The global pandemic of the 2019 coronavirus disease (COVID-19) has taken a staggering toll in terms of economies, livelihoods, and, most importantly, lives. As with any emerging disease, many uncertainties remain about significant disease characteristics such as transmission and immunity. Predicting disease spread and the impacts of interventions is a challenge with imperfect and incomplete information about the disease itself. Moreover, an accurate accounting of the outbreak requires knowledge about the number of cases, which necessitates fast and accurate testing. However, the current use and speed of testing varies significantly across different locations. While testing alone will not stop transmission, widespread testing gives richer information on how an outbreak is progressing, particularly with a disease such as COVID-19 with hidden transmission. Furthermore, knowledge of person’s disease status leads to better adherence to the strict isolation and social distancing measures used to curb transmission. While many models consider the role of interventions in flattening the curve, this project will address a key gap in modeling of COVID-19: the impact of testing strategies on the ability to assess and intervene against the ongoing outbreak. This project will develop new mathematical models of transmission of SARS-CoV-2, the cause of COVID-19 along with intervention strategies using testing. Two graduate students will be involved in the research.
This project will develop compartmental models of the COVID-19 outbreak incorporating the use of testing in conjunction with other non-pharmaceutical interventions such as social distancing. Testing capacity, accuracy, and delays will be built into these models to closely resemble real-world challenges in implementation, which will differ by location. The PI will analyze and simulate these deterministic models to expand understanding of the requirements of testing for successful slowing of the outbreak. The results from the novel models will be validated with simulations from similarly structured stochastic models and actual data. Results will be presented through a publicly accessible dashboard where information on local testing procedures can be entered and examined to determine locally appropriate strategies. This publicly accessible dashboard will help policy- and decision-makers develop policies in the context of local testing capacity to best slow the spread of COVID-19. In addition, this work will provide a framework for developing priorities for testing and evaluation of intervention in future emerging epidemics.
This award is co-funded with the Office of Multidisciplinary Activities (OMA) program of MPS.
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.
