An empirical test of the role of small-scale transmission in large-scale disease dynamicsAuthor(s): Joseph R. Mihaljevic; Carlos M. Polivka; Constance J. Mehmel; Chentong Li; Vanja Dukic; Greg Dwyer
Source: The American Naturalist. 195(4): 616-635.
Publication Series: Scientific Journal (JRNL)
Station: Pacific Northwest Research Station
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A key assumption of epidemiological models is that population-scale disease spread is driven by close contact between hosts and pathogens. At larger scales, however, mechanisms such as spatial structure in host and pathogen populations and environmental heterogeneity could alter disease spread. The assumption that small-scale transmission mechanisms are sufficient to explain large-scale infection rates, however, is rarely tested. Here, we provide a rigorous test using an insect-baculovirus system. We fit a mathematical model to data from forest-wide epizootics while constraining the model parameters with data from branch-scale experiments, a difference in spatial scale of four orders of magnitude. This experimentally constrained model fits the epizootic data well, supporting the role of small-scale transmission, but variability is high. We then compare this model’s performance to an unconstrained model that ignores the experimental data, which serves as a proxy for models with additional mechanisms. The unconstrained model has a superior fit, revealing a higher transmission rate across forests compared with branch-scale estimates. Our study suggests that small-scale transmission is insufficient to explain baculovirus epizootics. Further research is needed to identify the mechanisms that contribute to disease spread across large spatial scales, and synthesizing models and multiscale data are key to understanding these dynamics.
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CitationMihaljevic, Joseph R.; Polivka, Carlos M.; Mehmel, Constance J.; Li, Chentong; Dukic, Vanja; Dwyer, Greg. 2020. An empirical test of the role of small-scale transmission in large-scale disease dynamics. The American Naturalist. 195(4): 616-635. https://doi.org/10.1086/707457.
KeywordsDisease ecology, epidemiological modeling, Bayesian inference, microbial control.
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