Modeling dynamic introduction of Chikungunya virus in the United States.
Modeling dynamic introduction of Chikungunya virus in the United States.
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DOI：
10.1371/journal.pntd.0001918
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发表时间：
2012
影响因子：
3.8
通讯作者：
Harrington LC
中科院分区：
文献类型：
--
作者：
Ruiz-Moreno D;Vargas IS;Olson KE;Harrington LC
基孔肯雅热是一种蚊子传播的人类病毒感染，以前仅限于中非地区。然而，在本世纪，随着病毒入侵其他国家，包括温带地区，它显示出令人惊讶的地理扩张潜力。由于没有疫苗和特异性治疗，基孔肯雅热的主要控制战略仍然是对蚊子种群进行预防性控制。考虑到基孔肯雅热传入美国的风险，我们开发了一个基于单个病毒传入的疾病传入模型。我们的研究将基于气候的蚊子种群动态随机模型与流行病学模型相结合，以确定具有流行风险的时间窗口。我们将该模型与不同地点的温度数据一起运行，以研究流行病潜力的地理敏感性。我们发现，在气温季节性变化明显的地区，也存在与蚊子种群生存和生长的年份相匹配的流行病风险季节。在这些地区，控制蚊子的数量可能是一种有效的策略。但是，在其他温度全年支持蚊子生长的地方，流行病的风险很高，而且（实际上）是恒定的。在这些地区，单独控制蚊子数量可能不是一种有效的疾病控制策略，应实施其他方法作为补充。我们的研究结果强烈表明，如果基孔肯雅热在美国引入和建立，将首先出现流行区和流行区，主要由控制蚊子年种群周期的环境因素确定。应确定这些区域，规划不同的干预措施。此外，减少病媒与人的比例可以降低季节性温度模式强烈的地区暴发的可能性和规模。这是美国第一个考虑基孔肯雅热风险的模型，可以应用于其他媒介传播疾病。基孔肯雅热是一种蚊媒病毒感染，具有惊人的地域扩展潜力。与其他没有疫苗和特异性治疗的热带传染病类似，基孔肯雅热的主要控制策略仍然是减少蚊子种群规模。我们开发了一个疾病传入模型，将基于气候的蚊子种群动态随机模型与流行病学模型相结合，以确定一个暴露个体的疾病传入可能危及整个人群健康状况的时间窗口。我们用不同地点的温度数据运行了这个模型，显示了这种风险的地理敏感性。在不同空间位置识别具有流行风险的时间窗口是指导蚊虫种群控制工作的关键。季节变化明显的地点也存在与蚊子种群生存和生长期相匹配的高流行风险季节，因此控制蚊子种群规模可能是这些地区的最佳策略。然而，具有其他温度模式的地点可能需要额外的控制策略以避免流行病。据我们所知，这是探索基孔肯雅热在美国传入的第一个模型。我们的建模方法可用于其他媒介传播疾病，并可扩展以比较不同控制策略的结果。
Chikungunya is a mosquito-borne viral infection of humans that previously was confined to regions in central Africa. However, during this century, the virus has shown surprising potential for geographic expansion as it invaded other countries including more temperate regions. With no vaccine and no specific treatment, the main control strategy for Chikungunya remains preventive control of mosquito populations. In consideration for the risk of Chikungunya introduction to the US, we developed a model for disease introduction based on virus introduction by one individual. Our study combines a climate-based mosquito population dynamics stochastic model with an epidemiological model to identify temporal windows that have epidemic risk. We ran this model with temperature data from different locations to study the geographic sensitivity of epidemic potential. We found that in locations with marked seasonal variation in temperature there also was a season of epidemic risk matching the period of the year in which mosquito populations survive and grow. In these locations controlling mosquito population sizes might be an efficient strategy. But, in other locations where the temperature supports mosquito development all year the epidemic risk is high and (practically) constant. In these locations, mosquito population control alone might not be an efficient disease control strategy and other approaches should be implemented to complement it. Our results strongly suggest that, in the event of an introduction and establishment of Chikungunya in the US, endemic and epidemic regions would emerge initially, primarily defined by environmental factors controlling annual mosquito population cycles. These regions should be identified to plan different intervention measures. In addition, reducing vector: human ratios can lower the probability and magnitude of outbreaks for regions with strong seasonal temperature patterns. This is the first model to consider Chikungunya risk in the US and can be applied to other vector borne diseases. Chikungunya fever is a mosquito-borne viral infection showing a surprising potential for geographic expansion. Similar to other tropical infectious diseases having no vaccine and no specific treatment, the main control strategy for Chikungunya remains reduction of mosquito population size. We developed a model for disease introduction that combines a climate based mosquito population dynamics stochastic model with an epidemiological model in order to identify temporal windows during which disease introduction through one exposed individual might compromise the health status of the entire human population. We ran this model with temperature data from different locations showing the geographic sensitivity of this risk. The identification of temporal windows with epidemic risk at different spatial locations is key to guiding mosquito population control campaigns. Locations with marked seasonal variation also have a season with high epidemic risk matching the period in which mosquito populations survive and grow, therefore controlling mosquito population sizes might be an optimal strategy in those areas. However, locations with other temperature patterns may need additional control strategies to avoid epidemics. To our knowledge, this is the first model to explore Chikungunya introduction in the USA. Our modeling approach can be used for other vector borne diseases and can be expanded to compare the outcome with different control strategies.
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作者：
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DOI：
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