Assessment of the Risk of Severe Acute Respiratory Syndrome Coronavirus 2 (SARS-CoV-2) Reinfection in an Intense Reexposure Setting

Global prevalence of coronavirus disease 2019 reinfection: a systematic review and meta-analysis

BACKGROUND

In December 2019, severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) emerged with a high transmissibility rate and resulted in numerous negative impacts on global life. Preventive measures such as face masks, social distancing, and vaccination helped control the pandemic. Nonetheless, the emergence of SARS-CoV-2 variants, such as Omega and Delta, as well as coronavirus disease 2019 (COVID-19) reinfection, raise additional concerns. Therefore, this study aimed to determine the overall prevalence of reinfection on global and regional scales.

METHODS

A systematic search was conducted across three databases, PubMed, Scopus, and ProQuest Central, including all articles pertaining to COVID-19 reinfection without language restriction. After critical appraisal and qualitative synthesis of the identified relevant articles, a meta-analysis considering random effects was used to pool the studies.

RESULTS

We included 52 studies conducted between 2019 and 2022, with a total sample size of 3,623,655 patients. The overall prevalence of COVID-19 reinfection was 4.2% (95% confidence interval [CI]: 3.7-4.8%; n = 52), with high heterogeneity between studies. Africa had the highest prevalence of 4.7% (95% CI: 1.9-7.5%; n = 3), whereas Oceania and America had lower estimates of 0.3% (95% CI: 0.2-0.4%; n = 1) and 1% (95% CI: 0.8-1.3%; n = 7), respectively. The prevalence of reinfection in Europe and Asia was 1.2% (95% CI: 0.8-1.5%; n = 8) and 3.8% (95% CI: 3.4-4.3%; n = 43), respectively. Studies that used a combined type of specimen had the highest prevalence of 7.6% (95% CI: 5.8-9.5%; n = 15) compared with those that used oropharyngeal or nasopharyngeal swabs only that had lower estimates of 6.7% (95% CI: 4.8-8.5%; n = 8), and 3.4% (95% CI: 2.8-4.0%; n = 12) respectively.

CONCLUSION

COVID-19 reinfection occurs with varying prevalence worldwide, with the highest occurring in Africa. Therefore, preventive measures, including vaccination, should be emphasized to ensure control of the pandemic.

A retrospective cross-sectional observational study of SARS-CoV-2 reinfection in La Ribera Health Department, Valencia, Spain

Introduction. The possibility of reinfection by severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) is a widely proven fact and may have clinical implications.

Hypothesis /Gap Statement. It is not known whether there have been cases of reinfection by SARS-CoV-2 in La Ribera Health Department.

Aim. To determine whether there have been cases of reinfection by SARS-CoV-2 in La Ribera Health Department and to identify their characteristics.

Methodology. Retrospective cross-sectional observational study of cases of reinfection by SARS-CoV-2 in the population of La Ribera Department between March 2020 and February 2021. The positive baseline cohort includes all cases positive by RT-PCR for SARS-CoV-2, with reinfection cases being those that, after resolution of the first episode according to the World Health Organization (WHO) criteria, presented a new positive RT-PCR result.

Results. Out of a total of 15 687 cases with positive RT-PCR, 40 were considered to be reinfections, which meant a cumulative incidence of 0.255 % and an incidence density of 5.05 cases per 100 000 person-days. Most of the cases occurred during the highest incidence peaks of the pandemic in the department. Seventy-five per cent of the patients in these cases were older than 40 years, 42.5 % were healthcare professionals or nursing home residents and 12.5 % had an immunosuppressive comorbidity. There were no severe, critical or death cases. In the reinfection episodes, with respect to the first episode, there was a tendency to be milder, they required fewer days of hospitalization, their RT-PCR became negative earlier, they developed a greater humoral response and the sick leave period was shorter. The median period between the RT-PCR in the first episode and the RT-PCR in the second episode was 127.5 days (range: 48–301; IQR: 89.5–256.25)

Conclusions. SARS-CoV-2 reinfection cases are rare, tend to be mild and can occur within a median period of 127.5 days.

Added value of anti-SARS-CoV-2 antibody testing in a Flemish nursing home during an acute COVID-19 outbreak in April 2020

OBJECTIVES

To examine the added value of anti-SARS-CoV-2 antibody testing in a nursing home during an acute COVID-19 outbreak. RT-PCR is the gold standard, but can be false-negative.

METHODS

119 residents and 93 staff members were tested with RT-PCR test and/or a rapid IgM/IgG test. Of these participants, 176 had both tests, 24 only RT-PCR, and 12 only IgM/IgG in the period April 14 to 16 April 2020.

RESULTS

40 (34%) residents and 11 (13%) staff were PCR-positive. Using a rapid IgM/IgG test, 17 (17%) residents and 18 (20%) staff were positive for IgM and/or IgG (IgM/IgG). Thirty-two PCR-positive residents had an IgM/IgG test: 9 (28%), 11 (34%), and 13 (41%) were positive for IgM, IgG, and IgM/IgG. Ten PCR-positive staff had an IgM/IgG test: 3 (30%), 6 (60%), and 6 (60%) were positive for IgM, IgG, and IgM/IgG. Additional IgM/IgG tests were performed in 9 residents 11 to 14 days after the positive RT-PCR test. Of those, 7 (78%) tested positive for IgM/IgG. When retested 3 weeks later, the 2 remaining residents also tested positive. Of the 134 PCR-negative participants who had an IgM/IgG test, 15 were positive for IgM/IgG (8% of the 200 participants tested with RT-PCR).

CONCLUSIONS

During an acute outbreak in a nursing home, 26% of residents and staff were PCR-positive. An additional 8% was diagnosed using IgM/IgG antibody testing. The use of RT-PCR alone as the sole diagnostic method for surveillance during an acute outbreak is insufficient to grab the full extent of the outbreak.

Characterizing the effective reproduction number during the COVID-19 pandemic: Insights from Qatar’s experience

Background

The effective reproduction number, R_t, is a tool to track and understand pandemic dynamics. This investigation of R_t estimations was conducted to guide the national COVID-19 response in Qatar, from the onset of the pandemic until August 18, 2021.

Methods

Real-time “empirical” R_t^Empirical was estimated using five methods, including the Robert Koch Institute, Cislaghi, Systrom-Bettencourt and Ribeiro, Wallinga and Teunis, and Cori et al. methods. R_t was also estimated using a transmission dynamics model (R_t^Model-based). Uncertainty and sensitivity analyses were conducted. Correlations between different R_t estimates were assessed by calculating correlation coefficients, and agreements between these estimates were assessed through Bland-Altman plots.

Results

R_t^Empirical captured the evolution of the pandemic through three waves, public health response landmarks, effects of major social events, transient fluctuations coinciding with significant clusters of infection, and introduction and expansion of the Alpha (B.1.1.7) variant. The various estimation methods produced consistent and overall comparable R_t^Empirical estimates with generally large correlation coefficients. The Wallinga and Teunis method was the fastest at detecting changes in pandemic dynamics. R_t^Empirical estimates were consistent whether using time series of symptomatic PCR-confirmed cases, all PCR-confirmed cases, acute-care hospital admissions, or ICU-care hospital admissions, to proxy trends in true infection incidence. R_t^Model-based correlated strongly with R_t^Empirical and provided an average R_t^Empirical.

Conclusions

R_t estimations were robust and generated consistent results regardless of the data source or the method of estimation. Findings affirmed an influential role for R_t estimations in guiding national responses to the COVID-19 pandemic, even in resource-limited settings.

Long-Lasting Immunity Against SARS-CoV-2: Dream or Reality?

Since its declaration as a pandemic in March 2020, SARS-CoV-2 has infected more than 217 million people worldwide and despite mild disease in the majority of the cases, more than 4.5 million cases of COVID-19-associated death have been reported as of September 2021. The question whether recovery from COVID-19 results in prevention of reinfection can be answered with a "no" since cases of reinfections have been reported. The more important question is whether during SARS-CoV-2 infection, a protective immunity is built and maintained afterwards in a way which protects from possibly severe courses of disease in case of a reinfection. A similar question arises with respect to vaccination: as of September 2021, globally, more than 5.2 billion doses of vaccines have been administered. Therefore, it is of utmost importance to study the cellular and humoral immunity toward SARS-CoV-2 in a longitudinal manner. In this study, reconvalescent COVID-19 patients have been followed up for more than 1 year after SARS-CoV-2 infection to characterize in detail the long-term humoral as well as cellular immunity. Both SARS-CoV-2-specific T cells and antibodies could be detected for a period of more than 1 year after infection, indicating that the immune protection established during initial infection is maintained and might possibly protect from severe disease in case of reinfection or infection with novel emerging variants. Moreover, these data demonstrate the opportunity for immunotherapy of hospitalized COVID-19 patients via adoptive transfer of functional antiviral T cells isolated from reconvalescent individuals.

Complete Analysis of the Epidemiological Scenario around a SARS-CoV-2 Reinfection: Previous Infection Events and Subsequent Transmission

The first descriptions of reinfection by SARS-CoV-2 have been recently reported. However, these studies focus exclusively on the reinfected case, without considering the epidemiological context of the event. Our objectives were to perform a complete analysis of the sequential infections and community transmission events around a SARS-CoV-2 reinfection, including the infection events preceding it, the exposure, and subsequent transmissions. Our analysis was supported by host genetics, viral whole-genome sequencing, phylogenomic viral population analysis, and refined epidemiological data obtained from interviews with the involved subjects. The reinfection involved a 53-year-old woman with asthma (Case A), with a first COVID-19 episode in April 2020 and a much more severe second episode 4-1/2 months later, with SARS-CoV-2 seroconversion in August, that required hospital admission. An extended genomic analysis allowed us to demonstrate that the strain involved in Case A's reinfection was circulating in the epidemiological context of Case A and was also transmitted subsequently from Case A to her family context. The reinfection was also supported by a phylogenetic analysis, including 348 strains from Madrid, which revealed that the strain involved in the reinfection was circulating by the time Case A suffered the second episode, August-September 2020, but absent at the time range corresponding to Case A's first episode. IMPORTANCE We present the first complete analysis of the epidemiological scenario around a reinfection by SARS-CoV-2, more severe than the first episode, including three cases preceding the reinfection, the reinfected case per se, and the subsequent transmission to another seven cases.

Reinfections in COVID-19 Patients: Impact of Virus Genetic Variability and Host Immunity

The COVID-19 pandemic is still posing a devastating threat to social life and economics. Despite the modest decrease in the number of cases during September–November 2020, the number of active cases is on the rise again. This increase was associated with the emergence and spread of the new SARS-CoV-2 variants of concern (VOCs), such as the U.K. (B1.1.7), South Africa (B1.351), Brazil (P1), and Indian (B1.617.2) strains. The rapid spread of these new variants has raised concerns about the multiple waves of infections and the effectiveness of available vaccines. In this review, we discuss SARS-CoV-2 reinfection rates in previously infected and vaccinated individuals in relation to humoral responses. Overall, a limited number of reinfection cases have been reported worldwide, suggesting long protective immunity. Most reinfected patients were asymptomatic during the second episode of infection. Reinfection was attributed to several viral and/or host factors, including (i) underlying immunological comorbidities; (ii) low antibody titers due to the primary infection or vaccination; (iii) rapid decline in antibody response after infection or vaccination; and (iv) reinfection with a different SARS-CoV-2 variant/lineage. Infections after vaccination were also reported on several occasions, but mostly associated with mild or no symptoms. Overall, findings suggest that infection- and vaccine-induced immunity would protect from severe illness, with the vaccine being effective against most VOCs.

SARS-CoV-2 infection hospitalization, severity, criticality, and fatality rates in Qatar

The SARS-CoV-2 pandemic resulted in considerable morbidity and mortality as well as severe economic and societal disruptions. Despite scientific progress, true infection severity, factoring both diagnosed and undiagnosed infections, remains poorly understood. This study aimed to estimate SARS-CoV-2 age-stratified and overall morbidity and mortality rates based on analysis of extensive epidemiological data for the pervasive epidemic in Qatar, a country where < 9% of the population are ≥ 50 years. We show that SARS-CoV-2 severity and fatality demonstrate a striking age dependence with low values for those aged < 50 years, but rapidly growing rates for those ≥ 50 years. Age dependence was particularly pronounced for infection criticality rate and infection fatality rate. With Qatar's young population, overall SARS-CoV-2 severity and fatality were not high with < 4 infections in every 1000 being severe or critical and < 2 in every 10,000 being fatal. Only 13 infections in every 1000 received any hospitalization in acute-care-unit beds and < 2 in every 1000 were hospitalized in intensive-care-unit beds. However, we show that these rates would have been much higher if Qatar's population had the demographic structure of Europe or the United States. Epidemic expansion in nations with young populations may lead to considerably lower disease burden than currently believed.

A review of mathematical model-based scenario analysis and interventions for COVID-19

Mathematical model-based analysis has proven its potential as a critical tool in the battle against COVID-19 by enabling better understanding of the disease transmission dynamics, deeper analysis of the cost-effectiveness of various scenarios, and more accurate forecast of the trends with and without interventions. However, due to the outpouring of information and disparity between reported mathematical models, there exists a need for a more concise and unified discussion pertaining to the mathematical modeling of COVID-19 to overcome related skepticism. Towards this goal, this paper presents a review of mathematical model-based scenario analysis and interventions for COVID-19 with the main objectives of (1) including a brief overview of the existing reviews on mathematical models, (2) providing an integrated framework to unify models, (3) investigating various mitigation strategies and model parameters that reflect the effect of interventions, (4) discussing different mathematical models used to conduct scenario-based analysis, and (5) surveying active control methods used to combat COVID-19.

The protective effect of previous COVID-19 infection in a high-prevalence hospital setting

OBJECTIVE

To assess the protective effect of previous COVID-19 infection for healthcare workers in a high-prevalence setting.

METHOD

The COVID-19 antibody and PCR results of 538 healthcare workers on wards with COVID-19 outbreaks from 1 March 2020 to 31 July 2020 were evaluated. Infection rates of the 'previously infected' and 'no evidence of previous infection' groups were compared during second-wave outbreaks between 29 September 2020 and 20 November 2020.

RESULTS

One out of 115 individuals previously infected developed infection compared with 104 out of 423 individuals with no evidence of previous infection. Attack rates in staff previously infected was reduced significantly from 24.59% to 0.87% (odds ratio 0.027, 95% CI 0.004-0.195, p<0.001) when compared to the 'no evidence of previous infection' group with the same exposure risk.

CONCLUSION

Prior SARS-CoV-2 infection offers significant protection against reinfection and this protection lasts 4 months for the majority of individuals.

mRNA-1273 COVID-19 vaccine effectiveness against the B.1.1.7 and B.1.351 variants and severe COVID-19 disease in Qatar

The SARS-CoV-2 pandemic continues to be a global health concern. The mRNA-1273 (Moderna) vaccine was reported to have an efficacy of 94.1% at preventing symptomatic COVID-19 due to infection with 'wild-type' variants in a randomized clinical trial. Here, we assess the real-world effectiveness of this vaccine against SARS-CoV-2 variants of concern, specifically B.1.1.7 (Alpha) and B.1.351 (Beta), in Qatar, a population that comprises mainly working-age adults, using a matched test-negative, case-control study design. We show that vaccine effectiveness was negligible for 2 weeks after the first dose, but increased rapidly in the third and fourth weeks immediately before administration of a second dose. Effectiveness against B.1.1.7 infection was 88.1% (95% confidence interval (CI): 83.7-91.5%) ≥14 days after the first dose but before the second dose, and was 100% (95% CI: 91.8-100.0%) ≥14 days after the second dose. Analogous effectiveness against B.1.351 infection was 61.3% after the first dose (95% CI: 56.5-65.5%) and 96.4% after the second dose (95% CI: 91.9-98.7%). Effectiveness against any severe, critical or fatal COVID-19 disease due to any SARS-CoV-2 infection (predominantly B.1.1.7 and B.1.351) was 81.6% (95% CI: 71.0-88.8%) and 95.7% (95% CI: 73.4-99.9%) after the first and second dose, respectively. The mRNA-1273 vaccine is highly effective against B.1.1.7 and B.1.351 infections, whether symptomatic or asymptomatic, and against any COVID-19 hospitalization and death, even after a single dose.

COVID-19 (SARS-CoV-2) outbreak monitoring using wastewater-based epidemiology in Qatar

Raw municipal wastewater from five wastewater treatment plants representing the vast majority of the Qatar population was sampled between the third week of June 2020 and the end of August 2020, during the period of declining cases after the peak of the first wave of infection in May 2020. The N1 region of the SARS-CoV-2 genome was used to quantify the viral load in the wastewater using RT-qPCR. The trend in Ct values in the wastewater samples mirrored the number of new daily positive cases officially reported for the country, confirmed by RT-qPCR testing of naso-pharyngeal swabs. SARS-CoV-2 RNA was detected in 100% of the influent wastewater samples (7889 ± 1421 copy/L - 542,056 ± 25,775 copy/L, based on the N1 assay). A mathematical model for wastewater-based epidemiology was developed and used to estimate the number of people in the population infected with COVID-19 from the N1 Ct values in the wastewater samples. The estimated number of infected population on any given day using the wastewater-based epidemiology approach declined from 542,313 ± 51,159 to 31,181 ± 3081 over the course of the sampling period, which was significantly higher than the officially reported numbers. However, seroprevalence data from Qatar indicates that diagnosed infections represented only about 10% of actual cases. The model estimates were lower than the corrected numbers based on application of a static diagnosis ratio of 10% to the RT-qPCR identified cases, which is assumed to be due to the difficulty in quantifying RNA losses as a model term. However, these results indicate that the presented WBE modeling approach allows for a realistic assessment of incidence trend in a given population, with a more reliable estimation of the number of infected people at any given point in time than can be achieved using human biomonitoring alone.

The risk of symptomatic infection during a second COVID-19 wave, in SARS-CoV-2 seropositive individuals

We analyzed 221 coronavirus disease 2019 cases identified between June 2020 and January 2021 in 6074 individuals screened for immunoglobulin G antibodies in May 2020, representing 77% of residents of 5 Italian municipalities. The relative risk of developing symptomatic infection in seropositive participants was 0.055 (95% confidence interval, .014–.220).

Analytic comparison between three high-throughput commercial SARS-CoV-2 antibody assays reveals minor discrepancies in a high-incidence population

Performance of three automated commercial serological IgG-based assays was investigated for assessing SARS-CoV-2 “ever” (past or current) infection in a population-based sample in a high exposure setting. PCR and serological testing was performed on 394 individuals. SARS-CoV-2-IgG seroprevalence was 42.9% (95% CI 38.1–47.8%), 40.6% (95% CI 35.9–45.5%), and 42.4% (95% CI 37.6–47.3%) using the CL-900i, VidasIII, and Elecsys assays, respectively. Between the three assays, overall, positive, and negative percent agreements ranged between 93.2–95.7%, 89.3–92.8%, and 93.8–97.8%, respectively; Cohen’s kappa statistic ranged from 0.86 to 0.91; and 35 specimens (8.9%) showed discordant results. Among all individuals, 12.5% (95% CI 9.6–16.1%) had current infection, as assessed by PCR. Of these, only 34.7% (95% CI 22.9–48.7%) were seropositive by at least one assay. A total of 216 individuals (54.8%; 95% CI 49.9–59.7%) had evidence of ever infection using antibody testing and/or PCR during or prior to this study. Of these, only 78.2%, 74.1%, and 77.3% were seropositive in the CL-900i, VidasIII, and Elecsys assays, respectively. All three assays had comparable performance and excellent agreement, but missed at least 20% of individuals with past or current infection. Commercial antibody assays can substantially underestimate ever infection, more so when infection rates are high.

Possible COVID-19 reinfection case in Duhok City, Kurdistan: A case report

Since the discovery of severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2), the coronavirus disease (COVID-19) pandemic has become the most important health-care crisis globally, having spread to millions of people worldwide. Patients who recover from COVID-19 are still susceptible to reinfection. In this report, we present the case of a patient who had recovered from COVID-19. Recovery was defined as the resolution of symptoms accompanied by two consecutive SARS-CoV-2-negative real-time reverse transcriptase-polymerase chain reaction (RT-PCR) test results. Two months after the first infection, the patient tested positive for anti-SARS-CoV-2 antibodies. Three months after this test, the patient presented with mild COVID-19 symptoms that was confirmed by RT-PCR. These findings indicate a possible reinfection case. If the occurrence of reinfections is demonstrated to be true, then it may change the strategy of community-based disease prevention. More research is needed to confirm the concept of reinfection.

What we know and dont know on SARS-CoV-2 and COVID-19

Coronavirus disease 2019 (COVID-19), caused by the SARS-CoV-2 virus discovered in December 2019 in Wuhan, China, has had an enormous impact on public health worldwide due to its rapid spread and pandemic behavior, challenges in its control and mitigation, and few therapeutic alternatives. In this review, we summarize the pathophysiological mechanisms, clinical presentation, and diagnostic techniques. In addition, the main lineages and the different strategies for disease prevention are reviewed, with emphasis on the development of vaccines and their different platforms. Finally, some of the currently available therapeutic strategies are summarized. Throughout the article, we point out the current knowns and unknowns at the time of writing this article.

COVID-19 Vaccination Scenarios: A Cost-Effectiveness Analysis for Turkey

As of March 2021, COVID-19 has claimed the lives of more than 2.7 million people worldwide. Vaccination has started in most countries around the world. In this study, we estimated the cost-effectiveness of strategies for COVID-19 vaccination for Turkey compared to a baseline in the absence of vaccination and imposed measures by using an enhanced SIRD (Susceptible, Infectious, Recovered, Death) model and various scenarios for the first year after vaccination. The results showed that vaccination is cost-effective from a health care perspective, with an incremental cost-effectiveness ratio (ICER) of 511 USD/QALY and 1045 USD/QALY if vaccine effectiveness on transmission is equal or reduced to only 50% of effectiveness on disease, respectively, at the 90% baseline effectiveness of the vaccine. From a societal perspective, cost savings were estimated for both scenarios. Other results further showed that the minimum required vaccine uptake to be cost-effective would be at least 30%. Sensitivity and scenario analyses, as well as the iso-ICER curves, showed that the results were quite robust and that major changes in cost-effectiveness outcomes cannot be expected. We can conclude that COVID-19 vaccination in Turkey is highly cost-effective or even cost-saving.

SARS-CoV-2 infection rates of antibody-positive compared with antibody-negative health-care workers in England: a large, multicentre, prospective cohort study (SIREN)

BACKGROUND

Increased understanding of whether individuals who have recovered from COVID-19 are protected from future SARS-CoV-2 infection is an urgent requirement. We aimed to investigate whether antibodies against SARS-CoV-2 were associated with a decreased risk of symptomatic and asymptomatic reinfection.

METHODS

A large, multicentre, prospective cohort study was done, with participants recruited from publicly funded hospitals in all regions of England. All health-care workers, support staff, and administrative staff working at hospitals who could remain engaged in follow-up for 12 months were eligible to join The SARS-CoV-2 Immunity and Reinfection Evaluation study. Participants were excluded if they had no PCR tests after enrolment, enrolled after Dec 31, 2020, or had insufficient PCR and antibody data for cohort assignment. Participants attended regular SARS-CoV-2 PCR and antibody testing (every 2-4 weeks) and completed questionnaires every 2 weeks on symptoms and exposures. At enrolment, participants were assigned to either the positive cohort (antibody positive, or previous positive PCR or antibody test) or negative cohort (antibody negative, no previous positive PCR or antibody test). The primary outcome was a reinfection in the positive cohort or a primary infection in the negative cohort, determined by PCR tests. Potential reinfections were clinically reviewed and classified according to case definitions (confirmed, probable, or possible) and symptom-status, depending on the hierarchy of evidence. Primary infections in the negative cohort were defined as a first positive PCR test and seroconversions were excluded when not associated with a positive PCR test. A proportional hazards frailty model using a Poisson distribution was used to estimate incidence rate ratios (IRR) to compare infection rates in the two cohorts.

FINDINGS

From June 18, 2020, to Dec 31, 2020, 30 625 participants were enrolled into the study. 51 participants withdrew from the study, 4913 were excluded, and 25 661 participants (with linked data on antibody and PCR testing) were included in the analysis. Data were extracted from all sources on Feb 5, 2021, and include data up to and including Jan 11, 2021. 155 infections were detected in the baseline positive cohort of 8278 participants, collectively contributing 2 047 113 person-days of follow-up. This compares with 1704 new PCR positive infections in the negative cohort of 17 383 participants, contributing 2 971 436 person-days of follow-up. The incidence density was 7·6 reinfections per 100 000 person-days in the positive cohort, compared with 57·3 primary infections per 100 000 person-days in the negative cohort, between June, 2020, and January, 2021. The adjusted IRR was 0·159 for all reinfections (95% CI 0·13-0·19) compared with PCR-confirmed primary infections. The median interval between primary infection and reinfection was more than 200 days.

INTERPRETATION

A previous history of SARS-CoV-2 infection was associated with an 84% lower risk of infection, with median protective effect observed 7 months following primary infection. This time period is the minimum probable effect because seroconversions were not included. This study shows that previous infection with SARS-CoV-2 induces effective immunity to future infections in most individuals.

FUNDING

Department of Health and Social Care of the UK Government, Public Health England, The National Institute for Health Research, with contributions from the Scottish, Welsh and Northern Irish governments.

Adaptive Susceptible-Infectious-Removed Model for Continuous Estimation of the COVID-19 Infection Rate and Reproduction Number in the United States: Modeling Study

BACKGROUND

The dynamics of the COVID-19 pandemic vary owing to local population density and policy measures. During decision-making, policymakers consider an estimate of the effective reproduction number Rt, which is the expected number of secondary infections spread by a single infected individual.

OBJECTIVE

We propose a simple method for estimating the time-varying infection rate and the Rt.

METHODS

We used a sliding window approach with a Susceptible-Infectious-Removed (SIR) model. We estimated the infection rate from the reported cases over a 7-day window to obtain a continuous estimation of Rt. A proposed adaptive SIR (aSIR) model was applied to analyze the data at the state and county levels.

RESULTS

The aSIR model showed an excellent fit for the number of reported COVID-19 cases, and the 1-day forecast mean absolute prediction error was <2.6% across all states. However, the 7-day forecast mean absolute prediction error approached 16.2% and strongly overestimated the number of cases when the Rt was rapidly decreasing. The maximal Rt displayed a wide range of 2.0 to 4.5 across all states, with the highest values for New York (4.4) and Michigan (4.5). We found that the aSIR model can rapidly adapt to an increase in the number of tests and an associated increase in the reported cases of infection. Our results also suggest that intensive testing may be an effective method of reducing Rt.

CONCLUSIONS

The aSIR model provides a simple and accurate computational tool for continuous Rt estimation and evaluation of the efficacy of mitigation measures.

Immunity to SARS-CoV-2: Lessons Learned

In the year since the emergence of severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) and with understanding of the etiology of the coronavirus disease 2019 (COVID-19) pandemic, it has become clear that most infected individuals achieve some form of immunity against the virus with relatively few reported reinfections. A number of vaccines have already achieved emergency use authorization based on data from large phase 3 field efficacy clinical trials. However, our knowledge about the extent and durability of this immunity, and the breadth of vaccine coverage against SARS-CoV-2 variants is still evolving. In this narrative review, we summarize the latest and rapidly developing understanding of immunity to SARS-CoV-2 infection, including what we have learned about the key antigens of SARS-CoV-2 (i.e., the spike protein and its receptor-binding domain), their importance in vaccine development, the immediate immune response to SARS-CoV-2, breadth of coverage of emerging SARS-CoV-2 variants, contributions of preexisting immunity to related coronaviruses, and duration of immunity. We also discuss lessons from newer approaches, such as systems serology, that provide insights into molecular and cellular immune responses elicited and how they relate to the trajectory of infection, and potentially inform immune correlates of protection. We also briefly examine the limited research literature on immune responses in special populations, such as pregnant women and children.

The Importance and Challenges of Identifying SARS-CoV-2 Reinfections

Reports of severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) reinfection have raised important questions about the strength and durability of the immune response to primary infection, which are key factors in predicting the course of the pandemic. Identifying reinfection requires detecting the virus at two different time points and using viral genomic data to distinguish reinfection from persistent viral carriage. This process is hindered by challenges of logistics and capacity, such as banking samples from primary infection and performing viral genome sequencing. These challenges may help to explain why very few cases have been described to date. In addition, reinfection may be a rare phenomenon, but detailed prospective studies are needed to rigorously assess its frequency. To provide context for future investigations of SARS-CoV-2 reinfection, we review 16 cases that have been published to date or are available in preprint. Reinfection occurred across demographic spectra and in patients whose initial infections were both asymptomatic/mild and moderate/severe. For cases in which severity could be compared between episodes, half of reinfections were less severe, raising the possibility of partial immune protection. Although many patients had a positive total immunoglobulin or IgG result at the time of reinfection, very little examination of their immune response was performed. Further work is needed to elucidate the frequency, determinants, and consequences of SARS-CoV-2 reinfection. Establishing the necessary frameworks for surveillance and investigation will rely heavily on clinical laboratories and clinical investigators, and we propose several considerations to guide the medical community in identifying and characterizing SARS-CoV-2 reinfections.

Mathematical modeling of the SARS-CoV-2 epidemic in Qatar and its impact on the national response to COVID-19

BACKGROUND

Mathematical modeling constitutes an important tool for planning robust responses to epidemics. This study was conducted to guide the Qatari national response to the severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) epidemic. The study investigated the epidemic's time-course, forecasted health care needs, predicted the impact of social and physical distancing restrictions, and rationalized and justified easing of restrictions.

METHODS

An age-structured deterministic model was constructed to describe SARS-CoV-2 transmission dynamics and disease progression throughout the population.

RESULTS

The enforced social and physical distancing interventions flattened the epidemic curve, reducing the peaks for incidence, prevalence, acute-care hospitalization, and intensive care unit (ICU) hospitalizations by 87%, 86%, 76%, and 78%, respectively. The daily number of new infections was predicted to peak at 12 750 on May 23, and active-infection prevalence was predicted to peak at 3.2% on May 25. Daily acute-care and ICU-care hospital admissions and occupancy were forecast accurately and precisely. By October 15, 2020, the basic reproduction number R0 had varied between 1.07-2.78, and 50.8% of the population were estimated to have been infected (1.43 million infections). The proportion of actual infections diagnosed was estimated at 11.6%. Applying the concept of Rt tuning, gradual easing of restrictions was rationalized and justified to start on June 15, 2020, when Rt declined to 0.7, to buffer the increased interpersonal contact with easing of restrictions and to minimize the risk of a second wave. No second wave has materialized as of October 15, 2020, five months after the epidemic peak.

CONCLUSIONS

Use of modeling and forecasting to guide the national response proved to be a successful strategy, reducing the toll of the epidemic to a manageable level for the health care system.