Simple Framework for Real-Time Forecast in a Data-Limited Situation: The Zika Virus (ZIKV) Outbreaks in Brazil From 2015 to 2016 as an Example

Parasit Vectors. 2019 Jul 12;12(1):344. doi: 10.1186/s13071-019-3602-9.


Background: In 2015-2016, Zika virus (ZIKV) caused serious epidemics in Brazil. The key epidemiological parameters and spatial heterogeneity of ZIKV epidemics in different states in Brazil remain unclear. Early prediction of the final epidemic (or outbreak) size for ZIKV outbreaks is crucial for public health decision-making and mitigation planning. We investigated the spatial heterogeneity in the epidemiological features of ZIKV across eight different Brazilian states by using simple non-linear growth models.

Results: We fitted three different models to the weekly reported ZIKV cases in eight different states and obtained an R2 larger than 0.995. The estimated average values of basic reproduction numbers from different states varied from 2.07 to 3.41, with a mean of 2.77. The estimated turning points of the epidemics also varied across different states. The estimation of turning points nevertheless is stable and real-time. The forecast of the final epidemic size (attack rate) is reasonably accurate, shortly after the turning point. The knowledge of the epidemic turning point is crucial for accurate real-time projection of the outbreak.

Conclusions: Our simple models fitted the epidemic reasonably well and thus revealed the spatial heterogeneity in the epidemiological features across Brazilian states. The knowledge of the epidemic turning point is crucial for real-time projection of the outbreak size. Our real-time estimation framework is able to yield a reliable prediction of the final epidemic size.

Keywords: Brazil; Epidemic size; Modeling analysis; Reproduction number; Spatial heterogeneity; Zika virus.

MeSH terms

  • Basic Reproduction Number
  • Brazil / epidemiology
  • Computer Systems*
  • Disease Outbreaks / statistics & numerical data*
  • Epidemics / prevention & control
  • Epidemics / statistics & numerical data
  • Humans
  • Incidence
  • Models, Theoretical*
  • Public Health
  • Zika Virus
  • Zika Virus Infection / epidemiology*
  • Zika Virus Infection / transmission