University of Kansas
COVID-19 is a respiratory disease caused by a recently discovered, novel coronavirus. Since its discovery in Wuhan, China, in 2019, COVID-19 has already led to over 2 million cases globally. It has spread globally, including to many vulnerable countries without adequate healthcare infrastructures. Many different responses have been tried, including social distancing, school and event closings, and travel bans. This project will develop mathematical models to address three fundamental questions: 1) how much participation and coordinated control is needed for effective protection? 2) what independent control efforts can compensate for lack of coordination to achieve effective protection? and 3) how do community population demographics, socioeconomic conditions, and health care infrastructure impact outcomes? This project aims to inform coordination of disease control policies at all scales (local, regional, national, international) to aid in curtailing the ongoing and future outbreaks. This project will advance fundamental understanding of the impacts of control efforts via a new risk perception-driven infectious disease model, and predict which drivers of public demand for community-level control efforts might lead to potentially harmful long-term decisions. The project will involve training two doctoral students in techniques related to mathematical modeling of disease dynamics and spread. Many mitigation options are being weighed and implemented for COVID-19, with different decisions made at different administrative levels, including alternative quarantine strategies and different degrees of ?lockdown?. All of these decisions come with different perceptions of risk. The PIs will develop and analyze disease transmission models that incorporate various factors including public perception of risk, age-structure with a hospitalized population, and spatial structure with different scales spanning local communities to an entire country. These models will be used to explore impacts of community population demographics, socioeconomic conditions, and health care infrastructures, and how these factors impact control efforts under different social and economic settings. With the results of these models, policy- and decision-makers can consider the impacts of specific features of the communities under their administration as contributors to a broader network of public health efforts and choose the optimal mitigation steps. This work will inform coordination of disease control policies to curtail the ongoing outbreak directly. The results of this project, while tailored specifically to inform COVID-19 virus control strategies, will be applicable to any novel infectious disease outbreak in the future.