Rapid review of COVID-19 epidemic estimation studies for Iran

BMC Public Health

Mohsen Rezaei Hemami¹  Leila Janani²  Maziar Moradi-Lakeh³  Farshad Pourmalek⁴ 

[1] Aberdeen Centre for Health Data Sciences, University of Aberdeen, Aberdeen, UK;Department of Biostatistics, School of Public Health, Iran University of Medical Sciences, Tehran, Iran;Preventive Medicine and Public Health Research Center, Psychosocial Health Research Institute, Community and Family Medicine Department, School of Medicine, Iran University of Medical Sciences, Tehran, Iran;University of British Columbia, Vancouver, Canada;

关键词: COVID-19;    Epidemic;    Pandemic;    Iran;    Cases;    Deaths;    Model;    Prediction;    Estimation;   

DOI  :  10.1186/s12889-021-10183-3

来源: Springer

【 摘 要 】

BackgroundTo inform researchers about the methodology and results of epidemic estimation studies performed for COVID-19 epidemic in Iran, we aimed to perform a rapid review.MethodsWe searched for and included published articles, preprint manuscripts and reports that estimated numbers of cumulative or daily deaths or cases of COVID-19 in Iran. We found 131 studies and included 29 of them.ResultsThe included studies provided outputs for a total of 84 study-model/scenario combinations. Sixteen studies used 3–4 compartmental disease models. At the end of month two of the epidemic (2020-04-19), the lowest (and highest) values of predictions were 1,777 (388,951) for cumulative deaths, 20,588 (2,310,161) for cumulative cases, and at the end of month four (2020-06-20), were 3,590 (1,819,392) for cumulative deaths, and 144,305 (4,266,964) for cumulative cases. Highest estimates of cumulative deaths (and cases) for latest date available in 2020 were 418,834 on 2020-12-19 (and 41,475,792 on 2020-12-31). Model estimates predict an ominous course of epidemic progress in Iran. Increase in percent population using masks from the current situation to 95% might prevent 26,790 additional deaths (95% confidence interval 19,925–35,208) by the end of year 2020.ConclusionsMeticulousness and degree of details reported for disease modeling and statistical methods used in the included studies varied widely. Greater heterogeneity was observed regarding the results of predicted outcomes. Consideration of minimum and preferred reporting items in epidemic estimation studies might better inform future revisions of the available models and new models to be developed. Not accounting for under-reporting drives the models’ results misleading.

【 授权许可】

CC BY