Data_Sheet_1_Predicting the Spread of Vector-Borne Diseases in a Warming World.PDF

Predicting how climate warming affects vector borne diseases is a key research priority. The prevailing approach uses the basic reproductive number (R0) to predict warming effects. However, R0 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 R0 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.

预测气候变暖如何影响虫媒传染病（vector-borne diseases），是当前核心的研究重点之一。主流研究方法采用基本再生数（basic reproductive number，R0）来预测气候变暖的影响。然而，R0的推导基于热环境稳态的假设，若将其用于非稳态热环境下的疾病传播预测，则可能得到错误的结果。本研究构建了一种基于性状的数学模型，可在任意非稳态热环境下预测任意虫媒传染病的传播态势与流行率。我们以疟疾媒介与病原体的性状响应数据对模型进行参数化，用以验证政府间气候变化专门委员会（Intergovernmental Panel on Climate Change，IPCC）最新发布的暖冬与酷暑预测情景。本研究得到三项核心结论：其一，当前常用于探究气候变暖对疾病传播影响的R0公式，违背了其作为线性化宿主-媒介模型主导特征值推导时的核心假设。该公式会在低温环境下高估疾病传播水平，在高温环境下低估传播水平，即便在稳态热环境中，其预测结果也并不可靠。其二，相较于暖冬，酷暑会同时缩小疾病流行的热耐受区间，并进一步降低该区间内的流行率，其影响程度远大于暖冬，凸显了极端高温事件对疾病负担的驱动作用。其三，气候变暖通过成虫死亡率的复合效应降低感染媒介种群规模，通过死亡率与传播的交互效应降低感染宿主种群规模，但未感染媒介种群对气候变暖却表现出意料之外的抗干扰能力。这意味着，针对气候变暖可降低疾病负担的生态学预测，应当结合媒介生物对冷、热气候的进化适应可能性予以审慎修正。