Real-Time Assessment of Health-Care Requirements During the Zika Virus Epidemic in Martinique

Abstract

The spread of Zika virus in the Americas has been associated with a surge in Guillain-Barré syndrome (GBS) cases. Given the severity of GBS, territories affected by Zika virus need to plan health-care resources to manage GBS patients. To inform such planning in Martinique, we analyzed Zika virus surveillance and GBS data from Martinique in real time with a modeling framework that captured dynamics of the Zika virus epidemic, the risk of GBS in Zika virus–infected persons, and the clinical management of GBS cases. We compared our estimates with those from the 2013–2014 Zika virus epidemic in French Polynesia. We were able to predict just a few weeks into the epidemic that, due to lower transmission potential and lower probability of developing GBS following infection in Martinique, the total number of GBS cases in Martinique would be substantially lower than suggested by simple extrapolations from French Polynesia. We correctly predicted that 8 intensive-care beds and 7 ventilators would be sufficient to treat GBS cases. This study showcased the contribution of modeling to inform local health-care planning during an outbreak. Timely studies that estimate the proportion of infected persons that seek care are needed to improve the predictive power of such approaches.

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

“…Also, using informative priors to make up for the lack of information during the early stages of an epidemic has been done before. For instance, a priori information from the ZIKV epidemics in French Polynesia has been used to support the early forecasts of health-care requirements for the ZIKV epidemic in Martinique [38]. In this case, however, authors concluded that a prior built from an epidemic in a different location resulted in inaccurate predictions at the early stage.…”

“…• the peak incidence, defined as the maximal value of the upper bound of the trajectory-wise 95% prediction band, as it corresponds to the capacity needed to ensure continuity of care [38];…”

Quels éléments président à l’émergence d’une épidémie virale ? Peut-on la prévoir ?

“…Les objectifs des activités de recherche sont d'identifier la source, le mode de transmission, les facteurs de risque et l'évolutivité potentielle du problème pour proposer rapidement à l'autorité sanitaire des mesures de prévention et de contrôle scientifiquement argumentées et pertinentes au regard de la situation (en termes d'efficacité attendue, d'acceptabilité et de coût, par exemple pour un programme de vaccination). Par exemple lors de l'épidémie de virus Zika aux Antilles, les travaux de modélisation ont permis d'estimer les besoins en termes d'offre de soins pour gérer les cas de syndromes de Guillain-Baré [33] ; de même des travaux contribuent à identifier les meilleures stratégies de lutte contre les arboviroses en France métropolitaine [34]. Dans ce domaine de la recherche également, l'approche multidisciplinaire est indispensable pour fédérer les compétences, expertises et moyens.…”

Projecting the end of the Zika virus epidemic in Latin America: a modelling analysis

“…It is apparent from the incidence in 2017 that many countries no longer reported an increasing incidence of cases. Due to either data unavailability or inaccuracies in the reported number of Zika cases in each country at the time of analysis, such approaches have either not used incidence data at all, [15][16][17] fit models to data on other arboviruses 14 or used selected Zika-related incidence data from particular countries 5,12,13,[18][19][20][21] to calibrate their models. Additionally, only a small number of studies have validated their model findings, either through comparison to serological surveys or comparing model outputs to incidence data not used within model fitting.…”

“…Additionally, only a small number of studies have validated their model findings, either through comparison to serological surveys or comparing model outputs to incidence data not used within model fitting. 13,[19][20][21] Considerably more data are now available across LAC and spanning multiple arboviral transmission seasons. This provides a valuable opportunity to examine the nature of ZIKV transmission and the importance of connectivity and seasonality in assessing ZIKV persistence in specific locations throughout LAC.…”