Forecasting hospital-level COVID-19 admissions using real-time mobility data.

BACKGROUND: For each of the COVID-19 pandemic waves, hospitals have had to plan for deploying surge capacity and resources to manage large but transient increases in COVID-19 admissions. While a lot of effort has gone into predicting regional trends in COVID-19 cases and hospitalizations, there are far fewer successful tools for creating accurate hospital-level forecasts. METHODS: Large-scale, anonymized mobile phone data has been shown to correlate with regional case counts during the first two waves of the pandemic (spring 2020, and fall/winter 2021). Building off this success, we developed a multi-step, recursive forecasting model to predict individual hospital admissions; this model incorporates the following data: (i) hospital-level COVID-19 admissions, (ii) statewide test positivity data, and (iii) aggregate measures of large-scale human mobility, contact patterns, and commuting volume. RESULTS: Incorporating large-scale, aggregate mobility data as exogenous variables in prediction models allows us to make hospital-specific COVID-19 admission forecasts 21 days ahead. We show this through highly accurate predictions of hospital admissions for five hospitals in Massachusetts during the first year of the COVID-19 pandemic. CONCLUSIONS: The high predictive capability of the model was achieved by combining anonymized, aggregated mobile device data about users' contact patterns, commuting volume, and mobility range with COVID hospitalizations and test-positivity data. Mobility-informed forecasting models can increase the lead-time of accurate predictions for individual hospitals, giving managers valuable time to strategize how best to allocate resources to manage forthcoming surges.

During the COVID-19 pandemic, hospitals have needed to make challenging decisions around staffing and preparedness based on estimates of the number of admissions multiple weeks ahead. Forecasting techniques using methods from machine learning have been successfully applied to predict hospital admissions statewide, but the ability to accurately predict individual hospital admissions has proved elusive. Here, we incorporate details of the movement of people obtained from mobile phone data into a model that makes accurate predictions of the number of people who will be hospitalized 21 days ahead. This model will be useful for administrators and healthcare workers to plan staffing and discharge of patients to ensure adequate capacity to deal with forthcoming hospital admissions.

Coronavirus disease 2019 (COVID-19) screening system utilizing daily symptom attestation helps identify hospital employees who should be tested to protect patients and coworkers.

OBJECTIVE: To investigate the effectiveness of a daily attestation system used by employees of a multi-institutional academic medical center, which comprised of symptom-screening, self-referrals to the Occupational Health Services team, and/or a severe acute respiratory coronavirus virus 2 (SARS-CoV-2) test. DESIGN: We conducted a retrospective cohort study of all employee attestations and SARS-CoV-2 tests performed between March and June 2020. SETTING: A large multi-institutional academic medical center, including both inpatient and ambulatory settings. PARTICIPANTS: All employees who worked at the study site. METHODS: Data were combined from the attestation system (COVIDPass), the employee database, and the electronic health records and were analyzed using descriptive statistics including χ2, Wilcoxon, and Kruskal-Wallis tests. We investigated whether an association existed between symptomatic attestations by the employees and the employee testing positive for SARS-CoV-2. RESULTS: After data linkage and cleaning, there were 2,117,298 attestations submitted by 65,422 employees between March and June 2020. Most attestations were asymptomatic (99.9%). The most commonly reported symptoms were sore throat (n = 910), runny nose (n = 637), and cough (n = 570). Among the 2,026 employees who ever attested that they were symptomatic, 905 employees were tested within 14 days of a symptomatic attestation, and 114 (13%) of these tests were positive. The most common symptoms associated with a positive SARS-CoV-2 test were anosmia (23% vs 4%) and fever (46% vs 19%). CONCLUSIONS: Daily symptom attestations among healthcare workers identified a handful of employees with COVID-19. Although the number of positive tests was low, attestations may help keep unwell employees off campus to prevent transmissions.

A SARS-CoV-2 Cluster in an Acute Care Hospital.

BACKGROUND: Little is known about clusters of severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) infection in acute care hospitals. OBJECTIVE: To describe the detection, mitigation, and analysis of a large cluster of SARS-CoV-2 infections in an acute care hospital with mature infection control policies. DESIGN: Descriptive study. SETTING: Brigham and Women's Hospital, Boston, Massachusetts. PARTICIPANTS: Patients and staff with cluster-related SARS-CoV-2 infections. INTERVENTION: Close contacts of infected patients and staff were identified and tested every 3 days, patients on affected units were preemptively isolated and repeatedly tested, affected units were cleaned, room ventilation was measured, and specimens were sent for whole-genome sequencing. A case-control study was done to compare clinical interactions, personal protective equipment use, and breakroom and workroom practices in SARS-CoV-2-positive versus negative staff. MEASUREMENTS: Description of the cluster, mitigation activities, and risk factor analysis. RESULTS: Fourteen patients and 38 staff members were included in the cluster per whole-genome sequencing and epidemiologic associations. The index case was a symptomatic patient in whom isolation was discontinued after 2 negative results on nasopharyngeal polymerase chain reaction testing. The patient subsequently infected multiple roommates and staff, who then infected others. Seven of 52 (13%) secondary infections were detected only on second or subsequent tests. Eight of 9 (89%) patients who shared rooms with potentially contagious patients became infected. Potential contributing factors included high viral loads, nebulization, and positive pressure in the index patient's room. Risk factors for transmission to staff included presence during nebulization, caring for patients with dyspnea or cough, lack of eye protection, at least 15 minutes of exposure to case patients, and interactions with SARS-CoV-2-positive staff in clinical areas. Whole-genome sequencing confirmed that 2 staff members were infected despite wearing surgical masks and eye protection. LIMITATION: Findings may not be generalizable. CONCLUSION: SARS-CoV-2 clusters can occur in hospitals despite robust infection control policies. Insights from this cluster may inform additional measures to protect patients and staff. PRIMARY FUNDING SOURCE: None.