The Effects of City Streets on an Urban Disease Vector

PLoS Comput Biol PLOS Computational Biology, 17 January 2013

Corentin M. Barbu, Andrew Hong, Jennifer M. Manne, Dylan S. Small, Javier E. Quintanilla Calderón, Karthik Sethuraman, Víctor Quispe-Machaca, Jenny Ancca-Juárez, Juan G. Cornejo del Carpio, Fernando S. Málaga Chavez, César Náquira, and Michael Z. Levy

“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.

Spatial distribution of Triatoma infestans presence in households of Paucarpata, Arequipa, Peru.

Spatial distribution of Triatoma infestans presence in households of Paucarpata, Arequipa, Peru. Map of the study area. Black indicates infested households, white non-infested households, and grey non-inspected households. The area encircled by dashes was used to fit the Gaussian Field Latent Class model; the remaining area was used as a validation dataset. The close-up shows the urban grid underneath and the aggregation of vectors within city blocks.

“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.”

2 thoughts on “The Effects of City Streets on an Urban Disease Vector

  1. We (Getis, Morrison, Scott) did similar work in the 1990s on dengue in Puerto Rico and Peru (Iquitos). That work should have been cited. A. Getis

  2. Dear Mr. Getis,
    I do not know of this work. Using the information you give I actually found work from you and your colleagues on the spatial aggregation in Puerto Rico and Peru but did not notice the presence of an analysis on the streets. Would you be nice enough to give the reference of this work? I would be glad to cite in a future work. Thanks. C. Barbu

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