Background Until recently, the Chagas disease vector, Triatoma infestans, was widespread in Arequipa, Perú , but as a result of a decades-long campaign in which over 70,000 houses were treated with insecticides, infestation prevalence is now greatly reduced. To monitor for T. infestans resurgence, the city is currently in a surveillance phase in which a sample of houses is selected for inspection each year. Despite extensive data from the control campaign that could be used to inform surveillance, the selection of houses to inspect is often carried out haphazardly or by convenience. Therefore, we asked, how can we enhance efforts toward preventing T. infestans resurgence by creating the opportunity for vector surveillance to be informed by data?

In Arequipa, Peru, a large-scale vector control campaign has successfully reduced urban infestations of the Chagas disease vector, Triatoma infestans. In addition to preventing new infections with Trypanosoma cruzi (etiological agent of Chagas disease), the campaign produced a wealth of information about the distribution and density of vector infestations. We used these data to create vector infestation risk maps for the city in order to target the last few remaining infestations, which are unevenly distributed and difficult to pinpoint. Our maps, which are provided on a mobile app, display color-coded, individual house-level estimates of T. infestans infestation risk. Entomologic surveillance personnel can use the maps to select homes to inspect based on estimated risk of infestation, as well as keep track of which parts of a given neighborhood they have inspected to ensure even surveillance throughout the zone. However, the question then becomes, how do we encourage surveillance personnel to actually use these two functionalities of the risk map? As such, we carried out a series of rolling trials to test different incentive schemes designed to encourage the following two behaviors by entomologic surveillance personnel in Arequipa: (i) preferential inspections of homes shown as high risk on the maps, and (ii) even surveillance across the geographical distribution of a given area, which we term, ‘spatial coverage.’ These two behaviors together constituted what we termed, ‘optimal map use.’ We found that several incentives resulted in one of the two target behaviors, but just one incentive scheme based on the game of poker resulted in optimal map use. This poker-based incentive structure may be well-suited to improve entomological surveillance activities and other complex multi-objective tasks.

Large-scale vector control campaigns have successfully reduced infectious disease incidence around the world. In addition to preventing new infections, these campaigns produce a wealth of information about the distribution and density of insect vectors, which can be incorporated into risk maps. These maps can effectively communicate risk map data to technicians on the ground, although encouraging them to use the data remains a challenge. We carried out a series of rolling trials in which we evaluated risk map use under different incentive schemes. Participants in the studies were trained field technicians tasked with house-to-house surveillance for insect vectors of Chagas disease in Arequipa, Peru. A novel incentive scheme based on poker best achieved a dual objective: to encourage technicians to preferentially visit higher-risk houses while surveilling evenly across the search zone. The poker incentive structure may be well-suited to improve entomological surveillance activities and other complex multi-objective tasks.