Department of Biology, Stanford University, Stanford, CA, USA.
Department of Pediatrics, Division of Infectious Diseases, Stanford University, Stanford, CA, USA.
Nat Commun. 2021 Feb 23;12(1):1233. doi: 10.1038/s41467-021-21496-7.
Climate drives population dynamics through multiple mechanisms, which can lead to seemingly context-dependent effects of climate on natural populations. For climate-sensitive diseases, such as dengue, chikungunya, and Zika, climate appears to have opposing effects in different contexts. Here we show that a model, parameterized with laboratory measured climate-driven mosquito physiology, captures three key epidemic characteristics across ecologically and culturally distinct settings in Ecuador and Kenya: the number, timing, and duration of outbreaks. The model generates a range of disease dynamics consistent with observed Aedes aegypti abundances and laboratory-confirmed arboviral incidence with variable accuracy (28-85% for vectors, 44-88% for incidence). The model predicted vector dynamics better in sites with a smaller proportion of young children in the population, lower mean temperature, and homes with piped water and made of cement. Models with limited calibration that robustly capture climate-virus relationships can help guide intervention efforts and climate change disease projections.
气候通过多种机制驱动种群动态，这可能导致气候对自然种群的影响在不同背景下看似不同。对于登革热、基孔肯雅热和寨卡等对气候敏感的疾病，气候在不同情况下似乎有相反的影响。在这里，我们展示了一个模型，该模型使用实验室测量的气候驱动的蚊子生理学进行参数化，可捕捉厄瓜多尔和肯尼亚在生态和文化上截然不同的环境中的三个关键流行特征：疫情的数量、时间和持续时间。该模型生成了一系列与观察到的埃及伊蚊丰度和实验室确认的虫媒病毒发病率一致的疾病动态，其准确性不同（28-85%用于载体，44-88%用于发病率）。该模型在人口中儿童比例较小、平均温度较低、家庭有管道供水和用水泥建造的地点，对蚊虫动态的预测更为准确。能够稳健捕捉气候-病毒关系的有限校准模型可以帮助指导干预措施和气候变化疾病预测。
