PreprintCited by 1,037SARS-CoV-2 and COVID-19 ResearchCOVID-19 epidemiological studiesCOVID-19 Clinical Research Studies

The DOI matches its Crossref record; 25 of the 25 references it cites were found in a registry or an index; none of the 21 sources its claims cite is checked for support yet.

Increased risk of SARS-CoV-2 reinfection associated with emergence of Omicron in South Africa

Open access

Abstract

Summary: Introduction: Globally, there have been more than 404 million cases of SARS-CoV-2, with 5.8 million confirmed deaths, as of February 2022. South Africa has experienced four waves of SARS-CoV-2 transmission, with the second, third, and fourth waves being driven by the Beta, Delta, and Omicron variants, respectively. A key question with the emergence of new variants is the extent to which they are able to reinfect those who have had a prior natural infection. Rationale: We developed two approaches to monitor routine epidemiological surveillance data to examine whether SARS-CoV-2 reinfection risk has changed through time in South Africa, in the context of the emergence of the Beta (B.1.351), Delta (B.1.617.2), and Omicron (B.1.1.529) variants. We analyze line list data on positive tests for SARS-CoV-2 with specimen receipt dates between 04 March 2020 and 31 January 2022, collected through South Africa’s National Notifiable Medical Conditions Surveillance System. Individuals having sequential positive tests at least 90 days apart were considered to have suspected reinfections. Our routine monitoring of reinfection risk included comparison of reinfection rates to the expectation under a null model (approach 1) and estimation of the time-varying hazards of infection and reinfection throughout the epidemic (approach 2) based on model-based reconstruction of the susceptible populations eligible for primary and second infections. Results: 105,323 suspected reinfections were identified among 2,942,248 individuals with laboratory-confirmed SARS-CoV-2 who had a positive test result at least 90 days prior to 31 January 2022. The number of reinfections observed through the end of the third wave in September 2021 was consistent with the null model of no change in reinfection risk (approach 1). Although increases in the hazard of primary infection were observed following the introduction of both the Beta and Delta variants, no corresponding increase was observed in the reinfection hazard (approach 2). Contrary to expectation, the estimated hazard ratio for reinfection versus primary infection was lower during waves driven by the Beta and Delta variants than for the first wave (relative hazard ratio for wave 2 versus wave 1: 0.71 (CI 95 : 0.60–0.85); for wave 3 versus wave 1: 0.54 (CI 95 : 0.45–0.64)). In contrast, the recent spread of the Omicron variant has been associated with an increase in reinfection hazard coefficient. The estimated hazard ratio for reinfection versus primary infection versus wave 1 was 1.75 (CI 95 : 1.48–2.10) for the period of Omicron emergence (01 November 2021 to 30 November 2021) and 1.70 (CI 95 : 1.44–2.04) for wave 4 versus wave 1. Individuals with identified reinfections since 01 November 2021 had experienced primary infections in all three prior waves, and an increase in third infections has been detected since mid-November 2021. Many individuals experiencing third infections had second infections during the third (Delta) wave that ended in September 2021, strongly suggesting that these infections resulted from immune evasion rather than waning immunity. Conclusion: Population-level evidence suggests that the Omicron variant is associated with substantial ability to evade immunity from prior infection. In contrast, there is no population-wide epidemiological evidence of immune escape associated with the Beta or Delta variants. This finding has important implications for public health planning, particularly in countries like South Africa with high rates of immunity from prior infection. Further development of methods to track reinfection risk during pathogen emergence, including refinements to assess the impact of waning immunity, account for vaccine-derived protection, and monitor the risk of multiple reinfections will be an important tool for future pandemic preparedness.

Overview

AI snapshot

Written from the paper’s full text.

TL;DR

Using national SARS-CoV-2 surveillance data from South Africa, the authors found no population-level evidence of increased reinfection risk associated with Beta or Delta, but clear evidence of immune evasion by Omicron in previously infected individuals. The increase in reinfections began in November 2021 and was temporally consistent with Omicron’s emergence.

Key findings

  • No evidence of increased reinfection risk was associated with circulation of Beta (B.1.351) or Delta (B.1.617.2); in contrast, reinfections rose sharply with Omicron (B.1.1.529), with observed reinfections exceeding the 95% projection interval from the null model from early November 2021.
  • Among 105,323 individuals with at least two suspected infections, 1,778 had at least three suspected infections and 18 had four; 1,492 of those with multiple reinfections (83.9%) experienced their third infection after 31 October 2021.
  • During the fourth wave, the 7-day moving average of suspected second infections reached nearly 2,700, and all suspected reinfections reached approximately 2,750.
  • The mean ratio of reinfection hazard to primary infection hazard increased to 0.25 for 01 November 2021 to the beginning of the fourth wave and 0.27 during the fourth wave; in a generalized linear mixed model, the relative hazard ratio was 1.75 (CI95: 1.48–2.10) for Omicron emergence versus wave 1 and 1.70 (CI95: 1.44–2.04) for wave 4 versus wave 1.

Methods

  • The study used routinely collected National Institute for Communicable Diseases (NICD) line list data with specimen receipt dates from 04 March 2020 to 31 January 2022; a suspected reinfection was a positive SARS-CoV-2 test at least 90 days after the most recent positive test.
  • Two approaches were used: a catalytic null model assuming a constant reinfection hazard proportional to the 7-day moving average of detected infections, fitted through 28 February 2021 and projected forward; and a time-varying hazard reconstruction of susceptible groups, plus a Poisson generalized linear mixed model with log link to estimate relative hazard by wave.
  • Analyses were conducted in R version 4.0.5, with descriptive analysis of time between successive positive tests and of individuals with multiple suspected reinfections.

Limitations

  • The paper states that changes in testing practices, health-seeking behavior, or access to care were not accounted for; rapid antigen tests may be under-reported; civil unrest in July 2021 disrupted testing in Gauteng and KwaZulu-Natal and violated the model assumption that force of infection is proportional to positive tests; reinfections were not confirmed by sequencing or a negative test between episodes; vaccination status was unknown and coverage was low (22.5% fully vaccinated by 30 November 2021); and estimates of the extent of immune evasion should be treated with caution.
  • The paper also states that it aimed to detect changing trends rather than precisely estimate reinfection risk, and that undetected primary infections, undetected reinfections, and deaths not linked to test data could inflate the estimated denominator at risk and reduce apparent reinfection risk.
  • Evident from the text: the temporal-trend analyses considered only suspected second infections, while third and fourth infections were described descriptively; observation probabilities were assumed (for example, pobs = 0.1 and pobs2 = 0.5), and the authors note that absolute hazard values are sensitive to these assumptions.

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Integrity signals

Registry

Crossref · 10.1101/2021.11.11.21266068

Cites

25 of 25 references verified0 citation errors · 0 not checkable

Statistics

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Figures

Fig. 1. Daily numbers of detected primary infections, individuals eligible to be considered for reinfection, and suspected reinfections in South Africa. (A) Time series of detected primary infections. Black line indicates 7-day moving average; black points are daily values. Colored bands represent wave periods, defined as the period for which the 7-day moving average of cases (detected infections and reinfections) was at least 15% of the corresponding wave peak (purple = wave 1, pink = wave 2, orange = wave 3, turquoise = wave 4). (B) Population at risk for reinfection (individuals who tested positive at least 90 days ago and have not yet had a suspected reinfection). (C) Time series of suspected second infections. Blue line indicates 7-day moving average; blue points are daily values.
Fig. 1. Daily numbers of detected primary infections, individuals eligible to be considered for reinfection, and suspected reinfections in South Africa. (A) Time series of detected primary infections. Black line indicates 7-day moving average; black points are daily values. Colored bands represent wave periods, defined as the period for which the 7-day moving average of cases (detected infections and reinfections) was at least 15% of the corresponding wave peak (purple = wave 1, pink = wave 2, orange = wave 3, turquoise = wave 4). (B) Population at risk for reinfection (individuals who tested positive at least 90 days ago and have not yet had a suspected reinfection). (C) Time series of suspected second infections. Blue line indicates 7-day moving average; blue points are daily values.CC BY · PubMed Central
Fig. 2. Time between consecutive infections, based on the time between successive positive tests. Note that the time since the previous positive test must be at least 90 days to be considered a reinfection. (A) Time in days between the last positive test of the putative first infection and the first positive test of the suspected second infection. (B) Time in days between the last positive test of the putative second infection and the first positive tests of the suspected third infection. Colors represent suspected reinfections diagnosed on or after 01 November 2021. In both panels, bars for these individuals are colored by the wave during which the previous infection occurred (purple = wave 1, pink = wave 2, orange = wave 3, light grey = inter-wave).
Fig. 2. Time between consecutive infections, based on the time between successive positive tests. Note that the time since the previous positive test must be at least 90 days to be considered a reinfection. (A) Time in days between the last positive test of the putative first infection and the first positive test of the suspected second infection. (B) Time in days between the last positive test of the putative second infection and the first positive tests of the suspected third infection. Colors represent suspected reinfections diagnosed on or after 01 November 2021. In both panels, bars for these individuals are colored by the wave during which the previous infection occurred (purple = wave 1, pink = wave 2, orange = wave 3, light grey = inter-wave).CC BY · PubMed Central
Fig. 3. Timing of infections for individuals with multiple suspected reinfections. Circles represent the first positive test of the first detected infection; triangles represent the first positive test of the suspected second infection; squares represent the first positive test of the suspected third infection; crosses represent the first positive test of the suspected fourth infection. Colored bands represent wave periods, defined as the period for which the 7-day moving average of cases was at least 15% of the corresponding wave peak (purple = wave 1, pink = wave 2, orange = wave 3, turquoise = wave 4).
Fig. 3. Timing of infections for individuals with multiple suspected reinfections. Circles represent the first positive test of the first detected infection; triangles represent the first positive test of the suspected second infection; squares represent the first positive test of the suspected third infection; crosses represent the first positive test of the suspected fourth infection. Colored bands represent wave periods, defined as the period for which the 7-day moving average of cases was at least 15% of the corresponding wave peak (purple = wave 1, pink = wave 2, orange = wave 3, turquoise = wave 4).CC BY · PubMed Central
Fig. 4. Observed and expected temporal trends in reinfection numbers. Blue lines (points) represent the 7-day moving average (daily values) of suspected reinfections. Grey lines (bands) represent median predictions (95% projection intervals) from the null model. (A and C) The null model was fit to data on suspected reinfections through 28 February 2021. (B and D) Comparison of data to projections from the null model over the projection period. The divergence observed reinfections from the projection interval in November suggests immune escape. (A) and (B): National. (C) and (D) Gauteng.
Fig. 4. Observed and expected temporal trends in reinfection numbers. Blue lines (points) represent the 7-day moving average (daily values) of suspected reinfections. Grey lines (bands) represent median predictions (95% projection intervals) from the null model. (A and C) The null model was fit to data on suspected reinfections through 28 February 2021. (B and D) Comparison of data to projections from the null model over the projection period. The divergence observed reinfections from the projection interval in November suggests immune escape. (A) and (B): National. (C) and (D) Gauteng.CC BY · PubMed Central
Fig. 5. Estimates of infection and reinfection hazards. (A) Estimated time-varying hazard coefficients for primary infection (black) and second infections (blue). Colored bands represent wave periods, defined as the period for which the 7-day moving average of cases was at least 15% of the corresponding wave peak (purple = wave 1, pink = wave 2, orange = wave 3, turquoise = wave 4). (B) Ratio of the estimated hazard for reinfections to the estimated hazard for primary infections.
Fig. 5. Estimates of infection and reinfection hazards. (A) Estimated time-varying hazard coefficients for primary infection (black) and second infections (blue). Colored bands represent wave periods, defined as the period for which the 7-day moving average of cases was at least 15% of the corresponding wave peak (purple = wave 1, pink = wave 2, orange = wave 3, turquoise = wave 4). (B) Ratio of the estimated hazard for reinfections to the estimated hazard for primary infections.CC BY · PubMed Central

Verification

Claims the paper makes

25 Own result · 16 Own method · 14 Not checked yet · 11 Own interpretation · 2 No basis given

Verify all 21 references — free account
  • It may state two separate claims as one. 25
  • Probably not a substantial claim. 16
36 of 36
  • The authors state this as an assumption.

    The reinfection hazard coefficient is constant through time in the null model.

    “under the assumption of a constant reinfection hazard coefficient (i.e., a null model of no change in reinfection risk)”
  • The authors state this as an assumption.

    The reinfection hazard is proportional to the 7-day moving average of the total number of diagnosed infections (primary infections and reinfections).

    “The model assumes the reinfection hazard is proportional to the 7-day moving average of the total number of diagnosed infections (primary infections and reinfections).”
  • The authors state this as an assumption.

    The force of infection is proportional to the number of positive tests.

    “a key assumption of our models - that the force of infection is proportional to the number of positive tests - was violated during this period”
  • The authors state this as an assumption.

    Observation probabilities for primary infections and reinfections are assumed for the hazard estimates.

    “The absolute values of the hazard coefficients and hazard ratio are sensitive to assumed observation probabilities for primary infections and reinfections”
  • The authors state this as an assumption.

    The reinfection hazard experienced by previously diagnosed individuals is proportional to the incidence of detected infections.

    “First, we constructed a simple null model based on the assumption that the reinfection hazard experienced by previously diagnosed individuals is proportional to the incidence of detected infections and fit this model to the pattern of suspected second infections observed through 28 February 2021.”
  • The authors state this as an assumption.

    The reinfection hazard coefficient does not change through time.

    “The null model assumes no change in the reinfection hazard coefficient through time.”
  • The authors state this as an assumption.

    For a case testing positive on day t, the reinfection hazard is 0 for each day from t+1 to t+89 and λI^τ for each day τ≥t+90.

    “For a case testing positive on day t (by specimen receipt date), we assumed the reinfection hazard is 0 for each day from t+1 to t+89 and λI^τ for each day τ≥t+90 , where I^τ is the 7-day moving average of the detected case incidence (first infections and reinfections) for day τ .”
  • The authors state this as an assumption.

    The observed reinfection incidence data are negative binomially distributed with mean Dx.

    “The model was fitted to observed reinfection incidence through 28 February 2021 assuming data are negative binomially distributed with mean Dx .”
  • The authors state this as an assumption.

    Individuals whose first infection was missed experience the same infection hazard as individuals whose primary infection was diagnosed and who have not yet been reinfected.

    “These individuals are assumed to experience the same infection hazard as individuals whose primary infection was diagnosed and who have not yet been reinfected, estimated as h2t=X^t/pobs2S2t .”
  • The authors state this as an assumption.

    Individuals are not eligible for reinfection until at least 90 days after their primary infection.

    “Because individuals are not eligible for reinfection until at least 90 days after their primary infection, we set Ut=h2t=0 when t<90 .”
  • The authors state this as an assumption.

    The hazard of infection is proportional to the 7-day moving average of infection incidence.

    “If we assume that the hazard of infection is proportional to the 7-day moving average of infection incidence ( Yt^=P^t+U^t+Xt^/pobs2 ), we can then examine the infectiousness of the virus through time as λ1t=h1t/Yt^ and λ2t=h2t/Yt^ .”
  • The authors state this as an assumption.

    The probability of detection for primary infections (pobs) is 0.1 and for individuals with a previously identified infection (pobs2) is 0.5.

    “For this analysis, we assume pobs=0.1 and pobs2=0.5 , which falls within the plausible range of observation probabilities (fig. S8).”
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Citations

Cites

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Research works printed but not linked: 3

  1. 10.1016/j.cell.2021.06.020/attachment/10183911-f97e-4da5-ae05-227854f207f4/mmc1.pdf · DOI
  2. 10.1101/2021.11.11.21266068v1 · DOI
  3. Sahai H , Khurshid A. Statistics in Epidemiology: Methods, Techniques and Applications. 1st ed. CRC Press; 1995. 352 p.

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Metadata

Authors
Pulliam, J. R. C., van Schalkwyk, C., Govender, N., Gottberg, A. V., Cohen, C., Groome, M. J., Dushoff, J., Mlisana, K., & Moultrie, H.
Affiliations
  • Stellenbosch University (ZA)
  • National Health Laboratory Service (ZA)
Corresponding author
Pulliam, Juliet R.C.
Venue
Science
Publisher
openRxiv
Published
2021-11-11
Volume
376
Issue
6593
Pages
eabn4947-eabn4947
ISSN
0036-8075 (ISSN-L)
OpenAlex
W4220987557
Cited by
1,037
References
25
Citations by year
2026: 24 · 2025: 48 · 2024: 161 · 2023: 260 · 2022: 485 · 2021: 58
FWCI
116.60
Type
journal article
Language
en
Open access
Licence
cc-by
Locations
  • Science (publishedVersion, cc-by)
  • medRxiv (acceptedVersion, cc-by)
  • PubMed Central (publishedVersion, cc-by)
  • PubMed (publishedVersion)
Funders
  • Wellcome Trust (221003/Z/20/Z)
  • Department of Science and Innovation, South Africa
  • National Research Foundation
  • Foreign, Commonwealth and Development Office
Topics
  • Health Sciences › Medicine › Infectious Diseases › SARS-CoV-2 and COVID-19 Research
  • Physical Sciences › Mathematics › Modeling and Simulation › COVID-19 epidemiological studies
  • Health Sciences › Medicine › Infectious Diseases › COVID-19 Clinical Research Studies
Keywords
SARS-CoV-2 reinfection, Omicron variant, immune evasion, Beta variant, Delta variant
MeSH terms
Reinfection, COVID-19, SARS-CoV-2, Humans, South Africa

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