tetano
Editor, Senior Moderator
J Infect Public Health
. 2026 Jan 8;19(3):103140.
doi: 10.1016/j.jiph.2026.103140. Online ahead of print.
Modeling the emergence of divergent mutants of SARS-CoV-2, "Omicron-like events": A time-to-event analysis
Haruka Hayashi[SUP] 1 [/SUP], Yuta Okada[SUP] 1 [/SUP], Taishi Kayano[SUP] 1 [/SUP], Katsuma Hayashi[SUP] 1 [/SUP], Tetsuro Kobayashi[SUP] 1 [/SUP], Hiroshi Nishiura[SUP] 2 [/SUP]
Affiliations
Background: During the COVID-19 pandemic, several divergent mutants including the Omicron (B.1.1.529) BA.1 variant emerged, having a distinct mechanism of emergence compared with pre-existing variants of concern. Apart from playing a major role in causing recurrent epidemic waves, the highly divergent mutants also contributed to changing the fate of the pandemic by exhibiting large differences in phenotypic characteristics among even closely related variants. Given that several different variants emerged during the pandemic, the present study aimed to quantitatively evaluate the risk of emergence of divergent severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) mutants and understand the mechanism of such emergence.
Methods: Upon identifying the emergence of phylogenetically distinct variants in "Omicron-like events" that have been recognized to date, a time-to-event analysis was carried out to estimate the monthly hazard rate of emergence. Four statistical models were established and compared using the Akaike Information Criterion.
Results: The model using the number of hospitalized cases was determined to be the best fit. The risk of Omicron-like events is not independent of time; instead, the monthly risk of emergence is likely to increase over time due to an increasing number of infection events.
Conclusions: Ongoing virus genomic surveillance is vital, and possible prevention among immunosuppressed individuals should be considered.
Keywords: Evolution; Mathematical model; Mutation; Severe acute respiratory syndrome coronavirus 2; Statistical estimation; Survival analysis.
. 2026 Jan 8;19(3):103140.
doi: 10.1016/j.jiph.2026.103140. Online ahead of print.
Modeling the emergence of divergent mutants of SARS-CoV-2, "Omicron-like events": A time-to-event analysis
Haruka Hayashi[SUP] 1 [/SUP], Yuta Okada[SUP] 1 [/SUP], Taishi Kayano[SUP] 1 [/SUP], Katsuma Hayashi[SUP] 1 [/SUP], Tetsuro Kobayashi[SUP] 1 [/SUP], Hiroshi Nishiura[SUP] 2 [/SUP]
Affiliations
- PMID: 41564542
- DOI: 10.1016/j.jiph.2026.103140
Background: During the COVID-19 pandemic, several divergent mutants including the Omicron (B.1.1.529) BA.1 variant emerged, having a distinct mechanism of emergence compared with pre-existing variants of concern. Apart from playing a major role in causing recurrent epidemic waves, the highly divergent mutants also contributed to changing the fate of the pandemic by exhibiting large differences in phenotypic characteristics among even closely related variants. Given that several different variants emerged during the pandemic, the present study aimed to quantitatively evaluate the risk of emergence of divergent severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) mutants and understand the mechanism of such emergence.
Methods: Upon identifying the emergence of phylogenetically distinct variants in "Omicron-like events" that have been recognized to date, a time-to-event analysis was carried out to estimate the monthly hazard rate of emergence. Four statistical models were established and compared using the Akaike Information Criterion.
Results: The model using the number of hospitalized cases was determined to be the best fit. The risk of Omicron-like events is not independent of time; instead, the monthly risk of emergence is likely to increase over time due to an increasing number of infection events.
Conclusions: Ongoing virus genomic surveillance is vital, and possible prevention among immunosuppressed individuals should be considered.
Keywords: Evolution; Mathematical model; Mutation; Severe acute respiratory syndrome coronavirus 2; Statistical estimation; Survival analysis.