Department of Physical Geography, Stockholm University, 106 91 Stockholm, Sweden.
Bolin Centre for Climate Research, Stockholm University, 106 91 Stockholm, Sweden.
Int J Environ Res Public Health. 2020 Sep 17;17(18):6786. doi: 10.3390/ijerph17186786.
Hydroclimatic change may affect the range of some infectious diseases, including tularemia. Previous studies have investigated associations between tularemia incidence and climate variables, with some also establishing quantitative statistical disease models based on historical data, but studies considering future climate projections are scarce. This study has used and combined hydro-climatic projection outputs from multiple global climate models (GCMs) in phase six of the Coupled Model Intercomparison Project (CMIP6), and site-specific, parameterized statistical tularemia models, which all imply some type of power-law scaling with preceding-year tularemia cases, to assess possible future trends in disease outbreaks for six counties across Sweden, known to include tularemia high-risk areas. Three radiative forcing (emissions) scenarios are considered for climate change projection until year 2100, incuding low (2.6 Wm), medium (4.5 Wm), and high (8.5 Wm) forcing. The results show highly divergent changes in future disease outbreaks among Swedish counties, depending primarily on site-specific type of the best-fit disease power-law scaling characteristics of (mostly positive, in one case negative) sub- or super-linearity. Results also show that scenarios of steeper future climate warming do not necessarily lead to steeper increase of future disease outbreaks. Along a latitudinal gradient, the likely most realistic medium climate forcing scenario indicates future disease decreases (intermittent or overall) for the relatively southern Swedish counties Örebro and Gävleborg (Ockelbo), respectively, and disease increases of considerable or high degree for the intermediate (Dalarna, Gävleborg (Ljusdal)) and more northern (Jämtland, Norrbotten; along with the more southern Värmland exception) counties, respectively.
水文气候的变化可能会影响一些传染病的传播范围，包括野兔热。先前的研究已经调查了野兔热发病率与气候变量之间的关系，其中一些研究还根据历史数据建立了定量统计疾病模型，但考虑未来气候预测的研究很少。本研究使用了第六阶段耦合模式比较计划（CMIP6）中多个全球气候模式（GCM）的水文气候预测输出数据，以及特定地点、参数化的统计野兔热模型，这些模型都暗示了与前一年野兔热病例存在某种类型的幂律关系，以评估瑞典六个县（已知包括野兔热高风险地区）未来疾病爆发的可能趋势。该研究考虑了三种气候变化预测的辐射强迫（排放）情景，直至 2100 年，包括低（2.6 Wm）、中（4.5 Wm）和高（8.5 Wm）强迫。结果表明，瑞典各县未来疾病爆发的变化趋势差异很大，主要取决于特定地点的最佳拟合疾病幂律缩放特征的类型（主要是正相关，在一种情况下为负相关）的次线性或超线性。结果还表明，未来气候变暖更为陡峭的情景并不一定导致未来疾病爆发的增加更为陡峭。在纬度梯度上，最现实的中等气候强迫情景表明，相对较南部的瑞典县Örebro 和 Gävleborg（Ockelbo）的未来疾病呈下降趋势（间歇性或总体下降），而中间（Dalarna、Gävleborg（Ljusdal））和更北部（Jämtland、Norrbotten；以及更南部的Värmland 是例外）的县的未来疾病呈显著或高度增加。
