Wednesday, May 21, 2025
5/21/2025
A climate and host community driven Ixodes population and infection dynamics model - Project Summary Lyme disease is an emerging infectious disease with an estimated 300,000 cases every year in the United States. The disease is transmitted from its animal reservoirs to humans by ticks. There is currently no vaccine available, as such Lyme disease prevention and control are of the utmost importance. Lyme disease control methods (e.g., deer or rodent-targeted acaricide, host removal, rodent-targeted vaccines, etc.) are effective over small spatial and temporal scales in research settings. But largescale tick and Lyme disease control have been unsuccessful. One reason for this is that the efficacy of a control strategy depends on the climate and tick-host community where it is applied. A tool to evaluate potential tick strategies is needed. A tick population and infection dynamics model would provide such a tool. This model could predict the reduction in the infected tick density for a control strategy in a given environmental context. Here we propose to develop, parameterize, and validate such a model. This model will then be able to test tick control strategies; it will provide a valuable tool for tick control. For the model to be accurate it needs to be encoded with a realistic representation of the mechanisms which drive tick population and infection dynamics. Tick populations are largely driven by climate and the tick-host community. We will construct the model together with an empirical study designed to parameterize it. This tight coupling of theory and empiricism is very powerful. We will parameterize the model using a novel field-based, climate-manipulation method. We will encode a realistic tick host community structure into the model. Previous tick population models had a simplistic representation of host community structure, which limited their ability to predict tick density. Finally, validation — testing a model's prediction against an independent data set — is a necessary step for any modeling study, however previous tick population models' predictions of tick density have not been validated. Here we will validate our model's prediction of tick density and infection against an independently collected nation-wide tick density and infection data set. The final parameterized and validated model will be a powerful tool when designing and implementing tick control strategies. This will have critical human health benefits for the control of Lyme disease and other tick-borne diseases. We will make the computer code to run the model publicly available. This will be a great resource to tick-borne disease researchers.
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