The aim of this project is to understand the effect of disease severity on the spread and evolution of infectious diseases, and its significance for infectious disease management and control. Recent and current epidemics emphasize that there is a pressing need to understand what makes some infectious diseases so devastating. Pathogens from the common cold through seasonal flu, SARS-1, SARS-2, and Ebola vary remarkably in how deadly they are and how well they transmit between people. Moreover, there is considerable variation in the impact that a specific disease will have on different individuals in a population. For example, some individuals get sicker than others, and importantly some will act as superspreaders of the pathogen. This type of variation is typical of infectious diseases in humans and other species, including livestock, crops and wildlife. To answer these questions the researchers will combine mathematical and computational models to develop new evolutionary theory that will explain the important factors responsible for variation in the outcome of an infection. The findings from the theory will be tested experimentally using an insect disease system in the laboratories at UC Berkeley. The mathematical modelling and experimental analysis can then be applied to real systems. A particular focus will be to examine the impact of different agricultural management practice on the severity of disease in agricultural systems, for example, which farming practices lead to the evolution of more virulent disease. Understanding what determines the virulence of infectious disease is critical to the effective management of current and emerging disease threats.

Individual hosts vary in their susceptibility and transmissibility through genetic and epigenetic effects, their condition, and their immune memory. Individual hosts also vary in their disease contacts within a population due to how individuals are arranged in space, how they move, and how they interact, all of which can generate population structure even in the absence of heterogeneities in the environment. These individual heterogeneities and the heterogeneity in transmission due to population structure can interact with heterogeneities that arise from specific interactons between host and parasites genotype. This project will test theories about the effects each of these three sources of heterogeneity (individual, population and interaction) have on the epidemiology of disease. How these different heterogeneities interact to determine the evolution of disease virulence and host defense is little studied. New theory is therefore required to test these interactions’ implications to both long-term evolutionary outcomes and short-term transient dynamics. This project will: (1) develop theory to predict how these heterogeneities interact to determine long-term outcomes and transient evolutionary behavior, (2) test these predictions in a tractable laboratory model system (larvae of the moth Plodia interpunctella infected with PiGV), and (3) develop models to predict the impact of heterogeneities on the evolution of pathogens in agricultural systems.

This award reflects NSF's statutory mission and has been deemed worthy of support through evaluation using the Foundation's intellectual merit and broader impacts review criteria.

National Science Foundation (NSF)
Division of Environmental Biology (DEB)
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Samuel Scheiner
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University of California Berkeley
United States
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