Improving early epidemiological assessment of emerging Aedes-transmitted epidemics using historical data

Abstract

Model-based epidemiological assessment is useful to support decision-making at the beginning of an emerging Aedes-transmitted outbreak. However, early forecasts are generally unreliable as little information is available in the first few incidence data points. Here, we show how past Aedes-transmitted epidemics help improve these predictions. The approach was applied to the 2015-2017 Zika virus epidemics in three islands of the French West Indies, with historical data including other Aedes-transmitted diseases (chikungunya and Zika) in the same and other locations. Hierarchical models were used to build informative a priori distributions on the reproduction ratio and the reporting rates. The accuracy and sharpness of forecasts improved substantially when these a priori distributions were used in models for prediction. For example, early forecasts of final epidemic size obtained without historical information were 3.3 times too high on average (range: 0.2 to 5.8) with respect to the eventual size, but were far closer (1.1 times the real value on average, range: 0.4 to 1.5) using information on past CHIKV epidemics in the same places. Likewise, the 97.5% upper bound for maximal incidence was 15.3 times (range: 2.0 to 63.1) the actual peak incidence, and became much sharper at 2.4 times (range: 1.3 to 3.9) the actual peak incidence with informative a priori distributions. Improvements were more limited for the date of peak incidence and the total duration of the epidemic. The framework can adapt to all forecasting models at the early stages of emerging Aedes-transmitted outbreaks.

Fig 1

(A) Weekly number of Zika virus (ZIKV) cases reported by the surveillance systems in the French West Indies during 2016–2017 (S1 Dataset). The dotted line shows the threshold defining high epidemic activity, “S” and “E” mark the start and the end of the period of high epidemic activity and “P” marks the date of peak incidence. (B-C) Weekly incidence (per 1,000 population) during the epidemics of chikungunya virus (CHIKV) in the French West Indies in 2013–2015 (S2 Dataset ($\mathcal{D}2$)) and of ZIKV then CHIKV in French Polynesia in 2013–2015 (S3 Dataset ($\mathcal{D}3$)).

Fig 2. A priori distributions considered for the reporting rate ρ_Z (panel A) and the basic reproduction number R0,Z (panel B) during the Zika virus epidemics in the French West Indies: Non-informative, regional and island-specific.

Fig 3. Predictive distribution of weekly incidence of Zika virus infections in Guadeloupe, Martinique and Saint-Martin using either non-informative (NI, panel A), informative regional (R, panel B) or informative local (L, panel C) priors, and calibrated using data available up to the vertical dashed line (here chosen two weeks after date “S”).

Continuous lines correspond to mean prediction of future incidence, dark and light grey areas to 50% and 95% prediction intervals, respectively, and circles to observed incidence.

Fig 4. Accuracy (panel A, values closer to zero indicate better accuracy) and sharpness (panel B, values closer to zero indicate better sharpness) of the predictive distribution of future incidence based on epidemiological assessments conducted each week.

Colours correspond to different a priori distributions on the parameters: non-informative priors or informative priors based on historical data, either considered at the regional or the local level.

Fig 5. Posterior distributions (mean and 95% credible intervals) of the basic reproduction number R0,Z (panel A) and the reporting rate ρ_Z (panel B) throughout the ZIKV epidemics of the French West Indies.

Colours correspond to different a priori distributions on the parameters: non-informative priors or informative priors based on historical data considered either at the regional or the local level.

Fig 6. Early forecasts regarding four indicators of operational interest: (A) final epidemic size (total observed cases); (B) maximal weekly observed incidence; (C) date of peak incidence (difference with the date observed thereafter, in months); and (D) duration of the period of high epidemic activity (from date “S” to date “E”, in months).

The dashed lines represent the values observed after the end of the epidemic. Colours correspond to different a priori distributions on the parameters: non-informative priors or informative priors based on historical data, either considered at the regional or the local level.