class: center, middle, inverse, title-slide .title[ # Análisis Espacial del Dengue ] .author[ ### Felipe Antonio Dzul Manzanilla ] .date[ ### 2022: Last compiled 2022-11-08 ] ---
### Ciclo de Vida del Programa de Prevención y Control del Dengue
--- ### **Análisis Espacial de la Transmisión del Dengue** <hr style="height:2px;border-width:0;color:#330019;background-color:#330019">
--- ### **Hotspots de la Transmisión del Dengue** <hr style="height:2px;border-width:0;color:#330019;background-color:#330019"> .pull-left[ 1. Bajar las bases de datos del **[SINAVE](https://www.sinave.gob.mx/)**. 2. Geocodificar las bases. 3. Bajar los shapefile del **[INEGI](https://www.inegi.org.mx/)**. 4. Seleccionar la localidad de interes y extraer los AGEBs. 5. Contar el número de casos por AGEB. 6. Cálcular el Z-score de los casos. 7. Generar la matriz de adjacencias. 8. Cálcular el estadístico espacial local Getis&Ord `\(\color{#2ECC40}G_{\color{#2ECC40}i}^{\color{#2ECC40}*}\)`. 9. Realizar la la corrección de Bonferroni. 10. Cálcular los hotspots. 11. Visualizar los hotspots. ] .pull-right[
] .tiny[.blue[.footnote[ [Dzul-Manzanilla et al 2021](https://www.thelancet.com/journals/lanplh/article/PIIS2542-5196(21)00030-9/fulltext) ]]] --- ### Estadístico Espacial Local `\(\color{#2ECC40}G_{\color{#2ECC40}i}^{\color{#2ECC40}*}\)` (Hotspots) <hr style="height:2px;border-width:0;color:#330019;background-color:#330019"> `$$\color{#2ECC40}G_{\color{#2ECC40}i}^{\color{#2ECC40}*} = \frac{\color{#FF4136}\sum_{\color{#FF4136}j \color{#FF4136}= \color{#FF4136}1}^\color{#FF4136}{n} \color{#FF4136}w_{\color{#FF4136}i\color{#FF4136}j}\color{#FF4136}x_{\color{#FF4136}j}} {\color{#0074D9}\sum_{\color{#0074D9}j \color{#0074D9}= \color{#0074D9}1}^{\color{#0074D9}n} \color{#0074D9}x_{\color{#0074D9}j}}$$` donde: `\(\color{#FF4136}\sum_{\color{#FF4136}j \color{#FF4136}= \color{#FF4136}1}^\color{#FF4136}{n} \color{#FF4136}w_{\color{#FF4136}i\color{#FF4136}j}\color{#FF4136}x_{\color{#FF4136}j}\)` el numerador, es la suma de los valores `\(x_{i}\)` de la unidad espacial de interes con sus vecinos `\(x_{j}\)` & `\(\frac{}{\color{#0074D9}\sum_{\color{#0074D9}j \color{#0074D9}= \color{#0074D9}1}^{\color{#0074D9}n} \color{#0074D9}x_{\color{#0074D9}j}}\)` el denominador, es la suma de todos los valores `\(x\)` en toda la localidad de interes. ### **Hotspots** son las áreas o las unidades espaciales con valores altos de `\(\color{#2ECC40}G_{\color{#2ECC40}i}^{\color{#2ECC40}*}\)` y homogéneos de la unidad espaciales de interes `\(x_{ij}\)`. En otras palabras el estadístico espacial, identifica las unidades espaciales `\(x_{ij}\)` con valores altos comparados con el valor promedio de todas la unidades espaciales en la localidad de interes. .tiny[.blue[.footnote[Getis & Ord 1991; Ord & Getis 1995]]] --- ### **Cadenas de Transmisión Knox test** <hr style="height:2px;border-width:0;color:#330019;background-color:#330019"> .pull-left[ 1. Bajar las bases de datos del **[SINAVE](https://www.sinave.gob.mx/)**. 2. Geocodificar las bases. 3. Generar la base (onset y coordenadas). 4. Aplicar el [**Knox test**](). 5. Definir los [**Space-Time link**](). 6. Visualizar [**Space-Time link**](). ] .pull-right[
] .tiny[.blue[.footnote[]]] --- ### **Cadenas de Transmisión Knox test** <hr style="height:2px;border-width:0;color:#330019;background-color:#330019"> `$$\color{Orange}{Knox} = \frac{1}{2} \sum_{i=1}^{n} \sum_{i=1}^{n} \color{Green}{S_{ij}} \color{red}{T_{ij}}$$` donde: `\(\color{Green}{S_{ij}}\)` = 1 si el caso `\(( i\ne j)\)` & la `\(d_{ij} \le \delta^s\)` (metros = 400), de lo contrario 0. `\(\color{red}{T_{ij}}\)` = 1 si el caso `\(( i\ne j)\)` & la `\(d_{ij} \le \delta^t\)` (dÍas = 20), de los contrario 0. **Hipotesis Nula** las distancias temporales entre pares de casos son independientes de las distancias espaciales. **Hipotesis Alternativa** existe dependencia de las distancias espaciales y temporales entre los pares de casos (existen cadenas de transmisión). <table class=" lightable-paper lightable-striped" style='font-family: "Arial Narrow", arial, helvetica, sans-serif; width: auto !important; margin-left: auto; margin-right: auto;'> <thead> <tr> <th style="empty-cells: hide;" colspan="1"></th> <th style="padding-bottom:0; padding-left:3px;padding-right:3px;text-align: center; " colspan="3"><div style="TRUE">Time</div></th> </tr> <tr> <th style="text-align:left;"> </th> <th style="text-align:center;"> Close </th> <th style="text-align:center;"> Not.close </th> <th style="text-align:center;"> Total </th> </tr> </thead> <tbody> <tr> <td style="text-align:left;"> Close in Space </td> <td style="text-align:center;"> `\(0_{1}\)` </td> <td style="text-align:center;"> `\(0_{2}\)` </td> <td style="text-align:center;"> `\(S_{1}\)` </td> </tr> <tr> <td style="text-align:left;"> Not close in Space </td> <td style="text-align:center;"> `\(0_{3}\)` </td> <td style="text-align:center;"> `\(0_{4}\)` </td> <td style="text-align:center;"> `\(S_{2}\)` </td> </tr> <tr grouplength="1"><td colspan="4" style="border-bottom: 1px solid;"><strong></strong></td></tr> <tr> <td style="text-align:left;padding-left: 2em;" indentlevel="1"> Total </td> <td style="text-align:center;"> `\(S_{3}\)` </td> <td style="text-align:center;"> `\(S_{4}\)` </td> <td style="text-align:center;"> `\(N\)` </td> </tr> </tbody> </table> .tiny[.blue[.footnote[Viet et al 2015]]] --- ### **Log-Gaussian Cox Process** <hr style="height:2px;border-width:0;color:#330019;background-color:#330019"> .pull-left[ 1. Bajar las bases de datos del **[SINAVE](https://www.sinave.gob.mx/)**. 2. Geocodificar las bases. 4. Aplicar el [**LGCP**](). 5. Visualizar [** el modelo**](). ] .pull-right[
] .tiny[.blue[.footnote[ [Moraga, 2020](https://journal.r-project.org/archive/2021/RJ-2021-017/RJ-2021-017.pdf) ]]] --- ### **Log Gaussian Cox Process** <hr style="height:2px;border-width:0;color:#330019;background-color:#330019"> **Modelo General** $$ \varLambda{_s} = exp(n{_s})$$ El modelo asume que los casos han son una parcial realización de un proceso Gausiano (log-Gaussian). **Modelo en un Grid** `$$\varLambda_{ij} = \int\limits_{s_{ij}}^{} exp(n(s))ds$$` `$$\varLambda_{ij} \approx |s_{ij}| exp(n_{ij})$$` donde `\(|s_{ij}|\)` es el área de la celda `\(s_{ij}\)` `\(y_{ij}|n_{ij} \sim Poisson(|s_{ij}|exp(n_{ij}))\)` `\(n_{ij} = \beta{_0} + \beta{_1} \space x \space cov (s_{ij}) + f{_s}(s_{ij}) + f{_u}(s_{ij})\)` --- ### **Análisis Espacial del Vector del Dengue** <hr style="height:2px;border-width:0;color:#330019;background-color:#330019">
--- ### **Análisis Espacial del Vector del Dengue** <hr style="height:2px;border-width:0;color:#330019;background-color:#330019">
.tiny[.blue[.footnote[ [Modificado de Zuur et al 2017](http://www.highstat.com/index.php/beginner-s-guide-to-regression-models-with-spatial-and-temporal-correlation); [Dzul-Manzanilla et al 2019](http://acaentmex.org/entomologia/revista/2019/EMF/EMF%20497-501.pdf)]]] --- ### **Análisis Espacial del Vector del Dengue** <hr style="height:2px;border-width:0;color:#330019;background-color:#330019">
.tiny[.blue[.footnote[ [Modificado de Zuur et al 2017](http://www.highstat.com/index.php/beginner-s-guide-to-regression-models-with-spatial-and-temporal-correlation); [Dzul-Manzanilla et al 2019](http://acaentmex.org/entomologia/revista/2019/EMF/EMF%20497-501.pdf)]]] --- ### **Analisis Geoéstadistico con INLA** <hr style="height:2px;border-width:0;color:#330019;background-color:#330019"> .pull-left[ <img src="index_files/figure-html/unnamed-chunk-10-1.png" width="100%" height="100%" style="display: block; margin: auto;" /> ] .pull-right[ `\(s{_i}\)` sitios de colecta con coordenadas geográficas (longitud. latitud). `\(D\)` area de estudio (zona metropolitana de Guadalajara). `\(Y{_i}\)` es la variable de respuesta (Número de Huevos por Ovitrampa o Manzana). `\(y{_i}\)` tiene una distribución (binomial negativa ó zibn). `\(U{_s{_i}}\)` el efecto espacial & el proceso ocurre en un campo gaussiano continuo (Gaussian Field). ] Se usa SPDE & Elemento Finito para aproximar la matriz de covarianzas de `\(U{_s{_i}}\)` `\(\color{#2ECC40}{el \space proceso \space se \space encuentra \space implementado \space en \space INLA}\)` --- ### **Análisis Espacial del Dengue de México en R** <hr style="height:2px;border-width:0;color:#330019;background-color:#330019"> Paquetes en R desarrollados por el Programa ([denhotspots](https://fdzul.github.io/denhotspots/) & [deneggs](https://github.com/fdzul/deneggs)) para el análisis espacial. <table class=" lightable-classic" style='font-family: "Arial Narrow", "Source Sans Pro", sans-serif; margin-left: auto; margin-right: auto;'> <thead> <tr> <th style="text-align:left;"> Análisis </th> <th style="text-align:left;"> denhotspots </th> <th style="text-align:left;"> deneggs </th> </tr> </thead> <tbody> <tr> <td style="text-align:left;"> Hotspots </td> <td style="text-align:left;"> TRUE </td> <td style="text-align:left;"> FALSE </td> </tr> <tr> <td style="text-align:left;"> Visualización de los Hotspots </td> <td style="text-align:left;"> TRUE </td> <td style="text-align:left;"> FALSE </td> </tr> <tr> <td style="text-align:left;"> Cadenas de Transmisión </td> <td style="text-align:left;"> TRUE </td> <td style="text-align:left;"> FALSE </td> </tr> <tr> <td style="text-align:left;"> Visualiación de las Cadenas de Transmisión </td> <td style="text-align:left;"> TRUE </td> <td style="text-align:left;"> FALSE </td> </tr> <tr> <td style="text-align:left;"> LGCP </td> <td style="text-align:left;"> TRUE </td> <td style="text-align:left;"> FALSE </td> </tr> <tr> <td style="text-align:left;"> Visualización de LGCP </td> <td style="text-align:left;"> TRUE </td> <td style="text-align:left;"> FALSE </td> </tr> <tr> <td style="text-align:left;"> Hotspots de Huevos </td> <td style="text-align:left;"> FALSE </td> <td style="text-align:left;"> TRUE </td> </tr> <tr> <td style="text-align:left;"> Visualización de Hotspots </td> <td style="text-align:left;"> FALSE </td> <td style="text-align:left;"> TRUE </td> </tr> </tbody> </table> --- ### **Hotspots de la Transmisión del Dengue** <hr style="height:2px;border-width:0;color:#330019;background-color:#330019"> .panelset[ .panel[.panel-name[Dataset] ```r # Step 1. load the dengue geocoded dataset #### load("C:/Users/HOME/OneDrive/proyects/priority_research_projects/hotspots_high_risk_localities_138/8.RData/geocoded_dataset.RData") ``` ] .panel[.panel-name[Extract Locality] ```r # Step 2. extract the locality ##### x <- rgeomex::extract_ageb(locality = c("Guadalajara", "Zapopan", "Tlaquepaque", "Tonalá"), cve_geo = "14") ``` ] .panel[.panel-name[Cases by AGEB] ```r library(magrittr) z <- denhotspots::point_to_polygons(x = y, y = x$ageb, ids = names(x$ageb)[-10], time = ANO, coords = c("long", "lat"), crs = 4326, dis = "DENV") ``` ] .panel[.panel-name[Hotspots] ```r hotspots <- denhotspots::gihi(x = z, id = names(z)[c(1:9)], time = "year", dis = "DENV", gi_hi = "gi", alpha = 0.95) ``` ] ] --- ### **Hotspots de la Transmisión del Dengue** <hr style="height:2px;border-width:0;color:#330019;background-color:#330019"> .panelset.sideways[ .panel[.panel-name[Static Map Code] ```r denhotspots::staticmap_intensity(x = hotspots, pal = rcartocolor::carto_pal, pal_name = TRUE, name = "OrYel", breaks = 1, dir_pal = -1, x_leg = 0.5, y_leg = 0.1, ageb = TRUE) ``` ] .panel[.panel-name[Static Map] <!-- --> ] .panel[.panel-name[Interactive Map Code] ```r source("C:/Users/HOME/Dropbox/r_developments/r_dashboards/github_pages/test_dashboard/3.Functions/hotspots_map.R") hotspots_map(cve_ent = "14", locality = c("Guadalajara", "Zapopan", "Tlaquepaque", "Tonalá"), hotspots = hotspots, static_map = FALSE) ``` ] .panel[.panel-name[Interactive Map]
] ] --- ### **Cadenas de Transmisión Knox test** <hr style="height:2px;border-width:0;color:#330019;background-color:#330019"> .panelset.sideways[ .panel[.panel-name[Código] ```r # Step 1. load the dengue cases geocoded load("C:/Users/HOME/OneDrive/proyects/geocoding_mex/2022/9.RData_geocoded/geo_den_mx_31_merida_2022_10_31.RData") # Step 2. visualize the space-time link denhotspots::transmission_chains_map(geocoded_dataset = y, cve_edo = "31", locality = "Merida", dengue_cases = "Probable") ``` ] .panel[.panel-name[Probables]
] .panel[.panel-name[Código] ```r # Step 1. load the dengue cases geocoded load("C:/Users/HOME/OneDrive/proyects/geocoding_mex/2022/9.RData_geocoded/geo_den_mx_31_merida_2022_10_31.RData") # Step 2. visualize the space-time link denhotspots::transmission_chains_map(geocoded_dataset = y, cve_edo = "31", locality = "Merida", dengue_cases = "Confirmado") ``` ] .panel[.panel-name[Confirmados]
] ] --- ### **Log Gaussian Cox Process** <hr style="height:2px;border-width:0;color:#330019;background-color:#330019"> .panelset.sideways[ .panel[.panel-name[Código] ```r # Step 1. load the dengue geocoded dataset #### load("C:/Users/HOME/OneDrive/proyects/geocoding_mex/2022/9.RData_geocoded/den2022_positivos.RData") # Step 2. extract the dengue cases ##### geocoded_dataset <- z |> dplyr::filter(ESTATUS_CASO == 2) |> dplyr::mutate(onset = FEC_INI_SIGNOS_SINT, date = as.character(onset), id = VEC_ID) |> dplyr::mutate(x = long, y = lat) |> sf::st_as_sf(coords = c("long", "lat"), crs = 4326) |> dplyr::select(x, y, onset) # Step 3. extract the locality #### loc <- rgeomex::extract_locality(cve_edo = 31, locality = "Mérida") # Step 4. extract the dengue cases of the locality geocoded_dataset <- geocoded_dataset[loc,] |> sf::st_drop_geometry() # Step 5. map of lgcp library(magrittr) library(ggplot2) library(sf) library(sp) library(plotly) library(htmlwidgets) (denhotspots::spatial_lgcp(dataset = geocoded_dataset, locality = "Merida", cve_edo = "31", longitude = "x", latitude = "y", k = 30, plot = FALSE, aproximation = "gaussian", integration = "laplace", resolution = 0.015, approach = "lattice", cell_size = 1500, name = "YlGnBu")$map) ``` ] .panel[.panel-name[Merida]
] .panel[.panel-name[Código] ```r # Step 1. load the dengue geocoded dataset #### load("C:/Users/HOME/OneDrive/proyects/geocoding_mex/2022/9.RData_geocoded/den2022_positivos.RData") # Step 2. extract the dengue cases ##### geocoded_dataset <- z |> dplyr::filter(ESTATUS_CASO == 2) |> dplyr::mutate(onset = FEC_INI_SIGNOS_SINT, date = as.character(onset), id = VEC_ID) |> dplyr::mutate(x = long, y = lat) |> sf::st_as_sf(coords = c("long", "lat"), crs = 4326) |> dplyr::select(x, y, onset) # Step 3. extract the locality #### loc <- rgeomex::extract_locality(cve_edo = 26, locality = "Hermosillo") # Step 4. extract the dengue cases of the locality geocoded_dataset <- geocoded_dataset[loc,] |> sf::st_drop_geometry() # Step 5. map of lgcp library(magrittr) library(ggplot2) library(sf) library(sp) library(plotly) library(htmlwidgets) (denhotspots::spatial_lgcp(dataset = geocoded_dataset, locality = "Hermosillo", cve_edo = "26", longitude = "x", latitude = "y", k = 30, plot = FALSE, aproximation = "gaussian", integration = "laplace", resolution = 0.015, approach = "lattice", cell_size = 1500, name = "YlGnBu")$map) ``` ] .panel[.panel-name[Hermosillo]
] .panel[.panel-name[Código] ```r # Step 1. load the dengue geocoded dataset #### load("C:/Users/HOME/OneDrive/proyects/geocoding_mex/2022/9.RData_geocoded/den2022_positivos.RData") # Step 2. extract the dengue cases ##### geocoded_dataset <- z |> dplyr::filter(ESTATUS_CASO == 2) |> dplyr::mutate(onset = FEC_INI_SIGNOS_SINT, date = as.character(onset), id = VEC_ID) |> dplyr::mutate(x = long, y = lat) |> sf::st_as_sf(coords = c("long", "lat"), crs = 4326) |> dplyr::select(x, y, onset) # Step 3. extract the locality #### loc <- rgeomex::extract_locality(cve_edo = 26, locality = "Navojoa") # Step 4. extract the dengue cases of the locality geocoded_dataset <- geocoded_dataset[loc,] |> sf::st_drop_geometry() # Step 5. map of lgcp library(magrittr) library(ggplot2) library(sf) library(sp) library(plotly) library(htmlwidgets) (denhotspots::spatial_lgcp(dataset = geocoded_dataset, locality = "Navojoa", cve_edo = "26", longitude = "x", latitude = "y", k = 30, plot = FALSE, aproximation = "gaussian", integration = "laplace", resolution = 0.012, approach = "lattice", cell_size = 1500, name = "YlGnBu")$map) ``` ] .panel[.panel-name[navojoa]
] ] --- ### **Análisis Geoéstadístico del Vector del Dengue** <hr style="height:2px;border-width:0;color:#330019;background-color:#330019"> .panelset.sideways[ .panel[.panel-name[Código 1] ```r deneggs::eggs_hotspots(path_lect = "C:/Users/HOME/Dropbox/cenaprece_datasets/2022/14_jalisco", cve_ent = "14", locality = c("Guadalajara", "Zapopan", "Tlaquepaque", "Tonala"), path_coord = "C:/Users/HOME/Dropbox/cenaprece_datasets/2022/14_jalisco/DescargaOvitrampasMesFco.txt", longitude = "Pocision_X", latitude = "Pocision_Y", aproximation = "gaussian", integration = "eb", fam = "zeroinflatednbinomial1", k = 40, palette_vir = "magma", leg_title = "Huevos", plot = FALSE, hist_dataset = FALSE, sem = 40, var = "eggs", cell_size = 2000, alpha = .99)$map ``` ] .panel[.panel-name[Semana 40] <img src="index_files/figure-html/unnamed-chunk-27-1.png" width="100%" /> ] .panel[.panel-name[Codigo 2] ```r deneggs::eggs_hotspots(path_lect = "C:/Users/HOME/Dropbox/cenaprece_datasets/2022/14_jalisco", cve_ent = "14", locality = c("Guadalajara", "Zapopan", "Tlaquepaque", "Tonala"), path_coord = "C:/Users/HOME/Dropbox/cenaprece_datasets/2022/14_jalisco/DescargaOvitrampasMesFco.txt", longitude = "Pocision_X", latitude = "Pocision_Y", aproximation = "gaussian", integration = "eb", fam = "zeroinflatednbinomial1", k = 30, palette_vir = "magma", leg_title = "Huevos", plot = FALSE, hist_dataset = FALSE, sem = 41, var = "eggs", cell_size = 2000, alpha = .99)$map ``` ] .panel[.panel-name[Semana 41] <img src="index_files/figure-html/unnamed-chunk-28-1.png" width="100%" /> ] .panel[.panel-name[Codigo 3] ```r deneggs::eggs_hotspots(path_lect = "C:/Users/HOME/Dropbox/cenaprece_datasets/2022/14_jalisco", cve_ent = "14", locality = c("Guadalajara", "Zapopan", "Tlaquepaque", "Tonala"), path_coord = "C:/Users/HOME/Dropbox/cenaprece_datasets/2022/14_jalisco/DescargaOvitrampasMesFco.txt", longitude = "Pocision_X", latitude = "Pocision_Y", aproximation = "gaussian", integration = "eb", fam = "zeroinflatednbinomial1", k = 30, palette_vir = "magma", leg_title = "Huevos", plot = FALSE, hist_dataset = FALSE, sem = 42, var = "eggs", cell_size = 2000, alpha = .99)$map ``` ] .panel[.panel-name[Semana 42] <img src="index_files/figure-html/unnamed-chunk-29-1.png" width="100%" /> ] .panel[.panel-name[Codigo 4] ```r deneggs::eggs_hotspots(path_lect = "C:/Users/HOME/Dropbox/cenaprece_datasets/2022/14_jalisco", cve_ent = "14", locality = c("Guadalajara", "Zapopan", "Tlaquepaque", "Tonala"), path_coord = "C:/Users/HOME/Dropbox/cenaprece_datasets/2022/14_jalisco/DescargaOvitrampasMesFco.txt", longitude = "Pocision_X", latitude = "Pocision_Y", aproximation = "gaussian", integration = "eb", fam = "zeroinflatednbinomial1", k = 30, palette_vir = "magma", leg_title = "Huevos", plot = FALSE, hist_dataset = FALSE, sem = 43, var = "eggs", cell_size = 2000, alpha = .99)$map ``` ] .panel[.panel-name[Semana 43] <img src="index_files/figure-html/unnamed-chunk-30-1.png" width="100%" /> ] ] --- ### **Análisis Geoéstadístico y Hotspots del Vector del Dengue** <hr style="height:2px;border-width:0;color:#330019;background-color:#330019"> .panelset.sideways[ .panel[.panel-name[Código] ```r #step 1. define the path for the historic dataset #### path_vect <- "C:/Users/HOME/Dropbox/cenaprece_datasets/2022/31_yucatan" path_coord <- paste(path_vect, "DescargaOvitrampasMesFco.txt", sep = "/") # Step 2. run the spde model #### x22 <- deneggs::eggs_hotspots_week(cve_mpo = "102", cve_edo = "31", year = "2022", hist_dataset = FALSE, locality = "Valladolid", path_vect = path_vect, path_coord = path_coord, integration_strategy = "eb", aproximation_method = "gaussian", fam_distribution = "zeroinflatednbinomial1", plot = FALSE, kvalue = 40, palette.viridis = "viridis", cell.size = 500, alpha.value = .99) # Step 3. Visualize the hotspots intensity #### deneggs::eggs_hotspots_intensity_map(spde_betas = x22$betas, years = 2022, locality = "Valladolid", cve_ent = "31", palette = rcartocolor::carto_pal, name = "SunsetDark") ``` ] .panel[.panel-name[Mapa de Intensidad] <img src="index_files/figure-html/unnamed-chunk-32-1.png" width="100%" /> ] ] --- ### **Análisis Geoéstadístico y Hotspots del Vector del Dengue** <hr style="height:2px;border-width:0;color:#330019;background-color:#330019">  --- class: center, middle ### **Modelo de Estratificación del Riesgo de Transmisión de Dengue** <hr style="height:2px;border-width:0;color:#330019;background-color:#330019"> .pull-left[ .blue[Concepto] <img src = "figs/stacked_maps_cases.jpg"> ] .pull-right[ .blue[Mapa de Riesgo] <img src = "figs/map_risk_guadalajara.jpg"> ] .tiny[.blue[.footnote[Dzul-Manzanilla et al 2021.]]] --- ## Definición del Modelo estratégico de focalización .pull-left[ - .red[**Áreas con riesgo muy alto de transmisión**.] Hotposts de casos + hotspots del vector. - .orange[**Áreas con riesgo alto de transmisión**.] Hotspots de transmisión de casos. - .yellow[**Áreas con riesgo medio de transmisión**.] Hotspots del vector. - .green[**Áreas con riesgo bajo de transmisión**]. Sin hotspots del vector o hotspots de casos. ] .pull-right[ Guadalajara <img src="figs/map_risk_guadalajara.jpg" width="90%" /> ] --- ### Control Espacio-Temporal del Dengue <hr style="height:2px;border-width:0;color:#330019;background-color:#330019"> .pull-left[.panel-name[Paradigma Tradicional] <!-- --> ] .pull-right[.panel-name[Nuevo Paradigma] <!-- --> ] .tiny[.blue[.footnote[[Dzul-Manzanilla et al 2021](https://www.thelancet.com/journals/lanplh/article/PIIS2542-5196(21)00030-9/fulltext)]]] --- ### **Desarrollo en R** <hr style="height:2px;border-width:0;color:#330019;background-color:#330019"> Dashboards - [Programa de Prevención y Control de las Arbovirosis del Estado de Yucatan](https://fdzul.github.io/denv_dash_yuc/#cadenas-de-transmisi%C3%B3n) - [Estratificacion del Riesgo de Transmisión de Dengue del Estado de Veracruz](https://fdzul.github.io/dengue_risk_map_veracruz/#dengue-risk-maps) Manuales - [Manual para la identificación de los hotspots en áreas úrbanas](https://fdzul.github.io/manual_hotspots/) - [Modelo Estratégico de Focalización del Dengue en áreas urbanas de México](https://fdzul.github.io/strategic_mod_den_targeting/) Paquetes - [denhotspots](https://fdzul.github.io/denhotspots/), [deneggs](https://github.com/fdzul/deneggs), [rgeomex](https://github.com/fdzul/rgeomex), & [boldenr](https://github.com/fdzul/boldenr) Cursos - [Análisis Espacial del Dengue en R](https://fdzul.github.io/spatial_analysis_dengue_R/) En honor al Dr. **Leandro Hernández Barrios** que en paz descanse. --- ## Dios Botic! <hr style="height:2px;border-width:0;color:#330019;background-color:#330019"> - ***Bio*** : https://fdzul.github.io/web_site_fadm/ - ***email*** : [felipe.dzul.m@gmail.com]() - ***celular*** : [8139945623]() - ***slides***: https://animated-longma-729cee.netlify.app/talks/spatial_analysis_dengue/#1 .footnote[La presentación fue creada via [**xaringan**](https://github.com/yihui/xaringan), [**revealjs**](https://revealjs.com/), [remark.js](https://remarkjs.com), [**knitr**](http://yihui.name/knitr), & [R Markdown](https://rmarkdown.rstudio.com) en [R]() & [RStudio](2.R_Scripts/libs/rstudio_leaflet/rstudio_leaflet.css).]