Zoonotic vector-borne diseases are determined by a complex set of ecological factors driving transmission in nature and drivers of human disease risk, including social factors. Although these ecological and social factors are interrelated, they are often studied separately. We propose a coupled natural human system to explore the complex interactions between these factors driving transmission of Lyme disease in Northeast U.S. We used an agent-based model (ABM) to examine how human-tick contact rates are derived from the interaction between vector density and human behaviors, including those related to adaptation to perceived risk. Herein, we present an ABM in which we simulated an environment encompassing patches of urban forests and residential properties with different densities of infected Ixodes scapularis nymphs. Human agents were assigned a random movement within this environment and 3 counterfactual scenarios were simulated after a first tick encounter by the agent: i) avoidance of urban parks; ii) use of personal protection measures; iii) and spray against ticks at the household level. These scenarios were compared to a scenario with no adaptive human response. In addition, we tested whether different density of forests and their aggregation affected human-tick contact rates. Landscapes were simulated using the secr R package.
Results/Conclusions
All adaptive behaviors reduced human-tick encounter significantly and avoidance of urban parks showed the highest reduction (Kruskal-Wallis, p<0.001) compared to the null model, across all types of landscapes simulated. However, there were differences in human-tick encounter rates depending on the density of forested areas (number of forest patches) and aggregation of patches. Human-tick contact rates increased with the density of forests and fragmentation (less aggregated patches) since the probability of finding a forest patch was higher and agents moved randomly across the space. In this model tick density was kept contact for the forest patches, independently of the landscape configuration to understand the effects of human behavior. In a future model we will introduce variation in tick density as observed in the field, to better understand the system dynamics. This ABM is part of a broader project to build an empirically-based ABM combining ecological data collected on Staten Island, NY, and human behavioral data derived from a smartphone application (The Tick App). Combining these data will provide insights into the relative importance of factors driven by humans and nature and potential targets for intervention strategies.