Gnanvi Janyce Eunice, Salako Kolawolé Valère, Kotanmi Gaëtan Brezesky, Glèlè Kakaï Romain
Laboratoire de Biomathématiques et d'Estimations Forestières, Université d'Abomey-Calavi, 04 BP 1525, Cotonou, Benin.
Infect Dis Model. 2021;6:258-272. doi: 10.1016/j.idm.2020.12.008. Epub 2021 Jan 12.
Since the emergence of the novel 2019 coronavirus pandemic in December 2019 (COVID-19), numerous modellers have used diverse techniques to assess the dynamics of transmission of the disease, predict its future course and determine the impact of different control measures. In this study, we conducted a global systematic literature review to summarize trends in the modelling techniques used for Covid-19 from January 1st 2020 to November 30th 2020. We further examined the accuracy and precision of predictions by comparing predicted and observed values for cumulative cases and deaths as well as uncertainties of these predictions. From an initial 4311 peer-reviewed articles and preprints found with our defined keywords, 242 were fully analysed. Most studies were done on Asian (78.93%) and European (59.09%) countries. Most of them used compartmental models (namely SIR and SEIR) (46.1%) and statistical models (growth models and time series) (31.8%) while few used artificial intelligence (6.7%), Bayesian approach (4.7%), Network models (2.3%) and Agent-based models (1.3%). For the number of cumulative cases, the ratio of the predicted over the observed values and the ratio of the amplitude of confidence interval (CI) or credibility interval (CrI) of predictions and the central value were on average larger than 1 indicating cases of inaccurate and imprecise predictions, and large variation across predictions. There was no clear difference among models used for these two ratios. In 75% of predictions that provided CI or CrI, observed values fall within the 95% CI or CrI of the cumulative cases predicted. Only 3.7% of the studies predicted the cumulative number of deaths. For 70% of the predictions, the ratio of predicted over observed cumulative deaths was less or close to 1. Also, the Bayesian model made predictions closer to reality than classical statistical models, although these differences are only suggestive due to the small number of predictions within our dataset (9 in total). In addition, we found a significant negative correlation (rho = - 0.56, = 0.021) between this ratio and the length (in days) of the period covered by the modelling, suggesting that the longer the period covered by the model the likely more accurate the estimates tend to be. Our findings suggest that while predictions made by the different models are useful to understand the pandemic course and guide policy-making, some were relatively accurate and precise while other not.
自2019年12月新型冠状病毒大流行（COVID-19）出现以来，众多建模人员运用了各种技术来评估该疾病的传播动态、预测其未来发展趋势并确定不同防控措施的影响。在本研究中，我们进行了一项全球系统性文献综述，以总结2020年1月1日至2020年11月30日期间用于COVID-19建模技术的趋势。我们还通过比较累计病例和死亡的预测值与观测值以及这些预测的不确定性，进一步检验了预测的准确性和精确性。从最初用我们定义的关键词找到的4311篇同行评审文章和预印本中，对242篇进行了全面分析。大多数研究是针对亚洲（78.93%）和欧洲（59.09%）国家开展的。其中大多数使用了 compartmental模型（即SIR和SEIR）（46.1%）和统计模型（增长模型和时间序列）（31.8%），而很少有人使用人工智能（6.7%）、贝叶斯方法（4.7%）、网络模型（2.3%）和基于主体的模型（1.3%）。对于累计病例数，预测值与观测值的比率以及预测的置信区间（CI）或可信区间（CrI）的幅度与中心值的比率平均大于1，表明存在预测不准确和不精确的情况，且不同预测之间差异很大。用于这两个比率的模型之间没有明显差异。在提供CI或CrI的预测中，75%的观测值落在预测的累计病例的95%CI或CrI范围内。只有3.7%的研究预测了累计死亡人数。对于70%的预测，预测的累计死亡人数与观测值的比率小于或接近1。此外，贝叶斯模型的预测比经典统计模型更接近实际情况，尽管由于我们数据集中的预测数量较少（总共9个），这些差异只是暗示性的。此外，我们发现这个比率与建模所涵盖时间段的长度（以天为单位）之间存在显著的负相关（rho = - 0.56，p = 0.021），这表明模型涵盖的时间段越长，估计往往越准确。我们的研究结果表明，虽然不同模型所做的预测有助于理解大流行的发展趋势并指导政策制定，但有些预测相对准确和精确，而有些则不然。
