Median age varicella at infection according to pre-school attendance before age 3.

<p>Data from OECD and median age at varicella infection in European countries <a href="http://www.ploscompbiol.org/article/info:doi/10.1371/journal.pcbi.1002105#pcbi.1002105-Nardone1" target="_blank">[3]</a>, <a href="http://www.ploscompbiol.org/article/info:doi/10.1371/journal.pcbi.1002105#pcbi.1002105-Khoshnood1" target="_blank">[16]</a>.</p

Best fitted model predictions: cumulated incidences and weekly incidence.

<p>(A) Cumulated incidence in first-borns (observed: plain, simulated: grey zone) and others (observed: dashed, simulated: hatched zone). (B) Cumulated incidence in urban (observed: plain, simulated: grey zone) and rural municipalities (observed: dashed, simulated: hatched zone). (C) Observed weekly incidence (plain) and simulated (grey zone) starting from the first week of September to the end of August. Hatches correspond to school holidays. In all simulations, the maximum and minimum from over 100 years of simulation are reported.</p

Varicella CI according to different scenarios.

<p>(A) Varicella CI according to school exclusion (school exclusion - current: plain, no school exclusion: dashed). (B) Varicella CI according to age at first-school enrolment: at 3 y.o. (current policy) (bold), at 2 y.o. (dashed), at 4 y.o. (dotted), at 5 y.o. (dash dotted).</p

Simulation of a realistic children population in Corsica.

<p>(A) Distribution of household final number of children. (B) Observed (white) and simulated (grey) distribution of time lag between successive siblings (in years). (C) Observed (white) and simulated (grey) number of children in Corsica according to age. (D) Observed (white) and simulated (grey) number of children aged less than 12 years old per household. (E) Commuting to school outside the municipality of residence.</p

Observed and simulated cumulated incidence of varicella.

<p>Observed cumulated incidence of varicella (red), simulated by the RAS model (green), and simulated allowing Households only (dashed dotted dark), Households and Municipalities (dashed dark), Households and Schools (dotted dark), Households and Schools and municipality (plain dark). Hatches correspond to the 95% CI of the “Households and Schools and Municipality” model.</p

Model sensitivity analysis on the number of contacts in the municipality and in the school.

<p>Model sensitivity analysis on the number of contacts in the municipality and in the school.</p

Model parameters values and goodness of fit based on cumulated incidence of varicella.

<p>Model parameters values and goodness of fit based on cumulated incidence of varicella.</p

Improving early epidemiological assessment of emerging <i>Aedes</i>-transmitted epidemics using historical data

<div><p>Model-based epidemiological assessment is useful to support decision-making at the beginning of an emerging <i>Aedes</i>-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 <i>Aedes</i>-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 <i>Aedes</i>-transmitted diseases (chikungunya and Zika) in the same and other locations. Hierarchical models were used to build informative <i>a priori</i> distributions on the reproduction ratio and the reporting rates. The accuracy and sharpness of forecasts improved substantially when these <i>a priori</i> 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 <i>a priori</i> 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 <i>Aedes</i>-transmitted outbreaks.</p></div

Improving early epidemiological assessment of emerging <i>Aedes</i>-transmitted epidemics using historical data - Fig 1

<p>(A) Weekly number of Zika virus (ZIKV) cases reported by the surveillance systems in the French West Indies during 2016–2017 (<a href="http://www.plosntds.org/article/info:doi/10.1371/journal.pntd.0006526#pntd.0006526.s003" target="_blank">S1 Dataset</a>). The dotted line shows the threshold defining high epidemic activity, “<i>S</i>” and “<i>E</i>” mark the start and the end of the period of high epidemic activity and “<i>P</i>” 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 (<a href="http://www.plosntds.org/article/info:doi/10.1371/journal.pntd.0006526#pntd.0006526.s004" target="_blank">S2 Dataset</a> ()) and of ZIKV then CHIKV in French Polynesia in 2013–2015 (<a href="http://www.plosntds.org/article/info:doi/10.1371/journal.pntd.0006526#pntd.0006526.s005" target="_blank">S3 Dataset</a> ()).</p

Typical pathways according to initial infective location.

<p>For each district, values were averaged over all neighbors less than 100 km away. Basins of attraction were identified by clustering.</p