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Objectives: The SARS-CoV-2 BQ.1* variant rapidly spread globally in late 2022, posing a challenge due to its increased immune evasion. Methods: We conducted a prevalence survey in Brazil from November 16 to December 22, 2022, as part of a cohort study. We conducted interviews and collected nasal samples for reverse transcription-polymerase chain reaction (RT-PCR) testing and whole-genome sequencing. Cumulative incidence was estimated using RT-PCR positivity, cycle threshold values, and external data on the dynamics of RT-PCR positivity following infection. Results: Among 535 participants, 54% had documented SARS-CoV-2 exposure before this outbreak and 74% had received COVID-19 vaccination. In this study, 14.8% tested positive for SARS-CoV-2, with BQ.1* identified in 90.7% of cases. Using case data and cycle threshold values, cumulative incidence was estimated at 56% (95% confidence interval, 36-88%). Of the 79 positive participants, 48.1% had a symptomatic illness, with a lower proportion fulfilling the World Health Organization COVID-19 case definition compared to prior Omicron waves. No participants required medical attention. Conclusions: Despite high population-level hybrid immunity, the BQ.1* variant attacked 56% of our population. Lower disease severity was associated with BQ.1* compared to prior Omicron variants. Hybrid immunity may provide protection against future SARS-CoV-2 variants but in this case was not able to prevent widespread transmission.
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Epidemics from the Eye of the Pathogen
While a common trend in disease modeling is to develop models of increasing complexity, it was recently pointed out that outbreaks appear remarkably simple when viewed in the incidence vs. cumulative cases (ICC) plane. This article details the theory behind this phenomenon by analyzing the stochastic Susceptible, Infected, Recovered (SIR) model in the cumulative cases domain. We prove that the Markov chain associated with this model reduces, in the ICC plane, to a pure birth chain for the cumulative number of cases, whose limit leads to an independent increments Gaussian process that fluctuates about a deterministic ICC curve. We calculate the associated variance and quantify the additional variability due to estimating incidence over a finite period of time. We also illustrate the universality brought forth by the ICC concept on real-world data for Influenza A and for the COVID-19 outbreak in Arizona.
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- Award ID(s):
- 2028401
- PAR ID:
- 10478514
- Publisher / Repository:
- SIAM
- Date Published:
- Journal Name:
- SIAM Journal on Applied Mathematics
- Volume:
- 82
- Issue:
- 6
- ISSN:
- 0036-1399
- Page Range / eLocation ID:
- 2036 to 2056
- Subject(s) / Keyword(s):
- epidemics stochastic modeling complexity reduction Gaussian process cumulative cases
- Format(s):
- Medium: X
- Sponsoring Org:
- National Science Foundation
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- Free Publicly Accessible Full Text
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Accepted Manuscript1.0
- Journal Article:
- https://doi.org/10.1137/21M1450719
