School of Public Health and Preventive Medicine, Monash University, Melbourne, Victoria, Australia.
Department of Virology, Bangabandhu Sheikh Mujib Medical University, Dhaka, Bangladesh.
PLoS Negl Trop Dis. 2023 Feb 13;17(2):e0010631. doi: 10.1371/journal.pntd.0010631. eCollection 2023 Feb.
Dengue is among the fastest-spreading vector-borne infectious disease, with outbreaks often overwhelm the health system and result in huge morbidity and mortality in its endemic populations in the absence of an efficient warning system. A large number of prediction models are currently in use globally. As such, this study aimed to systematically review the published literature that used quantitative models to predict dengue outbreaks and provide insights about the current practices. A systematic search was undertaken, using the Ovid MEDLINE, EMBASE, Scopus and Web of Science databases for published citations, without time or geographical restrictions. Study selection, data extraction and management process were devised in accordance with the 'Checklist for Critical Appraisal and Data Extraction for Systematic Reviews of Prediction Modelling Studies' ('CHARMS') framework. A total of 99 models were included in the review from 64 studies. Most models sourced climate (94.7%) and climate change (77.8%) data from agency reports and only 59.6% of the models adjusted for reporting time lag. All included models used climate predictors; 70.7% of them were built with only climate factors. Climate factors were used in combination with climate change factors (13.4%), both climate change and demographic factors (3.1%), vector factors (6.3%), and demographic factors (5.2%). Machine learning techniques were used for 39.4% of the models. Of these, random forest (15.4%), neural networks (23.1%) and ensemble models (10.3%) were notable. Among the statistical (60.6%) models, linear regression (18.3%), Poisson regression (18.3%), generalized additive models (16.7%) and time series/autoregressive models (26.7%) were notable. Around 20.2% of the models reported no validation at all and only 5.2% reported external validation. The reporting of methodology and model performance measures were inadequate in many of the existing prediction models. This review collates plausible predictors and methodological approaches, which will contribute to robust modelling in diverse settings and populations.
登革热是传播速度最快的虫媒传染病之一，如果没有有效的预警系统，其在流行地区的疫情经常会使卫生系统不堪重负，并导致大量发病和死亡。目前全球有大量的预测模型正在使用。因此，本研究旨在系统地回顾使用定量模型预测登革热疫情的已发表文献，并提供有关当前实践的见解。使用 Ovid MEDLINE、EMBASE、Scopus 和 Web of Science 数据库对已发表的引文进行了系统搜索，没有时间或地理限制。研究选择、数据提取和管理过程是根据“预测建模研究的批判性评价和数据提取清单”（“CHARMS”）框架制定的。从 64 项研究中共有 99 个模型被纳入审查。大多数模型从机构报告中获取气候（94.7%）和气候变化（77.8%）数据，只有 59.6%的模型调整了报告时间滞后。所有纳入的模型都使用了气候预测因子；其中 70.7%的模型仅使用气候因素构建。气候因素与气候变化因素（13.4%）、气候变化和人口因素（3.1%）、媒介因素（6.3%）和人口因素（5.2%）一起使用。39.4%的模型使用了机器学习技术。其中，随机森林（15.4%）、神经网络（23.1%）和集成模型（10.3%）较为突出。在统计学（60.6%）模型中，线性回归（18.3%）、泊松回归（18.3%）、广义加性模型（16.7%）和时间序列/自回归模型（26.7%）较为突出。约 20.2%的模型根本没有报告任何验证，只有 5.2%的模型报告了外部验证。许多现有的预测模型在方法学和模型性能指标的报告方面都不够充分。本综述汇集了合理的预测因子和方法学方法，这将有助于在不同环境和人群中进行稳健的建模。
