf of the U.S. Food and Drug Administration. Attendees also proposed strategies to expand the accuracy and capacity of these tests to distinguish between naturally acquired and vaccine-induced antibodies, which will be critical to evaluating COVID-19 vaccine candidates.

Both community-based and large-scale serology surveillance efforts such as the RESPONSE study sponsored by NIAID and NHLBI are collecting critical data to improve epidemiological models and inform public health decision-making. Ideally, attendees noted, federal partners will expand this activity to establish an interactive serological database that will help public health officials monitor and quickly respond to changes in SARS-CoV-2 infection patterns.

A Lerner et al. COVID-19 Serology Studies Workshop: Meeting Report. Immunity DOI: 10.1016/j.immuni.2020.06.012 (2020).

Cristina Cassetti, Ph.D., deputy director of NIAIDs Division of Microbiology and Infectious Diseases, is available for comment.

NIAID conducts and supports research at NIH, throughout the United States, and worldwide to study the causes of infectious

About the National Institutes of Health (NIH):NIH, the nation's medical research agency, includes 27 Institutes and Centers and is a component of the U.S. Department of Health and Human Services. NIH is the primary federal agency conducting and supporting basic, clinical, and translational medical research, and is investigating the causes, treatments, and cures for both common and rare diseases. For more information about NIH and its programs, visit http://www.nih.gov.

NIHTurning Discovery Into Health

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Experts identify steps to expand and improve antibody tests in COVID-19 response - National Institutes of Health

Covid-19 is similar in important ways to the diseases caused by other recent coronaviruses, such as SARS and MERS. Like Covid-19, SARS and MERS exhibited male-female differences in fatalities. As with Covid-19, this difference was initially claimed as a sex difference. But careful analysis showed that gendered behaviors, pre-existing conditions, and gender-segregated occupational exposures explained these sex differences. All signs point to Covid-19 following a comparable pattern.

SARS emerged in early 2003 and quickly reached pandemic levels. Men overall indeed died at a higher rate than women. But a closer inspection of the data soon showed that sex differences varied considerably by age group. At older ages, there was no significant difference between the female and male fatality rates, but younger men died at markedly higher rates than younger women. For instance, in Hong Kong, only 5.9 percent of women ages 35 to 44 died, compared with 15.3 percent of men. Between the ages of 35 and 64, men who developed SARS were 10 percent more likely to die than women.

Taking a cue from these patterns, researchers ran analyses accounting for age, occupation and pre-existing conditions. The results showed that after accounting for these factors, women and men actually had similar fatality rates for SARS for all age groups. The lower fatality rate among women was driven by particularly high infection rates among health care workers, who were predominantly young, healthy and female. So women were both disproportionately likely to be infected and disproportionately likely to survive, compared with men in that age group. Among older women and men, and those with comorbidities such as heart disease, cancer, asthma and liver disease, there was little difference in SARS outcomes. The apparent sex difference was caused by gender-related occupational differences and diseases with complex, often socially rooted causes.

MERS offers an even more clear-cut example. The disease overwhelmingly affected, and continues to affect, older men. Primary transmission from camels remains a key source of infections, and camel handling and slaughtering are predominantly male occupations in Saudi Arabia. As with SARS, a comprehensive study published in 2017 found that fatalities did not differ by sex after accounting for age and pre-existing health status. The sex difference here, in other words, is produced by who is getting infected, not who dies once theyre infected.

A key factor most likely related to male-female differences in Covid-19 fatalities is that men overall are in a poorer state of health than women. In a study examining sex differences in outcomes among Covid-19 patients in China, men were more likely than women to have any comorbidity or two or more of them. Of people with Covid-19 and chronic obstructive pulmonary disease, 83.3 percent were male. Of people with diabetes and cardiovascular disease, 58.9 percent and 62.1 percent, respectively, were male. To be sure, sex-linked biology may play a role in the development of some chronic diseases, but always in complex interaction with class, race or ethnicity, and gender-related variables. Several analyses have already demonstrated that in places where men have higher Covid-19 fatality rates than women, men also, on average, have far higher rates of behaviors such as smoking and comorbidities related to smoking, such as heart disease.

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Whats Really Behind the Gender Gap in Covid-19 Deaths? - The New York Times

Late last week, a group of researchers posted a letter that they had sent to the Proceedings of National Academy of Sciences (PNAS) requesting the retraction of a study published the week before that purportedly showed mask use was the most effective intervention in slowing the spread of COVID-19 in New York City.

Though PNAS editors have yet to respond to the request, scientists have roundly criticized the study's methodology, and the entire kerfuffle has highlighted the difficulty of "doing science" amid a full-blown pandemic.

The paper in question, "Identifying airborne transmission as the dominant route for the spread of COVID-19" states, "After April 3, the only difference in the regulatory measures between NYC and the United States lies in face covering on April 17 in NYC."

The group of scientists, many from Stanford and Johns Hopkins universities, took umbrage with that conclusion and said it is verifiably false on several accounts: Other parts of the country had mandated mask use, and different parts of the United States had different degrees of "lockdown."

"While masks are almost certainly an effective public health measure for preventing and slowing the spread of SARS-CoV-2, the claims presented in this study are dangerously misleading and lack any basis in evidence," they wrote in a letter to the PNAS editorial board, requesting retraction. "Unfortunately, since its publication on June 11th, this article has been distributed and shared widely in traditional and social media, where its claims are being interpreted as rigorous science."

Noah Haber, ScD, a postdoctoral fellow at Stanford University, said he has heard from PNAS editors that they have received the letter. Haber was the first co-signer of the letter requesting retraction.

"The policy implications of this paper is immediate, so we hope response is commensurate with the decisions that need to be made," he told CIDRAP News.

Haber said he and his colleagues are not arguing the usefulness of masks, but instead pointing out that the study in question could not evaluate how effective masking policies are relative to other policies.

"There are an enormous number of severe errors with the paper," Haber said. "Unfortunately, this is not a new problem in science, but the stakes are much higher than before."

Haber said the paper also highlights the problems of doing science in the midst of a pandemic caused by a novel virus: An enormous, unprecedented volume of studies have been published on COVID-19. But unfortunately, many don't hold up and are methodologically flawed.

"Under normal circumstances, years-long debate would filter wheat from the chaff, but everything is happening so immediately now," he said.

David Kriebel, ScD, a professor of epidemiology at the University of Massachusetts-Lowell, has followed the controversy. While he agrees that the PNAS study is flawed, he does not agree with a retraction at this time. The paper wasn't a failure of the peer review process, he said, but rather a failure of understanding the limits of science during a pandemic.

"The kind of science we are talking aboutand the public has become so remarkably informed aboutis applied science being used to inform decision-making on a mass scale," Kriebel said. "That kind of science is really quite different in important ways from the work of geologists, chemists, or astronomers. It has an urgency; it has to be translated to millions of people, and quickly."

Kriebel said that usually science is self-correcting, given enough time. But currently there is not enough time for science to self-correct when it's being used to craft public health policy. He said that's a problem for policy makers over-relying on "capital S" science to justify decisions.

"It's actually not helpful for scientists to hide behind a curtain of certainty. There is uncertainty about masks. But that doesn't mean we shouldn't be wearing them," Kriebel said. Instead of clamoring for scientific studies to back up mandates on mask use, Kriebel argues for more transparency in public health messaging.

"I would say, 'Mask use is our best judgment right now, and we will tell you if we get more evidence," he said.

Both Kriebel and Haber agree that masks probably do offer a level of protection, but right now there is no way to tease out how much protection masks offer versus physical distancing of 6 feet or more, or hand washing.

"The world is much messier than we would like to admit," said Kreibel. "We do our best and admit our uncertainty."

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Controversy on COVID-19 mask study spotlights messiness of science during a pandemic - CIDRAP