Project lead: Dr. Esteban Hernandez-Vargas
A quantitative understanding of disease transmission remains a central vexation for science as it involves several complex and dynamic processes at different scales. The link between the infection dynamics of the infected host to the susceptible population-level transmission is widely acknowledged – but missing is a transparent setting that would allow the integration of multiple data and mathematical modeling. The overarching goal of this project is to develop parameter fitting algorithms to establish the foundations for predictively simulating disease transmission across scales – from the infected host dynamics to the population level. Our central hypothesis is that within-host and between-hosts models adjusted with a wealth of data being accumulated in influenza research will provide a secure base to develop and fine-tune our algorithms for parameter fitting. To attain our overall objective, we will pursue the following two specific aims: (1) We will determine the architectures of parameter-fitting algorithms to integrate multiscale data into models. (2) We will define the potential of our algorithms with published data sets from domestic animals infected with influenza. This data-driven multiscale model will predict the effect of host influenza infection on contact-dependent transmission in domestic animals. The expected outcome is to provide computational evidence toward a comprehensive understanding of host infection and immunity to contact-dependent transmission. The project brings new features to multiscale modeling that will improve our technical capabilities to integrate data of different scales in biology and beyond.