The Effects of City Streets on an Urban Disease Vector

Abstract

With increasing urbanization vector-borne diseases are quickly developing in cities, and urban control strategies are needed. If streets are shown to be barriers to disease vectors, city blocks could be used as a convenient and relevant spatial unit of study and control. Unfortunately, existing spatial analysis tools do not allow for assessment of the impact of an urban grid on the presence of disease agents. Here, we first propose a method to test for the significance of the impact of streets on vector infestation based on a decomposition of Moran's spatial autocorrelation index; and second, develop a Gaussian Field Latent Class model to finely describe the effect of streets while controlling for cofactors and imperfect detection of vectors. We apply these methods to cross-sectional data of infestation by the Chagas disease vector Triatoma infestans in the city of Arequipa, Peru. Our Moran's decomposition test reveals that the distribution of T. infestans in this urban environment is significantly constrained by streets (p<0.05). With the Gaussian Field Latent Class model we confirm that streets provide a barrier against infestation and further show that greater than 90% of the spatial component of the probability of vector presence is explained by the correlation among houses within city blocks. The city block is thus likely to be an appropriate spatial unit to describe and control T. infestans in an urban context. Characteristics of the urban grid can influence the spatial dynamics of vector borne disease and should be considered when designing public health policies.

Residual Infestation and Recolonization during UrbanTriatoma infestansBug Control Campaign, Peru1

“…Estimated initial prevalence of infestation in households participating in a single treatment (range 6.9%–12.2%) was lower than that of households that participated twice (35.6%), which suggested a strong correlation between infestation status and participation. Estimated probability of detecting infestation in an infested household during a treatment ( s ) was 57% (95% CI 0.46–0.66) (Table 2) (http://www.spatcontrol.net/articles/Barbu2014/suppMet.pdf), which is comparable to previous estimates obtained by using other methods ( 25 ). …”

“…Recolonization in urban areas was perhaps predictable because insects rapidly move between households ( 25 , 31 ). However, the problem of residual vector populations is different in our urban study area.…”

“…The observation during the surveillance phase of T. cruzi infection in several households that participated in the initial treatment phase highlights this risk. Finally, although this study covered only 1 city, it encompasses a large part of what comprises one of the main infested urban areas in Latin America and is characterized by diverse landscapes ranging from semi-rural/periurban ( 37 ) to urban ( 25 ). …”

Is participation contagious? Evidence from a household vector control campaign in urban Peru

“…Household participation was clustered spatially, suggesting that neighbour participation may be an important cue to action. While the runs of participation we observed could have been due to spatial patterns of urban vector infestation19 or other sociodemographic predictors of participation, the positive association between neighbour participation and each household's participation decision was robust to the addition of vector infestation and neighbourhood type controls in multivariate models. Our results highlight the potential to nudge others towards participation if just one or two households assent to insecticide application.…”

A Door-to-Door Survey of Bed Bug (Cimex lectularius) Infestations in Row Homes in Philadelphia, Pennsylvania

“…Random simulation results were compared with the observed data to determine the presence of any significant clustering of infestation. Because this method is limited to assessing correlation within city blocks, a Moran's I and a stratified Moran's I test for autocorrelation 19 were performed. The latter test takes into account the effects of city streets on spatial patterns.…”