NSF Award
2031536
As COVID-19 spreads through communities across the world, and particularly within the USA, a number of questions remain unanswered. Of particular importance is to what extent different mitigation and containment strategies affect the resulting number of ICU cases and/or deaths? This question will become ever more nuanced as communities begin to relax current ?lockdown? orders to varying degrees. Additionally, to what extent do spatial and temporal changes in weather (temperature, precipitation, and humidity) as well as UV radiation modulate the disease?s evolution? Through a combination of unique data collection, model refinement, and scientific investigation, this study can shed valuable insight on these questions. The codes and derived data will be made available to the scientific community through GitHub repositories, CRAN packages, and web portals, and informal training will be provided for potentially interested stakeholders, such as county public health departments, the CDC, and DoD agencies.<br/><br/>This investigation will use an existing state-of-the-art modeling and forecasting framework, Dynamics of Interacting Community Epidemics (DICE), to examine the human ecology of COVID-19 dynamics. DICE is a unique tool that can help reveal the impact of different containment and non-pharmaceutical mitigation strategies, as well as climate forcing, on the transmission of COVID-19. Uniquely, it is an arbitrarily scaled hybrid spatial metapopulation model in which individual communities experience deterministic disease dynamics, but between which the process of one community seeding an outbreak in another community is stochastic. DICE can be run at the county, state, region, or national level, or, various combinations of these sub-units can be coupled, depending on what data are available. DICE solves the system of SE1?EnI1?ImRX equations producing a modeled incidence profile and estimates of the reproduction number as a function of time, R(t), the severity of the outbreak, and parameters quantifying the efficacy of interventions. DICE already has the capability of incorporating school vacation data, and uses climate data from NASA and NOAA, and specific humidity, in particular, which has been shown to be important in forecasting the evolution of influenza. A range of methodologies for incorporating interventions, such as school closures, social distancing, and shelter-in-place orders have been recently tested and explored using a complementary single-population prototype tool (DRAFT), specifically developed to rapidly explore refinements that can be incorporated into DICE. DICE can both simulate possible future scenarios as well as fit to available data to estimate the efficacy of different intervention profiles, and also captures joint estimates of severity (Sev) and transmissibility (R(t)). As COVID-19 spreads across the U.S., community transmission can be evaluated in R-Sev space, which will provide crucial and strategic information to assist policymakers in making more informed decisions. Through the use of a Monte Carlo Markov Chain (MCMC) approach, DICE produces robust estimates of the uncertainties in the projections. Additionally, DICE is a multi-model algorithm, allowing the generation of forecasts for more than 32 model variants, which provides not only an estimate of the impact that various factors may play (e.g., climate), but also produces hyper-ensembles of model realizations, which, in turn provide additional estimates of uncertainty. This RAPID award is made by the Ecology and Evolution of Infectious Disease Program in the Division of Environmental Biology, using funds from the Coronavirus Aid, Relief, and Economic Security (CARES) Act.<br/><br/>This award reflects NSF's statutory mission and has been deemed worthy of support through evaluation using the Foundation's intellectual merit and broader impacts review criteria.
