Multiple vector tick species and their pathogens continue to expand in the US, with an estimated >400,000 cases of Lyme disease, in addition to the other emerging infections. Process-based mechanistic models can be used to assess the ecological and environmental drivers of tick and pathogen population dynamics and simulate multiple scenarios of tick-borne disease interventions. We developed a multi-model ensemble incorporating five published dynamic population models. Model ensembles improve the predictive skill and reduce uncertainty from single models. We calibrated the ensemble using collections of host-seeking, host and human-derived ticks from six military bases (Fort Indiantown Gap, Fort Eustis Army Base, Fort Knox Veterans Affairs, Fort A.P. Hill, West Point USMA, Fort Drum), provided by the Army Public Health Center. We incorporated climatic exogenous variables to force the mathematical models, i.e., mean near-surface air temperature, saturation deficit, relative humidity, rainfall amount, and day length, which were obtained from satellite-gauge combined datasets to weather station records (downloaded from NOAA's National Centers for Environmental Information portal). Model outputs include total (gross) number of host-seeking and on-host immature and adult ticks for both equilibria (assuming normal annual cycles of climate variables) and observed weather records. Using validated model ensembles, we explored ‘what if’ scenarios including the implementation of existing or newly developed methods to control ticks in the environment and on hosts. Our ultimate goal is to provide information to stakeholders on the optimum combination of interventions that can synergistically contribute to the control of tick-borne diseases in an integrated vector management approach.
