Rapid review of COVID-19 epidemic estimation studies for Iran
Background: To 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.
Methods: We 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.
Results: The 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 1777 (388951) for cumulative deaths, 20588 (2310161) for cumulative cases, and at the end of month four (2020-06-20), were 3590 (1819392) for cumulative deaths, and 144305 (4266964) for cumulative cases. Highest estimates of cumulative deaths (and cases) for latest date available in 2020 were 418834 on 2020-12-19 (and 41475792 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 26790 additional deaths (95% confidence interval 19925-35208) by the end of year 2020.
Conclusions: Meticulousness 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.
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Posted 11 Jan, 2021
On 25 Dec, 2020
On 25 Dec, 2020
On 25 Dec, 2020
On 22 Sep, 2020
Received 17 Sep, 2020
On 06 Sep, 2020
Received 13 Aug, 2020
On 20 Jul, 2020
Invitations sent on 11 Jun, 2020
On 08 Jun, 2020
On 05 Jun, 2020
On 02 Jun, 2020
On 22 May, 2020
Rapid review of COVID-19 epidemic estimation studies for Iran
Posted 11 Jan, 2021
On 25 Dec, 2020
On 25 Dec, 2020
On 25 Dec, 2020
On 22 Sep, 2020
Received 17 Sep, 2020
On 06 Sep, 2020
Received 13 Aug, 2020
On 20 Jul, 2020
Invitations sent on 11 Jun, 2020
On 08 Jun, 2020
On 05 Jun, 2020
On 02 Jun, 2020
On 22 May, 2020
Background: To 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.
Methods: We 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.
Results: The 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 1777 (388951) for cumulative deaths, 20588 (2310161) for cumulative cases, and at the end of month four (2020-06-20), were 3590 (1819392) for cumulative deaths, and 144305 (4266964) for cumulative cases. Highest estimates of cumulative deaths (and cases) for latest date available in 2020 were 418834 on 2020-12-19 (and 41475792 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 26790 additional deaths (95% confidence interval 19925-35208) by the end of year 2020.
Conclusions: Meticulousness 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.
Figure 1
Figure 2
Figure 3
Figure 4
Figure 5