Inferring the risk factors behind the geographical spread and transmission of Zika in the Americas.
Inferring the risk factors behind the geographical spread and transmission of Zika in the Americas.
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DOI：
10.1371/journal.pntd.0006194
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发表时间：
2018-01
影响因子：
3.8
通讯作者：
Grubaugh ND
中科院分区：
文献类型：
--
作者：
Gardner LM;Bóta A;Gangavarapu K;Kraemer MUG;Grubaugh ND
2015年至2016年，美洲发生了前所未有的寨卡病毒疫情。疫情的规模以及新认识到的与病毒相关的健康风险引起了整个研究界的高度关注。我们的研究补充了最近几项绘制寨卡流行病学要素的研究，引入了一种新提出的方法，同时估计导致局部传播的地理传播的各种风险因素的贡献，并计算每对地区之间的传播（或重新引入）风险。我们分析的重点是美洲，其中的区域集包括所有国家、海外领土和美国各州。我们提出了一种新的应用广义逆感染模型（GIIM）。GIIM模型使用疫情的真实的观察结果，并试图估计推动传播的风险因素。观察结果来自每个地区报告的寨卡病毒本地传播日期，网络结构由所有地区之间的客运航空旅行活动定义，考虑的风险因素包括区域社会经济因素，病媒栖息地适宜性，旅行量和流行病学数据。GIIM依赖于基于多智能体的优化方法来估计参数，并利用数据驱动的随机动态流行病模型进行评估。正如预期的那样，我们发现蚊子的数量，起源地区的发病率和人口密度是寨卡病毒传播和传播的风险因素。令人惊讶的是，航空客运量的影响较小，最重要的因素是地区人均国内生产总值（GDP）。我们的模型生成了疫情期间国家一级的出口和进口风险概况，并对美洲所有始发地-目的地旅行对之间引入寨卡病毒导致本地传播的可能性进行了定量估计。我们的研究结果表明，当地的病媒控制，而不是旅行限制，将更有效地降低寨卡病毒传播和建立的风险。此外，寨卡病毒传播与国内生产总值之间的反比关系表明，寨卡病例更有可能发生在人们无法保护自己免受蚊子侵害的地区。该建模框架并不专门针对寨卡病毒，并且可以很容易地用于具有足够的流行病学和昆虫学数据的其他媒介传播病原体。自2015年5月巴西首次报告寨卡病毒以来，该病毒已传播到60多个国家和地区，全球范围内报告的寨卡病毒输入病例越来越多。然而，在导致这一流行病迅速出现的机制背后仍然存在许多不确定性。这项工作引入了一个新的建模框架，以提高我们对2015-2016年美洲流行期间导致Zika地理传播和局部传播的风险因素的理解。该模型由区域社会经济因素、蚊子丰度、旅行量和流行病学数据提供信息。正如预期的那样，我们的研究结果表明，蚊子、人类宿主和病毒的存在增加了蚊媒病毒传播的风险。然而，客运航空旅行的影响较小，这表明旅行限制对控制类似流行病的影响很小。重要的是，我们发现较低的地区GDP是寨卡病毒传播的最佳预测因素，这表明寨卡主要是一种贫困疾病。
An unprecedented Zika virus epidemic occurred in the Americas during 2015-2016. The size of the epidemic in conjunction with newly recognized health risks associated with the virus attracted significant attention across the research community. Our study complements several recent studies which have mapped epidemiological elements of Zika, by introducing a newly proposed methodology to simultaneously estimate the contribution of various risk factors for geographic spread resulting in local transmission and to compute the risk of spread (or re-introductions) between each pair of regions. The focus of our analysis is on the Americas, where the set of regions includes all countries, overseas territories, and the states of the US. We present a novel application of the Generalized Inverse Infection Model (GIIM). The GIIM model uses real observations from the outbreak and seeks to estimate the risk factors driving transmission. The observations are derived from the dates of reported local transmission of Zika virus in each region, the network structure is defined by the passenger air travel movements between all pairs of regions, and the risk factors considered include regional socioeconomic factors, vector habitat suitability, travel volumes, and epidemiological data. The GIIM relies on a multi-agent based optimization method to estimate the parameters, and utilizes a data driven stochastic-dynamic epidemic model for evaluation. As expected, we found that mosquito abundance, incidence rate at the origin region, and human population density are risk factors for Zika virus transmission and spread. Surprisingly, air passenger volume was less impactful, and the most significant factor was (a negative relationship with) the regional gross domestic product (GDP) per capita. Our model generates country level exportation and importation risk profiles over the course of the epidemic and provides quantitative estimates for the likelihood of introduced Zika virus resulting in local transmission, between all origin-destination travel pairs in the Americas. Our findings indicate that local vector control, rather than travel restrictions, will be more effective at reducing the risks of Zika virus transmission and establishment. Moreover, the inverse relationship between Zika virus transmission and GDP suggests that Zika cases are more likely to occur in regions where people cannot afford to protect themselves from mosquitoes. The modeling framework is not specific for Zika virus, and could easily be employed for other vector-borne pathogens with sufficient epidemiological and entomological data. Since May 2015, when Zika was first reported in Brazil, the virus has spread to over 60 countries and territories, and imported cases of Zika have been increasingly reported worldwide. However, there is still much uncertainty behind the mechanisms which dictated the rapid emergence of the epidemic. This work introduces a novel modeling framework to improve our understanding of the risk factors which contributed to the geographic spread and local transmission of Zika during the 2015-2016 epidemic in the Americas. The model is informed by data on regional socioeconomic factors, mosquito abundance, travel volumes, and epidemiological data. As expected, our results indicate that increased presence of mosquitoes, human hosts, and viruses increase the risk for mosquito-borne virus transmission. Passenger air travel, however, was less impactful, suggesting that travel restrictions will have minimal impact on controlling similar epidemics. Importantly, we found that a lower regional GDP was the best predictor of Zika virus transmission, suggesting that Zika is primarily a disease of poverty.
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DOI：
10.1126/science.aaf5036
发表时间：
2016-04-15
期刊：
Science (New York, N.Y.)
影响因子：
--
作者：
Faria NR;Azevedo RDSDS;Kraemer MUG;Souza R;Cunha MS;Hill SC;Thézé J;Bonsall MB;Bowden TA;Rissanen I;Rocco IM;Nogueira JS;Maeda AY;Vasami FGDS;Macedo FLL;Suzuki A;Rodrigues SG;Cruz ACR;Nunes BT;Medeiros DBA;Rodrigues DSG;Queiroz ALN;da Silva EVP;Henriques DF;da Rosa EST;de Oliveira CS;Martins LC;Vasconcelos HB;Casseb LMN;Simith DB;Messina JP;Abade L;Lourenço J;Alcantara LCJ;de Lima MM;Giovanetti M;Hay SI;de Oliveira RS;Lemos PDS;de Oliveira LF;de Lima CPS;da Silva SP;de Vasconcelos JM;Franco L;Cardoso JF;Vianez-Júnior JLDSG;Mir D;Bello G;Delatorre E;Khan K;Creatore M;Coelho GE;de Oliveira WK;Tesh R;Pybus OG;Nunes MRT;Vasconcelos PFC
通讯作者：
Vasconcelos PFC
影响因子：
3.7
作者：
Diagne CT;Diallo D;Faye O;Ba Y;Faye O;Gaye A;Dia I;Faye O;Weaver SC;Sall AA;Diallo M
通讯作者：
Diallo M
影响因子：
11.8
作者：
Foy BD;Kobylinski KC;Chilson Foy JL;Blitvich BJ;Travassos da Rosa A;Haddow AD;Lanciotti RS;Tesh RB
通讯作者：
Tesh RB
伊蚊物种的媒介状况决定了寨卡病毒本地传播的地理风险。
影响因子：
3.8
作者：
Gardner L;Chen N;Sarkar S
通讯作者：
Sarkar S
影响因子：
3.7
作者：
Gardner L;Sarkar S
通讯作者：
Sarkar S