Dataset Information

A Bayesian Mixture Model for Predicting the COVID-19 Pandemic in the United States.

ABSTRACT: An outbreak of SARS-CoV-2 has led to a global pandemic affecting virtually every country. As of August 31, 2020, globally, there have been approximately 25,500,000 confirmed cases and 850,000 deaths; in the United States (50 states plus District of Columbia), there have been more than 6,000,000 confirmed cases and 183,000 deaths. We propose a Bayesian mixture model to predict and monitor COVID-19 mortality across the United States. The model captures skewed unimodal (prolonged recovery) or multimodal (multiple surges) curves. The results show that across all states, the first peak dates of mortality varied between April 4, 2020 for Alaska and June 18, 2020 for Arkansas. As of August 31, 2020, 31 states had a clear bimodal curve showing a strong second surge. The peak date for a second surge ranged from July 1, 2020 for Virginia to September 12, 2020 for Hawaii. The first peak for the United States occurred about April 16, 2020-dominated by New York and New Jersey-and a second peak on August 6, 2020-dominated by California, Texas, and Florida. Reliable models for predicting the COVID-19 pandemic are essential to informing resource allocation and intervention strategies. A Bayesian mixture model was able to more accurately predict the shape of the mortality curves across the United States than other models, including the timing of multiple peaks. However, given the dynamic nature of the pandemic, it is important that the results be updated regularly to identify and better monitor future waves, and characterize the epidemiology of the pandemic.

SUBMITTER: Kaciroti NA

PROVIDER: S-EPMC8045639 | biostudies-literature |

REPOSITORIES: biostudies-literature

ACCESS DATA

Similar Datasets

Project description:BackgroundIn the United States, COVID-19 is a nationally notifiable disease, meaning cases and hospitalizations are reported by states to the Centers for Disease Control and Prevention (CDC). Identifying and reporting every case from every facility in the United States may not be feasible in the long term. Creating sustainable methods for estimating the burden of COVID-19 from established sentinel surveillance systems is becoming more important.ObjectiveWe aimed to provide a method leveraging surveillance data to create a long-term solution to estimate monthly rates of hospitalizations for COVID-19.MethodsWe estimated monthly hospitalization rates for COVID-19 from May 2020 through April 2021 for the 50 states using surveillance data from the COVID-19-Associated Hospitalization Surveillance Network (COVID-NET) and a Bayesian hierarchical model for extrapolation. Hospitalization rates were calculated from patients hospitalized with a lab-confirmed SARS-CoV-2 test during or within 14 days before admission. We created a model for 6 age groups (0-17, 18-49, 50-64, 65-74, 75-84, and ≥85 years) separately. We identified covariates from multiple data sources that varied by age, state, and month and performed covariate selection for each age group based on 2 methods, Least Absolute Shrinkage and Selection Operator (LASSO) and spike and slab selection methods. We validated our method by checking the sensitivity of model estimates to covariate selection and model extrapolation as well as comparing our results to external data.ResultsWe estimated 3,583,100 (90% credible interval [CrI] 3,250,500-3,945,400) hospitalizations for a cumulative incidence of 1093.9 (992.4-1204.6) hospitalizations per 100,000 population with COVID-19 in the United States from May 2020 through April 2021. Cumulative incidence varied from 359 to 1856 per 100,000 between states. The age group with the highest cumulative incidence was those aged ≥85 years (5575.6; 90% CrI 5066.4-6133.7). The monthly hospitalization rate was highest in December (183.7; 90% CrI 154.3-217.4). Our monthly estimates by state showed variations in magnitudes of peak rates, number of peaks, and timing of peaks between states.ConclusionsOur novel approach to estimate hospitalizations for COVID-19 has potential to provide sustainable estimates for monitoring COVID-19 burden as well as a flexible framework leveraging surveillance data.

Project description:BackgroundThe pandemic has resulted in an increase of deaths not directly related to COVID-19 infection. We aimed to use a national death dataset to determine the impact of the pandemic on people with liver disease in the U.S, focusing on alcohol-associated liver disease (ALD) and non-alcoholic fatty liver disease (NAFLD).MethodsUsing data from the National Vital Statistic System from the CDC WONDER platform and ICD-10 codes, we identified deaths associated with liver disease. We evaluated observed versus predicted mortality for 2020-2021 based on trends from 2010-2019 with joinpoint and prediction modeling analysis.ResultsAmong 626,090 chronic liver disease-related deaths between 2010 and 2021, Age-standardized mortality rates (ASMR) for ALD dramatically increased between 2010-2019 and 2020-2021 (annual percentage change [APC] 3.5% to 17.6%, P<0.01), leading to a higher observed ASMR (per 100,000 persons) than predicted for 2020 (15.67 vs.13.04) and 2021 (17.42 vs.13.41). ASMR for NAFLD also increased during the pandemic (APC:14.5%), while the rates for hepatitis B and C decreased. Notably, the ASMR rise for ALD was most pronounced in non-Hispanic Whites, Blacks, and Alaska Indians/Native Americans (APC: 11.7%, 10.8%, 18.0%, all P<0.05), with similar but less critical findings for NAFLD while rates were steady for non-Hispanic Asians throughout 2010-2021 (APC: 4.9%). The ASMR rise for ALD was particularly severe for the 25-44 age group (APC: 34.6%, versus 13.7% and 12.6% for 45-64 and ≥65, all P<0.01), which were also all higher than pre-COVID-19 rates (all P<0.01).ConclusionsASMR for ALD and NAFLD increased at an alarming rate during the COVID-19 pandemic with the largest disparities among the young, non-Hispanic White, and Alaska Indian/Native American populations.Lay summaryThe impact of the pandemic on people with liver disease in the U.S remains unclear. This study indicated that age-standardized mortality rates for alcohol associated liver disease and non-alcohol fatty liver disease greatly accelerated during the COVID-19 pandemic with the largest disparities among the young, non-Hispanic White, and Alaska Indian/Native American populations. Increasing awareness about the care importance of chronic liver disease in specific populations must be prioritized.

Project description:Background and purposeThe magnitude and drivers of excess cerebrovascular-specific mortality during the coronavirus disease 2019 (COVID-19) pandemic are unknown. We aim to quantify excess stroke-related deaths and characterize its association with social distancing behavior and COVID-19-related vascular pathology.MethodsUnited States and state-level excess cerebrovascular deaths from January to May 2020 were quantified using National Center for Health Statistic data and Poisson regression models. Excess cerebrovascular deaths were analyzed as a function of time-varying stroke-related emergency medical service (EMS) calls and cumulative COVID-19 deaths using linear regression. A state-level regression analysis was performed to determine the association between excess cerebrovascular deaths and time spent in residences, measured by Google Community Mobility Reports, during the height of the pandemic after the first COVID-19 death (February 29).ResultsForty states and New York City were included. Excess cerebrovascular mortality occurred nationally from the weeks ending March 28 to May 2, 2020, up to a 7.8% increase above expected levels during the week of April 18. Decreased stroke-related EMS calls were associated with excess stroke deaths one (70 deaths per 1000 fewer EMS calls [95% CI, 20-118]) and 2 weeks (85 deaths per 1000 fewer EMS calls [95% CI, 37-133]) later. Twenty-three states and New York City experienced excess cerebrovascular mortality during the pandemic height. A 10% increase in time spent at home was associated with a 4.3% increase in stroke deaths (incidence rate ratio, 1.043 [95% CI, 1.001-1.085]) after adjusting for COVID-19 deaths.ConclusionsExcess US cerebrovascular deaths during the COVID-19 pandemic were observed and associated with decreases in stroke-related EMS calls nationally and mobility at the state level. Public health measures are needed to identify and counter the reticence to seeking medical care for acute stroke during the COVID-19 pandemic.

Dataset Information

A Bayesian Mixture Model for Predicting the COVID-19 Pandemic in the United States.

Similar Datasets

OmicsDI is part of the ELIXIR infrastructure

Tweets