Exploring the association between precipitation and population cases of ocular toxoplasmosis in Colombia

pal <- colorBin("YlOrRd",domain = NULL, n=5)
state_popup <- paste0("<strong>Departamento: </strong>",  map$NOMBRE_DPT,  "<br><strong>Precipitation: </strong>",map$precipitation)

leaflet(map) %>%
  addTiles() %>%
  addPolygons(
    color = "white", fillColor = ~ pal(precipitation),
    fillOpacity = 1, popup = state_popup
  ) %>%
  addLegend(pal = pal, values = ~precipitation, opacity = 1)

pal <- colorBin("YlOrRd",domain = NULL, n=5)
state_popup <- paste0("<strong>Departamento: </strong>",  map$NOMBRE_DPT,  "<br><strong>Precipitation: </strong>",map$RR)

leaflet(map) %>%
  addTiles() %>%
  addPolygons(
    color = "white", fillColor = ~ pal(RR),
    fillOpacity = 1, popup = state_popup
  ) %>%
  addLegend(pal = pal, values = ~RR, opacity = 1)

Fig 4. Precipitation in Colombia and incidence rate of ocular toxoplasmosis (OT). (A) The precipitation map of the departments in Colombia, which shows the average annual value of precipitation in each department for the year 2019; we were able to collect the information for all the departments from the National Institute of Hydrology, Meteorology, and Environmental Studies (IDEAM)[25]. (B) The estimated relative risk of OT with the fitted conditional autoregressive (CAR) model for the data available in 2019. Caquetá, Caldas, and Quindío have a higher relative risk, and departments with less precipitation, such as Atlántico and La Guajira, have a lower relative risk.