Real-time predictive modeling for public health departments to control infectious diseases


PROJECT SUMMARY Public health departments increasingly use predictive modeling to guide decisions and resource allocation for the control of infectious diseases in the United States, especially during the COVID-19 pandemic. These novel predictive models offer promise to better identify high-risk populations to precisely deploy interventions such as vaccination, yet there is limited evidence on how these models are used by public health departments and whether they translate into policy that reduces infectious diseases. The major scientific problem I seek to address is to identify whether, and to what degree, predictive models can be incorporated into public health practice and translated into policy by public health departments to improve the control of infectious diseases. By leveraging a key collaboration with the California Department of Public Health (CDPH) and rich epidemiologic data sources, I will address a key public health challenge of how to optimally allocate limited resources for targeted vaccination against pertussis, seasonal influenza, and hepatitis A. The goal is to target vaccines to the highest-risk locations and populations to reduce the number of outbreaks and infections. My hypothesis is that public health departments can effectively incorporate predictive mathematical models on optimal targeting of vaccination into their policy decisions. I will apply my expertise in predictive modeling and infectious diseases to develop open-source, predictive modeling tools for county public health departments to allocate targeted vaccination to the highest-risk populations, and study the step-by-step implementation of these models in public health use. My broad, long-range goal is to evaluate the causal public health impact of using predictive models to guide decisions on vaccination in public health departments. In Aim 1, I will develop and validate predictive models to optimally target vaccines to high-risk locations and populations (age, demographic and risk factor) for pertussis, seasonal influenza, and hepatitis A. The model will provide comparative effectiveness and costs of various targeted vaccination strategies, and an overall vaccine recommendation specific to the county and infectious disease. In Aim 2, I will apply methods from implementation science to optimize the user experience for public health officials to maximize usability, communication, and uptake of model-based vaccine recommendations. In Aim 3, I will implement the predictive models of targeted vaccination in California public health departments and measure implementation outcomes in a pilot study. This work will provide the foundation for a future innovative trial with CDPH that randomizes county public health departments and evaluates whether using model-based predictions on optimal vaccine allocation can causally reduce cases and outbreaks. This proposed work has the potential to unlock new scientific directions of translating predictive models into common practice in public health, which can then be applied across many infectious diseases.


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