Development and Delivery of a Real-time Hospital-onset COVID-19 Surveillance System Using Network Analysis

Spatiotemporal Data Modelling for Epidemiological Research in Hospitals

Nosocomial infections are a great source of concern for healthcare organizations. The spatial layout of hospitals and the movements of patients play significant roles in the spread of outbreaks. However, the existing models are ad-hoc for a specific hospital and research topic. This work shows the design of a data model to study the spread of infections among hospital patients. Its spatial dimension describes the hospital layout with several levels of detail, and the temporal dimension describes everything that happens to the patients in the form of events, which can relate to the spatial dimension. The model is meant to be sufficiently general to fit any hospital layout and to be used for different epidemiological research topics. We proved the model's suitability by defining six queries based on patients' movements and contacts that could assist in several epidemiological research tasks, such as discovering potential transmission routes. The model was implemented as an RDF* knowledge graph, and the queries were in SPARQL*. Finally, we designed two experiments in which two outbreaks of Clostridium difficile were analyzed using several queries (four in the first experiment and two in the second) on a knowledge graph (105,000 nodes, 185,000 edges) with synthetic data.

SARS-CoV-2 surface and air contamination in an acute healthcare setting during the first and second pandemic waves

BACKGROUND

Surfaces and air in healthcare facilities can be contaminated with severe acute respiratory syndrome coronavirus-2 (SARS-CoV-2). Previously, the authors identified SARS-CoV-2 RNA on surfaces and air in their hospital during the first wave of the coronavirus disease 2019 pandemic (April 2020).

AIM

To explore whether the profile of SARS-CoV-2 surface and air contamination had changed between April 2020 and January 2021.

METHODS

This was a prospective, cross-sectional, observational study in a multi-site London hospital. In January 2021, surface and air samples were collected from comparable areas to those sampled in April 2020, comprising six clinical areas and a public area. SARS-CoV-2 was detected using reverse transcription polymerase chain reaction and viral culture. Sampling was also undertaken in two wards with natural ventilation alone. The ability of the prevalent variants at the time of the study to survive on dry surfaces was evaluated.

FINDINGS

No viable virus was recovered from surfaces or air. Five percent (N=14) of 270 surface samples and 4% (N=1) of 27 air samples were positive for SARS-CoV-2, which was significantly lower than in April 2020 [52% (N=114) of 218 surface samples and 48% (N=13) of 27 air samples (P<0.001, Fisher's exact test)]. There was no clear difference in the proportion of surface and air samples positive for SARS-CoV-2 RNA based on the type of ventilation in the ward. All variants tested survived on dry surfaces for >72 h, with a <3-log10 reduction in viable count.

CONCLUSION

This study suggests that enhanced infection prevention measures have reduced the burden of SARS-CoV-2 RNA on surfaces and air in healthcare facilities.

Investigating healthcare worker mobility and patient contacts within a UK hospital during the COVID-19 pandemic

BACKGROUND

Insights into behaviours relevant to the transmission of infections are extremely valuable for epidemiological investigations. Healthcare worker (HCW) mobility and patient contacts within the hospital can contribute to nosocomial outbreaks, yet data on these behaviours are often limited.

METHODS

Using electronic medical records and door access logs from a London teaching hospital during the COVID-19 pandemic, we derive indicators for HCW mobility and patient contacts at an aggregate level. We assess the spatial-temporal variations in HCW behaviour and, to demonstrate the utility of these behavioural markers, investigate changes in the indirect connectivity of patients (resulting from shared contacts with HCWs) and spatial connectivity of floors (owing to the movements of HCWs).

RESULTS

Fluctuations in HCW mobility and patient contacts were identified during the pandemic, with the most prominent changes in behaviour on floors handling the majority of COVID-19 patients. The connectivity between floors was disrupted by the pandemic and, while this stabilised after the first wave, the interconnectivity of COVID-19 and non-COVID-19 wards always featured. Daily rates of indirect contact between patients provided evidence for reactive staff cohorting in response to the number of COVID-19 patients in the hospital.

CONCLUSIONS

Routinely collected electronic records in the healthcare environment provide a means to rapidly assess and investigate behaviour change in the HCW population, and can support evidence based infection prevention and control activities. Integrating frameworks like ours into routine practice will empower decision makers and improve pandemic preparedness by providing tools to help curtail nosocomial outbreaks of communicable diseases.

A district-level vulnerability assessment of next COVID-19 variant (Omicron BA.2) in Uttarakhand using quantitative SWOT analysis

COVID-19 has had an impact on the entire humankind and has been proved to spread in deadly waves. As a result, preparedness and planning are required to better deal with the epidemic's upcoming waves. Effective planning, on the other hand, necessitates detailed vulnerability assessments at all levels, from the national to the state or regional. There are several issues at the regional level, and each region has its own features. As a result, each region needs its own COVID-19 vulnerability assessment. In terms of climate, terrain and demographics, the state of Uttarakhand differs significantly from the rest of India. As a result, a vulnerability assessment of the next COVID-19 variation (Omicron BA.2) is required for district-level planning to meet regional concerns. A total of 17 variables were chosen for this study, including demographic, socio-economic, infrastructure, epidemiological and tourism-related factors. AHP was used to compute their weights. After applying min-max normalisation to the data, a district-level quantitative SWOT is created to compare the performance of 13 Uttarakhand districts. A COVID-19 vulnerability index (normalised R i ) ranging between 0 and 1 was produced, and district-level vulnerabilities were mapped. Quantitative SWOT results depict that Dehradun is a best performing district followed by Haridwar, while Bageshwar, Rudra Prayag, Champawat and Pithoragarh are on the weaker side and the normalised Ri proves Dehradun, Nainital, Champawat, Bageshwar and Chamoli to be least vulnerable to COVID-19 (normalised R i ≤ 0.25) and Pithoragarh to be the most vulnerable district (normalised R i > 0.90). Pauri Garwal and Uttarkashi are moderately vulnerable (normalised R i 0.50 to 0.75).

SARS-CoV-2 outbreak in a Canadian suburban tertiary hospital necessitating full facility closure: a descriptive observational study

BACKGROUND

SARS-CoV-2 can cause outbreaks in community- and hospital-based settings. The aim of this study was to provide a detailed epidemiologic account of a hospital-wide SARS-CoV-2 outbreak and provide a description of case evaluations, transmission networks and the interventions implemented to stem the outbreak.

METHODS

We conducted a retrospective descriptive study of a hospital-wide SARS-CoV-2 outbreak at the Misericordia Community Hospital (Edmonton) from June 21 to Aug. 14, 2020. We reviewed hospital chart, public health and occupational health records to determine demographics, case type (community- or hospital-acquired), need for critical care and outcome for each case linked to the outbreak (patients, hospital staff, and community and patient visitors). We developed detailed transmission networks using epidemiologic data to determine what variables may have contributed to transmission.

RESULTS

Fifty-eight cases of SARS-CoV-2 infection were linked to this hospital outbreak (31 patients, 25 staff members and 2 visitors; 66% female, age range 19-97 years). One patient required critical care, and 11 deaths were recorded (all among inpatients). Most cases were hospital-acquired (91%), and 28% were asymptomatic at the time of diagnosis. The outbreak was composed of 2 clusters driven by protective equipment breaches, premature removal of precautions, transmission in small staff quarters and infection of a staff member after exposure to a wandering patient with dementia and asymptomatic, undetected SARS-CoV-2 infection.

INTERPRETATION

A detailed epidemiologic review of this hospital-wide outbreak shows that a SARS-CoV-2 outbreak can involve complex transmission chains and clusters. Multipronged bundled approaches, aggressive contact tracing, and patient and staff prevalence screening are important to help bring such outbreaks under control, along with ongoing vigilance in detecting delayed cases.

Superspreaders drive the largest outbreaks of hospital onset COVID-19 infections

SARS-CoV-2 is notable both for its rapid spread, and for the heterogeneity of its patterns of transmission, with multiple published incidences of superspreading behaviour. Here, we applied a novel network reconstruction algorithm to infer patterns of viral transmission occurring between patients and health care workers (HCWs) in the largest clusters of COVID-19 infection identified during the first wave of the epidemic at Cambridge University Hospitals NHS Foundation Trust, UK. Based upon dates of individuals reporting symptoms, recorded individual locations, and viral genome sequence data, we show an uneven pattern of transmission between individuals, with patients being much more likely to be infected by other patients than by HCWs. Further, the data were consistent with a pattern of superspreading, whereby 21% of individuals caused 80% of transmission events. Our study provides a detailed retrospective analysis of nosocomial SARS-CoV-2 transmission, and sheds light on the need for intensive and pervasive infection control procedures.

Association between intrahospital transfer and hospital-acquired infection in the elderly: a retrospective case-control study in a UK hospital network

Background

Intrahospital transfers have become more common as hospital staff balance patient needs with bed availability. However, this may leave patients more vulnerable to potential pathogen transmission routes via increased exposure to contaminated surfaces and contacts with individuals.

Objective

This study aimed to quantify the association between the number of intrahospital transfers undergone during a hospital spell and the development of a hospital-acquired infection (HAI).

Methods

A retrospective case–control study was conducted using data extracted from electronic health records and microbiology cultures of non-elective, medical admissions to a large urban hospital network which consists of three hospital sites between 2015 and 2018 (n=24 240). As elderly patients comprise a large proportion of hospital users and are a high-risk population for HAIs, the analysis focused on those aged 65 years or over. Logistic regression was conducted to obtain the OR for developing an HAI as a function of intrahospital transfers until onset of HAI for cases, or hospital discharge for controls, while controlling for age, gender, time at risk, Elixhauser comorbidities, hospital site of admission, specialty of the dominant healthcare professional providing care, intensive care admission, total number of procedures and discharge destination.

Results

Of the 24 240 spells, 2877 cases were included in the analysis. 72.2% of spells contained at least one intrahospital transfer. On multivariable analysis, each additional intrahospital transfer increased the odds of acquiring an HAI by 9% (OR=1.09; 95% CI 1.05 to 1.13).

Conclusion

Intrahospital transfers are associated with increased odds of developing an HAI. Strategies for minimising intrahospital transfers should be considered, and further research is needed to identify unnecessary transfers. Their reduction may diminish spread of contagious pathogens in the hospital environment.

Nosocomial transmission and outbreaks of coronavirus disease 2019: the need to protect both patients and healthcare workers

OBJECTIVES

To compile current published reports on nosocomial outbreaks of severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2), evaluate the role of healthcare workers (HCWs) in transmission, and evaluate outbreak management practices.

METHODS

Narrative literature review.

SHORT CONCLUSION

The coronavirus disease 2019 (COVID-19) pandemic has placed a large burden on hospitals and healthcare providers worldwide, which increases the risk of nosocomial transmission and outbreaks to "non-COVID" patients or residents, who represent the highest-risk population in terms of mortality, as well as HCWs. To date, there are several reports on nosocomial outbreaks of SARS-CoV-2, and although the attack rate is variable, it can be as high as 60%, with high mortality. There is currently little evidence on transmission dynamics, particularly using genomic sequencing, and the role of HCWs in initiating or amplifying nosocomial outbreaks is not elucidated. There has been a paradigm shift in management practices of viral respiratory outbreaks, that includes widespread testing of patients (or residents) and HCWs, including asymptomatic individuals. These expanded testing criteria appear to be crucial in identifying and controlling outbreaks.