The emergence and global expansion of SARS-CoV-2 as a human pathogen over the last four months represents a nearly unprecedented challenge for the infectious disease modelling community. This pandemic has benefitted from huge volumes of data being generated, but the rate of dissemination of these data has often outpaced existing data pipelines. While the last decade has seen significant advances in real-time infectious disease forecasting ? spurred by rapid growth in data and computational methods ? these methods have primarily focused on seasonal endemic diseases based, are based on historical data, and so do not apply easily to this novel pathogen, or to pandemic scenarios. New methods are needed to leverage the wealth of surveillance data at fine spatial granularity, together with associated information about policy interventions and environmental conditions over space and time, to reason directly about the mechanisms to forecast and understand the transmission dynamics of SARS-CoV-2 transmission. These methods must use sound statistical and epidemiological principles and be flexible and computationally efficient to provide real- time forecasts to guide public health decision-making and respond to changing aspects of this global crisis. The central research activities of this project are (1) to develop scalable, computationally efficient Bayesian hierarchical compartmental models to flexibly respond to state-level public health forecasting needs, and (2) to design models and conduct analyses to draw robust inference about the effectiveness of interventions in impacting the reproductive rate of SARS-CoV-2 infections within the US to build an evidence-base for continued responses to COVID-19 and future pandemics.

Public Health Relevance

The SARS-CoV-2 pandemic is an emerging public health crisis. A fundamental challenge is how to turn data into evidence that can inform decision-making about managing resources, improving health outcomes, and controlling further spread of SARS-CoV-2. Real-time forecasting and flexible mechanistic models to understand the disease dynamics can provide policy-makers tools to manage public response. The goal of the proposed research is to adapt existing statistical modeling frameworks and develop new ones for making forecasts of COVID-19 in real-time and integrating these forecasts into public health decision making.

National Institute of Health (NIH)
National Institute of General Medical Sciences (NIGMS)
Unknown (R35)
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Ravichandran, Veerasamy
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University of Massachusetts Amherst
Public Health & Prev Medicine
Schools of Public Health
United States
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Ray, Evan L; Reich, Nicholas G (2018) Prediction of infectious disease epidemics via weighted density ensembles. PLoS Comput Biol 14:e1005910
Reich, Nicholas G; Lessler, Justin; Varma, Jay K et al. (2018) Quantifying the Risk and Cost of Active Monitoring for Infectious Diseases. Sci Rep 8:1093
Lauer, Stephen A; Sakrejda, Krzysztof; Ray, Evan L et al. (2018) Prospective forecasts of annual dengue hemorrhagic fever incidence in Thailand, 2010-2014. Proc Natl Acad Sci U S A 115:E2175-E2182
Tushar, Abhinav; Reich, Nicholas G (2017) flusight: interactive visualizations for infectious disease forecasts. J Open Source Softw 2: