Could the recent zika epidemic have been predicted?

ángel G. Muñoz, Madeleine C. Thomson, Anna M. Stewart-Ibarra, Gabriel Andres Vecchi, Xandre Chourio, Patricia Nájera, Zelda Moran, Xiaosong Yang

Research output: Contribution to journalArticlepeer-review

30 Scopus citations


Given knowledge at the time, the recent 2015-2016 zika virus (ZIKV) epidemic probably could not have been predicted. Without the prior knowledge of ZIKV being already present in South America, and given the lack of understanding of key epidemiologic processes and long-term records of ZIKV cases in the continent, the best related prediction could be carried out for the potential risk of a generic Aedes-borne disease epidemic. Here we use a recently published two-vector basic reproduction number model to assess the predictability of the conditions conducive to epidemics of diseases like zika, chikungunya, or dengue, transmitted by the independent or concurrent presence of Aedes aegypti and Aedes albopictus. We compare the potential risk of transmission forcing the model with the observed climate and with state-of-the-art operational forecasts from the North American Multi Model Ensemble (NMME), finding that the predictive skill of this new seasonal forecast system is highest for multiple countries in Latin America and the Caribbean during the December-February and March-May seasons, and slightly lower-but still of potential use to decision-makers-for the rest of the year. In particular, we find that above-normal suitable conditions for the occurrence of the zika epidemic at the beginning of 2015 could have been successfully predicted at least 1 month in advance for several zika hotspots, and in particular for Northeast Brazil: the heart of the epidemic. Nonetheless, the initiation and spread of an epidemic depends on the effect of multiple factors beyond climate conditions, and thus this type of approach must be considered as a guide and not as a formal predictive tool of vector-borne epidemics.

Original languageEnglish (US)
Article number1291
JournalFrontiers in Microbiology
Issue numberJUL
StatePublished - Jul 12 2017

All Science Journal Classification (ASJC) codes

  • Microbiology (medical)
  • Microbiology


  • Aedes-borne diseases
  • Chikungunya
  • Climate
  • Dengue
  • Predictability
  • R0 model
  • Zika


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