Department of Epidemiology, Mailman School of Public Health, Columbia University, New York, New York, United States of America.
Department of Environmental Health Sciences, Mailman School of Public Health, Columbia University, New York, New York, United States of America.
PLoS Comput Biol. 2021 Jun 9;17(6):e1009050. doi: 10.1371/journal.pcbi.1009050. eCollection 2021 Jun.
Climate drivers such as humidity and temperature may play a key role in influenza seasonal transmission dynamics. Such a relationship has been well defined for temperate regions. However, to date no models capable of capturing the diverse seasonal pattern in tropical and subtropical climates exist. In addition, multiple influenza viruses could cocirculate and shape epidemic dynamics. Here we construct seven mechanistic epidemic models to test the effect of two major climate drivers (humidity and temperature) and multi-strain co-circulation on influenza transmission in Hong Kong, an influenza epidemic center located in the subtropics. Based on model fit to long-term influenza surveillance data from 1998 to 2018, we found that a simple model incorporating the effect of both humidity and temperature best recreated the influenza epidemic patterns observed in Hong Kong. The model quantifies a bimodal effect of absolute humidity on influenza transmission where both low and very high humidity levels facilitate transmission quadratically; the model also quantifies the monotonic but nonlinear relationship with temperature. In addition, model results suggest that, at the population level, a shorter immunity period can approximate the co-circulation of influenza virus (sub)types. The basic reproductive number R0 estimated by the best-fit model is also consistent with laboratory influenza survival and transmission studies under various combinations of humidity and temperature levels. Overall, our study has developed a simple mechanistic model capable of quantifying the impact of climate drivers on influenza transmission in (sub)tropical regions. This model can be applied to improve influenza forecasting in the (sub)tropics in the future.
气候驱动因素，如湿度和温度，可能在流感季节性传播动态中发挥关键作用。这种关系在温带地区已经得到了很好的定义。然而，迄今为止，还没有能够捕捉热带和亚热带气候中多样化季节性模式的模型。此外，多种流感病毒可能会共同传播并影响疫情动态。在这里，我们构建了七个机械流行病模型，以测试两个主要气候驱动因素（湿度和温度）以及多株共同传播对位于亚热带的流感流行中心香港的流感传播的影响。根据对 1998 年至 2018 年长期流感监测数据的模型拟合，我们发现，一个简单的模型，纳入了湿度和温度的影响，最好地再现了香港观察到的流感流行模式。该模型量化了绝对湿度对流感传播的双峰效应，其中低湿度和非常高湿度水平都以二次方式促进传播；该模型还量化了与温度的单调但非线性关系。此外，模型结果表明，在人口水平上，较短的免疫期可以近似流感病毒（亚型）的共同传播。由最佳拟合模型估计的基本繁殖数 R0 也与在不同湿度和温度组合下的实验室流感生存和传播研究一致。总体而言，我们的研究开发了一种简单的机械模型，能够量化气候驱动因素对（亚热带）地区流感传播的影响。该模型可用于未来改进（亚热带）地区的流感预测。
