The Epidemiology Section utilizes mathematical models and data analysis to understand the epidemiology, spatiotemporal patterns, clinical progression and transmission routes of infectious diseases. We aim to find possible pathways for disease eradication with the current and future tools.
We are working to create a biologically detailed colonization and transmission model of Streptococcus Pneumoniae. Through modeling we hope to understand herd effect, routes of acquisition and transmission for infants, immunity induced by natural colonization and vaccines, minimal population size to self-sustain the endemic circulation, mechanisms for co-existence, as well as RSV/flu co-infections. A primary goal of our mathematical modeling effort is to mechanistically understand and mathematically describe the routes and patterns of pneumococcal transmission between age groups within different epidemiological settings (i.e., different vaccination coverage, family structure, and historical force of infection). Such analysis will facilitate prediction of a number of potentially useful quantities such as the magnitude of vaccination herd effects in reducing transmission, and how this varies over time both within and between settings; the feasibility and time horizon for local elimination of transmission; and the critical community size, the minimum population size necessary to sustain endemic circulation in a given setting.
Our goal is to build and parametrize a realistic population-level model for a number of enteric infections, after understanding the pathogen and location specific exposure patterns and transmission pathways, for example, how fece disposal patterns, water sources, and hygiene behaviors are associated with symptomatic and asymptomatic enteric infections, and what are the household and community indicators of childhood diarrheal exposure risk. The model will be able to explore population-level epidemiological impact, show progress to reach herd immunity, and understand co-infection between enteric pathogens.
We are working to create a mathematical model of typhoid transmission featuring detailed geography and biology, to accurately reproduce current observed typhoid epidemiology and to test future intervention scenarios. We aim to answer questions relating to the feasibility of an elimination strategy in endemic regions, understand the role of person-to-person vs. environmental transmission in observed typhoid epidemiology, and project realistic impact measures for vaccination in varying locations and age groups.
Humans have varied social interactions that create complex contact patterns: parents taking care of children, individuals interacting at work, children playing at school, are just a few examples of how humans interact. For many diseases, including respiratory and enteric pathogens, disease transmission occurs in populations in a nonrandom way. Whether the disease is clustered within a family, city, or community, these assortative patterns of disease transmission are often most effectively captured and modeled through networks. Data including family structure and travel patterns may inform these networks, depending on the disease being studied. We are working to clarify the role of networks in the diseases we study, capture the nuanced ways people behave, as well as create tools to incorporate networks quickly and effectively into transmission modeling.