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      The Effects of City Streets on an Urban Disease Vector

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          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.

          Author Summary

          Chagas disease is a major parasitic disease in Latin America. It is transmitted by Triatoma infestans an insect common in Arequipa, the second largest city in Peru. We propose a method to demonstrate that streets strongly affect the spatial distribution of infestation by this insect in Arequipa. The effect of streets may be due to several external factors: 1) houses on different sides of a street may not be equally welcoming to the insects due to the presence of certain materials or animals, 2) people inspecting houses on the two sides of a street may not be equally efficient, and, 3) insects may disperse to neighboring houses but rarely reach houses across a street. We take these aspects into account in a second analysis and confirm that streets are important barriers to these insects. Our finding should allow for improvements in the control of insects that transmit Chagas disease in cities. More generally, our methods can be applied to other pests and disease vectors to better understand and control epidemics in cities.

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          Pyrethroid resistance in African anopheline mosquitoes: what are the implications for malaria control?

          The use of pyrethroid insecticides in malaria vector control has increased dramatically in the past decade through the scale up of insecticide treated net distribution programmes and indoor residual spraying campaigns. Inevitably, the major malaria vectors have developed resistance to these insecticides and the resistance alleles are spreading at an exceptionally rapid rate throughout Africa. Although substantial progress has been made on understanding the causes of pyrethroid resistance, remarkably few studies have focused on the epidemiological impact of resistance on current malaria control activities. As we move into the malaria eradication era, it is vital that the implications of insecticide resistance are understood and strategies to mitigate these effects are implemented. Copyright © 2010 Elsevier Ltd. All rights reserved.
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            Putting the "landscape" in landscape genetics.

            Landscape genetics has emerged as a new research area that integrates population genetics, landscape ecology and spatial statistics. Researchers in this field can combine the high resolution of genetic markers with spatial data and a variety of statistical methods to evaluate the role that landscape variables play in shaping genetic diversity and population structure. While interest in this research area is growing rapidly, our ability to fully utilize landscape data, test explicit hypotheses and truly integrate these diverse disciplines has lagged behind. Part of the current challenge in the development of the field of landscape genetics is bridging the communication and knowledge gap between these highly specific and technical disciplines. The goal of this review is to help bridge this gap by exposing geneticists to terminology, sampling methods and analysis techniques widely used in landscape ecology and spatial statistics but rarely addressed in the genetics literature. We offer a definition for the term "landscape genetics", provide an overview of the landscape genetics literature, give guidelines for appropriate sampling design and useful analysis techniques, and discuss future directions in the field. We hope, this review will stimulate increased dialog and enhance interdisciplinary collaborations advancing this exciting new field.
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              Spatial epidemiology: an emerging (or re-emerging) discipline.

              Spatial epidemiology is the study of spatial variation in disease risk or incidence. Several ecological processes can result in strong spatial patterns of such risk or incidence: for example, pathogen dispersal might be highly localized, vectors or reservoirs for pathogens might be spatially restricted, or susceptible hosts might be clumped. Here, we briefly describe approaches to spatial epidemiology that are spatially implicit, such as metapopulation models of disease transmission, and then focus on research in spatial epidemiology that is spatially explicit, such as the creation of risk maps for particular geographical areas. Although the spatial dynamics of infectious diseases are the subject of intensive study, the impacts of landscape structure on epidemiological processes have so far been neglected. The few studies that demonstrate how landscape composition (types of elements) and configuration (spatial positions of those elements) influence disease risk or incidence suggest that a true integration of landscape ecology with epidemiology will be fruitful.
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                Author and article information

                Contributors
                Role: Editor
                Journal
                PLoS Comput Biol
                PLoS Comput. Biol
                plos
                ploscomp
                PLoS Computational Biology
                Public Library of Science (San Francisco, USA )
                1553-734X
                1553-7358
                January 2013
                January 2013
                17 January 2013
                : 9
                : 1
                : e1002801
                Affiliations
                [1 ]Center for Clinical Epidemiology & Biostatistics - Department of Biostatistics & Epidemiology, University of Pennsylvania Perelman School of Medicine, Philadelphia, Pennsylvania, United States of America
                [2 ]Department of Statistics, The Wharton School University of Pennsylvania, Philadelphia, Pennsylvania, United States of America
                [3 ]Department of Global Health and Population, Harvard School of Public Health, Boston, Massachusetts, United States of America
                [4 ]Facultad de Ciencias y Filosofia, Universidad Peruana Cayetano Heredia, Lima, Peru
                [5 ]Dirección Regional del Ministerio de Salud, Arequipa, Peru
                University of Texas at Austin, United States of America
                Author notes

                The authors have declared that no competing interests exist.

                Managed the lab and data: JEQC JGCC VQM JAJ FSMC MZL. Conceived and designed the experiments: CMB MZL DSS AH. Performed the experiments: CMB AH. Analyzed the data: CMB. Contributed reagents/materials/analysis tools: FSMC CN JGCC. Wrote the paper: CMB JMM MZL KS DSS.

                Article
                PCOMPBIOL-D-12-00661
                10.1371/journal.pcbi.1002801
                3547802
                23341756
                381a8b7a-3fab-4d95-aad1-a9706f23db51
                Copyright @ 2013

                This is an open-access article distributed under the terms of the Creative Commons Attribution License, which permits unrestricted use, distribution, and reproduction in any medium, provided the original author and source are credited.

                History
                : 25 April 2012
                : 12 October 2012
                Page count
                Pages: 9
                Funding
                Funding for this study came from National Institutes of Health (NIH, http://www.nih.gov/) 5K01 AI079162-04 and 05, NIH 3K01AI079162-02S1 and 03S1, NIH P50 AI074285-03, and 04. The funders had no role in study design, data collection and analysis, decision to publish, or preparation of the manuscript.
                Categories
                Research Article
                Mathematics
                Statistics
                Statistical Methods
                Biology
                Microbiology
                Vector Biology
                Triatoma
                Computational Biology
                Population Modeling
                Infectious Disease Modeling
                Population Biology
                Epidemiology
                Epidemiological Methods
                Infectious Disease Epidemiology
                Spatial Epidemiology
                Ecology
                Population Ecology
                Spatial and Landscape Ecology
                Urban Ecology
                Zoology
                Entomology
                Computer Science
                Geoinformatics
                Spatial Autocorrelation
                Medicine
                Epidemiology
                Spatial Epidemiology
                Infectious Diseases
                Neglected Tropical Diseases
                Chagas Disease
                Vectors and Hosts
                Triatoma
                Infectious Disease Control
                Public Health
                Disease Ecology

                Quantitative & Systems biology
                Quantitative & Systems biology

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