Spatial Heterogeneity and Risk Maps of Community Infestation by Triatoma infestans in Rural Northwestern Argentina
Publication Date
August 14, 2012
Journal
PLOS Neglected Tropical Diseases
Authors
Gonzalo M. Vazquez Prokopec, Cynthia Spillmann, Mario Zaidenberg, Ricardo E. Gürtler, et al
Volume
6
Issue
8
Pages
e1788
DOI
https://dx.plos.org/10.1371/journal.pntd.0001788
Publisher URL
http://journals.plos.org/plosntds/article?id=10.1371%2Fjournal.pntd.0001788
PubMed
http://www.ncbi.nlm.nih.gov/pubmed/22905276
PubMed Central
http://www.ncbi.nlm.nih.gov/pmc/articles/PMC3419179
Europe PMC
http://europepmc.org/abstract/MED/22905276
Web of Science
000308497100024
Scopus
84865967875
Mendeley
http://www.mendeley.com/research/spatial-heterogeneity-risk-maps-community-infestation-triatoma-infestans-rural-northwestern-argentin
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Mendeley | Further Information

{"title"=>"Spatial Heterogeneity and Risk Maps of Community Infestation by Triatoma infestans in Rural Northwestern Argentina", "type"=>"journal", "authors"=>[{"first_name"=>"Gonzalo M.", "last_name"=>"Vazquez-Prokopec", "scopus_author_id"=>"6508232166"}, {"first_name"=>"Cynthia", "last_name"=>"Spillmann", "scopus_author_id"=>"6602212477"}, {"first_name"=>"Mario", "last_name"=>"Zaidenberg", "scopus_author_id"=>"7005142614"}, {"first_name"=>"Ricardo E.", "last_name"=>"Gürtler", "scopus_author_id"=>"7006137918"}, {"first_name"=>"Uriel", "last_name"=>"Kitron", "scopus_author_id"=>"7003784237"}], "year"=>2012, "source"=>"PLoS Neglected Tropical Diseases", "identifiers"=>{"issn"=>"19352727", "scopus"=>"2-s2.0-84865967875", "sgr"=>"84865967875", "pui"=>"365599543", "isbn"=>"1935-2735", "pmid"=>"22905276", "doi"=>"10.1371/journal.pntd.0001788"}, "id"=>"34c65697-16e6-332c-b0c3-248bd153338e", "abstract"=>"BACKGROUND: Fifty years of residual insecticide spraying to control Triatoma infestans in the Gran Chaco region of northern Argentina, Paraguay and Bolivia shows that vertically coordinated interventions aiming at full coverage have limited effects and are unsustainable. We quantified the spatial distribution of T. infestans domestic infestation at the district level, identified environmental factors associated with high infestation and then explored the usefulness of risk maps for the spatial stratification of interventions.\\n\\nMETHODS AND FINDINGS: We performed spatial analyses of house infestation data collected by the National Chagas Service in Moreno Department, northern Argentina (1999-2002). Clusters of high domestic infestation occurred in the southwestern extreme of the district. A multi-model selection approach showed that domestic infestation clustered in areas of low elevation, with few farmlands, high density of rural houses, high mean maximum land surface temperature, large NDVI, and high percentage of degraded and deforested lands. The best model classified 98.4% of the communities in the training dataset (sensitivity, 93.3%; specificity, 95.4%). The risk map evidenced that the high-risk area only encompassed 16% of the district. By building a network-based transportation model we assessed the operational costs of spatially contiguous and spatially targeted interventions. Targeting clusters of high infestation would have reached -80% of all communities slated for full-coverage insecticide spraying, reducing in half the total time and economic cost incurred by a spatially contiguous strategy.\\n\\nCONCLUSIONS AND SIGNIFICANCE: In disperse rural areas where control programs can accomplish limited coverage, consideration of infestation hot spots can contribute to the design and execution of cost-effective interventions against Chagas disease vectors. If field validated, targeted vertical control in high risk areas and horizontal control in medium to low risk areas may provide both a logistically and economically feasible alternative to blanket vertical insecticide spraying when resources are limited.", "link"=>"http://www.mendeley.com/research/spatial-heterogeneity-risk-maps-community-infestation-triatoma-infestans-rural-northwestern-argentin", "reader_count"=>61, "reader_count_by_academic_status"=>{"Unspecified"=>2, "Librarian"=>1, "Researcher"=>15, "Student > Doctoral Student"=>10, "Student > Ph. D. Student"=>8, "Student > Postgraduate"=>4, "Student > Master"=>9, "Other"=>4, "Student > Bachelor"=>6, "Professor"=>2}, "reader_count_by_user_role"=>{"Unspecified"=>2, "Librarian"=>1, "Researcher"=>15, "Student > Doctoral Student"=>10, "Student > Ph. D. Student"=>8, "Student > Postgraduate"=>4, "Student > Master"=>9, "Other"=>4, "Student > Bachelor"=>6, "Professor"=>2}, "reader_count_by_subject_area"=>{"Unspecified"=>3, "Environmental Science"=>7, "Nursing and Health Professions"=>1, "Agricultural and Biological Sciences"=>26, "Medicine and Dentistry"=>14, "Social Sciences"=>4, "Immunology and Microbiology"=>1, "Earth and Planetary Sciences"=>3, "Economics, Econometrics and Finance"=>2}, "reader_count_by_subdiscipline"=>{"Medicine and Dentistry"=>{"Medicine and Dentistry"=>14}, "Social Sciences"=>{"Social Sciences"=>4}, "Immunology and Microbiology"=>{"Immunology and Microbiology"=>1}, "Earth and Planetary Sciences"=>{"Earth and Planetary Sciences"=>3}, "Economics, Econometrics and Finance"=>{"Economics, Econometrics and Finance"=>2}, "Agricultural and Biological Sciences"=>{"Agricultural and Biological Sciences"=>26}, "Nursing and Health Professions"=>{"Nursing and Health Professions"=>1}, "Unspecified"=>{"Unspecified"=>3}, "Environmental Science"=>{"Environmental Science"=>7}}, "reader_count_by_country"=>{"Bangladesh"=>1, "Argentina"=>1, "United States"=>3, "Brazil"=>1, "Mexico"=>1}, "group_count"=>6}

Scopus | Further Information

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Figshare

  • {"files"=>["https://ndownloader.figshare.com/files/592109"], "description"=>"<p>(A) Map showing the predicted prevalence of domestic infestation. (B) Map showing the probability of membership in a cluster of high domestic infestation. Both maps were estimated from the coefficients of the best fitting models. The spatial resolution of the map is 1×1 km.</p>", "links"=>[], "tags"=>["maps"], "article_id"=>262608, "categories"=>["Biotechnology"], "users"=>["Gonzalo M. Vazquez-Prokopec", "Cynthia Spillmann", "Mario Zaidenberg", "Ricardo E. Gürtler", "Uriel Kitron"], "doi"=>"https://dx.doi.org/10.1371/journal.pntd.0001788.g003", "stats"=>{"downloads"=>0, "page_views"=>6, "likes"=>0}, "figshare_url"=>"https://figshare.com/articles/_Risk_maps_of_T_infestans_domestic_infestation_/262608", "title"=>"Risk maps of <i>T. infestans</i> domestic infestation.", "pos_in_sequence"=>0, "defined_type"=>1, "published_date"=>"2012-08-14 00:43:28"}
  • {"files"=>["https://ndownloader.figshare.com/files/592450"], "description"=>"1<p>Variables: Den, density of rural houses (# per sq. km); Dist, distance from a community to the nearest T. infestans infested community (meters); LST, mean maximum land surface temperature (°C); NDVI, Normalized Difference Vegetation Index (no units); Elev, mean elevation of each community (meters above sea level); Deg, percentage of landscape within 2 km of a village that was degraded (see text for details); Def, percentage of landscape within 2 km of a community that was deforested (see text for details); Crops, percentage of landscape within 2 km of a village that was modified for soy production.</p><p>Symbols: X (variable tested in model); — (variable not tested in model); − (negative association) + (positive association);</p>**<p>(<i>P</i>≤0,01);</p>*<p>(0,01<<i>P</i>≤0,05); NS (not significant).</p><p>Δ<sub>i</sub> = AIC<sub>i</sub>−AIC<sub>min</sub>.</p><p>ω<sub>i</sub> = exp (−1/2 Δ<sub>i</sub>)/Σ exp (−1/2 Δ<sub>i</sub>).</p><p>Σ ω<sub>i</sub>(<i>j</i>): sum of ω<sub>i</sub> values from every model in which variable <i>i</i> was present. Indicates the relative importance of each independent variable in predicting the data.</p>2<p>Lowest AIC = 60.8.</p>", "links"=>[], "tags"=>["infestation", "moreno", "santiago", "del"], "article_id"=>262939, "categories"=>["Biotechnology"], "users"=>["Gonzalo M. Vazquez-Prokopec", "Cynthia Spillmann", "Mario Zaidenberg", "Ricardo E. Gürtler", "Uriel Kitron"], "doi"=>"https://dx.doi.org/10.1371/journal.pntd.0001788.t002", "stats"=>{"downloads"=>0, "page_views"=>34, "likes"=>0}, "figshare_url"=>"https://figshare.com/articles/_Factors_associated_with_membership_of_a_community_in_a_cluster_of_high_T_infestans_infestation_in_the_Moreno_Department_Santiago_del_Estero_Argentina_/262939", "title"=>"Factors associated with membership of a community in a cluster of high <i>T. infestans</i> infestation in the Moreno Department, Santiago del Estero, Argentina.", "pos_in_sequence"=>0, "defined_type"=>3, "published_date"=>"2012-08-14 00:48:59"}
  • {"files"=>["https://ndownloader.figshare.com/files/592557"], "description"=>"1<p>Assumes all communities are visited. Blanket control is performed based on the rule of contiguity (i.e. the nearest neighbor first). Targeted control assumes only communities predicted as high-risk (from the risk map) are visited.</p>2<p>Refers to the city where spraying brigades are based.</p>3<p>Communities with prevalence of domestic infestation by <i>T. infestans</i> higher than 10% are slated for blanket spraying (Tintina = 66 communities and 880 houses; Quimili = 76 communities and 822 houses).</p>4<p>Selected from communities estimated in 3.</p>5<p>The total cost for a Blanket contiguous strategy was estimated to be US$69,779 and for a Targeted strategy US$35,552. Costs were based on Vazquez-Prokopec et al. 2009 <a href=\"http://www.plosntds.org/article/info:doi/10.1371/journal.pntd.0001788#pntd.0001788-VazquezProkopec1\" target=\"_blank\">[5]</a> estimates and include cost of insecticides (US$6.9 per sprayed house), salaries (US$22 per-diem and US$11.2 wages per technician per day) and mobility (US$1 per km).</p>", "links"=>[], "tags"=>["costs", "spraying", "communities", "high-risk", "infestation"], "article_id"=>263047, "categories"=>["Biotechnology"], "users"=>["Gonzalo M. Vazquez-Prokopec", "Cynthia Spillmann", "Mario Zaidenberg", "Ricardo E. Gürtler", "Uriel Kitron"], "doi"=>"https://dx.doi.org/10.1371/journal.pntd.0001788.t004", "stats"=>{"downloads"=>1, "page_views"=>4, "likes"=>0}, "figshare_url"=>"https://figshare.com/articles/_Assessing_the_costs_of_spraying_communities_predicted_to_be_at_high_risk_of_domestic_infestation_clustering_/263047", "title"=>"Assessing the costs of spraying communities predicted to be at high-risk of domestic infestation clustering.", "pos_in_sequence"=>0, "defined_type"=>3, "published_date"=>"2012-08-14 00:50:47"}
  • {"files"=>["https://ndownloader.figshare.com/files/592520"], "description"=>"1<p>Variables: Den, density of rural houses (# per sq. km); Dist, distance from a community to the nearest T. infestans infested community (meters); LST, mean maximum land surface temperature (°C); NDVI, Normalized Difference Vegetation Index (no units); Elev, mean elevation of each community (meters above sea level); Deg, percentage of landscape within 2 km of a village that was degraded (see text for details); Def, percentage of landscape within 2 km of a community that was deforested (see text for details); Crops, percentage of landscape within 2 km of a village that was modified for soy production.</p><p>Symbols: X (variable tested in model); — (variable not tested in model); − (negative association) + (positive association);</p>**<p>(<i>P</i>≤0,01);</p>*<p>(0,01<<i>P</i>≤0,05); NS (not significant).</p><p>Δ<sub>i</sub> = AIC<sub>i</sub>−AIC<sub>min</sub>.</p><p>ω<sub>i</sub> = exp (−1/2 Δ<sub>i</sub>)/Σ exp (−1/2 Δ<sub>i</sub>).</p><p>Σ ω<sub>i</sub>(<i>j</i>): sum of ω<sub>i</sub> values from every model in which variable <i>i</i> was present. Indicates the relative importance of each independent variable in predicting the data.</p>2<p>Lowest AIC = 701.8.</p>", "links"=>[], "tags"=>["prevalence", "infestation", "moreno", "santiago", "del"], "article_id"=>263014, "categories"=>["Biotechnology"], "users"=>["Gonzalo M. Vazquez-Prokopec", "Cynthia Spillmann", "Mario Zaidenberg", "Ricardo E. Gürtler", "Uriel Kitron"], "doi"=>"https://dx.doi.org/10.1371/journal.pntd.0001788.t001", "stats"=>{"downloads"=>0, "page_views"=>4, "likes"=>0}, "figshare_url"=>"https://figshare.com/articles/_Factors_associated_with_the_high_prevalence_of_domestic_infestation_by_T_infestans_in_the_Moreno_Department_Santiago_del_Estero_Argentina_/263014", "title"=>"Factors associated with the high prevalence of domestic infestation by <i>T. infestans</i> in the Moreno Department, Santiago del Estero, Argentina.", "pos_in_sequence"=>0, "defined_type"=>3, "published_date"=>"2012-08-14 00:50:14"}
  • {"files"=>["https://ndownloader.figshare.com/files/311059", "https://ndownloader.figshare.com/files/311136", "https://ndownloader.figshare.com/files/311175", "https://ndownloader.figshare.com/files/311218"], "description"=>"<div><h3>Background</h3><p>Fifty years of residual insecticide spraying to control <em>Triatoma infestans</em> in the Gran Chaco region of northern Argentina, Paraguay and Bolivia shows that vertically coordinated interventions aiming at full coverage have limited effects and are unsustainable. We quantified the spatial distribution of <em>T. infestans</em> domestic infestation at the district level, identified environmental factors associated with high infestation and then explored the usefulness of risk maps for the spatial stratification of interventions.</p> <h3>Methods and Findings</h3><p>We performed spatial analyses of house infestation data collected by the National Chagas Service in Moreno Department, northern Argentina (1999–2002). Clusters of high domestic infestation occurred in the southwestern extreme of the district. A multi-model selection approach showed that domestic infestation clustered in areas of low elevation, with few farmlands, high density of rural houses, high mean maximum land surface temperature, large NDVI, and high percentage of degraded and deforested lands. The best model classified 98.4% of the communities in the training dataset (sensitivity, 93.3%; specificity, 95.4%). The risk map evidenced that the high-risk area only encompassed 16% of the district. By building a network-based transportation model we assessed the operational costs of spatially contiguous and spatially targeted interventions. Targeting clusters of high infestation would have reached ∼80% of all communities slated for full-coverage insecticide spraying, reducing in half the total time and economic cost incurred by a spatially contiguous strategy.</p> <h3>Conclusions and Significance</h3><p>In disperse rural areas where control programs can accomplish limited coverage, consideration of infestation hot spots can contribute to the design and execution of cost-effective interventions against Chagas disease vectors. If field validated, targeted vertical control in high risk areas and horizontal control in medium to low risk areas may provide both a logistically and economically feasible alternative to blanket vertical insecticide spraying when resources are limited.</p> </div>", "links"=>[], "tags"=>["spatial", "heterogeneity", "maps", "infestation", "northwestern", "argentina"], "article_id"=>121360, "categories"=>["Biotechnology"], "users"=>["Gonzalo M. Vazquez-Prokopec", "Cynthia Spillmann", "Mario Zaidenberg", "Ricardo E. Gürtler", "Uriel Kitron"], "doi"=>["https://dx.doi.org/10.1371/journal.pntd.0001788.s001", "https://dx.doi.org/10.1371/journal.pntd.0001788.s002", "https://dx.doi.org/10.1371/journal.pntd.0001788.s003", "https://dx.doi.org/10.1371/journal.pntd.0001788.s004"], "stats"=>{"downloads"=>12, "page_views"=>8, "likes"=>0}, "figshare_url"=>"https://figshare.com/articles/Spatial_Heterogeneity_and_Risk_Maps_of_Community_Infestation_by_Triatoma_infestans_in_Rural_Northwestern_Argentina/121360", "title"=>"Spatial Heterogeneity and Risk Maps of Community Infestation by <em>Triatoma infestans</em> in Rural Northwestern Argentina", "pos_in_sequence"=>0, "defined_type"=>4, "published_date"=>"2012-08-14 00:22:40"}
  • {"files"=>["https://ndownloader.figshare.com/files/591798"], "description"=>"<p>Prevalence of domestic infestation by <i>T. infestans</i> (assessed by householders' collections) during 1999–2002 in the Moreno Department, Santiago del Estero, Argentina. ND refers to communities for which infestation data were not available.</p>", "links"=>[], "tags"=>["public health and epidemiology"], "article_id"=>262299, "categories"=>["Biotechnology"], "users"=>["Gonzalo M. Vazquez-Prokopec", "Cynthia Spillmann", "Mario Zaidenberg", "Ricardo E. Gürtler", "Uriel Kitron"], "doi"=>"https://dx.doi.org/10.1371/journal.pntd.0001788.g001", "stats"=>{"downloads"=>1, "page_views"=>6, "likes"=>0}, "figshare_url"=>"https://figshare.com/articles/_Spatial_distribution_of_T_infestans_domestic_infestation_/262299", "title"=>"Spatial distribution of <i>T. infestans</i> domestic infestation.", "pos_in_sequence"=>0, "defined_type"=>1, "published_date"=>"2012-08-14 00:38:19"}
  • {"files"=>["https://ndownloader.figshare.com/files/591942"], "description"=>"<p>Location of significant clusters of domestic infestation in the Moreno Department, Santiago del Estero, Argentina. Positive clusters indentify communities with significantly high prevalence values, whereas negative clusters identify communities with significantly low prevalence values.</p>", "links"=>[], "tags"=>["clustering"], "article_id"=>262442, "categories"=>["Biotechnology"], "users"=>["Gonzalo M. Vazquez-Prokopec", "Cynthia Spillmann", "Mario Zaidenberg", "Ricardo E. Gürtler", "Uriel Kitron"], "doi"=>"https://dx.doi.org/10.1371/journal.pntd.0001788.g002", "stats"=>{"downloads"=>1, "page_views"=>7, "likes"=>0}, "figshare_url"=>"https://figshare.com/articles/_Local_clustering_of_T_infestans_domestic_infestation_/262442", "title"=>"Local clustering of <i>T. infestans</i> domestic infestation.", "pos_in_sequence"=>0, "defined_type"=>1, "published_date"=>"2012-08-14 00:40:42"}
  • {"files"=>["https://ndownloader.figshare.com/files/592346"], "description"=>"<p>The proposed mixed strategy involves vertical control targeted at areas of predicted high risk of domestic infestation clustering (circles and solid lines) and horizontal control based on community participation in the communities predicted to be at medium to low risk (squares and dashed lines).</p>", "links"=>[], "tags"=>["spatially", "structured", "vector"], "article_id"=>262838, "categories"=>["Biotechnology"], "users"=>["Gonzalo M. Vazquez-Prokopec", "Cynthia Spillmann", "Mario Zaidenberg", "Ricardo E. Gürtler", "Uriel Kitron"], "doi"=>"https://dx.doi.org/10.1371/journal.pntd.0001788.g005", "stats"=>{"downloads"=>0, "page_views"=>3, "likes"=>0}, "figshare_url"=>"https://figshare.com/articles/_A_spatially_structured_mixed_vector_control_strategy_/262838", "title"=>"A spatially structured mixed vector control strategy.", "pos_in_sequence"=>0, "defined_type"=>1, "published_date"=>"2012-08-14 00:47:18"}
  • {"files"=>["https://ndownloader.figshare.com/files/592490"], "description"=>"1<p>Variables: Den, density of rural houses (# per sq. km); LST, mean maximum land surface temperature (°C); NDVI, Normalized Difference Vegetation Index (no units); Elev, mean elevation of each community (meters above sea level); Deg, percentage of landscape within 2 km of a village that was degraded (see text for details); Def, percentage of landscape within 2 km of a community that was deforested (see text for details); Crops, percentage of landscape within 2 km of a village that was modified for soy production.</p>", "links"=>[], "tags"=>["fitting", "logistic", "regression", "predicting"], "article_id"=>262982, "categories"=>["Biotechnology"], "users"=>["Gonzalo M. Vazquez-Prokopec", "Cynthia Spillmann", "Mario Zaidenberg", "Ricardo E. Gürtler", "Uriel Kitron"], "doi"=>"https://dx.doi.org/10.1371/journal.pntd.0001788.t003", "stats"=>{"downloads"=>1, "page_views"=>88, "likes"=>0}, "figshare_url"=>"https://figshare.com/articles/_Best_fitting_multiple_logistic_regression_model_predicting_membership_in_a_cluster_of_high_T_infestans_domestic_infestation_/262982", "title"=>"Best fitting multiple logistic regression model predicting membership in a cluster of high <i>T. infestans</i> domestic infestation.", "pos_in_sequence"=>0, "defined_type"=>3, "published_date"=>"2012-08-14 00:49:42"}
  • {"files"=>["https://ndownloader.figshare.com/files/592223"], "description"=>"<p>(A) Implementation of a spatially contiguous strategy (i.e., visiting the nearest neighbor of each community). (B) Strategy targeting interventions according to risk (i.e., only high-risk communities are treated). Color squares indicate the location of Moreno's main cities (Quimili in pink and Tintina in light blue) where spraying teams initiate their journeys. Spraying was performed by two trucks (one stationed on each city) with two technicians each (represented by lines of the same color as the square indicating the city where they are based at). Black circles indicate the communities first visited by each spraying team in each control scenario.</p>", "links"=>[], "tags"=>["insecticide", "spraying", "schemes", "moreno"], "article_id"=>262719, "categories"=>["Biotechnology"], "users"=>["Gonzalo M. Vazquez-Prokopec", "Cynthia Spillmann", "Mario Zaidenberg", "Ricardo E. Gürtler", "Uriel Kitron"], "doi"=>"https://dx.doi.org/10.1371/journal.pntd.0001788.g004", "stats"=>{"downloads"=>1, "page_views"=>8, "likes"=>0}, "figshare_url"=>"https://figshare.com/articles/_Spatially_explicit_insecticide_spraying_schemes_in_the_Moreno_Department_/262719", "title"=>"Spatially explicit insecticide spraying schemes in the Moreno Department.", "pos_in_sequence"=>0, "defined_type"=>1, "published_date"=>"2012-08-14 00:45:19"}

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Relative Metric

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