A real-time search strategy for finding urban disease vector infestations

Abstract

ObjectivesContaining domestic vector infestation requires the ability to swiftly locate and treat infested homes. In urban settings where vectors are heterogeneously distributed throughout a dense housing matrix, the task of locating infestations can be challenging. Here, we present a novel stochastic compartmental model developed to help locate infested homes in urban areas. We designed the model using infestation data for the Chagas disease vector species Triatoma infestans in Arequipa, Peru.MethodsOur approach incorporates disease vector counts at each observed house, and the vector’s complex spatial dispersal dynamics. We used a Bayesian method to augment the observed data, estimate the insect population growth and dispersal parameters, and determine posterior infestation probabilities of households. We investigated the properties of the model through simulation studies, followed by field testing in Arequipa.ResultsSimulation studies showed the model to be accurate in its estimates of two parameters of interest: the growth rate of a domestic triatomine bug colony and the probability of a triatomine bug successfully invading a new home after dispersing from an infested home. When testing the model in the field, data collection using model estimates was hindered by low household participation rates, which severely limited the algorithm and in turn, the model’s predictive power.ConclusionsWhile future optimization efforts must improve the model’s capabilities when household participation is low, our approach is nonetheless an important step toward integrating data with predictive modeling to carry out evidence-based vector surveillance in cities.

An immune System for the City: A New Paradigm for Surveillance and Control of Disease Vectors

“…Districts entered the surveillance phase in a rolling manner, six months following completion of insecticide application. Post-spray surveillance activities initially were mostly limited to passive surveillance, with occasional active search for infested households when personnel were available [26] [27] Following the completion of the attack phase of the campaign, a series of data-driven surveillance schemes were piloted in multiple districts of the city [21] [28] [29]. These studies included search for the insect in over 8,000 households based on data collected during the attack phase and new data that accumulated during the surveillance phase.…”

“…In our control arm, we endeavored to create as close to a best case scenario for the conventional approach, leveraging the immense amount of data available. We started from a spatial-temporal model that integrates information collected during the attack phase of the vector control campaign and the subsequent years of surveillance; data that is not normally used by vector control teams to direct their surveillance efforts from year to year [28]. The model provides estimates of the relative (to all the other houses) probability of infestation within the search area.…”

Bayesian Data Augmentation for Partially Observed Stochastic Compartmental Models

“…For Bayesian approaches using MCMC methods, sampling the unobserved part of the epidemic process as latent variables, and therefore obtaining the full likelihood and posterior distribution of the parameters when the infection or removal process is completely unobserved, has been described in Gibson and Renshaw (1998); O'Neill and Roberts (1999); Gibson and Renshaw (2001). Rose et al (2020) adopted this MCMC data augmentation approach to fit a stochastic compartmental model for infestation data to help locate infested homes in urban areas. Another recent application of this method is by Pooley et al (2020), who introduced a software tool called SIRE to estimate genetic and non-genetic effects in epidemic processes.…”