title: Assessing Chikungunya risk in a metropolitan area of Argentina through satellite images and mathematical models.
abstract: BACKGROUND
  Chikungunya fever is a viral disease that recently invaded the American continent. In America, it is transmitted mainly by the mosquito Aedes aegypti, but Aedes albopictus is the main vector in other regions of the world. This work estimates the risk of disease emergence and the corresponding population at risk for the case of a naive population in the metropolitan area of Buenos Aires, the capital city of Argentina.
  
  
  METHODS
  A classic metapopulation epidemiological model, that considers human and mosquito populations, was extended in order to include different environmental signals. First, the vital rates of the mosquitoes were affected by local temperature. Second, habitat availability estimated from satellite images was used to determine the carrying capacity for local mosquito populations. Disease invasion was proposed to occur at different moments of the year. For each scenario, Monte Carlo simulations were used to estimate the risk of disease invasion and the population at risk.
  
  
  RESULTS
  The risk of a Chikungunya outbreak displays strong temporal (seasonal) patterns as well as spatial variability at the level of neighborhoods in the study area. According to the model, Summer and Fall display high risk for a Chikungunya invasion. The population at risk displays less variation over the year underlying the importance of preventive actions.
  
  
  CONCLUSIONS
  The ability of mapping habitat quality for vector-borne diseases allows developing risk analysis at scales that are easily manageable for public health officers. For this location, the correlation of disease risk with the season of the year and the habitat availability could provide information to develop efficient control strategies. This also underlines the importance of involving the whole community when developing control measures for Chikungunya fever and other recently invading vector-borne diseases such as Zika fever.
authors.affiliation: Grupo de Ecologia Computacional, Instituto de Ciencias Sociales y Administración, Universidad Nacional Arturo Jauretche, Florencio Varela, Buenos Aires, Argentina. diego.ruizmoreno.phd@gmail.com.
authors.forename: Diego
authors.initials: D
authors.lastname: Ruiz-Moreno
mesh_terms: D000818:Animals; D001118:Argentina / Q000453:epidemiology; D065632:Chikungunya Fever / Q000453:epidemiology* / Q000821:virology; D004196:Disease Outbreaks; D006801:Humans; D008962:Models, Theoretical; D058998:Remote Sensing Technology; D018570:Risk Assessment; D012621:Seasons; D014504:Urban Health
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