SARS-CoV-2 variant trends in Ireland: Wastewater-based epidemiology and clinical surveillance.

SARS-CoV-2 RNA quantification in wastewater is an important tool for monitoring the prevalence of COVID-19 disease on a community scale which complements case-based surveillance systems. As novel variants of concern (VOCs) emerge there is also a need to identify the primary circulating variants in a community, accomplished to date by sequencing clinical samples. Quantifying variants in wastewater offers a cost-effective means to augment these sequencing efforts. In this study, SARS-CoV-2 N1 RNA concentrations and daily loadings were determined and compared to case-based data collected as part of a national surveillance programme to determine the validity of wastewater surveillance to monitor infection spread in the greater Dublin area. Further, sequencing of clinical samples was conducted to determine the primary SARS-CoV-2 lineages circulating in Dublin. Finally, digital PCR was employed to determine whether SARS-CoV-2 VOCs, Alpha and Delta, were quantifiable from wastewater. No lead or lag time was observed between SARS-CoV-2 wastewater and case-based data and SARS-CoV-2 trends in Dublin wastewater significantly correlated with the notification of confirmed cases through case-based surveillance preceding collection with a 5-day average. This demonstrates that viral RNA in Dublin's wastewater mirrors the spread of infection in the community. Clinical sequence data demonstrated that increased COVID-19 cases during Ireland's third wave coincided with the introduction of the Alpha variant, while the fourth wave coincided with increased prevalence of the Delta variant. Interestingly, the Alpha variant was detected in Dublin wastewater prior to the first genome being sequenced from clinical samples, while the Delta variant was identified at the same time in clinical and wastewater samples. This work demonstrates the validity of wastewater surveillance for monitoring SARS-CoV-2 infections and also highlights its effectiveness in identifying circulating variants which may prove useful when sequencing capacity is limited.

Establishing a COVID-19 pandemic severity assessment surveillance system in Ireland.

We developed a COVID-19 pandemic severity assessment (PSA) monitoring system in Ireland, in order to inform and improve public health preparedness, response and recovery. The system based on the World Health Organization (WHO) Pandemic Influenza Severity Assessment (PISA) project included a panel of surveillance parameters for the following indicators: transmissibility, impact and disease severity. Age-specific thresholds were established for each parameter and data visualised using heat maps. The findings from the first pandemic wave in Ireland have shown that the WHO PISA system can be adapted for COVID-19, providing a standardised tool for early warning and monitoring pandemic severity.

Underlying conditions and risk of hospitalisation, ICU admission and mortality among those with COVID-19 in Ireland: A national surveillance study.

BACKGROUND: To date, over 2 million people worldwide have died with severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) infection. To describe the experience in Ireland, this study examined associations between underlying conditions and the following outcomes: mortality, admission to hospital or admission to the intensive care unit (ICU) among those infected with COVID-19. METHODS: This study used data from the Health Protection Surveillance Centre in Ireland and included confirmed cases of COVID-19 from the first wave of the pandemic between March and July 2020. Two cohorts were included: all cases (community and hospital) and hospital admissions only. For all cases, health outcome data included mortality and hospitalisation. For hospitalised cases, outcome data included mortality and ICU admission. Logistic regression was used to examine associations between underlying conditions and outcomes across both cohorts. Results are presented as adjusted odds ratios (OR) and 95% confidence intervals (CIs). FINDINGS: There were 19,789 cases included in analysis, which encompassed 1,476 (7.5%) deaths, 2,811 (14.2%) hospitalisations, and 438 (2.2%) ICU admissions of whom 90 (20.5%) died. Significantly higher risk of mortality, hospitalisation and ICU admission was associated with having chronic heart disease, a BMI ≥40kg/m2 and male sex. Additionally, diagnosis of a chronic neurological condition (OR 1.41; 95%CI:1.17, 1.69), chronic kidney disease (OR 1.74; 95%CI:1.35, 2.24) and cancer (OR 2.77; 95%CI:2.21, 3.47) were significantly associated with higher risk of mortality among all cases, with similar patterns of association observed for mortality among hospitalised cases. INTERPRETATION: The identification of underlying conditions among COVID-19 cases may help identify those at highest risk of the worst health outcomes and inform preventive strategies to improve outcomes. FUNDING: This study was supported by the Health Service Executive, Health Protection Surveillance Centre. KEB and MM are funded by the Health Research Board (RL-15-1579 and EIA-2019-012 respectively).