Disrupting Vector-Borne Disease Transmission in Complex Urban Environments

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Investigator: Caryn L. Bern, MD, MPH
Sponsor: University of Pennsylvania

Location(s): Peru

Description

This proposal will improve modeling of vector-borne disease transmission by developing new methods to make inference on unobserved spatial processes that are robust to the inaccuracies and uncertainties inherent in spatial data collection. To develop and evaluate our methods, we will conduct fieldwork in the city of Arequipa, Peru, where transmission of the parasitic agent of Chagas disease, Trypanosoma cruzi, by the insect vector Triatoma infestans is a serious urban problem. Chagas disease is one of the most deadly vector-borne diseases in the Americas;over 8 million people are infected with T. cruzi. Of these 8 million individuals 20% to 30% are expected to progress to cardiac or digestive forms of chronic Chagas disease which are difficult to treat and often fatal. The dense environment of cities facilitates the spread of vectors and parasites, hindering control efforts and putting large numbers of individuals at risk for infection. Further complicating control is the grid of city strets that leads to complex patterns of vector dispersal. Our proposal consists of three specific aims;each addresses a broad challenge to elucidating unseen processes of the spread of vector-borne diseases: 1) Mapping: To create maps of vector infestation that account for imperfect entomological surveys and spatial barriers in a landscape;2) Modeling Spread: To predict T. infestans dispersal through a city despite imperfect maps of its initial occurrence;and, 3) Spatia Control: To detect foci of T. infestans re-emergence and micro-epidemics of T. cruzi infection through adaptive spatial sampling. Achieving these three interlocking aims will improve control of many vector-borne diseases in urban and other complex environments.
The overarching aim of this proposal is to improve maps, models and control of vector-borne disease transmission by developing realistic methods to make inference on unobserved spatial processes that are robust to the inaccuracies and uncertainties inherent in spatial data collection. Our proposal, which focuses on Chagas disease, has the potential to improve the control of many vector-borne diseases in complex environments