A computational modeling framework for COVID-19 vaccination

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Project Summary/Abstract Coronavirus disease 2019 (COVID-19), caused by severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2), remains a global pandemic at present. Quantitative research is urgently needed to clarify the impacts of the current vaccination campaign on the pandemic evolution and economic growth, and to guide future policy development. The overall objective of this proposal is to establish a new computational modeling framework for an investigation of the COVID-19 vaccination campaign in the US, and to incorporate real data to assess the impacts of COVID-19 vaccination on public health and the economy. To achieve this objective, the team will pursue three specific aims: (1) Modeling the transmission and spread of COVID-19 under the impact of vaccination; (2) Modeling the economic impact of COVID-19 vaccination; (3) Conducting a case study for the Chattanooga region in the state of Tennessee. The proposed research is significant because it will incorporate detailed characteristics and potential limitations of the current vaccination campaign (such as the vaccine efficacy, phased allocation schemes, public resistance to vaccination, and vaccine breakthrough due to new variants of SARS- CoV-2) into a sophisticated modeling framework, which will enable us to make more accurate forecasts on the progression and long-term evolution of the pandemic. As such, the project is expected to advance the current understanding of COVID-19 transmission and to quantify the interaction between epidemic spreading, economic growth, and disease prevention and intervention under the impact of COVID-19 vaccination, all of which are important for the control and management of the pandemic. The approach is innovative in the development of a computational framework that integrates novel mechanistic and machine learning models and that connects the epidemic and economic aspects of COVID-19. The innovation of this project is also reflected by the integration of sophisticated computational modeling, rigorous mathematical analysis, intensive numerical simulation, and detailed data validation. The project represents an interdisciplinary collaboration among an applied and computational mathematician with long-term interest in infectious disease modeling (Wang), an epidemiologist with extensive working experiences at CDC and a current member of the regional COVID-19 task force (Heath), a business and management professor with a background in public heath (Mullen), and a statistician with expertise in machine learning and biomedical data analytics (Ma). The success of this project will not only build a solid knowledge base for the complex transmission dynamics of SARS-CoV-2 and the health and economic impacts of COVID-19 vaccination, but also provide important guidelines for the government agencies and public health administrations in pandemic management and policy development.