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• {"link"=>{"url"=>"http://www.citeulike.org/user/phoenixzxl/article/11979899"}, "post_time"=>"2013-02-04 02:52:33", "tag"=>"public_health", "linkout"=>{"type"=>"DOI", "url"=>"http://dx.doi.org/10.1371/journal.pcbi.1002801"}, "username"=>"phoenixzxl", "article_id"=>"11979899"}
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{"title"=>"The Effects of City Streets on an Urban Disease Vector", "type"=>"journal", "authors"=>[{"first_name"=>"Corentin M.", "last_name"=>"Barbu", "scopus_author_id"=>"26535432700"}, {"first_name"=>"Andrew", "last_name"=>"Hong", "scopus_author_id"=>"55583909900"}, {"first_name"=>"Jennifer M.", "last_name"=>"Manne", "scopus_author_id"=>"26023397500"}, {"first_name"=>"Dylan S.", "last_name"=>"Small", "scopus_author_id"=>"14025065700"}, {"first_name"=>"Javier E.", "last_name"=>"Quintanilla Calderón", "scopus_author_id"=>"55584445500"}, {"first_name"=>"Karthik", "last_name"=>"Sethuraman", "scopus_author_id"=>"55584048000"}, {"first_name"=>"Víctor", "last_name"=>"Quispe-Machaca", "scopus_author_id"=>"25228089500"}, {"first_name"=>"Jenny", "last_name"=>"Ancca-Juárez", "scopus_author_id"=>"8360109200"}, {"first_name"=>"Juan G.", "last_name"=>"Cornejo del Carpio", "scopus_author_id"=>"24447940000"}, {"first_name"=>"Fernando S.", "last_name"=>"Málaga Chavez", "scopus_author_id"=>"36167292900"}, {"first_name"=>"César", "last_name"=>"Náquira", "scopus_author_id"=>"35559365000"}, {"first_name"=>"Michael Z.", "last_name"=>"Levy", "scopus_author_id"=>"27167569100"}], "year"=>2013, "source"=>"PLoS Computational Biology", "identifiers"=>{"pui"=>"368294182", "sgr"=>"84873515837", "issn"=>"1553734X", "pmid"=>"23341756", "scopus"=>"2-s2.0-84873515837", "doi"=>"10.1371/journal.pcbi.1002801", "isbn"=>"1553-7358 (Electronic)\\r1553-734X (Linking)"}, "id"=>"120b3315-5a4b-3da9-95d8-d61f26f13931", "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.", "link"=>"http://www.mendeley.com/research/effects-city-streets-urban-disease-vector", "reader_count"=>72, "reader_count_by_academic_status"=>{"Unspecified"=>1, "Professor > Associate Professor"=>6, "Librarian"=>2, "Researcher"=>17, "Student > Doctoral Student"=>7, "Student > Ph. D. 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• http://doi.org/10.3201/eid2012.131820
• http://doi.org/10.1371/journal.pntd.0006883
• http://doi.org/10.1098/rsif.2013.0194
• http://doi.org/10.1016/j.prevetmed.2015.04.014
• http://doi.org/10.1371/journal.pntd.0003425
• http://doi.org/10.1186/s13071-018-2779-7
• http://doi.org/10.1186/s13071-019-3454-3
• http://doi.org/10.1186/s40249-016-0198-6
• http://doi.org/10.4269/ajtmh.13-0714
• http://doi.org/10.1111/ecog.02575
• http://doi.org/10.1186/s12889-019-7525-3
• http://doi.org/10.1371/journal.pntd.0003614
• http://doi.org/10.1016/j.ecolmodel.2016.04.012
• http://doi.org/10.1016/j.sste.2018.08.003
• http://doi.org/10.1186/1476-072X-12-48
• http://doi.org/10.1111/1365-2435.12487
• http://doi.org/10.1111/jvec.12198

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• {"id"=>"292202444077809664", "text"=>"El entramado urbano influye en la dispersión de enfermedades. Ni los bichitos se atreven a cruzar la callle!\nhttp://t.co/kZ7SDtS2", "created_at"=>"2013-01-18T09:31:06Z", "user"=>"fjesteban", "user_name"=>"Francisco J. Esteban", "user_profile_image"=>"http://a0.twimg.com/profile_images/2514206989/image_normal.jpg"}
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Figshare

• {"files"=>["https://ndownloader.figshare.com/files/505204"], "description"=>"<p>Left: autocorrelation of the infestation status as a function of the distance. Solid line: Global Moran's index. Dot-Dashed line: Moran's Index for within blocks household pairs. Dashed line: Moran's Index for household pairs across streets. All Moran's I values are significantly different from the expected value under hypothesis of no spatial autocorrelation (). Right: significance of the difference between the correlation within city blocks and the correlation across streets. Box plots indicate the expected values under the null hypothesis using a permutation test. The boxes indicate the , and quantiles, and the whiskers depict the 95% CrI.</p>", "links"=>[], "tags"=>["autocorrelation", "peru"], "article_id"=>175707, "categories"=>["Microbiology", "Infectious Diseases", "Information And Computing Sciences", "Mathematics", "Biotechnology", "Biological Sciences"], "users"=>["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", "Michael Z. Levy"], "doi"=>"https://dx.doi.org/10.1371/journal.pcbi.1002801.g003", "stats"=>{"downloads"=>1, "page_views"=>4, "likes"=>0}, "figshare_url"=>"https://figshare.com/articles/_Spatial_autocorrelation_of_Triatoma_infestans_presence_in_Paucarpata_Arequipa_Peru_and_the_effects_of_streets_/175707", "title"=>"Spatial autocorrelation of <i>Triatoma infestans</i> presence in Paucarpata, Arequipa, Peru and the effects of streets.", "pos_in_sequence"=>0, "defined_type"=>1, "published_date"=>"2013-01-17 01:35:07"}
• {"files"=>["https://ndownloader.figshare.com/files/505121"], "description"=>"<p>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.</p>", "links"=>[], "tags"=>["households"], "article_id"=>175625, "categories"=>["Microbiology", "Infectious Diseases", "Information And Computing Sciences", "Mathematics", "Biotechnology", "Biological Sciences"], "users"=>["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", "Michael Z. Levy"], "doi"=>"https://dx.doi.org/10.1371/journal.pcbi.1002801.g002", "stats"=>{"downloads"=>1, "page_views"=>45, "likes"=>0}, "figshare_url"=>"https://figshare.com/articles/_Spatial_distribution_of_Triatoma_infestans_presence_in_households_of_Paucarpata_Arequipa_Peru_/175625", "title"=>"Spatial distribution of <i>Triatoma infestans</i> presence in households of Paucarpata, Arequipa, Peru.", "pos_in_sequence"=>0, "defined_type"=>1, "published_date"=>"2013-01-17 01:33:45"}
• {"files"=>["https://ndownloader.figshare.com/files/276111", "https://ndownloader.figshare.com/files/276146", "https://ndownloader.figshare.com/files/276181", "https://ndownloader.figshare.com/files/276267"], "description"=>"<div><p>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 <em>Triatoma infestans</em> in the city of Arequipa, Peru. Our Moran's decomposition test reveals that the distribution of <em>T. infestans</em> 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 <em>T. infestans</em> 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.</p> </div>", "links"=>[], "tags"=>["effects", "streets", "vector"], "article_id"=>114417, "categories"=>["Cancer", "Microbiology", "Information And Computing Sciences", "Mathematics", "Biotechnology", "Biological Sciences"], "users"=>["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", "Michael Z. Levy"], "doi"=>["https://dx.doi.org/10.1371/journal.pcbi.1002801.s001", "https://dx.doi.org/10.1371/journal.pcbi.1002801.s002", "https://dx.doi.org/10.1371/journal.pcbi.1002801.s003", "https://dx.doi.org/10.1371/journal.pcbi.1002801.s004"], "stats"=>{"downloads"=>14, "page_views"=>15, "likes"=>0}, "figshare_url"=>"https://figshare.com/articles/The_Effects_of_City_Streets_on_an_Urban_Disease_Vector__/114417", "title"=>"The Effects of City Streets on an Urban Disease Vector", "pos_in_sequence"=>0, "defined_type"=>4, "published_date"=>"2013-01-17 01:13:37"}
• {"files"=>["https://ndownloader.figshare.com/files/505506"], "description"=>"a<p>The shape factor is indicated in meters.</p>b<p>Same Block Index: Percent of the spatial component of infestation explained by same city block neighbors (see Section 2 in <a href=\"http://www.ploscompbiol.org/article/info:doi/10.1371/journal.pcbi.1002801#pcbi.1002801.s003\" target=\"_blank\">Text S1</a>). In parentheses are the 95% Credible Intervals (CrI) according to the MCMC sampling. The probability of having no barrier effect of streets is indicated with the values of : ;</p>***<p>.</p>", "links"=>[], "tags"=>["kernels", "corresponding", "fitted"], "article_id"=>176011, "categories"=>["Microbiology", "Infectious Diseases", "Information And Computing Sciences", "Mathematics", "Biotechnology", "Biological Sciences"], "users"=>["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", "Michael Z. Levy"], "doi"=>"https://dx.doi.org/10.1371/journal.pcbi.1002801.t001", "stats"=>{"downloads"=>0, "page_views"=>0, "likes"=>0}, "figshare_url"=>"https://figshare.com/articles/_Spatial_kernels_and_corresponding_fitted_parameters_/176011", "title"=>"Spatial kernels and corresponding fitted parameters.", "pos_in_sequence"=>0, "defined_type"=>3, "published_date"=>"2013-01-17 01:40:11"}
• {"files"=>["https://ndownloader.figshare.com/files/505323"], "description"=>"<p>The autocorrelation of infestation in the generated data is compared to the autocorrelation in observed data. Infestation data were generated on the validation map using the estimated parameters for each of the kernels: exponential (first column), Cauchy (second column), Gaussian (third column), and geometric (fourth column). We calculated the standard Moran's I (first row) and the difference between within block and across street autocorrelation (second row) as a function of distance. The solid line indicates the values for the observed data. Box plots indicate the values obtained from generated data. The boxes indicate the , and quantiles, and the whiskers depict the 95% CrI.</p>", "links"=>[], "tags"=>["autocorrelation", "simulated", "gaussian", "latent"], "article_id"=>175832, "categories"=>["Microbiology", "Infectious Diseases", "Information And Computing Sciences", "Mathematics", "Biotechnology", "Biological Sciences"], "users"=>["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", "Michael Z. Levy"], "doi"=>"https://dx.doi.org/10.1371/journal.pcbi.1002801.g004", "stats"=>{"downloads"=>1, "page_views"=>6, "likes"=>0}, "figshare_url"=>"https://figshare.com/articles/_Spatial_autocorrelation_of_data_simulated_with_the_Gaussian_Field_Latent_Class_model_of_Triatoma_infestans_distribution_/175832", "title"=>"Spatial autocorrelation of data simulated with the Gaussian Field Latent Class model of <i>Triatoma infestans</i> distribution.", "pos_in_sequence"=>0, "defined_type"=>1, "published_date"=>"2013-01-17 01:37:12"}
• {"files"=>["https://ndownloader.figshare.com/files/504958"], "description"=>"<p>Working backward, we consider the infestation data to be the result of a latent infestation status , observed by imperfect inspectors of sensitivity . The true infestation is a binary manifestation of an underlying continuous infestation predictor . Cofactors and a local error term, , form the local component. The spatial component is modeled as a Gaussian field. The fit parameters, and , respectively tune how distances between neighbors and the streets define the spatial dependency between households in the spatial component.</p>", "links"=>[], "tags"=>["gaussian", "latent"], "article_id"=>175461, "categories"=>["Microbiology", "Infectious Diseases", "Information And Computing Sciences", "Mathematics", "Biotechnology", "Biological Sciences"], "users"=>["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", "Michael Z. Levy"], "doi"=>"https://dx.doi.org/10.1371/journal.pcbi.1002801.g001", "stats"=>{"downloads"=>1, "page_views"=>5, "likes"=>0}, "figshare_url"=>"https://figshare.com/articles/_General_structure_of_the_Gaussian_Field_Latent_Class_model_/175461", "title"=>"General structure of the Gaussian Field Latent Class model.", "pos_in_sequence"=>0, "defined_type"=>1, "published_date"=>"2013-01-17 01:31:01"}

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Relative Metric

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F1000Prime