Covid-19: The fatal attraction of herd immunity

Abbasi, Kamran

doi:10.1136/bmj.m3714

Cited by 2 publications

(2 citation statements)

References 18 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…Official report of cumulative deaths on 2020-05-20 was 7183, and all of these end up in three possibilities: (1) correction factor of 21 (8–42) for COVID-19 deaths in Iran, or (2) the IFR in Iran was 0.024% at that time, or (3) that seroprevalence finding was wrong. While some politicians and researchers may wish, advocate, or publish for herd immunity – knowingly or unknowingly [ 76 – 79 ], research evidence yet describe why “COVID-19 herd immunity is unethical and unachievable” [ 80 , 81 ]. On a rather daily basis, more and more announcements are being made by authorities and officials about higher correction factors for COVID-19 deaths and cases in Iran, or unpreparedness of testing system or hospital infrastructure for combating the epidemic; for which continued quotation and referencing here would not further help anyone.…”

Section: Discussionmentioning

confidence: 99%

Rapid review of COVID-19 epidemic estimation studies for Iran

et al. 2021

View full text Add to dashboard Cite

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 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. 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.

show abstract

Section: Discussionmentioning

confidence: 99%

Rapid review of COVID-19 epidemic estimation studies for Iran

et al. 2021

View full text Add to dashboard Cite

show abstract

“…While some politicians and researchers may wish, advocate, or publish for herd immunity -knowingly or unknowingly[72][73][74][75], research evidence yet describe why "COVID-19 herd immunity is unethical and unachievable"[76,77]. On a rather daily basis, more and more announcements are being made by authorities and o cials about higher correction factors for COVID-19 deaths and cases in Iran, or unpreparedness of testing system or hospital infrastructure for combating the epidemic; for which continued quotation and referencing here would not further help anyone.Undercounting, under-ascertainment, or under-reporting is a known issue with the number of o cial con rmed cases and deaths, almost in all countries.…”

mentioning

confidence: 99%

Rapid review of COVID-19 epidemic estimation studies for Iran

Pourmalek

Hemami

Janani

et al. 2021

Preprint

View full text Add to dashboard Cite

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.

show abstract

Covid-19: The fatal attraction of herd immunity

Cited by 2 publications

References 18 publications

Rapid review of COVID-19 epidemic estimation studies for Iran

Rapid review of COVID-19 epidemic estimation studies for Iran

Rapid review of COVID-19 epidemic estimation studies for Iran

Contact Info

Product

Resources

About