The COVID-19 pandemic, caused by SARS-CoV-2, represents a true global emergency and crisis that needs to be addressed immediately. Currently we do not have a good understanding of quantitative estimates of key epidemiological parameters, such as the time from infection to becoming infectious, the duration of infectiousness, and how these durations are affected by symptom severity. We do not know how much transmission is driven by asymptomatic individuals and individuals with mild, moderate, or severe symptoms. Lastly, we do not know how the viral load relates to the infectiousness of an individual. By constructing multiscale models and integrating multiple streams of data from different biological scales into a coherent model framework, the project will address these unknowns through mathematical modeling and thus advance our understanding of COVID-19 transmission. This understanding will be then used to make precise predictions and evaluations of the impacts of interventions, i.e. much needed intellectual advancement to address the current COVID-19 global pandemic. The results of the project will be presented to public health professionals and government officials to aid decision making through regular meetings and connections that the team members participate and maintain. For example, co-PI Ke regularly participates in weekly CDC modeling group meetings and in monthly meetings with a working group commissioned by the White House called PPFST. Co-PIs Hengartner and Romero-Severnson have close connections with the New Mexico Department of Health. The PIs will ensure that the project is designed and formulated to address critical public health questions and that the results can be used by public health officials.
The objective of this research project is to advance the fundamental understanding of key epidemiological parameters and determinants for SARS-CoV-2 that are essential to better quantify and predict SARS-CoV-2 transmission dynamics. The investigators will use mathematical modeling of SARS-CoV-2 dynamics across multiple scales. First, the PI will develop within-host models of SARS-CoV-2 infection and will fit the model to data from literature to estimate parameters such as the time from cell infection to release of virus into bodily fluids, the rate of viral production from infected cells, the infected cell lifespan, and other factors that link to transmission and disease severity. Another aim is to use the model to predict the effectiveness of drug therapy as a function of person's viral load. Second, the PI will tie the within-host dynamics to clinical factors, such as the time between infection and viral shedding, time to symptoms, and in some cases to time of death, which are important in understanding transmission dynamics and intervention effectiveness. Ultimately, this project will provide a quantitative framework to evaluate the effectiveness of pharmaceutical and non-pharmaceutical interventions, e.g., quarantine, school closures, and other means of social distancing.
This grant is being awarded using funds made available by the Coronavirus Aid, Relief, and Economic Security (CARES) Act supplement allocated to 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.
