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An open repository of real-time COVID-19 indicators.

ABSTRACT: The COVID-19 pandemic presented enormous data challenges in the United States. Policy makers, epidemiological modelers, and health researchers all require up-to-date data on the pandemic and relevant public behavior, ideally at fine spatial and temporal resolution. The COVIDcast API is our attempt to fill this need: Operational since April 2020, it provides open access to both traditional public health surveillance signals (cases, deaths, and hospitalizations) and many auxiliary indicators of COVID-19 activity, such as signals extracted from deidentified medical claims data, massive online surveys, cell phone mobility data, and internet search trends. These are available at a fine geographic resolution (mostly at the county level) and are updated daily. The COVIDcast API also tracks all revisions to historical data, allowing modelers to account for the frequent revisions and backfill that are common for many public health data sources. All of the data are available in a common format through the API and accompanying R and Python software packages. This paper describes the data sources and signals, and provides examples demonstrating that the auxiliary signals in the COVIDcast API present information relevant to tracking COVID activity, augmenting traditional public health reporting and empowering research and decision-making.

SUBMITTER: Reinhart A

PROVIDER: S-EPMC8713778 | biostudies-literature | 2021 Dec

REPOSITORIES: biostudies-literature

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An open repository of real-time COVID-19 indicators.

Reinhart Alex A Brooks Logan L Jahja Maria M Rumack Aaron A Tang Jingjing J Agrawal Sumit S Al Saeed Wael W Arnold Taylor T Basu Amartya A Bien Jacob J Cabrera Ángel A ÁA Chin Andrew A Chua Eu Jing EJ Clark Brian B Colquhoun Sarah S DeFries Nat N Farrow David C DC Forlizzi Jodi J Grabman Jed J Gratzl Samuel S Green Alden A Haff George G Han Robin R Harwood Kate K Hu Addison J AJ Hyde Raphael R Hyun Sangwon S Joshi Ananya A Kim Jimi J Kuznetsov Andrew A La Motte-Kerr Wichada W Lee Yeon Jin YJ Lee Kenneth K Lipton Zachary C ZC Liu Michael X MX Mackey Lester L Mazaitis Kathryn K McDonald Daniel J DJ McGuinness Phillip P Narasimhan Balasubramanian B O'Brien Michael P MP Oliveira Natalia L NL Patil Pratik P Perer Adam A Politsch Collin A CA Rajanala Samyak S Rucker Dawn D Scott Chris C Shah Nigam H NH Shankar Vishnu V Sharpnack James J Shemetov Dmitry D Simon Noah N Smith Benjamin Y BY Srivastava Vishakha V Tan Shuyi S Tibshirani Robert R Tuzhilina Elena E Van Nortwick Ana Karina AK Ventura Valérie V Wasserman Larry L Weaver Benjamin B Weiss Jeremy C JC Whitman Spencer S Williams Kristin K Rosenfeld Roni R Tibshirani Ryan J RJ

Proceedings of the National Academy of Sciences of the United States of America 20211201 51

The COVID-19 pandemic presented enormous data challenges in the United States. Policy makers, epidemiological modelers, and health researchers all require up-to-date data on the pandemic and relevant public behavior, ideally at fine spatial and temporal resolution. The COVIDcast API is our attempt to fill this need: Operational since April 2020, it provides open access to both traditional public health surveillance signals (cases, deaths, and hospitalizations) and many auxiliary indicators of CO ...[more]

PMID: 34903654

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Project description:The COVID-19 pandemic emerged in late December 2019. In the first six months of the global outbreak, the U.S. reported more cases and deaths than any other country in the world. Effective modeling of the course of the pandemic can help assist with public health resource planning, intervention efforts, and vaccine clinical trials. However, building applied forecasting models presents unique challenges during a pandemic. First, case data available to models in real time represent a nonstationary fraction of the true case incidence due to changes in available diagnostic tests and test-seeking behavior. Second, interventions varied across time and geography leading to large changes in transmissibility over the course of the pandemic. We propose a mechanistic Bayesian model that builds upon the classic compartmental susceptible-exposed-infected-recovered (SEIR) model to operationalize COVID-19 forecasting in real time. This framework includes nonparametric modeling of varying transmission rates, nonparametric modeling of case and death discrepancies due to testing and reporting issues, and a joint observation likelihood on new case counts and new deaths; it is implemented in a probabilistic programming language to automate the use of Bayesian reasoning for quantifying uncertainty in probabilistic forecasts. The model has been used to submit forecasts to the U.S. Centers for Disease Control through the COVID-19 Forecast Hub under the name MechBayes. We examine the performance relative to a baseline model as well as alternate models submitted to the forecast hub. Additionally, we include an ablation test of our extensions to the classic SEIR model. We demonstrate a significant gain in both point and probabilistic forecast scoring measures using MechBayes, when compared to a baseline model, and show that MechBayes ranks as one of the top two models out of nine which regularly submitted to the COVID-19 Forecast Hub for the duration of the pandemic, trailing only the COVID-19 Forecast Hub ensemble model of which which MechBayes is a part.

Project description:BackgroundThe coronavirus disease (COVID) pandemic caused disruption globally and was particularly distressing in low- and middle-income countries such as India. This study aimed to provide population representative estimates of COVID-related outcomes in India over time and characterize how COVID-related changes and impacts differ by key socioeconomic groups across the life course.MethodsThe sample was leveraged from an existing nationally representative study on cognition and dementia in India: Harmonized Diagnostic Assessment of Dementia for the Longitudinal Aging Study in India (LASI-DAD). The wave-1 of LASI-DAD enrolled 4096 older adults aged 60 years and older in 3316 households from 18 states and union territories of India. Out of the 3316 LASI-DAD households, 2704 with valid phone numbers were contacted and invited to participate in the Real-Time Insights COVID-19 in India (RTI COVID-India) study. RTI COVID-India was a bi-monthly phone survey that provided insight into the individual's knowledge, attitudes, and behaviour towards COVID-19 and changes in the household's economic and health conditions throughout the pandemic. The survey was started in May 2020 and 9 rounds of data have been collected.Findings till dateOut of the 2704 LASI-DAD households with valid phone numbers, 1766 households participated in the RTI COVID-India survey at least once. Participants were in the age range of 18-102 years, 49% were female, 66% resided in rural area. Across all rounds, there was a higher report of infection among respondents aged 60-69 years. There was a greater prevalence of COVID-19 diagnosis reported in urban (23.0%) compared to rural areas (9.8%). Respondents with higher education had a greater prevalence of COVID-19 diagnosis compared to those with lower or no formal education. Highest prevalence of COVID-19 diagnosis was reported from high economic status compared to middle and low economic status households. Comparing education gradients in experiencing COVID-19 symptoms and being diagnosed, we observe an opposite pattern: respondents with no formal schooling reported the highest level of experiencing COVID-19 symptoms, whereas the greatest proportion of the respondents with secondary school or higher education reported being diagnosed with COVID-19.Future plansThe study group will analyse the data collected showing the real-time changes throughout the pandemic and will make the data widely available for researchers to conduct further studies.

Dataset Information

An open repository of real-time COVID-19 indicators.

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An open repository of real-time COVID-19 indicators.

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