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      Bayesian estimation of SARS-CoV-2 prevalence in Indiana by random testing

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          Infection with the novel coronovirus severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) has resulted in a worldwide pandemic of COVID-19 disease. Efforts to design local, regional, and national responses to the virus are constrained by a lack of information on the extent of the epidemic as well as inaccuracies in newly developed diagnostic tests. In this study we analyze data from testing randomly selected Indiana state residents for infection or previous exposure to SARS-CoV-2 and derive estimates of the statewide COVID-19 prevalence in an attempt to address potential biases arising from nonresponse and diagnostic testing errors.

          Abstract

          From 25 to 29 April 2020, the state of Indiana undertook testing of 3,658 randomly chosen state residents for the novel severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) virus, the agent causing COVID-19 disease. This was the first statewide randomized study of COVID-19 testing in the United States. Both PCR and serological tests were administered to all study participants. This paper describes statistical methods used to address nonresponse among various demographic groups and to adjust for testing errors to reduce bias in the estimates of the overall disease prevalence in Indiana. These adjustments were implemented through Bayesian methods, which incorporated all available information on disease prevalence and test performance, along with external data obtained from census of the Indiana statewide population. Both adjustments appeared to have significant impact on the unadjusted estimates, mainly due to upweighting data in study participants of non-White races and Hispanic ethnicity and anticipated false-positive and false-negative test results among both the PCR and antibody tests utilized in the study.

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          An interactive web-based dashboard to track COVID-19 in real time

          In December, 2019, a local outbreak of pneumonia of initially unknown cause was detected in Wuhan (Hubei, China), and was quickly determined to be caused by a novel coronavirus, 1 namely severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2). The outbreak has since spread to every province of mainland China as well as 27 other countries and regions, with more than 70 000 confirmed cases as of Feb 17, 2020. 2 In response to this ongoing public health emergency, we developed an online interactive dashboard, hosted by the Center for Systems Science and Engineering (CSSE) at Johns Hopkins University, Baltimore, MD, USA, to visualise and track reported cases of coronavirus disease 2019 (COVID-19) in real time. The dashboard, first shared publicly on Jan 22, illustrates the location and number of confirmed COVID-19 cases, deaths, and recoveries for all affected countries. It was developed to provide researchers, public health authorities, and the general public with a user-friendly tool to track the outbreak as it unfolds. All data collected and displayed are made freely available, initially through Google Sheets and now through a GitHub repository, along with the feature layers of the dashboard, which are now included in the Esri Living Atlas. The dashboard reports cases at the province level in China; at the city level in the USA, Australia, and Canada; and at the country level otherwise. During Jan 22–31, all data collection and processing were done manually, and updates were typically done twice a day, morning and night (US Eastern Time). As the outbreak evolved, the manual reporting process became unsustainable; therefore, on Feb 1, we adopted a semi-automated living data stream strategy. Our primary data source is DXY, an online platform run by members of the Chinese medical community, which aggregates local media and government reports to provide cumulative totals of COVID-19 cases in near real time at the province level in China and at the country level otherwise. Every 15 min, the cumulative case counts are updated from DXY for all provinces in China and for other affected countries and regions. For countries and regions outside mainland China (including Hong Kong, Macau, and Taiwan), we found DXY cumulative case counts to frequently lag behind other sources; we therefore manually update these case numbers throughout the day when new cases are identified. To identify new cases, we monitor various Twitter feeds, online news services, and direct communication sent through the dashboard. Before manually updating the dashboard, we confirm the case numbers with regional and local health departments, including the respective centres for disease control and prevention (CDC) of China, Taiwan, and Europe, the Hong Kong Department of Health, the Macau Government, and WHO, as well as city-level and state-level health authorities. For city-level case reports in the USA, Australia, and Canada, which we began reporting on Feb 1, we rely on the US CDC, the government of Canada, the Australian Government Department of Health, and various state or territory health authorities. All manual updates (for countries and regions outside mainland China) are coordinated by a team at Johns Hopkins University. The case data reported on the dashboard aligns with the daily Chinese CDC 3 and WHO situation reports 2 for within and outside of mainland China, respectively (figure ). Furthermore, the dashboard is particularly effective at capturing the timing of the first reported case of COVID-19 in new countries or regions (appendix). With the exception of Australia, Hong Kong, and Italy, the CSSE at Johns Hopkins University has reported newly infected countries ahead of WHO, with Hong Kong and Italy reported within hours of the corresponding WHO situation report. Figure Comparison of COVID-19 case reporting from different sources Daily cumulative case numbers (starting Jan 22, 2020) reported by the Johns Hopkins University Center for Systems Science and Engineering (CSSE), WHO situation reports, and the Chinese Center for Disease Control and Prevention (Chinese CDC) for within (A) and outside (B) mainland China. Given the popularity and impact of the dashboard to date, we plan to continue hosting and managing the tool throughout the entirety of the COVID-19 outbreak and to build out its capabilities to establish a standing tool to monitor and report on future outbreaks. We believe our efforts are crucial to help inform modelling efforts and control measures during the earliest stages of the outbreak.
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            Temporal dynamics in viral shedding and transmissibility of COVID-19

            We report temporal patterns of viral shedding in 94 patients with laboratory-confirmed COVID-19 and modeled COVID-19 infectiousness profiles from a separate sample of 77 infector-infectee transmission pairs. We observed the highest viral load in throat swabs at the time of symptom onset, and inferred that infectiousness peaked on or before symptom onset. We estimated that 44% (95% confidence interval, 25-69%) of secondary cases were infected during the index cases' presymptomatic stage, in settings with substantial household clustering, active case finding and quarantine outside the home. Disease control measures should be adjusted to account for probable substantial presymptomatic transmission.
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              COVID-19 exacerbating inequalities in the US

              COVID-19 does not affect everyone equally. In the US, it is exposing inequities in the health system. Aaron van Dorn, Rebecca E Cooney, and Miriam L Sabin report from New York. In the US, New York City has so far borne the brunt of the coronavirus disease 2019 (COVID-19) pandemic, with the highest reported number of cases and the highest death toll in the country. The first COVID-19 case in the city was reported on March 1, but community transmission was firmly established on March 7. As of April 14, New York State has tested nearly half a million people, among whom 195 031 have tested positive. In New York City alone, 106 763 people have tested positive and 7349 have died. “New York is the canary in the coal mine. What happens to New York is going to wind up happening to California, and Washington State and Illinois. It's just a matter of time”, said New York Governor Andrew Cuomo, while asking for greater federal assistance. The response within New York City, known for its historically strong public health responses, has been to ramp up for the surge, but also to tailor the approach to address some of the most basic touchpoints that could worsen health outcomes, including providing three meals a day to all New York residents in need. Oxiris Barbot, commissioner of the New York City Department of Health and Mental Hygiene stated, “Our primary focus at this moment has to be on keeping our city's communities safe. This means supporting the public hospitals with supplies; connecting underserved people to free access to care; and delivering health guidance through the trusted voices of community organizations. The COVID-19 pandemic will come to an end eventually, but what is needed afterward is a renewed focus to ensure that health is not a byproduct of privilege. Public health has a fundamental role to play in shaping our future to be more just and equitable.” Confirming existing disparities, within New York City and other urban centres, African American and other communities of colour have been especially affected by the COVID-10 pandemic. Across the country, deaths due to COVID-19 are disproportionately high among African Americans compared with the population overall. In Milwaukee, WI, three quarters of all COVID-19 related deaths are African American, and in St Louis, MO, all but three people who have died as a result of COVID-19 were African American. According to Sharrelle Barber of Drexel University Dornsife School of Public Health (Philadelphia, PA, USA), the pre-existing racial and health inequalities already present in US society are being exacerbated by the pandemic. “Black communities, Latino communities, immigrant communities, Native American communities—we're going to bear the disproportionate brunt of the reckless actions of a government that did not take the proper precautions to mitigate the spread of this disease”, Barber said. “And that's going to be overlaid on top of the existing racial inequalities.” Part of the disproportionate impact of the COVID-19 pandemic on communities of colour has been structural factors that prevent those communities from practicing social distancing. Minority populations in the US disproportionally make up “essential workers” such as retail grocery workers, public transit employees, and health-care workers and custodial staff. “These front-line workers, disproportionately black and brown, then are typically a part of residentially segregated communities”, said Barber. “They don't have that privilege of quote unquote ‘staying at home’, connecting those individuals to the communities they are likely to be a part of because of this legacy of residential segregation, or structural racism in our major cities and most cities in the United States.” The negative consequences of health disparities for people who live in rural areas in the US were already a problem before the pandemic. Underserved African Americans face higher HIV incidence and greater maternal and infant mortality rates. Undocumented Latino communities working in rural industries such as farming, poultry, and meat production often have no health insurance. Poor white communities have been badly hit by the opioid crisis and across rural areas, especially in the southern states, high rates of non-communicable diseases are driven by conditions such as obesity. With higher COVID-19 mortality among those with underlying health conditions, these areas could be hit hard. © 2020 Spencer Platt/Getty Images 2020 Since January 2020 Elsevier has created a COVID-19 resource centre with free information in English and Mandarin on the novel coronavirus COVID-19. The COVID-19 resource centre is hosted on Elsevier Connect, the company's public news and information website. Elsevier hereby grants permission to make all its COVID-19-related research that is available on the COVID-19 resource centre - including this research content - immediately available in PubMed Central and other publicly funded repositories, such as the WHO COVID database with rights for unrestricted research re-use and analyses in any form or by any means with acknowledgement of the original source. These permissions are granted for free by Elsevier for as long as the COVID-19 resource centre remains active. 14 US states (mostly in the south and the Plains) have refused to accept the Affordable Care Act Medicaid expansion, leaving millions of the poorest and sickest Americans without access to health care, with the added effect of leaving many regional and local hospitals across the US closed or in danger of closing because of the high cost of medical care and a high proportion of rural uninsured and underinsured people. People with COVID-19 in those states will have poor access to the kind of emergency and intensive care they will need. Native American populations also have disproportionately higher levels of underlying conditions, such as heart disease and diabetes, that would make them particularly at risk of complications from COVID-19. Health care for Native American communities has a unique place in the US. As part of treaty obligations owed by the US government to tribal groups, the Indian Health Service (IHS) provides direct point of care health care for the 2·6 million Native Americans living on tribal reservations. According to the IHS, there are currently 985 confirmed cases of COVID-19 on tribal reservations, and 536 cases in the Navajo Nation alone (the largest reservation). However, the IHS's ability to respond to the crisis might be limited: according to according to Kevin Allis, Chief Executive Officer of the National Congress of American Indians, the largest Native American advocacy organisation, the IHS has only 1257 hospital beds and 36 intensive care units, and many people covered by the IHS are hours away from the nearest IHS facility. The IHS also does not cover care from external providers. Although there is a provision of the CARES Act stimulus bill that is intended to cover those costs, it is unclear how effective it would be if someone covered by the IHS is transferred to a non-IHS facility. © 2020 Reuters/Kevin Lamarque 2020 Since January 2020 Elsevier has created a COVID-19 resource centre with free information in English and Mandarin on the novel coronavirus COVID-19. The COVID-19 resource centre is hosted on Elsevier Connect, the company's public news and information website. Elsevier hereby grants permission to make all its COVID-19-related research that is available on the COVID-19 resource centre - including this research content - immediately available in PubMed Central and other publicly funded repositories, such as the WHO COVID database with rights for unrestricted research re-use and analyses in any form or by any means with acknowledgement of the original source. These permissions are granted for free by Elsevier for as long as the COVID-19 resource centre remains active. The CARES Act also included US$8 billion to supplement the health and economies of Native Americans and Alaska Natives. Even that number was an increase from what President Donald Trump's administration originally wanted. “We knew the White House wanted to give us nothing”, Allis said. “And senate Republicans were okay with a billion and it fine-tuned its way to $8 billion.” But the deep history of injustice by the US government towards these people means that the US response will be looked on with suspicion. At the national level, the response has varied widely by state, with many states that voted for Trump in 2016—notably Florida, Texas, and Georgia—responding to the emerging pandemic later and with more lax measures. Florida Governor Ron DeSantis, a Republican Trump ally, was slow to implement social-distancing measures and close non-essential businesses, and Georgia Governor Brian Kemp ordered beaches closed by local authorities to be reopened on April 3. However, the trend has not been universal: in Ohio, Republican Governor Mike DeWine was swift in issuing orders to shut non-essential businesses and in responding to the crisis. The federal response has also been overtly political. States with governors that Trump sees as political allies (such as Florida), have received the full measure of requested personal protective equipment from the federal stockpile, while states with governors whom Trump identifies as political enemies (such as New York's Cuomo, Oregon's Jay Inslee, and Michigan's Gretchen Whitmer, all Democrats) have received only a fraction of their requests. Trump has also publicly attacked the responses of those governors on Twitter and during his daily briefings. In distributing funds made available by the CARES Act, Trump also appears to be playing favourites: New York received only a fraction of the $30 billion hospital relief funds from the bill ($12 000 per patient), while other states much more lightly affected received more ($300 000 per patient in Montana and Nebraska, and more than $470 000 per patient in West Virginia, all states that voted for Trump in 2016). Although the numbers of reported cases seem to be levelling off in New York City and other urban areas, perhaps evidence that social-distancing measures are beginning to have an effect, emerging morbidity and mortality data have already clearly demonstrated what many have feared: a pandemic in which the brunt of the effects fall on already vulnerable US populations, and in which the deeply rooted social, racial, and economic health disparities in the country have been laid bare.
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                Author and article information

                Journal
                Proc Natl Acad Sci U S A
                Proc Natl Acad Sci U S A
                pnas
                pnas
                PNAS
                Proceedings of the National Academy of Sciences of the United States of America
                National Academy of Sciences
                0027-8424
                1091-6490
                02 February 2021
                13 January 2021
                13 January 2021
                : 118
                : 5
                : e2013906118
                Affiliations
                [1] aDepartment of Biostatistics, Indiana University Fairbanks School of Public Health, Indianapolis, IN 46202;
                [2] bDepartment of Health Policy and Management, Indiana University Fairbanks School of Public Health, Indianapolis, IN 46202;
                [3] cRegenstrief Institute, Inc., Indianapolis, IN 46202
                Author notes
                1To whom correspondence may be addressed. Email: cyiannou@ 123456iu.edu .

                Edited by Adrian E. Raftery, University of Washington, Seattle, WA, and approved December 2, 2020 (received for review July 2, 2020)

                Author contributions: C.T.Y., P.K.H., and N.M. designed research; C.T.Y. performed research; C.T.Y. analyzed data; C.T.Y. wrote the paper; P.K.H. and N.M. edited the paper; N.M. was principal investigator of the Indiana State contract to perform the study; and P.K.H. and N.M. liaised with the Indiana Department of Health and the Governor’s Office.

                Author information
                http://orcid.org/0000-0001-9014-3651
                http://orcid.org/0000-0002-3411-2700
                Article
                202013906
                10.1073/pnas.2013906118
                7865174
                33441450
                fe649248-f0c9-4c4d-a1d4-5e8ef2cb04e8
                Copyright © 2021 the Author(s). Published by PNAS.

                This open access article is distributed under Creative Commons Attribution-NonCommercial-NoDerivatives License 4.0 (CC BY-NC-ND).

                History
                Page count
                Pages: 8
                Funding
                Funded by: Indiana State Department of Health (IDOH) 100006977
                Award ID: State contract
                Award Recipient : Constantin T Yiannoutsos Award Recipient : Nir Menachemi Award Recipient : Paul K Halverson
                Categories
                433
                535
                Physical Sciences
                Statistics
                Custom metadata
                free

                covid-19,sars-cov-2,random sample
                covid-19, sars-cov-2, random sample

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