title: Forecasting Chikungunya spread in the Americas via data-driven empirical approaches.
abstract: BACKGROUND
  Chikungunya virus (CHIKV) is endemic to Africa and Asia, but the Asian genotype invaded the Americas in 2013. The fast increase of human infections in the American epidemic emphasized the urgency of developing detailed predictions of case numbers and the potential geographic spread of this disease.
  
  
  METHODS
  We developed a simple model incorporating cases generated locally and cases imported from other countries, and forecasted transmission hotspots at the level of countries and at finer scales, in terms of ecological features.
  
  
  RESULTS
  By late January 2015, >1.2 M CHIKV cases were reported from the Americas, with country-level prevalences between nil and more than 20 %. In the early stages of the epidemic, exponential growth in case numbers was common; later, however, poor and uneven reporting became more common, in a phenomenon we term "surveillance fatigue." Economic activity of countries was not associated with prevalence, but diverse social factors may be linked to surveillance effort and reporting.
  
  
  CONCLUSIONS
  Our model predictions were initially quite inaccurate, but improved markedly as more data accumulated within the Americas. The data-driven methodology explored in this study provides an opportunity to generate descriptive and predictive information on spread of emerging diseases in the short-term under simple models based on open-access tools and data that can inform early-warning systems and public health intelligence.
authors.affiliation: Veterinary Population Medicine, College of Veterinary Medicine, University of Minnesota, St. Paul, MN, USA. ecoguate2003@gmail.com.|Minnesota Aquatic Invasive Species Research Center, University of Minnesota, St. Paul, MN, USA. ecoguate2003@gmail.com.|Center for Global Health and Translational Science, SUNY Upstate Medical University, Syracuse, NY, USA. ecoguate2003@gmail.com.
authors.forename: Luis E
authors.initials: LE
authors.lastname: Escobar
authors.affiliation: Key Laboratory of Animal Ecology and Conservation Biology, Institute of Zoology, Chinese Academy of Sciences, Beijing, China. qiaohj@ioz.ac.cn.
authors.forename: Huijie
authors.initials: H
authors.lastname: Qiao
authors.affiliation: Biodiversity Institute, University of Kansas, Lawrence, KS, USA. town@ku.edu.
authors.forename: A Townsend
authors.initials: AT
authors.lastname: Peterson
mesh_terms: D000569:Americas / Q000453:epidemiology; D065632:Chikungunya Fever / Q000453:epidemiology* / Q000635:transmission*; D004812:Epidemiologic Methods; D005544:Forecasting; D006801:Humans; D015233:Models, Statistical; D015995:Prevalence; D012959:Socioeconomic Factors