Micro-climate to macro-risk: mapping fine scale differences in mosquito-borne disease risk using remote sensing
Micro-climate to macro-risk: mapping fine scale differences in mosquito-borne disease risk using remote sensing
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DOI：
10.1088/1748-9326/ac3589
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发表时间：
2021-12-01
影响因子：
6.7
通讯作者：
MacDonald, Andrew
中科院分区：
文献类型：
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作者：
Boser, Anna;Sousa, Daniel;MacDonald, Andrew
蚊子传播的疾病（MBD）威胁着世界80%以上的人口，并且随着土地使用和气候变化，其强度和地理范围正在增加。缓解措施取决于对特定疾病风险状况的了解，但目前的风险地图在空间分辨率方面受到严重限制。MBD风险的一个重要决定因素是温度，尽管温度和风险之间的关系已经得到了广泛的研究，但地图通常是使用无法捕捉小气候条件的稀疏数据创建的。在这里，我们利用高分辨率的地表温度（LST）测量，结合空气温度和MBD风险因素（如蚊子叮咬率和传播概率）之间建立的关系，以产生MBD风险成分的精细分辨率（70米）地图。我们把我们的案例研究集中在西尼罗河病毒（WNV）在圣华金河谷的加州，那里的温度变化很大，在一天和不同的农业/城市景观。我们首先使用现场测量来建立LST与气温之间的关系，并将其应用于WNV传输高峰月份（6月至9月）的生态系统星载热辐射计实验数据（2018-2020年）。然后，我们使用先前推导的方程来估计空间上明确的蚊子叮咬和西尼罗河病毒传播率。我们使用这些地图来揭示不同土地覆盖类型的风险差异，并确定一天中导致不同土地覆盖高风险的时间。此外，我们评估的价值，高分辨率的空间和时间的数据，以避免偏见的风险估计，由于詹森的不平等，并发现，使用汇总数据导致高达40.5%的风险值的可能范围内的显着偏差。通过这种分析，我们表明，新的遥感技术和疾病生态学的基本原理之间的协同作用，可以解锁新的见解MBDs的时空动态。
Mosquito-borne diseases (MBD) threaten over 80% of the world's population, and are increasing in intensity and shifting in geographical range with land use and climate change. Mitigation hinges on understanding disease-specific risk profiles, but current risk maps are severely limited in spatial resolution. One important determinant of MBD risk is temperature, and though the relationships between temperature and risk have been extensively studied, maps are often created using sparse data that fail to capture microclimatic conditions. Here, we leverage high resolution land surface temperature (LST) measurements, in conjunction with established relationships between air temperature and MBD risk factors like mosquito biting rate and transmission probability, to produce fine resolution (70 m) maps of MBD risk components. We focus our case study on West Nile virus (WNV) in the San Joaquin Valley of California, where temperatures vary widely across the day and the diverse agricultural/urban landscape. We first use field measurements to establish a relationship between LST and air temperature, and apply it to Ecosystem Spaceborne Thermal Radiometer Experiment data (2018-2020) in peak WNV transmission months (June-September). We then use the previously derived equations to estimate spatially explicit mosquito biting and WNV transmission rates. We use these maps to uncover significant differences in risk across land cover types, and identify the times of day which contribute to high risk for different land covers. Additionally, we evaluate the value of high resolution spatial and temporal data in avoiding biased risk estimates due to Jensen's inequality, and find that using aggregate data leads to significant biases of up to 40.5% in the possible range of risk values. Through this analysis, we show that the synergy between novel remote sensing technology and fundamental principles of disease ecology can unlock new insights into the spatio-temporal dynamics of MBDs.