School of Geography, Earth and Atmospheric Sciences, Faculty of Science, The University of Melbourne, 221 Bouverie St, Carlton, VIC 3053, Australia.
Department of Infectious Diseases, Melbourne Medical School, University of Melbourne at the Peter Doherty Institute for Infection and Immunity, Melbourne, VIC 3000, Australia.
Epidemiol Infect. 2023 Mar 14;151:e55. doi: 10.1017/S0950268823000365.
Ross River virus (RRV) is the most common mosquito-borne infection in Australia. RRV disease is characterised by joint pain and lethargy, placing a substantial burden on individual patients, the healthcare system and economy. This burden is compounded by a lack of effective treatment or vaccine for the disease. The complex RRV disease ecology cycle includes a number of reservoirs and vectors that inhabit a range of environments and climates across Australia. Climate is known to influence humans, animals and the environment and has previously been shown to be useful to RRV prediction models. We developed a negative binomial regression model to predict monthly RRV case numbers and outbreaks in the Darling Downs region of Queensland, Australia. Human RRV notifications and climate data for the period July 2001 - June 2014 were used for model training. Model predictions were tested using data for July 2014 - June 2019. The final model was moderately effective at predicting RRV case numbers (Pearson's = 0.427) and RRV outbreaks (accuracy = 65%, sensitivity = 59%, specificity = 73%). Our findings show that readily available climate data can provide timely prediction of RRV outbreaks.
罗斯河病毒（RRV）是澳大利亚最常见的蚊媒传染病。RRV 病的特征是关节疼痛和乏力，给个体患者、医疗保健系统和经济带来了巨大负担。由于缺乏针对该疾病的有效治疗方法或疫苗，这种负担更加严重。RRV 疾病的复杂生态循环包括许多宿主和媒介，它们栖息在澳大利亚各地的各种环境和气候中。众所周知，气候会影响人类、动物和环境，并且先前已经表明气候对 RRV 预测模型有用。我们开发了一个负二项回归模型，以预测澳大利亚昆士兰州达令唐斯地区的每月 RRV 病例数和疫情爆发情况。该模型使用了 2001 年 7 月至 2014 年 6 月期间的人类 RRV 通知和气候数据进行训练。使用 2014 年 7 月至 2019 年 6 月的数据对模型预测进行了测试。最终模型在预测 RRV 病例数（Pearson's = 0.427）和 RRV 疫情爆发（准确率=65%，灵敏度=59%，特异性=73%）方面的效果相当。我们的研究结果表明，现成的气候数据可以及时预测 RRV 疫情爆发。
