First reported nosocomial SARS-CoV-2 outbreak in a hospital-based laundry facility.

Nosocomial severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) outbreaks among health care workers have been scarcely reported so far. This report presents the results of an epidemiologic and molecular investigation of a SARS-CoV-2 outbreak among laundromat facility workers in a large tertiary centre in Israel. Following the first three reported cases of SARS-CoV-2 among laundromat workers, all 49 laundromat personnel were screened by qRT-PCR tests using naso- and oropharingeal swabs. Epidemiologic investigations included questionnaires, interviews and observations of the laundromat facility. Eleven viral RNA samples were then sequenced, and a phylogenetic analysis was performed using MEGAX.The integrated investigation defined three genetic clusters and helped identify the index cases and the assumed routes of transmission. It was then deduced that shared commute and public showers played a role in SARS-CoV-2 transmission in this outbreak, in addition to improper PPE use and social gatherings (such as social eating and drinking). In this study, we present an integrated epidemiologic and molecular investigation may help detect the routes of SARS-CoV-2 transmission, emphasising such routes that are less frequently discussed. Our work reinforces the notion that person-to-person transmission is more likely to cause infections than environmental contamination (e.g. from handling dirty laundry).

Viral co-pathogens in COVID-19 acute respiratory syndrome - what did we learn from the first year of pandemic?

OBJECTIVE: This study aimed to describe the distribution of respiratory pathogens and the occurrence of co-pathogens during the first year of the COVID-19 pandemic. METHODS: We used a multiplex polymerase chain reaction (PCR) panel targeting 23 microorganisms to analyze the oro-pharyngeal samples of patients admitted to our hospital with acute respiratory infection (ARI) between March 1, 2020, and February 28, 2021. We matched 40 to 50 patients who were SARS-CoV-2 positive and SARS-CoV-2 negative per month for age and sex. RESULTS: A total of 939 patients with multiplex PCR test results were included in the study. Respiratory pathogens where detected in only 8/476 (1.6%) patients with COVID-19 versus 87/463 (18.7%) patients with non-COVID-19 ARI patients. Diversity and rates of pathogens vastly differed from previous years but showed seasonal variance. CONCLUSION: Patients with SARS-CoV-2 infection presenting with ARI during the first year of the COVID-19 pandemic demonstrated paucity of respiratory co-pathogens.

The Sheba Medical Center healthcare workers' children's school: can we open schools safely?

OBJECTIVE: The role of school closure in mitigating coronavirus disease 2019 (COVID-19) transmission has been questioned. In our medical centre, during a 9-week national lockdown, an alternative school was opened for health-care workers' (HCW) children with a small number of children per class and strict symptom surveillance. After lockdown was lifted we screened children and their parents for severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) serology. METHODS: We conducted a cross-sectional study of HCW parents and their children after one teacher contracted COVID-19 following exposure at home and 53 children were exposed, isolated and tested by RT-PCR. We compared families with children attending the alternative school with families whose children who remained at home during the 9-week lockdown. Epidemiological and medical data were collected using a short questionnaire; nasopharyngeal and oropharyngeal swabs were obtained and tested for SARS-CoV-2 by RT-PCR, and blood was collected for SARS-CoV-2 IgA and IgG titres. RESULTS: A total of 435 children attended the Sheba alternative school. Among the 53 children exposed to the infected teacher, none tested positive by RT-PCR. Of these, 18 children-parent pairs were tested for serology and all were negative. A total of 106/435 (24%) children and their 78 parents were recruited for the cross-sectional study; 70 attended the Sheba school and 36 did not. Approximately 16% of children in either group reported symptoms (11/70 in the school group and 6/36 in the 'stay home' group), but SARS-CoV-2 was not detected by PCR in any, and previous exposure, as determined by serological tests, was low and not significantly different between the groups. CONCLUSION: In an alternative school for children of HCWs, active during COVID-19 national outbreak, we found no evidence of increased infection compared with children that stayed home.

Full genome viral sequences inform patterns of SARS-CoV-2 spread into and within Israel.

Full genome sequences are increasingly used to track the geographic spread and transmission dynamics of viral pathogens. Here, with a focus on Israel, we sequence 212 SARS-CoV-2 sequences and use them to perform a comprehensive analysis to trace the origins and spread of the virus. We find that travelers returning from the United States of America significantly contributed to viral spread in Israel, more than their proportion in incoming infected travelers. Using phylodynamic analysis, we estimate that the basic reproduction number of the virus was initially around 2.5, dropping by more than two-thirds following the implementation of social distancing measures. We further report high levels of transmission heterogeneity in SARS-CoV-2 spread, with between 2-10% of infected individuals resulting in 80% of secondary infections. Overall, our findings demonstrate the effectiveness of social distancing measures for reducing viral spread.

Utilization of machine-learning models to accurately predict the risk for critical COVID-19.

Among patients with Coronavirus disease (COVID-19), the ability to identify patients at risk for deterioration during their hospital stay is essential for effective patient allocation and management. To predict patient risk for critical COVID-19 based on status at admission using machine-learning models. Retrospective study based on a database of tertiary medical center with designated departments for patients with COVID-19. Patients with severe COVID-19 at admission, based on low oxygen saturation, low partial arterial oxygen pressure, were excluded. The primary outcome was risk for critical disease, defined as mechanical ventilation, multi-organ failure, admission to the ICU, and/or death. Three different machine-learning models were used to predict patient deterioration and compared to currently suggested predictors and to the APACHEII risk-prediction score. Among 6995 patients evaluated, 162 were hospitalized with non-severe COVID-19, of them, 25 (15.4%) patients deteriorated to critical COVID-19. Machine-learning models outperformed the all other parameters, including the APACHE II score (ROC AUC of 0.92 vs. 0.79, respectively), reaching 88.0% sensitivity, 92.7% specificity and 92.0% accuracy in predicting critical COVID-19. The most contributory variables to the models were APACHE II score, white blood cell count, time from symptoms to admission, oxygen saturation and blood lymphocytes count. Machine-learning models demonstrated high efficacy in predicting critical COVID-19 compared to the most efficacious tools available. Hence, artificial intelligence may be applied for accurate risk prediction of patients with COVID-19, to optimize patients triage and in-hospital allocation, better prioritization of medical resources and improved overall management of the COVID-19 pandemic.