UC Davis

Advances in computational performance and the advent of big data have allowed the emergence of new methodologies for modeling infectious diseases as complex systems representing aspects such as non-linearity, emergence, and feedback loops, among others, with increasing spatio-temporal resolution. Agent based models have been used in many other disciplines such as social sciences, ecology, city planing, and, more recently, epidemiology. With the objective of better integration of data and theory, we used agent based modeling and global sensitivity analysis of complex models under different epidemiological scenarios and with varying degrees of data availability and quality. In the first chapter, we integrated data from publications between February 2020-February 2021 and from phone interviews in a typical nursing home in California to develop an agent based model to explore the impact of non-pharmaceutical interventions and vaccination in the prevention and control of COVID-19 in nursing homes. In the second chapter, we used information from the literature and from sow farm production records in the Midwestern United States to develop a model that evaluate the effect of herd management and vaccination in the control of porcine reproductive and respiratory syndrome (PRRS), an endemic disease in the US pork industry. In the third chapter, we used data provided by the national veterinary services (AGROCALIDAD) of Ecuador to evaluate the impact of simulated re-introductions of foot and mouth disease (FMD), which is in the last phase of disease eradication in Ecuador, and to evaluate strategies for control of FMD. The findings for each of the presented chapters can be used to support disease eradication efforts of communicable disease outbreaks through approaches centering local epidemiological dynamics under different settings.