RAPID: Modeling COVID-19 transmission and mitigation using smaller contained populations

In the midst of the COVID-19 pandemic the state of Hawaii, being an archipelago, is in an exclusive position to carry out measures no other state could do ? it essentially sealed its borders to virtually all travel-related infections including inter-island ones by instituting a two-week quarantine of incoming air, water, and inter-island passengers, thus providing a critical data set that can help researchers understand the spread of the virus and the effectiveness of mitigation and isolation strategies. Hawaii also tracks the currently limited arrivals onto the various islands, and this collection of information will continue as mitigation levels change. This project will use the unique data from Hawaii to provide a predictive understanding of the virus through modeling of spread and mitigation effects, focusing on a critical gap in understanding variability of COVID-19 spread within different communities and a lack of dynamic modeling. Incorporation of data sets from a controlled environment will greatly enhance predictive understanding and enable mitigation approaches with better certainty based on real data. The project will use advanced computational techniques to make the models run efficiently and make them readily available to the public and decision makers involved in the COVID-19 response strategy.

Many current COVID-19 models only consider a totality of the population of any given state/county and do not take into account patterns of spatial activities or specificity of the region under consideration. This project will implement new dynamic and computationally-optimized models that incorporate compartmentalized populations to study variability in the spread of the disease as well as rapidly changing mitigation strategies. These elastic models are easily adaptable to different environments and employable locally and around the world, thus helping to minimize the negative effects of COVID-19 on public health at a global level. Use of discrete compartmentalized epidemiological models, as well as models based on spatio-temporal stochastic processes, can take into account different population communities distinguished through a variety of attributes that potentially affect the susceptibility of individuals to the disease. Such enhanced granularity will improve predictive capability of the models and provide better insights into the spread of COVID-19. The project will also engage students thus providing training for the future generation of researchers in data-driven sciences using a critical and urgent topic.

This project is jointly funded by CCF Division Software and Hardware Foundations Program and the Established Program to Stimulate Competitive Research (EPSCoR).

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.