Leveraging multiple data types to estimate the size of the Zika epidemic in the Americas

Abstract

Several hundred thousand Zika cases have been reported across the Americas since 2015. Incidence of infection was likely much higher, however, due to a high frequency of asymptomatic infection and other challenges that surveillance systems faced. Using a hierarchical Bayesian model with empirically-informed priors, we leveraged multiple types of Zika case data from 15 countries to estimate subnational reporting probabilities and infection attack rates (IARs). Zika IAR estimates ranged from 0.084 (95% CrI: 0.067-0.096) in Peru to 0.361 (95% CrI: 0.214-0.514) in Ecuador, with significant subnational variability in every country. Totaling infection estimates across these and 33 other countries and territories, our results suggest that 132.3 million (95% CrI: 111.3-170.2 million) people in the Americas had been infected by the end of 2018. These estimates represent the most extensive attempt to determine the size of the Zika epidemic in the Americas, offering a baseline for assessing the risk of future Zika epidemics in this region.

Fig 1. Model schematic.

Model fitting was performed separately for each country or territory. The subscript i indicates administrative unit i within a modeled country. The subscript x represents either the total population (x = T) or pregnant women only (x = P). The top row represents the different data types (C = confirmed cases, S = suspected cases, G = Guillan-Barré syndrome cases, M = microcephaly cases). The second row includes the latent variables ($\mathcal{Z}$ = Symptomatic infections, and $\mathcal{I}$ = Infections). The third row includes the symptompatic probability (${\rho }_{\mathcal{Z}}$), the reporting probabilities for confirmed cases (${\rho }_{C_{x,i}}$) and suspected cases(${\rho }_{S_x}$), the probability that a symptomatic infection leads to a reported GBS case (ρ_G), and the probability that a ZIKV infection in a pregnant woman leads to a reported microcephaly case (ρ_M). The parameters in the bottom row are the hyperparameters for the reporting probabilities ${\rho }_{C_{x,i}}$ and ${\rho }_{S_{x,i}}$. See text and S2 Table for description of model parameters and variables.

Fig 2. Posterior distributions of national ZIKV infection attack rate (IAR) and total ZIKV infections for 15 different countries and territories.

(A) ZIKV IAR for each modeled country or territory ordered by median IAR. (B) Estimated number of ZIKV infections for each country or territory ordered by median number of infections.

Fig 3. Posterior distribution of subnational ZIKV infection attack rates (IAR) for five different territories (Bolivia, Brazil, Ecuador, Nicaragua, and Puerto Rico).

Colored circles and whiskers are the median and 95% credible intervals for each administrative unit. Black circles with dashed lines are seroprevalence estimates from the literature (see S7 Table). The dashed lines are the 95% confidence intervals for the seroprevalence estimates assuming a binomial distribution with the exception of the 95% CI estimate from [17] for Bahia, Brazil which was taken directly from their analysis.

Fig 4. Posterior parameter estimates.

(A) Posterior and prior symptomatic probability estimates for each country or territory. (B) Posterior estimates from each country and territory of the probability that a ZIKV infection in a pregnant woman results in a reported case of microcephaly. Dashed line represents range for estimated risk of Zika-associated microcephaly from published observational studies (see text for references). (C) Posterior estimates from each country and territory of the probability that a symptomatic infection results in a reported Guillan-Barré syndrome (GBS) case.

Fig 5. Posterior predictive checks at the national level for each data type used in the Bayesian models.

Vertical lines are the observed cases and circles are the predicted number of cases with 95% credible intervals for each country and data type.