SARS-CoV-2 incidence, transmission and reinfection in a rural and an urban setting: results of the PHIRST-C cohort study, South Africa, 2020-2021

Long-Term Follow-Up of COVID-19 Convalescents-Immune Response Associated with Reinfection Rate and Symptoms

SARS-CoV-2 has spread worldwide, causing millions of deaths and leaving a significant proportion of people with long-term sequelae of COVID-19 ("post-COVID syndrome"). Whereas the precise mechanism of post-COVID syndrome is still unknown, the immune response after the first infection may play a role. Here, we performed a long-term follow-up analysis of 110 COVID-19 convalescents, analyzing the first SARS-CoV-2-directed immune response, vaccination status, long-term symptoms (approximately 2.5 years after first infection), and reinfections. A total of 96% of convalescents were vaccinated at least once against SARS-CoV-2 after their first infection. A reinfection rate of 47% was observed, and lower levels of anti-spike IgG antibodies after the first infection were shown to associate with reinfection. While T-cell responses could not be clearly associated with persistent postinfectious symptoms, convalescents with long-term symptoms showed elevated SARS-CoV-2-specific antibody levels at the first infection. Evaluating the immune response after the first infection might be a useful tool for identifying individuals with increased risk for re-infections and long-term symptoms.

Colonization and transmission of Staphylococcus aureus in schools: a citizen science project

Aggregation of children in schools has been established to be a key driver of transmission of infectious diseases. Mathematical models of transmission used to predict the impact of control measures, such as vaccination and testing, commonly depend on self-reported contact data. However, the link between self-reported social contacts and pathogen transmission has not been well described. To address this, we used Staphylococcus aureus as a model organism to track transmission within two secondary schools in England and test for associations between self-reported social contacts, test positivity and the bacterial strain collected from the same students. Students filled out a social contact survey and their S. aureus colonization status was ascertained through self-administered swabs from which isolates were sequenced. Isolates from the local community were also sequenced to assess the representativeness of school isolates. A low frequency of genome-linked transmission precluded a formal analysis of links between genomic and social networks, suggesting that S. aureus transmission within schools is too rare to make it a viable tool for this purpose. Whilst we found no evidence that schools are an important route of transmission, increased colonization rates found within schools imply that school-age children may be an important source of community transmission.

Emergence and spread of two SARS-CoV-2 variants of interest in Nigeria

Identifying the dissemination patterns and impacts of a virus of economic or health importance during a pandemic is crucial, as it informs the public on policies for containment in order to reduce the spread of the virus. In this study, we integrated genomic and travel data to investigate the emergence and spread of the SARS-CoV-2 B.1.1.318 and B.1.525 (Eta) variants of interest in Nigeria and the wider Africa region. By integrating travel data and phylogeographic reconstructions, we find that these two variants that arose during the second wave in Nigeria emerged from within Africa, with the B.1.525 from Nigeria, and then spread to other parts of the world. Data from this study show how regional connectivity of Nigeria drove the spread of these variants of interest to surrounding countries and those connected by air-traffic. Our findings demonstrate the power of genomic analysis when combined with mobility and epidemiological data to identify the drivers of transmission, as bidirectional transmission within and between African nations are grossly underestimated as seen in our import risk index estimates.

Estimation of disease burden and clinical severity of COVID-19 caused by Omicron BA.2 in Shanghai, February-June 2022

An outbreak of COVID-19 caused by the SARS-CoV-2 Omicron BA.2 sublineage occurred in Shanghai, China from February 26 to June 30, 2022. We use official reported data retrieved from Shanghai municipal Health Commissions to estimate the incidence of infections, severe/critical infections, and deaths to assess the disease burden. By adjusting for right censoring and RT PCR sensitivity, we provide estimates of clinical severity, including the infection fatality ratio, symptomatic case fatality ratio, and risk of developing severe/critical disease upon infection. The overall infection rate, severe/critical infection rate, and mortality rate were 2.74 (95% CI: 2.73-2.74) per 100 individuals, 6.34 (95% CI: 6.02-6.66) per 100,000 individuals and 2.42 (95% CI: 2.23-2.62) per 100,000 individuals, respectively. The severe/critical infection rate and mortality rate increased with age, noted in individuals aged 80 years or older. The overall fatality ratio and risk of developing severe/critical disease upon infection were 0.09% (95% CI: 0.09-0.10%) and 0.27% (95% CI: 0.24-0.29%), respectively. Having received at least one vaccine dose led to a 10-fold reduction in the risk of death for infected individuals aged 80 years or older. Under the repeated population-based screenings and strict intervention policies implemented in Shanghai, our results found a lower disease burden and mortality of the outbreak compared to other settings and countries, showing the impact of the successful outbreak containment in Shanghai. The estimated low clinical severity of this Omicron BA.2 epidemic in Shanghai highlight the key contribution of vaccination and availability of hospital beds to reduce the risk of death.

Estimating the time-varying reproduction number for COVID-19 in South Africa during the first four waves using multiple measures of incidence for public and private sectors across four waves

Objectives

We aimed to quantify transmission trends in South Africa during the first four waves of the COVID-19 pandemic using estimates of the time-varying reproduction number (R) and to compare the robustness of R estimates based on three different data sources and using data from public and private sector service providers.

Methods

We estimated R from March 2020 through April 2022, nationally and by province, based on time series of rt-PCR-confirmed cases, hospitalizations, and hospital-associated deaths, using a method which models daily incidence as a weighted sum of past incidence. We also estimated R separately using public and private sector data.

Results

Nationally, the maximum case-based R following the introduction of lockdown measures was 1.55 (CI: 1.43-1.66), 1.56 (CI: 1.47-1.64), 1.46 (CI: 1.38-1.53) and 3.33 (CI: 2.84-3.97) during the first (Wuhan-Hu), second (Beta), third (Delta), and fourth (Omicron) waves respectively. Estimates based on the three data sources (cases, hospitalisations, deaths) were generally similar during the first three waves but case-based estimates were higher during the fourth wave. Public and private sector R estimates were generally similar except during the initial lockdowns and in case-based estimates during the fourth wave.

Discussion

Agreement between R estimates using different data sources during the first three waves suggests that data from any of these sources could be used in the early stages of a future pandemic. High R estimates for Omicron relative to earlier waves is interesting given a high level of exposure pre-Omicron. The agreement between public and private sector R estimates highlights the fact that clients of the public and private sectors did not experience two separate epidemics, except perhaps to a limited extent during the strictest lockdowns in the first wave.

Epidemiological characteristics and transmission dynamics of the outbreak caused by the SARS-CoV-2 Omicron variant in Shanghai, China: a descriptive study

Background

In early March 2022, a major outbreak of the severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) Omicron variant spread rapidly throughout Shanghai, China. Here we aimed to provide a description of the epidemiological characteristics and spatiotemporal transmission dynamics of the Omicron outbreak under the population-based screening and lockdown policies implemented in Shanghai.

Methods

We extracted individual information on SARS-CoV-2 infections reported between January 1 and May 31, 2022, and on the timeline of the adopted non-pharmacological interventions. The epidemic was divided into three phases: i) sporadic infections (January 1-February 28), ii) local transmission (March 1-March 31), and iii) city-wide lockdown (April 1 to May 31). We described the epidemic spread during these three phases and the subdistrict-level spatiotemporal distribution of the infections. To evaluate the impact on the transmission of SARS-CoV-2 of the adopted targeted interventions in Phase 2 and city-wide lockdown in Phase 3, we estimated the dynamics of the net reproduction number ( R _t ).

Findings

A surge in imported infections in Phase 1 triggered cryptic local transmission of the Omicron variant in early March, resulting in the largest coronavirus disease 2019 (COVID-19) outbreak in mainland China since the original wave. A total of 626,000 SARS-CoV-2 infections were reported in 99.5% (215/216) of the subdistricts of Shanghai. The spatial distribution of the infections was highly heterogeneous, with 40% of the subdistricts accounting for 80% of all infections. A clear trend from the city center towards adjacent suburban and rural areas was observed, with a progressive slowdown of the epidemic spread (from 544 to 325 meters/day) prior to the citywide lockdown. During Phase 2, R _t remained well above 1 despite the implementation of multiple targeted interventions. The citywide lockdown imposed on April 1 led to a marked decrease in transmission, bringing R _t below the epidemic threshold in the entire city on April 14 and ultimately leading to containment of the outbreak.

Interpretation

Our results highlight the risk of widespread outbreaks in mainland China, particularly under the heightened pressure of imported infections. The targeted interventions adopted in March 2022 were not capable of halting transmission, and the implementation of a strict, prolonged city-wide lockdown was needed to successfully contain the outbreak, highlighting the challenges for successfully containing Omicron outbreaks.

Funding

Key Program of the National Natural Science Foundation of China (82130093).

Research in context

Evidence before this study: On May 24, 2022, we searched PubMed and Europe PMC for papers published or posted on preprint servers after January 1, 2022, using the following query: ("SARS-CoV-2" OR "Omicron" OR "BA.2") AND ("epidemiology" OR "epidemiological" OR "transmission dynamics") AND ("Shanghai"). A total of 26 studies were identified; among them, two aimed to describe or project the spread of the 2022 Omicron outbreak in Shanghai. One preprint described the epidemiological and clinical characteristics of 376 pediatric SARS-CoV-2 infections in March 2022, and the other preprint projected the epidemic progress in Shanghai, without providing an analysis of field data. In sum, none of these studies provided a comprehensive description of the epidemiological characteristics and spatiotemporal transmission dynamics of the outbreak.Added value of this study: We collected individual information on SARS-CoV-2 infection and the timeline of the public health response. Population-based screenings were repeatedly implemented during the outbreak, which allowed us to investigate the spatiotemporal spread of the Omicron BA.2 variant as well as the impact of the implemented interventions, all without enduring significant amounts of underreporting from surveillance systems, as experienced in other areas. This study provides the first comprehensive assessment of the Omicron outbreak in Shanghai, China.Implications of all the available evidence: This descriptive study provides a comprehensive understanding of the epidemiological features and transmission dynamics of the Omicron outbreak in Shanghai, China. The empirical evidence from Shanghai, which was ultimately able to curtail the outbreak, provides invaluable information to policymakers on the impact of the containment strategies adopted by the Shanghai public health officials to prepare for potential outbreaks caused by Omicron or novel variants.

SARS-CoV-2 Infection Is Associated with Uncontrolled HIV Viral Load in Non-Hospitalized HIV-Infected Patients from Gugulethu, South Africa

In South Africa, high exposure to SARS-CoV-2 occurs primarily in densely populated, low-income communities, which are additionally burdened by highly prevalent Human Immunodeficiency Virus (HIV). With the aim to assess SARS-CoV-2 seroprevalence and its association with HIV-related clinical parameters in non-hospitalized patients likely to be highly exposed to SARS-CoV-2, this observational cross-sectional study was conducted at the Gugulethu Community Health Centre Antiretroviral clinic between October 2020 and June 2021, after the first COVID-19 wave in South Africa and during the second and beginning of the third wave. A total of 150 adult (median age 39 years [range 20−65 years]) HIV-infected patients (69% female; 31% male) were recruited. 95.3% of the cohort was on antiretroviral therapy (ART), had a median CD4 count of 220 cells/µL (range 17−604 cells/µL) and a median HIV viral load (VL) of 49 copies/mL (range 1−1,050,867 copies/mL). Furthermore, 106 patients (70.7%) were SARS-CoV-2 seropositive, and 0% were vaccinated. When stratified for HIV VL, patients with uncontrolled HIV viremia (HIV VL > 1000 copies/mL) had significantly higher odds of SARS-CoV-2 seropositivity than patients with HIV VL < 1000 copies/mL, after adjusting for age, sex and ART status (p = 0.035, adjusted OR 2.961 [95% CI: 1.078−8.133]). Although the cause−effect relationship could not be determined due to the cross-sectional study design, these results point towards a higher risk of SARS-CoV-2 susceptibility among viremic HIV patients, or impaired HIV viral control due to previous co-infection with SARS-CoV-2.

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

We provide two methods for monitoring reinfection trends in routine surveillance data to identify signatures of changes in reinfection risk and apply these approaches to data from South Africa's severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) epidemic to date. Although we found no evidence of increased reinfection risk associated with circulation of the Beta (B.1.351) or Delta (B.1.617.2) variants, we did find clear, population-level evidence to suggest immune evasion by the Omicron (B.1.1.529) variant in previously infected individuals in South Africa. Reinfections occurring between 1 November 2021 and 31 January 2022 were detected in individuals infected in all three previous waves, and there has been an increase in the risk of having a third infection since mid-November 2021.

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

We provide two methods for monitoring reinfection trends in routine surveillance data to identify signatures of changes in reinfection risk and apply these approaches to data from South Africa's severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) epidemic to date. Although we found no evidence of increased reinfection risk associated with circulation of the Beta (B.1.351) or Delta (B.1.617.2) variants, we did find clear, population-level evidence to suggest immune evasion by the Omicron (B.1.1.529) variant in previously infected individuals in South Africa. Reinfections occurring between 1 November 2021 and 31 January 2022 were detected in individuals infected in all three previous waves, and there has been an increase in the risk of having a third infection since mid-November 2021.

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

We provide two methods for monitoring reinfection trends in routine surveillance data to identify signatures of changes in reinfection risk and apply these approaches to data from South Africa's severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) epidemic to date. Although we found no evidence of increased reinfection risk associated with circulation of the Beta (B.1.351) or Delta (B.1.617.2) variants, we did find clear, population-level evidence to suggest immune evasion by the Omicron (B.1.1.529) variant in previously infected individuals in South Africa. Reinfections occurring between 1 November 2021 and 31 January 2022 were detected in individuals infected in all three previous waves, and there has been an increase in the risk of having a third infection since mid-November 2021.

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

We provide two methods for monitoring reinfection trends in routine surveillance data to identify signatures of changes in reinfection risk and apply these approaches to data from South Africa's severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) epidemic to date. Although we found no evidence of increased reinfection risk associated with circulation of the Beta (B.1.351) or Delta (B.1.617.2) variants, we did find clear, population-level evidence to suggest immune evasion by the Omicron (B.1.1.529) variant in previously infected individuals in South Africa. Reinfections occurring between 1 November 2021 and 31 January 2022 were detected in individuals infected in all three previous waves, and there has been an increase in the risk of having a third infection since mid-November 2021.

The REinfection in COVID-19 Estimation of Risk (RECOVER) study: Reinfection and serology dynamics in a cohort of Canadian healthcare workers

Background

Understanding the immune response to natural infection by SARS‐CoV‐2 is key to pandemic management, especially in the current context of emerging variants. Uncertainty remains regarding the efficacy and duration of natural immunity against reinfection.

Methods

We conducted an observational prospective cohort study in Canadian healthcare workers (HCWs) with a history of PCR‐confirmed SARS‐CoV‐2 infection to (i) measure the average incidence rate of reinfection and (ii) describe the serological immune response to the primary infection.

Results

Our cohort comprised 569 HCWs; median duration of individual follow‐up was 371 days. We detected six cases of reinfection in absence of vaccination between August 21, 2020, and March 1, 2022, for a reinfection incidence rate of 4.0 per 100 person‐years. Median duration of seropositivity was 415 days in symptomatics at primary infection compared with 213 days in asymptomatics (p < 0.0001). Other characteristics associated with prolonged seropositivity for IgG against the spike protein included age over 55 years, obesity, and non‐Caucasian ethnicity.

Conclusions

Among unvaccinated healthcare workers, reinfection with SARS‐CoV‐2 following a primary infection remained rare.

SARS‐CoV‐2 reinfections remained rare events among unvaccinated healthcare workers. Prior natural infection confers some protection against reinfection and clinical disease, but waning of serum antibodies suggests this protection may not last in the long term.

Household Secondary Attack Rates of SARS-CoV-2 by Variant and Vaccination Status: An Updated Systematic Review and Meta-analysis

IMPORTANCE

An overall household secondary attack rate (SAR) of 18.9% (95% CI, 16.2%-22.0%) through June 17, 2021 was previously reported for SARS-CoV-2. Emerging variants of concern and increased vaccination have affected transmission rates.

OBJECTIVE

To evaluate how reported household SARs changed over time and whether SARs varied by viral variant and index case and contact vaccination status.

DATA SOURCES

PubMed and medRxiv from June 18, 2021, through March 8, 2022, and reference lists of eligible articles. Preprints were included.

STUDY SELECTION

Articles with original data reporting the number of infected and total number of household contacts. Search terms included SARS-CoV-2, COVID-19, variant, vaccination, secondary attack rate, secondary infection rate, household, index case, family contacts, close contacts, and family transmission.

DATA EXTRACTION AND SYNTHESIS

The Preferred Reporting Items for Systematic Reviews and Meta-Analyses reporting guideline was followed. Meta-analyses used generalized linear mixed models to obtain SAR estimates and 95% CIs.

MAIN OUTCOMES AND MEASURES

SAR stratified by covariates according to variant, index case and contact vaccination status, and index case identification period. SARs were used to estimate vaccine effectiveness on the basis of the transmission probability for susceptibility to infection (VES,p), infectiousness given infection (VEI,p), and total vaccine effectiveness (VET,p).

RESULTS

Household SARs were higher for 33 studies with midpoints in 2021 to 2022 (37.3%; 95% CI, 32.7% to 42.1%) compared with 63 studies with midpoints through April 2020 (15.5%; 95% CI, 13.2% to 18.2%). Household SARs were 42.7% (95% CI, 35.4% to 50.4%) for Omicron (7 studies), 36.4% (95% CI, 33.4% to 39.5%) for Alpha (11 studies), 29.7% (95% CI, 23.0% to 37.3%) for Delta (16 studies), and 22.5% (95% CI, 18.6% to 26.8%) for Beta (3 studies). For full vaccination, VES,p was 78.6% (95% CI, 76.0% to 80.9%) for Alpha, 56.4% (95% CI, 54.6% to 58.1%) for Delta, and 18.1% (95% CI, -18.3% to 43.3%) for Omicron; VEI,p was 75.3% (95% CI, 69.9% to 79.8%) for Alpha, 21.9% (95% CI, 11.0% to 31.5%) for Delta, and 18.2% (95% CI, 0.6% to 32.6%) for Omicron; and VET,p was 94.7% (95% CI, 93.3% to 95.8%) for Alpha, 64.4% (95% CI, 58.0% to 69.8%) for Delta, and 35.8% (95% CI, 13.0% to 52.6%) for Omicron.

CONCLUSIONS AND RELEVANCE

These results suggest that emerging SARS-CoV-2 variants of concern have increased transmissibility. Full vaccination was associated with reductions in susceptibility and infectiousness, but more so for Alpha than Delta and Omicron. The changes in estimated vaccine effectiveness underscore the challenges of developing effective vaccines concomitant with viral evolution.

Household secondary attack rates of SARS-CoV-2 by variant and vaccination status: an updated systematic review and meta-analysis

We previously reported a household secondary attack rate (SAR) for SARS-CoV-2 of 18.9% through June 17, 2021. To examine how emerging variants and increased vaccination have affected transmission rates, we searched PubMed from June 18, 2021, through January 7, 2022. Meta-analyses used generalized linear mixed models to obtain SAR estimates and 95%CI, disaggregated by several covariates. SARs were used to estimate vaccine effectiveness based on the transmission probability for susceptibility ( VE S,p ), infectiousness ( VE I,p ), and total vaccine effectiveness ( VE T,p ). Household SAR for 27 studies with midpoints in 2021 was 35.8% (95%CI, 30.6%-41.3%), compared to 15.7% (95%CI, 13.3%-18.4%) for 62 studies with midpoints through April 2020. Household SARs were 38.0% (95%CI, 36.0%-40.0%), 30.8% (95%CI, 23.5%-39.3%), and 22.5% (95%CI, 18.6%-26.8%) for Alpha, Delta, and Beta, respectively. VE I,p , VE S,p , and VE T,p were 56.6% (95%CI, 28.7%-73.6%), 70.3% (95%CI, 59.3%-78.4%), and 86.8% (95%CI, 76.7%-92.5%) for full vaccination, and 27.5% (95%CI, -6.4%-50.7%), 43.9% (95%CI, 21.8%-59.7%), and 59.9% (95%CI, 34.4%-75.5%) for partial vaccination, respectively. Household contacts exposed to Alpha or Delta are at increased risk of infection compared to the original wild-type strain. Vaccination reduced susceptibility to infection and transmission to others.

SUMMARY

Household secondary attack rates (SARs) were higher for Alpha and Delta variants than previous estimates. SARs were higher to unvaccinated contacts than to partially or fully vaccinated contacts and were higher from unvaccinated index cases than from fully vaccinated index cases.

Reinfection with SARS-CoV-2 among previously infected healthcare personnel and first responders

Background

SARS-CoV-2 virus testing among first responders and healthcare personnel who participated in a May-August 2020 serosurvey which assessed spike protein antibodies (S1 region) provided an opportunity to assess reinfection.

Methods

Serology survey data were merged with virus testing results from Rhode Island (March 1, 2020-February 17, 2021) and New York City (March 10-December 14, 2020). Participants with a positive virus test ≥14 days before their serology test were included. Reinfection was defined as a second positive SARS-CoV-2 test result ≥90 days after the first positive test. The association between serostatus and reinfection was assessed with a proportional hazards model adjusting for demographics, exposures, and virus testing frequency.

Results

Among 1,572 previously infected persons, 40 (2.5%) were reinfected. Reinfection differed by serostatus: 8.4% among seronegative versus 1.9% among seropositive participants (p<0.0001). Most reinfections occurred among Rhode Island nursing home and corrections (RINHC) personnel (n=30) who were most frequently tested (mean 30.3 tests versus 4.6 for other Rhode Island and 2.3 for New York City participants). The adjusted hazard ratio (aHR) for reinfection in seropositive versus seronegative persons was 0.41 (95% CI 0.20, 0.81). Exposure to a household member with COVID-19 before the serosurvey was also protective (aHR 0.34, 95% CI 0.13, 0.89).

Conclusions

Reinfections were uncommon among previously infected persons over a 9-month period that preceded widespread variant circulation. Seropositivity decreased reinfection risk. Lower reinfection risk associated with exposure to a household member with COVID-19 before the serosurvey may reflect subsequently reduced household transmission among members of previously infected households.