UMI IRD/UPMC 209 UMMISCO, Paris, France.
MIVEGEC, Université de Montpellier, IRD, CNRS, Montpellier, France.
Sci Rep. 2017 Jul 20;7(1):5967. doi: 10.1038/s41598-017-05957-y.
Understanding the spatio-temporal dynamics of endemic infections is of critical importance for a deeper understanding of pathogen transmission, and for the design of more efficient public health strategies. However, very few studies in this domain have focused on emerging infections, generating a gap of knowledge that hampers epidemiological response planning. Here, we analyze the case of a Chikungunya outbreak that occurred in Martinique in 2014. Using time series estimates from a network of sentinel practitioners covering the entire island, we first analyze the spatio-temporal dynamics and show that the largest city has served as the epicenter of this epidemic. We further show that the epidemic spread from there through two different propagation waves moving northwards and southwards, probably by individuals moving along the road network. We then develop a mathematical model to explore the drivers of the temporal dynamics of this mosquito-borne virus. Finally, we show that human behavior, inferred by a textual analysis of messages published on the social network Twitter, is required to explain the epidemiological dynamics over time. Overall, our results suggest that human behavior has been a key component of the outbreak propagation, and we argue that such results can lead to more efficient public health strategies specifically targeting the propagation process.
理解地方性传染病的时空动态对于深入了解病原体传播以及设计更有效的公共卫生策略至关重要。然而,该领域很少有研究关注新出现的传染病,这导致了知识上的差距,从而阻碍了流行病学应对规划。在这里,我们分析了 2014 年在马提尼克岛发生的基孔肯雅热疫情。我们利用覆盖全岛的哨兵医生网络的时间序列估计值,首先分析了时空动态,并表明最大的城市是该疫情的中心。我们进一步表明,疫情从那里通过向北和向南移动的两种不同传播波传播,可能是通过沿着道路网络移动的个人传播的。然后,我们开发了一个数学模型来探索这种蚊媒病毒时间动态的驱动因素。最后,我们表明,通过对社交网络 Twitter 上发布的消息进行文本分析推断出的人类行为,是解释随时间推移的流行病学动态所必需的。总的来说,我们的结果表明,人类行为一直是疫情传播的关键组成部分,我们认为这些结果可以导致更有效的公共卫生策略,专门针对传播过程。