Forecast of Barmah Forest Virus (BFV) disease setting rainfall constant

We obtained data on notified BFV cases, climate (maximum and minimum temperature and rainfall), socio-economic and tidal conditions for current period 2000–2008 for coastal regions in Queensland. Grid-data on future climate projections for 2025, 2050 and 2100 were also obtained. Logistic regression models were built to forecast the potential risk of BFV disease distribution under existing climatic, socio-economic and tidal conditions. The model was applied to estimate the potential geographic distribution of BFV outbreaks under climate change scenarios.

The predictive model had good model accuracy, sensitivity and specificity. Maps on potential risk of future BFV disease indicated that disease would vary significantly across coastal regions in Queensland by 2100 due to marked differences in future rainfall and temperature projections.

The mao figures show (a) Geographical distribution of BFV disease under current climatic conditions for Queensland entire coastal regions, (b) forecast of potential probabilities of risk of BFV disease under climate change scenarios setting rainfall constant for 2025, (c) 2050 and (d) 2100.

本研究收集了2000年至2008年间昆士兰州沿海地区通报的BFV病例数据，以及该时期的气候（最高和最低温度及降雨量）、社会经济状况和潮汐条件。同时，还获得了针对2025年、2050年和2100年未来气候预测的网格数据。基于现有的气候、社会经济和潮汐条件，构建了逻辑回归模型以预测BFV疾病传播的潜在风险。该模型被应用于估计气候变化情景下BFV疫情可能的地域分布。预测模型具有良好的模型准确度、敏感度和特异性。未来BFV疾病风险的预测地图显示，由于未来降雨和温度预测的显著差异，到2100年BFV疾病在昆士兰州沿海地区的分布将出现显著变化。图表中展示了：（a）昆士兰州整个沿海地区在当前气候条件下的BFV疾病地理分布，（b）在2025年气候变化情景下保持降雨量恒定的BFV疾病潜在风险概率预测，（c）2050年和（d）2100年的预测。