Results from a systematic programme of evaluating COVID-19 reinfection cases in the early phase of the pandemic, Singapore

OBJECTIVES

The objectives of this study were to describe the coronavirus disease caused by SARS-CoV-2 (COVID-19) reinfection evaluation algorithm used in the early phase of the pandemic in Singapore and analyze the clinical and laboratory characteristics of the cases evaluated.

METHODS

We performed a retrospective case-control analysis including all COVID-19 cases evaluated for possible reinfection under the local COVID-19 reinfection evaluation programme between 1 June 2020-30 June 2021. Whole genome sequencing (WGS) was used as confirmatory testing. We compared all reinfection ("RI") cases against those who were evaluated but eventually assessed not to be reinfection ("non-RI").

RESULTS

There were 74 possible reinfection cases evaluated through the programme, of which 32 were subsequently classified as RI. There was strong statistical evidence that RI cases had a longer interval between 1st and 2nd episode (mean 297 days; 95%-confidence interval (CI) 267-327) compared to non-RI cases (mean 186 days; 95%-CI 144-228). The cycle threshold (Ct) value of initial polymerase chain rection (PCR) at 2nd episode was also found to be significantly lower in RI cases (mean 23; 95%-CI 20-26) compared to non-RI cases (mean 34; 95%-CI 32-36). There was no significant difference in the proportion of individuals who had fever, acute respiratory symptoms or asymptomatic in both groups. Delta and beta variants were most commonly identified from WGS and provide indication of re-infection as these were not 'wild-type' and were not circulating during the time period of the index infection.

CONCLUSIONS

Using a combination of serologic, microbiologic and genomic criteria to evaluate possible reinfection cases is useful and can provide a framework for evaluation that may be modified for future similar situations.

The first nosocomial cluster of COVID-19 due to the Delta variant in a major acute-care hospital in Singapore - investigations and outbreak response

OBJECTIVES

The first large nosocomial cluster of COVID-19 in Singapore in April 2021 led to partial closure of a major acute-care hospital. We report our investigations and evaluate the effectiveness of the BNT162.b2 and mRNA1273 vaccines against the Delta variant.

METHODS

Close contacts of COVID-19 patients and the affected ward were identified and underwent surveillance for SARS-CoV-2 infection. Patient, staff and visitor cohorts were constructed and factors associated with infection evaluated. Phylogenetic analysis of patient samples was performed. Ward air exhaust filters were tested for SARS-CoV-2 virus.

RESULTS

There were 47 cases in total, comprising 29 patients, 9 staff, 6 ward visitors and 3 household contacts. All infections were of the Delta variant. Ventilation studies showed turbulent airflow and swabs from air exhaust filters were positive for SARS-Cov2. Vaccine breakthrough infections were seen in both patients and staff. Among patients, vaccination was associated with a 79% lower odds of infection with COVID-19 (adjusted OR =0.21, 95%CI 0.05-0.95) CONCLUSIONS: This cluster occurred despite an enhancement in infection control measures that the hospital had undertaken at the onset of this pandemic, but it was rapidly brought under control through case isolation, extensive contact tracing and quarantine measures, and led to enhancements in hospital personal protective equipment (PPE) use, introduction of routine rostered testing of inpatients and staff, and hospital infrastructure changes to improve ventilation within the general ward.

Cost-Effectiveness of Serum Galactomannan Surveillance during Mould-Active Antifungal Prophylaxis

Serial galactomannan (GM) monitoring can aid the diagnosis of invasive aspergillosis (IA) and optimise treatment decisions. However, widespread adoption of mould-active prophylaxis has reduced the incidence of IA and challenged its use. We evaluated the cost-effectiveness of prophylaxis-biomarker strategies. A Markov model simulating high-risk patients undergoing routine GM surveillance with mould-active versus non-mould-active prophylaxis was constructed. The incremental cost for each additional quality-adjusted life-year (QALY) gained over a lifetime horizon was calculated. In 40- and 60-year-old patients receiving mould-active prophylaxis coupled with routine GM surveillance, the total cost accrued was the lowest at SGD 11,227 (USD 8255) and SGD 9234 (USD 6790), respectively, along with higher QALYs gained (5.3272 and 1.1693). This strategy, being less costly and more effective, dominated mould-active prophylaxis with no GM monitoring or GM surveillance during non-mould-active prophylaxis. The prescription of empiric antifungal treatment was influential in the cost-effectiveness. When the GM test sensitivity was reduced from 80% to 30%, as might be anticipated with the use of mould-active prophylactic agents, the conclusion remained unchanged. The likelihood of GM surveillance with concurrent mould-active prophylaxis being cost-effective was 77%. Routine GM surveillance remained cost-effective during mould-active prophylaxis despite lower IA breakthroughs. Cost-saving from reduced empirical antifungal treatment was an important contributing factor.

Identifying COVID-19 cases in outpatient settings

Case identification is an ongoing issue for the COVID-19 epidemic, in particular for outpatient care where physicians must decide which patients to prioritise for further testing. This paper reports tools to classify patients based on symptom profiles based on 236 severe acute respiratory syndrome coronavirus 2 positive cases and 564 controls, accounting for the time course of illness using generalised multivariate logistic regression. Significant symptoms included abdominal pain, cough, diarrhoea, fever, headache, muscle ache, runny nose, sore throat, temperature between 37.5 and 37.9 °C and temperature above 38 °C, but their importance varied by day of illness at assessment. With a high percentile threshold for specificity at 0.95, the baseline model had reasonable sensitivity at 0.67. To further evaluate accuracy of model predictions, leave-one-out cross-validation confirmed high classification accuracy with an area under the receiver operating characteristic curve of 0.92. For the baseline model, sensitivity decreased to 0.56. External validation datasets reported similar result. Our study provides a tool to discern COVID-19 patients from controls using symptoms and day from illness onset with good predictive performance. It could be considered as a framework to complement laboratory testing in order to differentiate COVID-19 from other patients presenting with acute symptoms in outpatient care.