Research Project
10273685
TITLE: Harnessing coupled models to uncover mechanisms of disease transmission PROJECT SUMMARY Sustainable mitigation and control of infectious diseases rely on accurate predictions, risk assessments, and prioritization of intervention strategies. However, these are dynamic, vary in time, space, and/or with outbreak size, and are driven by intrinsic properties of the pathogen and complex, extrinsic factors, e.g., human behavior changes, the availability of vac-cines and pharmaceuticals, evolving diagnostic practices, available health-care infrastructure, and coordination between regions. Mechanistic models that holistically capture these factors are critically important. I focus on coupled models that address different facets of control strategies, specifically: (i) developing an epi-demiology and evolutionary integrated framework to evaluate long-term drivers of re- emergence and evolution in vaccine preventable diseases, using whooping cough (pertussis) as a model system; (ii) developing epi-economic coupled mod-els to evaluate the dynamical consequences of behavior and policy on epidemic and economic management in ongoing emerging epidemics, using COVID-19 as a model system; and (iii) predicting unintended consequences of emerging epidemics? control on the dynamics of vaccine preventable diseases. The computational, statistical, and mathematical methods are readily applicable to a range of diseases. The proposed research will provide a framework for quantification of risk?both in space and time. The goal of this research is to create an actionable framework of coupled systems that are flexible to allow for targeted, sustainable public health interventions. This framework will conduct evolutionary, epidemiological and economic/ behavioral analyses from historical (longitudinal) to near real-time ongoing outbreaks. Finally it will allow me to leverage disparate data streams (e.g., genomes, socio-demographic, economics, epidemiological and mobility) into integrative coupled models to understand the mechanisms underlying complex disease dynamics, and to predict future disease risk and economic consequences in changing environments.
