Spread of Zika virus in the Americas

Abstract

We use a data-driven global stochastic epidemic model to analyze the spread of the Zika virus (ZIKV) in the Americas. The model has high spatial and temporal resolution and integrates real-world demographic, human mobility, socioeconomic, temperature, and vector density data. We estimate that the first introduction of ZIKV to Brazil likely occurred between August 2013 and April 2014 (90% credible interval). We provide simulated epidemic profiles of incident ZIKV infections for several countries in the Americas through February 2017. The ZIKV epidemic is characterized by slow growth and high spatial and seasonal heterogeneity, attributable to the dynamics of the mosquito vector and to the characteristics and mobility of the human populations. We project the expected timing and number of pregnancies infected with ZIKV during the first trimester and provide estimates of microcephaly cases assuming different levels of risk as reported in empirical retrospective studies. Our approach represents a modeling effort aimed at understanding the potential magnitude and timing of the ZIKV epidemic and it can be potentially used as a template for the analysis of future mosquito-borne epidemics.

Keywords: Zika virus; computational epidemiology; metapopulation network model; vector-borne diseases.

Fig. 1.

Posterior distribution for ZIKV introductions in 12 major transportation hubs in Brazil between April 2013 and June 2014, incorporating the likelihood of replicating the observed epidemic peak in Colombia. (A) Full posterior distribution as a function of location and time of introduction. (B) Marginal posterior distribution for time (month) of introduction. (C) Marginal posterior distribution for location of introduction.

Fig. 2.

Estimated daily number of new ZIKV infections (per 1,000 people) in eight affected countries in the Americas between January 2014 and February 2017. The bold line and shaded area refer to the estimated median number of infections and 95% CI of the model projections. Rates include asymptomatic infections. The median incidence is calculated each week from the stochastic ensemble output of the model and may not be representative of specific epidemic realizations. Thin lines represent a sample of specific realizations. Note that the scales on the y axes of the subplots vary. *Puerto Rico curves are constrained under the condition that the peak of incidence curve is after March 1, 2016, based on the surveillance reports (72).

Fig. 3.

Monthly seasonality for the time- and location-dependent basic reproductive number, R₀. The equatorial regions display less seasonality than the nonequatorial regions, where the changes of the season have a strong impact on the temperature and consequently on the basic reproductive number, R₀.

Fig. 4.

Estimated daily number of births between October 2014 and December 2017 from women infected with ZIKV during the first trimester of pregnancy in eight affected countries in the Americas. The bold line and shaded area refer to the estimated median number of births and 95% CI of the model projections, respectively. Note that Brazil is plotted on a different scale. The median curve is calculated each week from the stochastic ensemble output of the model and may not be representative of specific epidemic realizations. Thin lines represent a sample of specific realizations.

Fig. 5.

(A) Correlation between the number of ZIKV cases by state in Colombia as reported by surveillance data through October 1, 2016 (38), compared with state-level model projections of infections (median with 95% CI). Pearson’s r correlation coefficient is reported for the linear association on the log scale. The outlier (in dark green) excluded from the statistical analysis corresponds to the Arauca region. (B) Timeline of microcephaly cases in Brazil through April 30, 2016. Bar plots show weekly definite (or highly probable cases) and moderately (or somewhat probable cases) from surveillance data (40). Line plots indicate estimated weekly new microcephaly cases given three levels of first trimester risk: 4.52% (circles) (37), 2.19% (squares) (37), and 0.95% (diamonds) (36). (C) Bar plot of ZIKV infections imported into the USA by state(s) as reported by CDC surveillance through October 5, 2016 (41), and compared to model projections (median with 95% CI) for the same period assuming 5.74% reporting/detection. (Inset) The correlation between CDC surveillance data and model projections (median with 95% CI).

Fig. 6.

Schematic representation of the integration of data layers and the computational flow chart defining the GLEAM model for ZIKV.

Fig. 7.

(A) Compartmental classification for ZIKV infection. Humans can occupy one of the four top compartments: susceptible, which can acquire the infection through contacts (bites) with infectious mosquitoes; exposed, where individuals are infected but are not able yet to transmit the virus; infectious, where individuals are infected and can transmit the disease to susceptible mosquitoes; and recovered or removed, where individuals are no longer infectious. The compartmental model for the mosquito vector is shown below. (B) Summary of the parameters of the model. $T_{\mathrm{dep}}$ denotes parameters that are temperature-dependent. $T\mathit{\text{,}}{G}_{\mathrm{dep}}$ denotes parameters that are temperature- and geolocation-dependent. Specific values for the parameters can be found in refs. , , , , and –.