Category: Covid-19

Two MIT professors have proposed a new approach to estimating the risks of exposure to Covid-19 under different indoor settings. The guideline they developed suggests a limit for exposure time, based on the number of people, the size of the space, the kinds of activity, whether masks are worn, and the ventilation and filtration rates. Their model offers a detailed, physics-based guideline for policymakers, businesses, schools, and individuals trying to gauge their own risks.

The guideline, appearing this week in the journal PNAS, was developed by Martin Z. Bazant, professor of chemical engineering and applied mathematics, and John W. M. Bush, professor of applied mathematics. They stress that one key feature of their model, which has received less attention in existing public-health policies, is providing a specific limit for the amount of time a person spends in a given setting.

Their analysis is based on the fact that in enclosed spaces, tiny airborne pathogen-bearing droplets emitted by people as they talk, cough, sneeze, sing, or eat will tend to float in the air for long periods and to be well-mixed throughout the space by air currents. There is now overwhelming evidence, they say, that such airborne transmission plays a major role in the spread of Covid-19. Bush says the study was initially motivated early last year by their concern that many decisions about policies were being guided primarily by the 6-foot rule, which doesnt adequately address airborne transmission in indoor spaces.

Using a strictly quantitative approach based on the best available data, the model produces an estimate of how long, on average, it would take for one person to become infected with the SARS-CoV-2 virus if an infected person entered the space, based on the key set of variables defining a given indoor situation. Rather than a simple yes or no answer about whether a given setting or activity is safe, it provides a guide as to just how long a person could safely expect to engage in that activity, whether it be a few minutes in a store, an hour in a restaurant, or several hours a day in an office or classroom, for example.

As scientists, weve tried to be very thoughtful and only go with what we see as hard data, Bazant says. Weve really tried to just stick to things we can carefully justify. We think our study is the most rigorous study of this type to date. While new data are appearing every day, and many uncertainties remain about the SARS-CoV-2 virus transmission, he says, We feel confident that weve made conservative choices at every point.

Bush adds: Its a quickly moving field. We submit a paper and the next day a dozen relevant papers come out, so we scramble to incorporate them. Its been like shooting at a moving target. For example, while their model was initially based on the transmissibility of the original strain of SARS-CoV-2 from epidemiological data on the best characterized early spreading events, they have since added a transmissibility parameter, which can be adjusted to account for the higher spreading rates of the new emerging variants. This adjustment is based on how any new strains transmissibility compares to the original strain; for example, for the U.K. strain, which has been estimated to be 60 percent more transmissible than the original, this parameter would be set at 1.6.

One thing thats clear, they say, is that simple rules, based on distance or capacity limits on certain types of businesses, dont reflect the full picture of the risk in a given setting. In some cases that risk may be higher than those simple rules convey; in others it may be lower. To help people, whether policymakers or individuals, to make more comprehensive evaluations, the researchers teamed with app developer Kasim Khan to put together an open-access mobile app and website where users can enter specific details about a situation size of the space, number of people, type of ventilation, type of activity, mask wearing, and the transmissibility factor for the predominant strain in the area at the time and receive an estimate of how long it would take, under those circumstances, for one new person to catch the virus if an infected person enters the space.

The calculations were based on inferences made from various mass-spreading events, where detailed data were available about numbers of people and their age range, sizes of the enclosed spaces, kinds of activities (singing, eating, exercising, etc.), ventilation systems, mask wearing, the amount of time spent, and the resulting rates of infections. Events they studied included, for example, the Skagit Valley Chorale in Washington state, where 86 percent of the seniors present became infected at a two-hour choir practice

While their guideline is based on well-mixed air within a given space, the risk would be higher if someone is positioned directly within a focused jet of particles emitted by a sneeze or a shout, for example. But in general the assumption of well-mixed air indoors seems to be consistent with the data from actual spreading events, they say.

When you look at this guideline for limiting cumulative exposure time, it takes in all of the parameters that you think should be there the number of people, the time spent in the space, the volume of the space, the air conditioning rate and so on, Bush says. All of these things are kind of intuitive, but its nice to see them appear in a single equation.

While the data on the crucial importance of airborne transmission has now become clear, Bazant says, public health organizations initially placed much more emphasis on handwashing and the cleaning of surfaces. Early in the pandemic, there was less appreciation for the importance of ventilation systems and the use of face masks, which can dramatically affect the safe levels of occupancy, he says.

Id like to use this work to establish the science of airborne transmission specifically for Covid-19, by just taking into account all factors, the available data, and the distribution of droplets for different kinds of activities, Bazant says. He hopes the information will help people make informed decisions for their own lives: If you understand the science, you can do things differently in your own home and your own business and your own school.

Bush offers an example: My mother is over 90 and lives in an elder care facility. Our model makes it clear that its useful to wear a mask and open a window this is what you have in your control. He was alarmed that his mother was planning to attend an exercise class in the facility, thinking it would be OK because people would be 6 feet apart. As the new study shows, because of the number of people and the activity level, that would actually be a highly risky activity, he says.

Already, since they made the app available in October, Bazant says, they have had about half a million users. Their feedback helped the researchers refine the model further, he says. And it has already helped to influence some decisions about reopening of businesses, he adds. For example, the owner of an indoor tennis facility in Washington state that had been shut down due to Covid restrictions says he was allowed to reopen in January, along with certain other low-occupancy sports facilities, based on an appeal he made based in large part on this guideline and on information from his participation in Bazants online course on the physics of Covid-19 transmission.

Bazant says that in addition to recommending guidelines for specific spaces, the new tools also provide a way to assess the relative merits of different intervention strategies. For example, they found that while improved ventilation systems and face mask use make a big difference, air filtration systems have a relatively smaller effect on disease spread. And their study can provide guidance on just how much ventilation is needed to reach a particular level of safety, he says.

Bazant and Bush have provided a valuable tool for estimating (among other things) the upper limit on time spent sharing the air space with others, says Howard Stone, a professor of mechanical and aerospace engineering at Princeton University who was not connected to this work. While such an analysis can only provide a rough estimate, he says the authors describe this kind of order of magnitude of estimate as a means for helping others judge the situation they might be in and how to minimize their risk. This is particularly helpful since a detailed calculation for every possible space and set of parameters is not possible.

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A method to assess Covid-19 transmission risks in indoor settings - MIT News

Going forward, whether you have to wear a mask or are able to get into a crowded restaurant will depend on what Colorado county you live in.

The states COVID-19 dial framework which looked at cases, hospitalizations and positive test percentages to determine capacity levels for businesses became optional Friday. Now, each county decides its own rules, though the state could intervene if hospitals start to run out of room.

Most of the Denver area elected to go to Level Blue, which moves last call to 2 a.m., doesnt set a maximum number of people in restaurants or gyms (though parties must be kept six feet apart) and limits bars to 25% capacity. Counties staying in Level Blue, or moving to it, are:

Six counties said they would remain in Level Green, where the only restrictions are 50% capacity for bars, indoor events and group sports played indoors. Those counties are:

Pueblo County said it will remain in Level Yellow, where most businesses are limited to 50% of capacity.

For the 28 counties that havent set their own public health orders, the only requirements most people will encounter are the statewide mask order and large events needing permission from the Colorado Department of Public Health and Environment. Those counties are:

Some counties have issued their own public health orders, with rules that dont correspond to a dial level.

This list will be updated with additional counties as information becomes available.

Denver Post reporter Jessica Seaman contributed to this report.

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After the dial: Which Colorado counties have eliminated all COVID-19 restrictions? - The Denver Post

According to Dr. Robert P. Lennon, The rise of social media has changed the way people around the world keep up with current events, with studies showing that up to 66% of Americans rely on social media for news.

Dr. Lennon, an author of a new paper from Penn State University in State College, Pennsylvania, makes the case that this misunderstanding is cause for alarm.

People who relied on TV news and social media were less knowledgeable about COVID-19 than other people during the early days of the pandemic.

Says Dr. Lennon:

This is worrying, as misinformation and misunderstanding about COVID-19 and how it spreads are likely to have fueled the pandemic, whose death toll now surpasses 2.5 million worldwide.

The studys conclusions also have implications for future pandemics, which experts consider inevitable.

The study appears in the peer-reviewed journal Current Medical Research & Opinion.

In March 2020, the researchers developed a survey designed to capture respondents preferred news sources, as well as their knowledge of COVID-19.

In the last week of March, the researchers emailed the surveys to adults listed in the Pennsylvania health systems marketing database, and 5,948 people completed the survey.

Stay informed with live updates on the current COVID-19 outbreak and visit our coronavirus hub for more advice on prevention and treatment.

This represented 4.8% of the whole database but 74% of those who opened the survey. This suggests a strong bias towards those who have an interest in the subject.

Most of those who completed the survey were older, white, educated females, although the study notes that responses were similar regardless of gender.

Participants over age 50 reported that they were more likely to trust television news sources than internet news sources than individuals younger than 50.

The authors of the study note that they have limited ability to generalize the studys conclusions due to the lack of diversity and urban representation in its sample.

For 42.8% of those completing surveys, the most trusted sources of information were governmental, including the Centers for Disease Control (CDC), National Institutes of Health (NIH), and the World Health Organization (WHO).

TV news was the most trusted source for 27.2% of participants, followed by 9.2% who trusted information from Pennsylvanias health system most. Just 7.4% of those surveyed said they trusted news from other internet sources.

The survey included 15 statements regarding COVID-19s transmission, severity, and treatment. The survey asked participants to identify each statement as true or false. For each statement, individuals also reported the degree to which they were sure of their answer. They used a 5-point scale from 1 for Very confident to 5 for Not at all confident [just guessing].

The individuals who scored highest on the survey were those who trusted governmental health sites. People who got their COVID-19 information from internet news sources came second.

The results did not clarify what the source was for the correct information. The researchers noted that they based some of their questions on CDC guidance, which provided an inherent bias toward government health sites.

The people who were most misinformed about COVID-19 were those who got their news from TV newscasters.

Coming in second-to-last for COVID-19 knowledge were people who said they got all or some of their health information from Facebook.

The researchers conducted the survey in late March 2020. Later research has now superseded some of the correct answers.

For example, participants designated one question about whether healthy people should wear facemasks to help prevent the spread of Covid-19 as false. At the time, people had little knowledge about the potential for spread amongst asymptomatic individuals.

While it is encouraging that such a large percentage of the studys sample turned to reliable governmental sources for COVID-19 information, there are still a great many people who are receiving misinformation from TV news.

Dr. Lennon hopes the studys insights can inform a more successful public-health response when future pandemics arise, saying:

Effective communication is a critical element of successfully managing a pandemic response; as for [containing] the disease spread, the public must comply with public health recommendations.

The first step in compliance, says Dr. Lennon, is an understanding of those recommendations, so it is vital that health communicators consider how the public gets their information and monitor these venues to correct misinformation when it appears.

Future studies should aim to capture the dynamic nature of peoples understanding of scientific truth as studies reveal new information.

Some of the new information may contradict original statements from trusted information sources, including government websites.

For live updates on the latest developments regarding the novel coronavirus and COVID-19, click here.

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TV news was the source of most early COVID-19 misinformation - Medical News Today

Abstract

Brazil has been severely hit by COVID-19, with rapid spatial spread of both cases and deaths. We use daily data on reported cases and deaths to understand, measure, and compare the spatiotemporal pattern of the spread across municipalities. Indicators of clustering, trajectories, speed, and intensity of the movement of COVID-19 to interior areas, combined with indices of policy measures show that while no single narrative explains the diversity in the spread, an overall failure of implementing prompt, coordinated, and equitable responses in a context of stark local inequalities fueled disease spread. This resulted in high and unequal infection and mortality burdens. With a current surge in cases and deaths and several variants of concern in circulation, failure to mitigate the spread could further aggravate the burden.

Brazil is the only country that, with a population larger than 100 million, has a universal, comprehensive, and free of charge health care system. Over three decades, this system contributed to reducing inequalities in access to health care and outcomes (1). It also facilitated the management of previous public health emergencies, such as the HIV/AIDS pandemic (2). Despite recent cuts in the health budget (3), it was expected that Brazils health system would place the country in a good position to mitigate the COVID-19 pandemic. With national coordination and through a vast network of community health agents, actions adapted to existing local inequalities (i.e., regional distribution of physicians and hospital beds) could have been implemented (4). However, Brazil is one of the countries most severely hit by COVID-19. As of March 11, 2021, 11,277,717 cases and 272,889 deaths have been reported. Those represent 9.5% and 10.4% of the worldwide cases and deaths, respectively; yet, Brazil shares only 2.7% of the worlds population. In late May, 2020, Latin America was declared the epicenter of the COVID-19 pandemic, mainly because of Brazil. Since June 7, 2020, Brazil ranks 2nd in deaths worldwide.

In Brazil, the federal response has been a dangerous combination of inaction and wrongdoing, including the promotion of chloroquine as treatment despite a lack of evidence (5, 6). Without a coordinated national strategy, local responses varied in form, intensity, duration, and start and end times, to some extent associated with political alignments (7, 8). The country has seen very high attack rates (9) and disproportionally higher burden among the most vulnerable (10, 11), illuminating local inequalities (12). Following multiple introductions of SARS-CoV-2, Brazil had an initial epidemic phase (February 15 to March 18, 2020) with restricted circulation (13), preceded by undetected virus circulation (14). While the initial spread was determined by existing socioeconomic inequalities, the lack of a coordinated, effective, and equitable response likely fueled the widespread spatial propagation of SARS-CoV-2 (12). The goal of this study was to understand, measure, and compare the pattern of spread of COVID-19 cases and deaths in Brazil at fine spatial and temporal scales. We use daily data from State Health Offices covering the period from epidemiological week 9 (February 23-29) to week 41 (October 4-10).

In all states, it took less than a month between the first case and the first death; only 11 days in Amazonas and 21 in So Paulo (table S1). Epidemiological curves for Brazil (fig. S1) hide distinct patterns of initial reporting, propagation, and containment of SARS-CoV-2 across administrative units. As states and cities imposed and relaxed restrictive measures at different times, population mobility facilitated the circulation of the virus and acted as a trigger of disease spread (15). Figure 1, A and B, show that cumulative cases and deaths, respectively, per 100,000 people were not uniformly distributed across municipalities. We used the space-time scan statistic (16) to identify areas that significantly recorded a high number of cases (Fig. 1C and table S2) or deaths (Fig. 1D and table S3) over a defined period.

Cumulative number of COVID-19 cases (A) and deaths (B) per 100,000 people by municipality. Dark lines on the maps show state boundaries. State acronyms by region, North: AC=Acre, AP=Amap, AM=Amazonas, PA=Par, RO=Rondnia, RR=Roraima, and TO=Tocantins; Northeast: AL=Alagoas, BA=Bahia, CE=Cear, MA=Maranho, PB=Paraba, PE=Pernambuco, PI=Piau, RN=Rio Grande do Norte, and SE=Sergipe; Center-West: DF=Distrito Federal, GO=Gois, MT=Mato Grosso, and MS=Mato Grosso do Sul; Southeast: ES=Esprito Santo; MG=Minas Gerais; RJ=Rio de Janeiro; and SP=So Paulo; South: PR=Paran; RS=Rio Grande do Sul; and SC=Santa Catarina. Spatio-temporal clustering of cases (C) and deaths (D) across Brazilian municipalities. Color and number codes in the clusters and the table on the left are the same, and the table indicates the interval during which each cluster was statistically significant. The color gradient (dark red to dark blue) indicates the temporal change based on the initial date of the cluster, and the cluster number indicates the rank of the relative risk for each cluster (tables S2 and S3). Clusters were assessed with the space-time scan statistic (see supplementary materials).

Deaths clustered about a month before cases. This likely reflects problems in surveillance, data reporting, and low testing capacity. The first significant cluster of COVID-19 deaths started on May 18 (Fig. 1D, #5), centered around Recife (capital of Pernambuco). Five other clusters of deaths occurred before the first cluster of cases was observed on June 16 (Fig. 1C, #7). Among those are clusters around Fortaleza and Rio de Janeiro (capital cities of Cear and Rio de Janeiro, respectively), and in a large area including Amazonas, Par, and Amap, states that have a disproportionally lower hospital capacity. Amazonas (whose capital is Manaus) has the highest mortality per 100,000 people in the country, more than double the rate for Brazil. By October, about 76% of its population was estimated to have been infected (9, 17). Except for one cluster in August (Fig. 1D, #1), the duration of death clusters did not reduce over time, ranging from 10 to 13 days. This is different than what was observed in South Korea, where successful containment reduced the duration and the geographic extent of clusters over time (18). A similar pattern was observed for COVID-19 cases (Fig. 1C). In the center and southern areas, clusters occurred later (August and September), corroborating a regional pattern of propagation of SARS-CoV-2 (19).

To understand and compare how COVID-19 cases and deaths spread across Brazil we calculated the geographic center of the epidemic. Trajectories of the center by epidemiological week show that after the introduction in So Paulo, both cases (Fig. 2A and movie S1) and deaths (Fig. 2B and movie S2) progressively moved north until week 20 (starting May 10), when the epidemic started to recede in Amazonas and Cear, but gained force in Rio de Janeiro and So Paulo. Comparing trajectories in each state (fig. S2) we calculated a ratio of the distance the center moved each week to the distance between the capital city and the most distant municipality (tables S4 and S5). In eight states the median weekly ratio for deaths was larger than cases (Fig. 2C), suggesting a faster movement of the focus of deaths.

COVID-19 case- (A) and death-weighted (B) geographic centers by epidemiological week. Thick lines show the geographic center for Brazil, thin lines show the trajectory of the center in each state, and the black dot indicates the state capital city (see supplementary materials). The first case in each state was recorded in the capital city, except for Rio de Janeiro, Rondnia, Bahia, Minas Gerais, and Rio Grande do Sul, and thus the trajectory of the center starts in the interior. This was more common for deaths (14 states did not report the first death in the capital: Rio de Janeiro, Amazonas, Par, Piau, Rio Grande do Norte, Paraba, Esprito Santo, Paran, Santa Catarina, Mato Grosso do Sul, Mato Grosso, and Gois). Figure S2 shows detailed maps for each state. (C) Scatterplot of the median distance that the geographical center of cases (X-axis) and deaths (Y-axis) shifted weekly in each state (measured as the ratio of the distance that the geographical center of cases shifted weekly in each state to the distance between the capital city and the furthest municipality in the state). (D) Scatterplot of the number of days that it took for a state to reach 50 COVID-19 cases (X-axis) after the first case was reported and 50 deaths after the first COVID-19 confirmed death (Y-axis). (E) Scatterplot of the standardized number of cases per 100,000 people (X-axis) and deaths per 100,000 people (Y-axis) by state. The 45-degree lines in (C), (D), and (E) describe equal values for variables in the scatterplot.

On average, it took 17.3 and 32.3 days to reach 50 cases and deaths, respectively. However, in four states deaths accumulated to a 50-count first (Fig. 2D), and in Amazonas, Cear, and Rio de Janeiro the difference between the time it took for cases and deaths to reach a 50-count was 6, 1, and 3 days, respectively (table S1). This short interval suggests undetected (and thus unmitigated) introduction and propagation of the virus for some time. This was confirmed in Cear (20) where a retrospective epidemiological investigation revealed that the virus was already circulating in January. Also, if the initial cases occurred in high-income areas, it is possible that consultations in private practices were not reported into national systems of the Ministry of Health (20) and remained silent to the surveillance system. In addition, testing capacity in Brazil was limited, and the first diagnostic RT-PCR test kits started to be produced in the country only in March. Although efforts of retrospective investigation were not scaled-up in the country, a comparison of standardized rates of cases and deaths per 100,000 people (Fig. 2E) show that in 11 states the death toll was larger than incidence, including Amazonas, Cear, and Rio de Janeiro.

To quantitatively measure the intensity of the spread of COVID-19 cases and deaths over time we used the locational Hoover Index (HI) (21, 22). Values closer to 100 indicate concentration in few municipalities, while those close to zero suggest more homogeneous spreading. If containment measures were effective, we would expect the index to decline slowly, remaining relatively high over time. Also, if measures were effective to avoid a collapse of the hospital system, we would expect a higher index for deaths, compared to cases. Figure 3A shows the HI for Brazil, and a clear trend toward extensive spread for both cases and deaths until about week 30 (July 19-25). The pattern, however, varied across states. In the first week with reported events, Amazonas, Roraima, and Amap had HI below 50 for both cases and deaths. This suggests either undetected circulation of the virus before initial reports (and therefore when reporting started there was already a large fraction of the population that had been infected), or fast and multiple introductions of the virus immediately followed by rapid spatial propagation (tables S6 and S7).

(A) Locational Hoover index (see supplementary materials) for cases (blue line) and deaths (red line) by epidemiological week. The area around each curve indicates the maximum and minimum index observed across states. (B) States and weeks when the locational Hoover index for cases was bigger than the index for deaths, indicating a faster spread of deaths. Bivariate choropleth map of the locational Hoover Index for cases and deaths in epidemiological week 14 (March 29-April 4) (C) and epidemiological week 41 (October 4-10) (D). Since SARS-CoV-2 reached states at different epidemiological weeks, (C) shows data from week 12 for RJ and SP; week 13 for AM, PI, RN, PE, PR, SC, RS, and GO; week 15 for AC; and week 16 for TO. Similarly, (D) shows data for week 33 for MT, and week 39 for ES.

Overall, the spread of COVID-19 was fast. By week 24 (June 7-13) and 32 (August 2-8), all states had HI for cases and deaths, respectively, lower than 50. In nine states, including Amazonas, Amap, Cear, and Rio de Janeiro, the spreading of deaths was faster than cases over several weeks (Fig. 3B), with some overlap with the time when clusters were observed in those areas (Fig. 1, C and D). Figure 3, C and D, show the first and last weekly HI for cases and deaths by states and there are marked contrasts in HI trajectory (tables S6 and S7). By week 41 (October 4-10), COVID-19 deaths in Amap (HI=31.3) had moved to the interior faster than cases (HI=42.9). Rio de Janeiro had the most intense interiorization of both cases (HI=14.9) and deaths (HI=21.9), followed by Amazonas (HI cases=20.2, HI deaths=30.4). Both experienced a shortage of ICU beds, but Amazonas has smaller availability (about 11 ICU beds per 100,000 people vs 23 in Rio de Janeiro), all concentrated in the capital city, Manaus. As the virus moved to the interior a higher demand for scarce and distant resources intensified, not all of which were fulfilled in time to prevent fatalities (23). In Rio de Janeiro, political chaos compromised a prompt and effective response. Leaders were immersed in corruption accusations, the governor was removed from office and face an impeachment trial, and the Secretary of Health was changed three times between May and September, one of whom was arrested (24). In contrast, although Cear also experienced a near-collapse of the hospital system late April to mid-May, and had silent circulation of the virus more than a month before the first case was officially reported (20), it ranked 6th in movement of cases (HI=31.3), but was the antepenultimate in deaths (HI=64.5). This suggests that even with the continued spread of the virus, local actions were successful in preventing fatality. No state had HI for cases higher than 50 by week 41, revealing an extensive pattern of disease spread toward the interior.

Overall, a higher percentage of COVID-19 cases and deaths were observed outside capital cities in weeks 20 (May 10-16) and 22 (May 24-30), respectively (Fig. 4A), with varied patterns across states (table S1). Rio Grande do Sul, Santa Catarina, and Paran, all in the South region, had earlier and concurrent shifts in cases and deaths (in March), and this was the last region to show a major surge in COVID-19. In Rio de Janeiro and Amazonas, the shift in deaths was much later than cases, 10 and 8 weeks, respectively.

(A) Percentage of cases (blue lines) and deaths (red lines) in the state capitals (solid lines) and the remaining municipalities (dashed lines) by epidemiological week. (B) Percentage of reported COVID-19 cases and deaths, and selected variables by epidemiological week. Variables: Stringency Index (STR), Containment Index (CTN), Social Distancing Index (SD), locational Hoover Index for cases (HIc), locational Hoover Index for deaths (HId), percentage of cases in each epidemiological week (PCTc), percentage of deaths in each epidemiological week (PCTd), normalized distance by which the national geographical center of cases shifted in each week (DSTc), and normalized distance by which the national geographical center of deaths shifted in each week (DSTd). Distances were normalized to vary between 0 and 100. The subscript min indicates the minimum value of the index observed among all states in each week; the subscript max denotes the maximum value. (C) Correlation matrix (Pearson). Cells in shades of red or blue are statistically significant: * <0.05, ** <0.01, and *** <0.001. (D) Hierarchical clustering dendrogram by state based on five variables: cumulative deaths per 100,000 people, maximum percentage of deaths in a week, maximum SD, epidemiological week when HId became lower than 50, and the maximum value of effective Rt over the study period (see supplementary materials).

To better capture policies adopted at the national and local levels and their associations with movement of COVID-19 toward the interior of states, we used three indicators, the Stringency Index (STR), the Containment Index (CTN all policies in STR except for the use of masks), and the Social Distancing Index (SD based on mobile devices). Because states introduced measures at different times with various duration, national indices hide much variation (Fig. 4B). We observed expected correlations (table S8) between policy indicators and HI for cases and deaths (Fig. 4C), but a positive correlation between HI and the distance by which the national geographical center of cases shifted weekly. This suggests a pattern of progressive concentration of cases and deaths in few but widespread areas. Considering each state (fig. S3), Amap showed a negative correlation between STR and HI for deaths, indicating that policy measures failed to prevent the movement of deaths (this was the only state where deaths moved to the interior faster than cases by week 41; Fig. 3D).

We used hierarchical clustering analysis (25) in an attempt to group states into categories based on measures that captured the overall COVID-19 mortality burden, intensity of transmission, speed of COVID-19 deaths toward the interior of states, and adoption of distancing measures (Fig. 4D). Categories 3 and 4 include the top 10 states in deaths/100,000 people, as well as those that observed the first spatiotemporal clustering of deaths, and fast reporting and movement of deaths. Category 2 has the highest number of contiguous states and the lowest death burden by week 41. However, all categories combine states with different levels of inequality and distinct political alignment.

In summary, our results highlight the fast spread of both cases and deaths of COVID-19 in Brazil, with distinct patterns and burden by state. They demonstrate that no single narrative explains the propagation of the virus across states in Brazil. Instead, layers of complex scenarios interweave, resulting in varied and concurrent COVID-19 epidemics across the country. First, Brazil is large and unequal, with disparities in quantity and quality of health resources (e.g., hospital beds, physicians), and income (e.g., an emergency cash transfer program started only in June 2020, and by November 41% of the households were receiving it). Second, a dense urban network that connects and influences municipalities through transportation, services, and business (26) was not fully interrupted during peaks in cases or deaths. Third, political alignment between governors and the president had a role in the timing and intensity of distancing measures (7), and polarization politicized the pandemic with consequences to adherence to control actions (27). Fourth, SARS-CoV-2 was circulating undetected in Brazil for more than a month (20), a result of the lack of well-structured genomic surveillance (28). Fifth, cities imposed and relaxed measures at different moments, based on distinct criteria, facilitating propagation (15). Our findings speak to those issues, but also show that some states were resilient, such as Cear, while others that comparatively had more resources failed to contain the propagation of COVID-19, such as Rio de Janeiro.

In such a scenario, prompt and equitable responses, coordinated at the federal level, are imperative to avoid fast virus propagation and disparities in outcomes (12). Yet, the COVID-19 response in Brazil was neither prompt nor equitable. It still isnt. Brazil is currently facing the worst moment of the pandemic, with a record number of cases and deaths, and near collapse of the hospital system. Vaccination has started but at a slow pace due to limited availability of doses. A new variant of concern (VOC), which emerged in Manaus (P1) in December, is estimated to be 1.4-2.2 times more transmissible, and able to evade immunity from previous non-P1 infection (29). That variant is spreading across the country. It became the most prevalent in circulation in six of eight states where investigations were performed (30). As of March 11, 2021, Brazil already reported 40% of the total COVID-19 deaths that occurred in 2020. In January 2021, Manaus witnessed a spike in cases and hospitalizations, a collapse of the hospital system, including a shortage of oxygen for patients (31). The death toll is unbearable, as Manaus already recorded 39.8% more COVID-19 deaths in 2021 than in 2020. Without immediate action, this could be a preview of what is yet to happen in other localities in Brazil. Without immediate containment, coordinated epidemiological and genomic surveillance measures, and an effort to vaccinate the largest number of people in the shortest possible time, the propagation of P1 will likely resemble the patterns here demonstrated, leading to unimaginable loss of lives. Failure to avoid this new round of propagation will facilitate the emergence of new VOCs, isolate Brazil as a threat to global health security, and lead to a completely avoidable humanitarian crisis.

G. James, D. Witten, T. Hastie, R. Tibshirani, An Introduction to Statistical Learning with Applications in R (Springer, 2017).

Instituto Brasileiro de Geografia e Estatistica, Regies de Influncia das Cidades: 2018 (IBGE, Coordenao de Geografia, 2020).

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Spatiotemporal pattern of COVID-19 spread in Brazil - Science

A new study by Texas A&M University researchers published in PLOS ONE details a new model for making short-term projections of daily COVID-19 cases that is accurate, reliable and easily used by public health officials and other organizations.

Led by Hongwei Zhao, professor of biostatistics at the Texas A&M School of Public Health, researchers used a method based on the SEIR (susceptible, exposed, infected and recovered states) framework to project COVID-19 incidence in the upcoming two to three weeks based on observed incidence cases only. This model assumes a constant or small change in the transmission rate of the virus that causes COVID-19 over a short period.

The model uses publicly available data on new reported cases of COVID-19 in Texas from the COVID-19 Data Repository by the Center for Systems Science and Engineering at Johns Hopkins University. Texas A&M researchers used this data on disease incidence for Texas and a selection of counties that included the Texas A&M campus to estimate the COVID-19 transmission rate.

Hongwei Zhao, professor of biostatistics at the School of Public Health.

Texas A&M School of Public Health

The results indicate that this model can be used to reasonably predict COVID-19 cases two to three weeks in advance using only current incidence numbers, Zhao said. The simplicity of this model is one of its greatest strengths as it can be easily implemented by organizations with few resources. Forecasts from this model can help health care organizations prepare for surges and help public health officials determine whether mask mandates or other policies will be needed.

They forecasted future infections under three possible scenarios: a sustained, constant rate of transmission; one where the transmission rate is five percent higher than current levels, reflecting a decrease in practices to prevent transmission or an increase in conditions that promote transmission; and one where transmission is five percent lower.

Estimating the current effective transmission rate can be tricky, since day-to-day variations in both infections and reporting can dramatically influence this estimate. Thus, the researchers smoothed daily reporting variations using a three-day weighted average and performed additional smoothing to account for data anomalies such as counties reporting several months of cases all at once.

The researchers compared their projections with reported incidence in Texas through four periods in 2020: April 15, June 15, August 15 and October 15. The number of new daily COVID-19 cases reported were relatively low in mid-April, when many businesses were shut down, and then started to increase in early May after phased re-openings began in Texas. The numbers increased sharply after Memorial Day, and then trended downward after a statewide mask mandate was enacted during the summer. Infections increased again after Labor Day, but then seemed to plateau until the middle of October, when the transmission rate was observed again to increase dramatically.

The statewide application of the model showed that it performed reasonably well, with only the second period forecast deviating from the actual recorded incidence, perhaps due to the dramatically changing numbers at the time when a great wave of COVID-19 occurred around the Memorial Day holiday. The model performed similarly well at the county level, though the smaller population and changes in population, such as students moving in and out of the area during the school year, influenced reporting of new cases.

However, the model is limited by the data it uses. Local testing and reporting policies and resources can affect data accuracy, and assumptions about transmission rate based on current incidence are less likely to be accurate further into the future. And as more people contract COVID-19 and recover, or are vaccinated, the susceptible population will change, possibly affecting transmission.

Despite these limitations, the researchers said the model can be a valuable tool for health care facilities and public health officials, especially when combined with other sources of information. The COVID-19 pandemic is not yet over, so having a tool that can determine when and where another surge might occur is important. Similarly, researchers hope to use these new tools at their disposal for future infectious disease needs.

Additionally, the model has been used to create a dashboard that provides real-time data on the spread of COVID-19 state-wide. It has been used locally by university administrators and public health officials.

Other School of Public health researchers involved in this study included Marcia Ory, Tiffany Radcliff, Murray Ct, Rebecca Fischer and Alyssa McNulty, along with Department of Statistics researchers Huiyan Sangand and Naveed Merchant.

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Reliable COVID-19 Short-Term Forecasting - Texas A&M Today - Texas A&M University Today

The global tally for the coronavirus-borne illness rose above 139 million on Friday, according to data aggregated by Johns Hopkins University, as the death toll climbed above 2.98 million. The U.S. leads the world in cases and deaths by wide margins, with 31.5 million cases, or about 23% of the global total, while the 565,289 death toll makes up about 19% of the global toll. The U.S. added at least 74,312 new cases and 909 new deaths on Thursday, according to a New York Times tracker. The U.S. has averaged 70,514 cases a day in the past week, up 8% from the average two weeks ago. Pfizer Inc. PFE, +1.16% Chief Executive Albert Bourla said it is likely that people who receive Covid-19 vaccines will need booster shots within a year afterward, and then annual vaccinations, to maintain protection against the virus as it evolves, the Wall Street Journal reported. "The variants will play a key role. It is extremely important to suppress the pool of people that can be susceptible to the virus," Bourla said during a virtual event hosted by CVS Health Corp. CVS, +0.93% that aired Thursday but was recorded April 1.Outside of the U.S., India has replaced Brazil as the country with the second highest number of cases at 14.3 million, and is fourth globally by deaths at 174,308. Brazil is third by cases at 13.7 million and second with a death toll of 365,444. Mexico is third by deaths at 211,213 and 14th highest by cases at 2.3 million. The U.K. has 4.4 million cases and 127,438 deaths, the highest in Europe and fifth highest in the world.

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Coronavirus tally: Global cases of COVID-19 top 139 million and U.S. death toll is above 565,000 - MarketWatch

For Immediate Release: April 14, 2021

The following quote is attributed to Acting FDA Commissioner Janet Woodcock, M.D.

During this worldwide public health emergency, the FDA has used a variety of tools to oversee facilities that manufacture FDA-regulated products. These tools include record requests in advance of or in lieu of a drug facility inspection, relying on information from trusted regulatory partners, and remote interactive evaluations (such as remote livestreaming video of operations, teleconferences and screen sharing). We have used some or all of these approaches to evaluate facilities for human and animal medical products during the public health emergency when inspections of drug facilities were not possible due to travel or quarantine restrictions.

Inspections are an important tool to keep Americans safe, and are part of a set of tools used for regulatory oversight. As part of the wide variety of tools we have deployed during the COVID-19 pandemic, remote interactive evaluations have informed the FDAs regulatory decision-making, contributed to ensuring drug quality and helped determine the scope, depth and timing of future inspections. By necessity, we have adapted by conducting more remote interactive evaluations throughout the public health emergency and are continuing to expand their use as appropriate. The purpose of this new guidance is to provide further clarity for regulated facilities on how the FDA will request and conduct these remote interactive evaluations during the COVID-19 public health emergency.

We recognize that remote interactive evaluations do not replace inspections, and that there are situations where only an inspection is appropriate based on risk and history of compliance with FDA regulations. Within the exceptional context of a global pandemic, we see remote interactive evaluations as part of a necessary strategy to evaluate medical product facilities by using all available approaches to ensure the medical products we regulate are safe, effective and of high quality.

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The FDA, an agency within the U.S. Department of Health and Human Services, protects the public health by assuring the safety, effectiveness, and security of human and veterinary drugs, vaccines and other biological products for human use, and medical devices. The agency also is responsible for the safety and security of our nations food supply, cosmetics, dietary supplements, products that give off electronic radiation, and for regulating tobacco products.

04/14/2021

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FDA Provides Guidance on Remote Interactive Evaluations for Oversight of Drug Facilities During COVID-19 - FDA.gov

News Release

Thursday, April 15, 2021

New RADx-UP research effort combines testing and safety measures in underserved populations.

The National Institutes of Health is awarding up to $33 million over two years to fund projects at 10 institutions across eight states to build evidence on safely returning students, teachers and support staff to in-person school in areas with vulnerable and underserved populations. This funding was made availableby the American Rescue Plan. Known as the Safe Return to School Diagnostic Testing Initiative, the awards are part of the NIH Rapid Acceleration of Diagnostics Underserved Populations (RADx-UP) program, which aims to increase COVID-19 testing access and uptake for vulnerable and underserved populations.Projects will combine frequent COVID-19 testing with proven safety measures to reduce the spread of the SARS-CoV-2 virus.

Many children have inequitable access to reliable virtual learning, and it is important they are able to participate safely in person while also maintaining the health and safety of the of the school and general communities, said Eliseo J. Prez-Stable, M.D., director of NIHs National Institute on Minority Health and Health Disparities and co-chair of the RADx-UP program. Establishing frequent COVID-19 testing protocols for schools in vulnerable and underserved communities is essential to the safe return to school effort, and these projects will inform decision makers on the best strategies to accomplish this.

Although many schools are offering both in-person and virtual learning options, some students face barriers to attending school remotely. For example, children may lack access to computer equipment and internet connectivity or may not have family members who can assist them with virtual learning. Moreover, without in-person schooling, many children forego school-based meals, speech or occupational therapy and after school programs. These barriers often disproportionately affect minorities, socially and economically disadvantaged children, and children with medical complexities and/or developmental disabilities.

Participating early childhood education and kindergarten through 12 schools include public, chartered, special education, and pediatric complex care that serve children in urban, rural and tribal communities. Attendance ranges from 50 to 3,500 children and populations are racially and ethnically diverse, including African Americans, American Indians/Alaska Natives, Latinos/Latinas, and Asian Americans. Schools were also selected for being in a school district with at least 50% of students receiving free or reduced-price lunch.

Some projects will involve at-home COVID-19 testing, while others will use pooled, in-school testing approaches. Study participants will receive either molecular or antigen tests, which can detect SARS-CoV-2 infection in samples from nasal swabs or saliva. Researchers will obtain parental consent prior to administering the diagnostic tests to children.

These awards will foster the development of comprehensive programs to meet the challenge of safely returning children to in-person schooling, particularly for children who are vulnerable to COVID-19 or who are at risk for significant disparities in access to testing, said Diana W. Bianchi, M.D., director of NIHs Eunice Kennedy Shriver National Institute of Child Health and Human Development (NICHD), which is managing the initiative.

In the coming months, the RADx-UP program will make additional awards,pending availability of funds, to expand the initiative across more locations.

RADx-UPSM is a registered service mark of the Department of Health and Human Services.

About the Eunice Kennedy Shriver National Institute of Child Health and Human Development (NICHD): NICHD leads research and training to understand human development, improve reproductive health, enhance the lives of children and adolescents, and optimize abilities for all. For more information, visit https://www.nichd.nih.gov.

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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funded COVID-19 testing initiative aims to safely return children to in-person school - National Institutes of Health

Ohio's northern counties, including Cuyahoga and Summit, are seeing increases in COVID-19 cases as Michigan and Canada are dealing with significant surges of a more contagious coronavirus variant, Gov. Mike DeWine said during a coronavirus update.

"About one-fourth of Ohioans live in these 11 counties, and most counties are in the northern part of the state where we are seeing a high level of variant," DeWine said Thursday, April 15.

Speaking at the University of Toledo mass vaccination site, the governor said that over the past two weeks, the state reported 200 cases per 100,000 Ohioans. Only four weeks ago, that statistic was 144 cases per 100,000. Summit County has the third-highest rate out of Ohio's 88 counties with 300.9 cases per 100,000 residents, and Cuyahoga County is just behind that with 280.9 cases per 100,000 residents.

DeWine said in early March that when the state has a case incidence rate of 50 cases per 100,000 or below for two weeks, all health orders would be rescinded. Recent daily case data, however, is far from that benchmark.

The number of COVID-19 cases are rising as more Ohioans are being vaccinated, even with the nationwide pause in the use of the one-dose Johnson & Johnson vaccine earlier this week. The state is set to receive 400,000 Pfizer and Moderna vaccine doses next week, he said.

Summit County will begin walk-in appointments at the Summit County Fairgrounds site Friday and Saturday for those wanting to be vaccinated but have not signed up online.

And in Cuyahoga County, the state's only federally run mass vaccination site at Cleveland State University's Wolstein Center will open a satellite clinic at 5398 Northfield Road in Maple Heights over the weekend, offering anyone 16 years and older the first dose of the Pfizer shot. Those interested can call 1-833-4-ASK-ODH or 1-833-427-5634. For free transportation, call 2-1-1.

There were 2,164 new positive coronavirus cases reported over the past 24 hours in the state, bringing the total cases to 1,048,109, according to the Ohio Health Department's Thursday, April 15, report. Total fatality count for state residents is 18,917. The state has seen 181 new hospitalizations and 31 new intensive care admissions in the past 24 hours. There are 1,305 Ohioans currently hospitalized with COVID-19.

There have been nearly 11.45 million coronavirus tests in the state. And nearly 4.25 million people, representing 36.4% of the total population, have received one dose of the vaccine since mid-December.

Originally posted here:

Cuyahoga, Summit counties among highest COVID-19 rates in the state - Crain's Cleveland Business

Despite trying to mitigate the challenges of staging a season during a pandemic, with a shortened 56-game regular season with only divisional play, the NHL has seen quite a few players miss time as a result of COVID-19. When a player is out, he lands on the COVID-19 protocol list, but a positive test isn't required to wind up on the list. Some players have tested positive COVID-19 while some may have just been exposed to someone that has tested positive among other factors.

Players that have appeared on the league's COVID-19 Protocols List can be the result of several factors including:

The Vancouver Canucksare one of the teams that have been heavily impacted by COVID-19 during the 2021 season. The Canucks have had 22 players and an additional four staff members test positive for COVID-19 since March 30. The team's games have been postponed through April 16 to give the team more time to recover and prepare to return to play.

In addition, the Buffalo Sabres had six games postponed as a result of COVID-19 protocols while the New Jersey Devils had five games postponed earlier this season.

Here's a closer look at which players have missed time and how many games they've missed as a result of COVID-19.

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NHL COVID-19 protocol list tracker: More than 150 players have missed time this season in the protocol - CBS Sports