Predicting the Spread of Vector-Borne Diseases in a Warming World
Predicting the Spread of Vector-Borne Diseases in a Warming World
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DOI：
10.3389/fevo.2022.758277
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发表时间：
2022-04-25
影响因子：
3
通讯作者：
Amarasekare, Priyanga
中科院分区：
文献类型：
--
作者：
Endo, Andrew;Amarasekare, Priyanga
预测气候变暖如何影响病媒传播的疾病是一个关键的研究重点。目前流行的方法使用基本再生数（R-0）来预测变暖效应。然而，R-0是在静态热环境的假设下得出的;使用它来预测非静态环境中的疾病传播可能会导致错误的预测。在这里，我们开发了一个基于特征的数学模型，可以预测疾病的传播和流行的任何类型的非平稳环境下的任何病媒传播的疾病。我们用疟疾病媒和病原体的性状反应数据对模型进行参数化，以测试IPCC关于冬季温度高于平均水平和夏季温度高于平均水平的最新预测。我们报告了三个关键发现。首先，通常用于调查变暖对疾病传播的影响的R-0公式违反了其推导的假设，作为线性化宿主-向量模型的主导特征值。结果，它高估了较冷环境中的疾病传播，低估了较暖环境中的疾病传播，证明即使在恒定的热环境中，它的预测也是不可靠的。其次，高于平均温度的夏季缩小了疾病流行的温度限制，并在这些限制范围内降低了流行率，其程度远远高于平均温度的冬季，突出了极端炎热在推动疾病负担方面的重要性。第三，虽然变暖通过成年人死亡率的复合效应减少了受感染的病媒种群，通过死亡率和传播的相互作用减少了受感染的宿主种群，但未受感染的病媒种群对变暖的影响令人惊讶。这表明，对变暖引起的疾病负担减少的生态预测应该受到病媒适应较冷和较暖气候的进化可能性的影响。
Predicting how climate warming affects vector borne diseases is a key research priority. The prevailing approach uses the basic reproductive number (R-0) to predict warming effects. However, R-0 is derived under assumptions of stationary thermal environments; using it to predict disease spread in non-stationary environments could lead to erroneous predictions. Here, we develop a trait-based mathematical model that can predict disease spread and prevalence for any vector borne disease under any type of non-stationary environment. We parameterize the model with trait response data for the Malaria vector and pathogen to test the latest IPCC predictions on warmer-than-average winters and hotter-than-average summers. We report three key findings. First, the R-0 formulation commonly used to investigate warming effects on disease spread violates the assumptions underlying its derivation as the dominant eigenvalue of a linearized host-vector model. As a result, it overestimates disease spread in cooler environments and underestimates it in warmer environments, proving its predictions to be unreliable even in a constant thermal environment. Second, hotter-than-average summers both narrow the thermal limits for disease prevalence, and reduce prevalence within those limits, to a much greater degree than warmer-than-average winters, highlighting the importance of hot extremes in driving disease burden. Third, while warming reduces infected vector populations through the compounding effects of adult mortality, and infected host populations through the interactive effects of mortality and transmission, uninfected vector populations prove surprisingly robust to warming. This suggests that ecological predictions of warming-induced reductions in disease burden should be tempered by the evolutionary possibility of vector adaptation to both cooler and warmer climates.