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

Parasites & Vectors

Daihai He¹  Salihu S. Musa¹  Hao Fu²  Shi Zhao³  Jing Qin³ 

[1] Department of Applied Mathematics, Hong Kong Polytechnic University;Department of Crop Science and Technology, College of Agriculture, South China Agricultural University;School of Nursing, Hong Kong Polytechnic University;

关键词: Zika virus;    Brazil;    Modeling analysis;    Reproduction number;    Epidemic size;    Spatial heterogeneity;   

DOI  :  10.1186/s13071-019-3602-9

来源: DOAJ

【 摘 要 】

Abstract 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 R 2 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.

【 授权许可】

Unknown