Shand L, Brown W M, Chaves L F, Goldberg T L, Hamer G L, Haramis L, Kitron U, Walker E D, Ruiz M O
Department of Statistics, University of Illinois, Urbana, IL 61801 (
Department of Pathobiology, University of Illinois, Urbana, IL 61801 (
J Med Entomol. 2016 Jul;53(4):935-944. doi: 10.1093/jme/tjw042. Epub 2016 Apr 25.
Mosquito-based surveillance is a practical way to estimate the risk of transmission of West Nile virus (WNV) to people. Variations in temperature and precipitation play a role in driving mosquito infection rates and transmission of WNV, motivating efforts to predict infection rates based on prior weather conditions. Weather conditions and sequential patterns of meteorological events can have particularly important, but regionally distinctive, consequences for WNV transmission, with high temperatures and low precipitation often increasing WNV mosquito infection. Predictive models that incorporate weather can thus be used to provide early indications of the risk of WNV infection. The purpose of this study was first, to assess the ability of a previously published model of WNV mosquito infection to predict infection for an area within the region for which it was developed, and second, to improve the predictive ability of this model by incorporating new weather factors that may affect mosquito development. The legacy model captured the primary trends in mosquito infection, but it was improved considerably when calibrated with local mosquito infection rates. The use of interaction terms between precipitation and temperature improved model performance. Specifically, temperature had a stronger influence than rainfall, so that lower than average temperature greatly reduced the effect of low rainfall on increased infection rates. When rainfall was lower, high temperature had an even stronger positive impact on infection rates. The final model is practical, stable, and operationally valid for predicting West Nile virus infection rates in future weeks when calibrated with local data.
基于蚊子的监测是评估西尼罗河病毒（WNV）向人类传播风险的一种实用方法。温度和降水的变化在推动蚊子感染率和WNV传播方面发挥着作用，这促使人们努力根据先前的天气状况预测感染率。天气状况和气象事件的连续模式对WNV传播可能产生特别重要但具有区域特色的影响，高温和低降水通常会增加WNV蚊子感染率。因此，纳入天气因素的预测模型可用于提供WNV感染风险的早期迹象。本研究的目的首先是评估先前发表的WNV蚊子感染模型对其开发区域内一个地区感染情况的预测能力，其次是通过纳入可能影响蚊子发育的新天气因素来提高该模型的预测能力。传统模型捕捉到了蚊子感染的主要趋势，但在用当地蚊子感染率进行校准时有了显著改进。降水与温度之间相互作用项的使用提高了模型性能。具体而言，温度的影响比降雨更强，因此低于平均温度会大大降低低降雨对感染率上升的影响。当降雨量较低时，高温对感染率的正向影响更强。最终模型在用当地数据进行校准时，对于预测未来几周的西尼罗河病毒感染率是实用、稳定且有效的。
