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SARS-CoV-2 re-infection: development of an epidemiological definition from India.

ABSTRACT: The current investigation was conducted with the objective to develop an epidemiological case definition of possible severe acute respiratory syndrome-coronavirus-2 (SARS-CoV-2) re-infection and assess its magnitude in India. The epidemiological case definition for SARS-CoV-2 re-infection was developed from literature review of data on viral kinetics. For achieving second objective, the individuals who satisfied the developed case definition for SARS-CoV-2 re-infection were contacted telephonically. Taking available evidence into consideration, re-infection with SARS-CoV-2 in our study was defined as any individual who tested positive for SARS-CoV-2 on two separate occasions by either molecular tests or rapid antigen test at an interval of at least 102 days with one negative molecular test in between. In this archive based, telephonic survey, 58 out of 1300 individuals (4.5%) fulfilled the above-mentioned definition; 38 individuals could be contacted with healthcare workers (HCWs) accounting for 31.6% of the cases. A large proportion of participants was asymptomatic and had higher Ct value during the first episode. While SARS-CoV-2 re-infection is still a rare phenomenon, there is a need for epidemiological definition of re-infection for establishing surveillance systems and this study contributes to such a goal.Severe acute respiratory syndrome-coronavirus-2 (SARS-CoV-2) re-infection is an emerging concern and there is a need to define it. Therefore, working epidemiological case definition for re-infection was developed and its magnitude was explored via archive-based, telephonic survey. Re-infection with SARS-CoV-2 was defined as two positive tests at an interval of at least 102 days with one interim negative test. Thirty-eight of the 58 eligible patients could be contacted with 12 (31.6%) being HCWs. Majority of the participants were asymptomatic and had higher Ct value during their first episode. To conclude, a working epidemiological case definition of SARS-CoV-2 re-infection is important to strengthen surveillance. The present investigation contributes to this goal and records reinfection in 4.5% of SARS-CoV-2 infected individuals in India.

SUBMITTER: Mukherjee A

PROVIDER: S-EPMC8027559 | biostudies-literature |

REPOSITORIES: biostudies-literature

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Project description:ImportanceSARS-CoV-2 infection is associated with persistent, relapsing, or new symptoms or other health effects occurring after acute infection, termed postacute sequelae of SARS-CoV-2 infection (PASC), also known as long COVID. Characterizing PASC requires analysis of prospectively and uniformly collected data from diverse uninfected and infected individuals.ObjectiveTo develop a definition of PASC using self-reported symptoms and describe PASC frequencies across cohorts, vaccination status, and number of infections.Design, setting, and participantsProspective observational cohort study of adults with and without SARS-CoV-2 infection at 85 enrolling sites (hospitals, health centers, community organizations) located in 33 states plus Washington, DC, and Puerto Rico. Participants who were enrolled in the RECOVER adult cohort before April 10, 2023, completed a symptom survey 6 months or more after acute symptom onset or test date. Selection included population-based, volunteer, and convenience sampling.ExposureSARS-CoV-2 infection.Main outcomes and measuresPASC and 44 participant-reported symptoms (with severity thresholds).ResultsA total of 9764 participants (89% SARS-CoV-2 infected; 71% female; 16% Hispanic/Latino; 15% non-Hispanic Black; median age, 47 years [IQR, 35-60]) met selection criteria. Adjusted odds ratios were 1.5 or greater (infected vs uninfected participants) for 37 symptoms. Symptoms contributing to PASC score included postexertional malaise, fatigue, brain fog, dizziness, gastrointestinal symptoms, palpitations, changes in sexual desire or capacity, loss of or change in smell or taste, thirst, chronic cough, chest pain, and abnormal movements. Among 2231 participants first infected on or after December 1, 2021, and enrolled within 30 days of infection, 224 (10% [95% CI, 8.8%-11%]) were PASC positive at 6 months.Conclusions and relevanceA definition of PASC was developed based on symptoms in a prospective cohort study. As a first step to providing a framework for other investigations, iterative refinement that further incorporates other clinical features is needed to support actionable definitions of PASC.

Project description:BackgroundSince the first recognition of the pandemic characteristics of SARS-CoV-2 infection, and before substantial case fatality data were available worldwide, public health agencies warned the public about the increased dangers of SARS-CoV-2 to persons with a variety of underlying physical conditions, many of which are more commonly found in persons over 50 years of age or in certain ethnic groups.ObjectiveTo investigate the statistical rather than the physiological basis in support of the abovementioned warnings, this study examines correlations globally on a nation-by-nation basis between the statistical data concerning COVID-19 fatalities and the statistics of potential comorbidities that may influence the severity of infection.MethodsThis study considers the statistics describing the populations of the 99 countries with the greatest numbers of SARS-CoV-2 infections at the time of the data cutoff. As national compilations of direct measures of immune system strength are not publicly available, the frequency of fatalities in those countries due to a variety of serious diseases is used as a proxy for the susceptibility of those populations to those same diseases.ResultsThe analysis produces plots and calculations of correlations and cross-correlations of COVID-19 case fatality rates and the risks of other potential cofactors. It exposes some reasons that may underlie the degree to which advanced age increases the risk of mortality of infection with SARS-CoV-2. In contrast with the strong influences of comorbidities on the seriousness of consequences of influenzas and their associated pneumonias, the correlations of the same set of risk factors with SARS-CoV-2 infection are considerably weaker. The general characteristics of the observed correlations strengthened through 3 cycles of analysis, starting in September 2020. The strongest correlations were with chronic kidney disease and coronary disease (approximately 0.28 and 0.20, respectively).ConclusionsThis study confirms early clinical observations that infection with SARS-CoV-2 presents an increased risk to persons over the age of 65 years. It does not support the suggestions presented by government agencies early in the pandemic that the risks are much greater for persons with certain common potential comorbidities.

Project description:BackgroundMany popular disease transmission models have helped nations respond to the COVID-19 pandemic by informing decisions about pandemic planning, resource allocation, implementation of social distancing measures, lockdowns, and other non-pharmaceutical interventions. We study how five epidemiological models forecast and assess the course of the pandemic in India: a baseline curve-fitting model, an extended SIR (eSIR) model, two extended SEIR (SAPHIRE and SEIR-fansy) models, and a semi-mechanistic Bayesian hierarchical model (ICM).MethodsUsing COVID-19 case-recovery-death count data reported in India from March 15 to October 15 to train the models, we generate predictions from each of the five models from October 16 to December 31. To compare prediction accuracy with respect to reported cumulative and active case counts and reported cumulative death counts, we compute the symmetric mean absolute prediction error (SMAPE) for each of the five models. For reported cumulative cases and deaths, we compute Pearson's and Lin's correlation coefficients to investigate how well the projected and observed reported counts agree. We also present underreporting factors when available, and comment on uncertainty of projections from each model.ResultsFor active case counts, SMAPE values are 35.14% (SEIR-fansy) and 37.96% (eSIR). For cumulative case counts, SMAPE values are 6.89% (baseline), 6.59% (eSIR), 2.25% (SAPHIRE) and 2.29% (SEIR-fansy). For cumulative death counts, the SMAPE values are 4.74% (SEIR-fansy), 8.94% (eSIR) and 0.77% (ICM). Three models (SAPHIRE, SEIR-fansy and ICM) return total (sum of reported and unreported) cumulative case counts as well. We compute underreporting factors as of October 31 and note that for cumulative cases, the SEIR-fansy model yields an underreporting factor of 7.25 and ICM model yields 4.54 for the same quantity. For total (sum of reported and unreported) cumulative deaths the SEIR-fansy model reports an underreporting factor of 2.97. On October 31, we observe 8.18 million cumulative reported cases, while the projections (in millions) from the baseline model are 8.71 (95% credible interval: 8.63-8.80), while eSIR yields 8.35 (7.19-9.60), SAPHIRE returns 8.17 (7.90-8.52) and SEIR-fansy projects 8.51 (8.18-8.85) million cases. Cumulative case projections from the eSIR model have the highest uncertainty in terms of width of 95% credible intervals, followed by those from SAPHIRE, the baseline model and finally SEIR-fansy.ConclusionsIn this comparative paper, we describe five different models used to study the transmission dynamics of the SARS-Cov-2 virus in India. While simulation studies are the only gold standard way to compare the accuracy of the models, here we were uniquely poised to compare the projected case-counts against observed data on a test period. The largest variability across models is observed in predicting the "total" number of infections including reported and unreported cases (on which we have no validation data). The degree of under-reporting has been a major concern in India and is characterized in this report. Overall, the SEIR-fansy model appeared to be a good choice with publicly available R-package and desired flexibility plus accuracy.

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SARS-CoV-2 re-infection: development of an epidemiological definition from India.

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