Reassessing Serosurvey-Based Estimates of the Symptomatic Proportion of Zika Virus Infections

Abstract

Since the 2007 Zika epidemic in the Micronesian state of Yap, it has been apparent that not all people infected with Zika virus (ZIKV) experience symptoms. However, the proportion of infections that result in symptoms remains unclear. Existing estimates have varied in their interpretation of symptoms due to other causes and the case definition used, and they have assumed perfect test sensitivity and specificity. Using a Bayesian model and data from ZIKV serosurveys in Yap (2007), French Polynesia (2013-2014), and Puerto Rico (2016), we found that assuming perfect sensitivity and specificity generally led to lower estimates of the symptomatic proportion. Incorporating reasonable assumptions for assay sensitivity and specificity, we estimated that 27% (95% credible interval (CrI): 15, 37) (Yap), 44% (95% CrI: 26, 66) (French Polynesia), and 50% (95% CrI: 34, 92) (Puerto Rico) of infections were symptomatic, with variation due to differences in study populations, study designs, and case definitions. The proportion of ZIKV infections causing symptoms is critical for surveillance system design and impact assessment. Here, we accounted for key uncertainties in existing seroprevalence data and found that estimates for the symptomatic proportion ranged from 27% to 50%, suggesting that while the majority of infections are asymptomatic or mildly symptomatic, symptomatic infections might be more common than previously estimated.

Figure 1.

Simulation showing the effect of sensitivity and specificity on estimates of the symptomatic proportion and Zika virus (ZIKV) infection prevalence in Yap (2007), French Polynesia (2013–2014), and Puerto Rico (2016). The top row shows the effect of different values of sensitivity (line style) and specificity (x-axis) on point estimates of the median proportion of ZIKV infections that were symptomatic in studies in Yap (A), French Polynesia (B), and Puerto Rico (C). The bottom row shows the corresponding estimates of infection prevalence for the study populations in Yap (D), French Polynesia (E), and Puerto Rico (F). In each of these simulations, we assumed that symptoms caused by ZIKV and other illnesses could overlap $(\mathrm{\gamma }=1)$.

Figure 2.

Estimated symptomatic proportion and Zika virus (ZIKV) infection prevalence under different models in Yap (2007), French Polynesia (2013–2014), and Puerto Rico (2016). The top row shows the estimated proportion of ZIKV infections that caused symptoms in studies in Yap (A), French Polynesia (B), and Puerto Rico (C), using 3 different models: “original” model, with a perfect assay and mutually exclusive causes of symptoms; “imperfect test,” with an imperfect assay and mutually exclusive causes of symptoms; and “imperfect test with symptom overlap,” with an imperfect assay and symptoms that could simultaneously result from both infection and other causes. Note that each study used unique case definitions for identifying symptomatic individuals. The bottom row shows the corresponding estimates of infection prevalence for the study populations in Yap (D), French Polynesia (E), and Puerto Rico (F). The boxes and bars show the 50% and 95% credible intervals.