Saw Swee Hock School of Public Health, National University Health Systems, National University of Singapore, Singapore, Singapore.
Saw Swee Hock School of Public Health, National University Health Systems, National University of Singapore, Singapore, Singapore.
Epidemics. 2020 Dec;33:100402. doi: 10.1016/j.epidem.2020.100402. Epub 2020 Aug 22.
Significant health risks arise in Thailand from dengue but little work has been conducted to quantify the extremities of dengue outbreaks - where health systems are likely to be most stretched. In this paper, we detail the utility of tools derived from extreme value theory (EVT) in modelling the extremes in dengue case counts observed during outbreaks using 25 years of province level dengue case count data in Thailand from 1993 to 2018. We assess the validity of the EVT toolkit by comparing them against 8 competing benchmarks. The inhomogeneous point process representation (IPP) was found to perform best on 5 in and out of sample criterion such as parameter stability, distributional characteristics and out of sample coverage. Lastly, by using the IPP to infer future extreme dengue events, IPP found stark differences at the province level in the mean level of dengue case counts that is expected to be exceeded over the next 10 years. The IPP model also found that high probability that dengue extreme events will reach levels above and beyond the observed historical maximums. EVT shows considerable potential in aiding health planners for the risk management of dengue. The results in this paper can be easily translatable to any infectious disease observed over a long period.
泰国存在严重的登革热健康风险，但几乎没有工作来量化登革热疫情的极端情况——在这些情况下，卫生系统可能面临最大的压力。本文详细介绍了使用泰国 1993 年至 2018 年 25 年的省级登革热病例数据，从极值理论（EVT）中得出的工具在建模疫情期间登革热病例数极值方面的应用。我们通过将其与 8 种竞争基准进行比较，评估了 EVT 工具包的有效性。结果发现，同质点过程表示法（IPP）在参数稳定性、分布特征和样本外覆盖率等 5 个内、样本外标准方面表现最好。最后，通过使用 IPP 推断未来的极端登革热事件，IPP 发现省级水平上的登革热病例数均值预计在未来 10 年内超过预期的差异很大。IPP 模型还发现，登革热极端事件达到并超过历史最高水平的可能性很高。EVT 在帮助登革热风险管理的卫生规划者方面显示出相当大的潜力。本文的结果可以很容易地转化为任何长期观察到的传染病。
