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Reconstructing the course of the COVID-19 epidemic over 2020 for US states and counties: results of a Bayesian evidence synthesis model.

ABSTRACT: Estimating the true magnitude of the United States (US) SARS-CoV-2 epidemic is crucial for understanding disease dynamics and, ultimately, for determining the effectiveness of interventions intended to interrupt transmission. We developed a Bayesian evidence synthesis model that explicitly accounts for reporting delays and secular variation in case ascertainment to generate estimates of incident COVID-19 infections on the basis of reported cases and deaths. We estimate time trends in COVID-19 epidemiology for every US state and county, from the first reported case (January 13, 2020) through January 1, 2021. Across counties, we estimate considerable variability in the level and pattern of incidence, producing major differences in the estimated proportion of the population infected by the end of 2020. Our estimates of COVID-19 deaths are consistent with independent estimates of excess mortality, and our estimates of cumulative incidence of infection are consistent with seroprevalence estimates from available antibody testing studies.

SUBMITTER: Chitwood MH

PROVIDER: S-EPMC8043480 | biostudies-literature |

REPOSITORIES: biostudies-literature

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Reconstructing the course of the COVID-19 epidemic over 2020 for US states and counties: Results of a Bayesian evidence synthesis model.

Project description:Reported COVID-19 cases and deaths provide a delayed and incomplete picture of SARS-CoV-2 infections in the United States (US). Accurate estimates of both the timing and magnitude of infections are needed to characterize viral transmission dynamics and better understand COVID-19 disease burden. We estimated time trends in SARS-CoV-2 transmission and other COVID-19 outcomes for every county in the US, from the first reported COVID-19 case in January 13, 2020 through January 1, 2021. To do so we employed a Bayesian modeling approach that explicitly accounts for reporting delays and variation in case ascertainment, and generates daily estimates of incident SARS-CoV-2 infections on the basis of reported COVID-19 cases and deaths. The model is freely available as the covidestim R package. Nationally, we estimated there had been 49 million symptomatic COVID-19 cases and 404,214 COVID-19 deaths by the end of 2020, and that 28% of the US population had been infected. There was county-level variability in the timing and magnitude of incidence, with local epidemiological trends differing substantially from state or regional averages, leading to large differences in the estimated proportion of the population infected by the end of 2020. Our estimates of true COVID-19 related deaths are consistent with independent estimates of excess mortality, and our estimated trends in cumulative incidence of SARS-CoV-2 infection are consistent with trends in seroprevalence estimates from available antibody testing studies. Reconstructing the underlying incidence of SARS-CoV-2 infections across US counties allows for a more granular understanding of disease trends and the potential impact of epidemiological drivers.

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Social associations and cardiovascular mortality in the United States counties, 2016 to 2020.

Project description:BackgroundThe positive aspects of social interaction on health have been described often, with considerably less attention to their negative aspect. This study aimed to assess the impact of social associations on cardiovascular mortality in the United States.MethodsThe Centers for Disease Control and Prevention Wide-Ranging Online Data for Epidemiologic Research (CDC WONDER) data sets from 2016 to 2020 were used to identify death records due to cardiovascular disease in the United States population aged 15 years and older. The social association rate defined as membership associations per 10,000 population, accessed from the 2020 County Health Rankings data was used as a surrogate for social participation. All United States counties were grouped into quartiles based on their social association rate; Q1 being the lowest quartile of social association, and Q4 the highest quartile. Age-adjusted mortality rate (AAMR) was calculated for each quartile. County health factor rankings for the state of Texas were used to adjust the AAMR for baseline comorbidities of county population, using Gaussian distribution linear regression.ResultsOverall, the AAMR was highest in the 4th social association rate quartile (306.73 [95% CI, 305.72-307.74]) and lowest in the 1st social association rate quartile (266.80 [95% CI, 266.41-267.20]). The mortality rates increased in a linear pattern from lowest to highest social association rate quartiles. After adjustment for the county health factor ranks of Texas, higher social association rate remained associated with a significantly higher AAMR (coefficient 15.84 [95% CI, 12.78-18.89]).ConclusionsOur study reported higher cardiovascular AAMR with higher social associations in the United States, with similar results after adjustment for County Health Rankings in the state of Texas.

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Clinical course and care requirements during the 2020 COVID-19 epidemic in South Australia.

Project description: Not available

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Trends in Number and Distribution of COVID-19 Hotspot Counties - United States, March 8-July 15, 2020.

Project description:The geographic areas in the United States most affected by the coronavirus disease 2019 (COVID-19) pandemic have changed over time. On May 7, 2020, CDC, with other federal agencies, began identifying counties with increasing COVID-19 incidence (hotspots) to better understand transmission dynamics and offer targeted support to health departments in affected communities. Data for January 22-July 15, 2020, were analyzed retrospectively (January 22-May 6) and prospectively (May 7-July 15) to detect hotspot counties. No counties met hotspot criteria during January 22-March 7, 2020. During March 8-July 15, 2020, 818 counties met hotspot criteria for ?1 day; these counties included 80% of the U.S. population. The daily number of counties meeting hotspot criteria peaked in early April, decreased and stabilized during mid-April-early June, then increased again during late June-early July. The percentage of counties in the South and West Census regions* meeting hotspot criteria increased from 10% and 13%, respectively, during March-April to 28% and 22%, respectively, during June-July. Identification of community transmission as a contributing factor increased over time, whereas identification of outbreaks in long-term care facilities, food processing facilities, correctional facilities, or other workplaces as contributing factors decreased. Identification of hotspot counties and understanding how they change over time can help prioritize and target implementation of U.S. public health response activities.

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Evidence for a continuous drift of the HIV-1 species towards higher resistance to neutralizing antibodies over the course of the epidemic.

Project description:We compared the neutralization sensitivity of early/transmitted HIV-1 variants from patients infected by subtype B viruses at 3 periods of the epidemic (1987-1991, 1996-2000, 2006-2010). Infectious pseudotyped viruses expressing envelope glycoproteins representative of the viral quasi-species infecting each patient were tested for sensitivity to neutralization by pools of sera from HIV-1 chronically infected patients and by an updated panel of 13 human monoclonal neutralizing antibodies (HuMoNAbs). A progressive significantly enhanced resistance to neutralization was observed over calendar time, by both human sera and most of the HuMoNAbs tested (b12, VRC01, VRC03, NIH45-46(G54W), PG9, PG16, PGT121, PGT128, PGT145). Despite this evolution, a combination of two HuMoNAbs (NIH45-46(G54W) and PGT128) still would efficiently neutralize the most contemporary transmitted variants. In addition, we observed a significant reduction of the heterologous neutralizing activity of sera from individuals infected most recently (2003-2007) compared to patients infected earlier (1987-1991), suggesting that the increasing resistance of the HIV species to neutralization over time coincided with a decreased immunogenicity. These data provide evidence for an ongoing adaptation of the HIV-1 species to the humoral immunity of the human population, which may add an additional obstacle to the design of an efficient HIV-1 vaccine.

| S-EPMC3701719 | biostudies-literature

A Bayesian functional approach to test models of life course epidemiology over continuous time.

Project description:BackgroundLife course epidemiology examines associations between repeated measures of risk and health outcomes across different phases of life. Empirical research, however, is often based on discrete-time models that assume that sporadic measurement occasions fully capture underlying long-term continuous processes of risk.MethodsWe propose (i) the functional relevant life course model (fRLM), which treats repeated, discrete measures of risk as unobserved continuous processes, and (ii) a testing procedure to assign probabilities that the data correspond to conceptual models of life course epidemiology (critical period, sensitive period and accumulation models). The performance of the fRLM is evaluated with simulations, and the approach is illustrated with empirical applications relating body mass index (BMI) to mRNA-seq signatures of chronic kidney disease, inflammation and breast cancer.ResultsSimulations reveal that fRLM identifies the correct life course model with three to five repeated assessments of risk and 400 subjects. The empirical examples reveal that chronic kidney disease reflects a critical period process and inflammation and breast cancer likely reflect sensitive period mechanisms.ConclusionsThe proposed fRLM treats repeated measures of risk as continuous processes and, under realistic data scenarios, the method provides accurate probabilities that the data correspond to commonly studied models of life course epidemiology. fRLM is implemented with publicly-available software.

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Monthly excess mortality across counties in the United States during the COVID-19 pandemic, March 2020 to February 2022.

Project description:Excess mortality is the difference between expected and observed mortality in a given period and has emerged as a leading measure of the COVID-19 pandemic's mortality impact. Spatially and temporally granular estimates of excess mortality are needed to understand which areas have been most impacted by the pandemic, evaluate exacerbating factors, and inform response efforts. We estimated all-cause excess mortality for the United States from March 2020 through February 2022 by county and month using a Bayesian hierarchical model trained on data from 2015 to 2019. An estimated 1,179,024 excess deaths occurred during the first 2 years of the pandemic (first: 634,830; second: 544,194). Overall, excess mortality decreased in large metropolitan counties but increased in nonmetropolitan counties. Despite the initial concentration of mortality in large metropolitan Northeastern counties, nonmetropolitan Southern counties had the highest cumulative relative excess mortality by July 2021. These results highlight the need for investments in rural health as the pandemic's rural impact grows.

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Robust estimation of SARS-CoV-2 epidemic in US counties.

Project description:The COVID-19 outbreak is asynchronous in US counties. Mitigating the COVID-19 transmission requires not only the state and federal level order of protective measures such as social distancing and testing, but also public awareness of time-dependent risk and reactions at county and community levels. We propose a robust approach to estimate the heterogeneous progression of SARS-CoV-2 at all US counties having no less than 2 COVID-19 associated deaths, and we use the daily probability of contracting (PoC) SARS-CoV-2 for a susceptible individual to quantify the risk of SARS-CoV-2 transmission in a community. We found that shortening by [Formula: see text] of the infectious period of SARS-CoV-2 can reduce around [Formula: see text] (or 78 K, [Formula: see text] CI: [66 K , 89 K ]) of the COVID-19 associated deaths in the US as of 20 September 2020. Our findings also indicate that reducing infection and deaths by a shortened infectious period is more pronounced for areas with the effective reproduction number close to 1, suggesting that testing should be used along with other mitigation measures, such as social distancing and facial mask-wearing, to reduce the transmission rate. Our deliverable includes a dynamic county-level map for local officials to determine optimal policy responses and for the public to better understand the risk of contracting SARS-CoV-2 on each day.

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Evidence of recombinant strains of porcine epidemic diarrhea virus, United States, 2013.

Project description:To investigate the evolutionary process by which porcine epidemic diarrhea virus (PEDV) in the United States hypothetically descended from strains in China, we analyzed PEDV-positive samples collected in China during January 2012-July 2013. Recombination in 2 strain sublineages was likely associated with identification of PEDV in the United States in 2013.

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