Hospital Occupancy Research Articles

Developing a length of stay prediction model for newborns, achieving better accuracy with greater usability

BackgroundOne in ten newborn children is born prematurely. The elongated length of stay (LOS) of these children in the Neonatal Intensive Care Unit (NICU) has important implications on hospital occupancy figures, healthcare and management costs, as well as the psychology of parents. In order to allow accurate planning and resource allocation, this study aims to create a generalizable and robust model to predict the NICU LOS of preterm newborns. MethodsData were collected from a large tertiary center NICU between 2011 and 2018 and relates to 5,362 newborns. The selected model was externally validated using a data set of 8,768 newborns from another tertiary center NICU. This report compares several models, such as Random Forest (RF), quantile RF, and other feature selection methods, including LASSO and AIC step-forward selection. In addition, a novel step-forward selection based on False Discovery Rate (FDR) for quantile regression is presented and evaluated. ResultsA high-orderquantile regression model for predicting preterm newborns’ LOS that uses only four features available at birth had more attractive properties than other richer ones. The model achieved a Mean Absolute Error (MAE) of 6.26 days on the internal validation set (average LOS 27.04) and an MAE of 6.04 days on the external validation set (average LOS 29.32). The suggested model surpassed the accuracy obtained by models in the literature. It is shown empirically that the FDR-based selection has better properties than the AIC-based step-forward selection approach. ConclusionThis paper demonstrates a process to create a predictive model for NICU LOS in preterm newborns, where each step is reasoned. We obtain a simple and robust model for NICU LOS prediction, which achieves far better results than the current model used for financing NICUs. Utilizing this model, we have created an easy-to-use online web application to ease parents' worries and to assist NICU management: https://tzviel.shinyapps.io/calcuLOS.

Concerning emergence of a new vancomycin-resistant Enterococcus faecium strain ST1299/CT1903/vanA at a tertiary university centre in South Germany

vanB-carrying vancomycin-resistant Enterococcus faecium (VREfm) of the sequence types 80 (ST80) and ST117 have dominated Germany in the past. In 2020, our hospital witnessed a sharp increase in the proportion of vanA-positive VREfm. To attempt to understand these dynamics through whole-genome sequencing (WGS) and analysis of nosocomial transmissions. At our hospital, the first VREfm isolate per patient, treated during 2020, was analysed retrospectively using specific vanA/vanB PCR, WGS, multi-locus sequence typing (MLST), and core-genome (cg) MLST. Epidemiologic links between VRE-positive patients were assessed using hospital occupancy data. Isolates from 319 out of 356 VREfm patients were available for WGS, of which 181 (56.7%) fulfilled the ECDC definition for nosocomial transmission. The high load of nosocomial cases is reflected in the overall high clonality rate with only three dominating sequence (ST) and complex types (CT), respectively: the new emerging strain ST1299 (100% vanA, 77.4% CT1903), and the well-known ST80 (90.0% vanB, 81.0% CT1065) and ST117 (78.0% vanB, 65.0% CT71). The ST1299 isolates overall, and the subtype CT1903 in particular, showed high isolate clonality, which demonstrates impressively high spreading potential. Overall, 152 out of 319 isolates had an allelic cgMLST difference of ≤3 to another, including 91 (59.6%) ST1299. Occupancy data identified shared rooms (3.7%), shared departments (6.2%), and VRE-colonized prior room occupants (0.6%) within 30 days before diagnosis as solid epidemiological links. A new emerging VREfm clone, ST1299/CT1903/vanA, dominated our institution in 2020 and has been an important driver of the increasing VREfm rates.

Safety of Same-Day Hospital Discharge Post Patent Foramen Ovale Closure: Findings from a Multicenter Study

Transcatheter patent foramen ovale (PFO) closure is indicated for patients with cryptogenic stroke. Although procedural safety is well established, there are limited data on the safety of same-day (SD) discharge. We aimed to review the outcomes of PFO closure with SD. Patients who underwent transcatheter PFO closure between January 2011 and May 2022 at 4 large US hospitals were retrospectively analyzed, comparing outcomes of SD versus delayed discharge (DD). The primary end point was a composite of access-site complication, stroke, device embolization, atrial arrhythmia, and bleeding. Secondary analysis comparing imaging modality and outcomes was performed. 554 patients (49.2% female) were analyzed (382 discharged SD). Average age was 54.3 ± 15. Baseline characteristics in both groups were broadly similar. Previous stroke (78.0% SD vs 76.2% DD, p=0.32) was the commonest indication for PFO closure. In the SD group, there was less general anesthesia use (5.5% vs 16.9%, p <0.001). Intraprocedural intracardiac echocardiography was used more frequently in SD cases (95.0% vs 81.4%, p <0.001). In the DD group, median stay was 1 night, and 34.9% stayed beyond 1 night. At 30days, there was no difference in the primary composite end point (14.9% vs 11.6%, p=0.15). There was no inter-group difference in individual adverse events (all p >0.05). When comparing imaging modality and outcomes, there was no difference in composite end points between transesophageal and intracardiac echocardiography (6.5% vs 14.7%, p=0.063). In conclusion, SD discharge after transcatheter PFO closure appears safe. This efficient approach may be advantageous in optimizing workflow and minimizing hospital occupancy.

Impacts of the COVID-19 pandemic on private hospitals in Brazil.

To analyze the operational and/or financial impacts of regulatory measures implemented by the National Agency for Supplementary Health and government health departments in response to the COVID-19 pandemic on a group of 118 private hospitals affiliated with the National Association of Private Hospitals. This study used a quantitative methodological design of descriptive, cross-sectional, and retrospective studies, utilizing secondary data provided by the National Agency for Supplementary Health. There was a -20.1% reduction in hospital admissions and hospital occupancy rate and a decrease of -4.4 percentage points in earnings before interest, taxes, depreciation, and amortization margins. Additionally, the average length of hospital stay increased by +0.5, while total expenses for hospital leaves and total net revenue for hospital leaves increased by +39.4% and +23.6%, respectively. Comparing the participation of the different International Classification of Diseases, according to ICD-10 in 2019 and 2020, revealed the following variations in percentage points of hospitalizations. Hospitalizations for infectious diseases (including COVID-19), treatment of neoplasms, and pregnancy increased by +2.1, +2.4, and +1.2, respectively. However, hospitalizations for respiratory diseases decreased by -4.1. The most critical period of the pandemic required the redirection of activities to concentrate efforts on caring for COVID-19 cases. This situation highlighted the non-prioritization of primary care, as many problems presented by patients not affected by COVID-19 had to be referred to emergency services when and if appropriate.

Effect of Implementing a Commercial Electronic Early Warning System on Outcomes of Hospitalized Patients.

Despite the widespread adoption of early warning systems (EWSs), it is uncertain if their implementation improves patient outcomes. The authors report a pre-post quasi-experimental evaluation of a commercially available EWS on patient outcomes at a 700-bed academic medical center. The EWS risk scores were visible in the electronic medical record by bedside clinicians. The EWS risk scores were also monitored remotely 24/7 by critical care trained nurses who actively contacted bedside nurses when a patient's risk levels increased. The primary outcome was inpatient mortality. Secondary outcomes were rapid response team calls and activation of cardiopulmonary arrest (code-4) response teams. The study team conducted a regression discontinuity analysis adjusting for age, gender, insurance, severity of illness, risk of mortality, and hospital occupancy at admission. The analysis included 53,229 hospitalizations. Adjusted analysis showed no significant change in inpatient mortality, rapid response team call, or code-4 activations after implementing the EWS. This study confirms the continued uncertainty in the effectiveness of EWSs and the need for further rigorous examinations of EWSs.

Empirical distributions of time intervals between COVID-19 cases and more severe outcomes in Scotland.

A critical factor in infectious disease control is the risk of an outbreak overwhelming local healthcare capacity. The overall demand on healthcare services will depend on disease severity, but the precise timing and size of peak demand also depends on the time interval (or clinical time delay) between initial infection, and development of severe disease. A broader distribution of intervals may draw that demand out over a longer period, but have a lower peak demand. These interval distributions are therefore important in modelling trajectories of e.g. hospital admissions, given a trajectory of incidence. Conversely, as testing rates decline, an incidence trajectory may need to be inferred through the delayed, but relatively unbiased signal of hospital admissions. Healthcare demand has been extensively modelled during the COVID-19 pandemic, where localised waves of infection have imposed severe stresses on healthcare services. While the initial acute threat posed by this disease has since subsided with immunity buildup from vaccination and prior infection, prevalence remains high and waning immunity may lead to substantial pressures for years to come. In this work, then, we present a set of interval distributions, for COVID-19 cases and subsequent severe outcomes; hospital admission, ICU admission, and death. These may be used to model more realistic scenarios of hospital admissions and occupancy, given a trajectory of infections or cases. We present a method for obtaining empirical distributions using COVID-19 outcomes data from Scotland between September 2020 and January 2022 (N = 31724 hospital admissions, N = 3514 ICU admissions, N = 8306 mortalities). We present separate distributions for individual age, sex, and deprivation of residing community. While the risk of severe disease following COVID-19 infection is substantially higher for the elderly and those residing in areas of high deprivation, the length of stay shows no strong dependence, suggesting that severe outcomes are equally severe across risk groups. As Scotland and other countries move into a phase where testing is no longer abundant, these intervals may be of use for retrospective modelling of patterns of infection, given data on severe outcomes.

Antimicrobial resistance landscape and COVID-19 impact in Egypt, Iraq, Jordan, and Lebanon: A survey-based study and expert opinion.

The objective of this study was to assess the antimicrobial resistance (AMR) landscape and the impact of COVID-19 on AMR in Egypt, Iraq, Jordan, and Lebanon, and to gather expert opinions on the barriers to the implementation of antimicrobial stewardship (AMS) initiatives in the region. A cross-sectional questionnaire survey was used to assess the current AMR landscape, existing AMS initiatives, barriers to implementing AMS initiatives, and the impact of COVID-19 on AMR in the four countries. The survey was completed by 204 physicians from Egypt (n = 82), Lebanon (n = 49), Iraq (n = 43), and Jordan (n = 30). Previous antibiotic use and previous bacterial colonization were perceived as the most common risk factors for an increase in AMR. According to the survey, multidrug-resistant (MDR) gram-negative bacteria were most common in lower respiratory tract infections, and Klebsiella pneumoniae and Escherichia coli were the most commonly identified gram-negative bacteria in hospital-acquired infections. Only 14.8% of pediatric physicians and 28.6% of adult physicians reported that target pathogen genotyping and phenotyping were done in hospitals, and the most commonly reported reasons for the lack of testing were technological and resource constraints. These constraints, coupled with the scarcity and high cost of newer antibiotics, have been identified as the most significant barriers to the successful management of MDR gram-negative bacterial infections in the region. It was reported that the spectrum of activity and safety of the antibiotic, the site of infection, the presence of comorbidities, and published guidelines and local antibiograms determined the choice of empirical antibiotic therapy for patients in the region. The four countries experienced a significant rise in AMR due to several factors during the COVID-19 pandemic, including an increase in hospital occupancy, a shift in priorities away from AMR surveillance, and changes in AMR epidemiology. Additionally, the large volumes of unnecessary and unsubstantiated antibiotic prescriptions during the COVID-19 pandemic has led to subsequent antibiotic shortages and significant increases in AMR in the region. Physicians also noted that the majority of COVID-19 patients were already on antibiotics before visiting the healthcare facility. MDR gram-negative bacteria were found in the majority of COVID-19 patients admitted to the intensive care unit. Despite the fact that various AMS initiatives have been implemented, they are not standardized across the region. Some of the main barriers to AMS implementation in the region are a lack of adequately trained AMS staff, lack of AMS knowledge and training among healthcare professionals, financial constraints, and the lack of AMR surveillance systems. These survey results provide valuable insights into the existing AMR and AMS landscape in the region, as well as the barriers that impede efficient AMS and AMR management. Based on these findings, the authors developed a call to action that suggests ways for each country in the region to address these challenges.

An interrupted time series study using administrative health data to examine the impact of the COVID-19 pandemic on alternate care level acute hospitalizations in Ontario, Canada.

Many health systems struggle with delayed discharges (known as alternate level of care [ALC] in Canada). Our objectives were to describe and compare patient and hospitalization characteristics by ALC status, and to examine the impact of the initial period of the COVID-19 pandemic on ALC rates in Ontario, Canada. We conducted an interrupted time series using linked administrative data for acute care hospital discharges in Ontario between Feb. 28, 2018, and Nov. 30, 2020. We measured the monthly ALC rate among discharges before and after the onset of the COVID-19 pandemic (Mar. 1, 2020). We used interrupted time series regressions to examine the association between the onset of the pandemic and average ALC monthly rates. We identified no meaningful differences in patient and admission characteristics, irrespective of time; however, differences were identified by ALC status. The overall average monthly rate of ALC discharges before the COVID-19 pandemic was 4.9% and after the onset of the pandemic was 5.0%. These discharges dropped to 4.3% (n = 3558) in March 2020 but then rebounded to their peak of 5.8% (n = 3915). There was no significant change in the average level of ALC rates per month after the onset of the pandemic (increase of 0.36% average per month, 95% confidence interval [CI] -0.11% to 0.83%) or monthly rate of change (slope) after the onset of the pandemic (-0.08%, 95% CI -0.15 to 0). We identified a continued high rate of hospital discharges with an ALC component despite the considerable efforts in hospital to reduce hospital occupancy during the COVID-19 pandemic. Future research should examine why ALC rates remain high despite hospital efforts.

Development of Patient Monitoring System Application: Integrating Design Thinking and QFD Method

COVID 19 and other diseases with high risk of transmission lead to hospital occupancy restrictions. On the other hand, the patient’s family needs to know the most recent patient condition. This research aims to design a patient monitoring system. Thus, the patient's family can monitor the patient's condition without visiting the hospital to reduce the transmission risk. The process of designing this monitoring system is carried out using a design thinking approach which consists of an empathy process, define, ideate, prototype, and test. This design thinking approach is combined with the Quality Function Deployment method. This approach was chosen because design thinking can solve problems effectively by understanding user needs and redefining the user experience problems. The result of this research is the patient monitoring system mockup. The mockup usability was tested using usability questionnaire. Respondents who are stakeholders in one of the hospitals agree that the developed patient monitoring system could solve existing problems.

A simulation model for predicting hospital occupancy for Covid-19 using archetype analysis

COVID-19 pandemic has sent millions of people to hospitals worldwide, exhausting on many occasions the capacity of healthcare systems to provide care patients required to survive. Although several epidemiological research works have contributed a variety of models and approaches to anticipate the pandemic spread, very few have tried to translate the output of these models into hospital service requirements, particularly in terms of bed occupancy, a key question for hospital managers. This paper proposes a tool for predicting the current and future occupancy associated with COVID-19 patients of a hospital to help managers make informed decisions to maximize the availability of hospitalization and intensive care unit (ICU) beds and ensure adequate access to services for confirmed COVID-19 patients. The proposed tool uses a discrete event simulation approach that uses archetypes (i.e., empirical models of trajectories) extracted from empirical analysis of actual patient trajectories. Archetypes can be fitted to trajectories observed in different regions or to the particularities of current and forthcoming variants using a rather small amount of data. Numerical experiments on realistic instances demonstrate the accuracy of the tool’s predictions and illustrate how it can support managers in their daily decisions concerning the system’s capacity and ensure patients the access the resources they require.

Wastewater-based modeling, reconstruction, and prediction for COVID-19 outbreaks in Hungary caused by highly immune evasive variants

(Motivation)Wastewater-based epidemiology (WBE) has emerged as a promising approach for monitoring the COVID-19 pandemic, since the measurement process is cost-effective and is exposed to fewer potential errors compared to other indicators like hospitalization data or the number of detected cases. Consequently, WBE was gradually becoming a key tool for epidemic surveillance and often the most reliable data source, as the intensity of clinical testing for COVID-19 drastically decreased by the third year of the pandemic. Recent results suggests that the model-based fusion of wastewater measurements with clinical data and other indicators is essential in future epidemic surveillance. (Method)In this work, we developed a wastewater-based compartmental epidemic model with a two-phase vaccination dynamics and immune evasion. We proposed a multi-step optimization-based data assimilation method for epidemic state reconstruction, parameter estimation, and prediction. The computations make use of the measured viral load in wastewater, the available clinical data (hospital occupancy, delivered vaccine doses, and deaths), the stringency index of the official social distancing rules, and other measures. The current state assessment and the estimation of the current transmission rate and immunity loss allow a plausible prediction of the future progression of the pandemic. (Results)Qualitative and quantitative evaluations revealed that the contribution of wastewater data in our computational epidemiological framework makes predictions more reliable. Predictions suggest that at least half of the Hungarian population has lost immunity during the epidemic outbreak caused by the BA.1 and BA.2 subvariants of Omicron in the first half of 2022. We obtained a similar result for the outbreaks caused by the subvariant BA.5 in the second half of 2022. (Applicability)The proposed approach has been used to support COVID management in Hungary and could be customized for other countries as well.

Design of effective outpatient sentinel surveillance for COVID-19 decision-making: a modeling study

BackgroundDecision-makers impose COVID-19 mitigations based on public health indicators such as reported cases, which are sensitive to fluctuations in supply and demand for diagnostic testing, and hospital admissions, which lag infections by up to two weeks. Imposing mitigations too early has unnecessary economic costs while imposing too late leads to uncontrolled epidemics with unnecessary cases and deaths. Sentinel surveillance of recently-symptomatic individuals in outpatient testing sites may overcome biases and lags in conventional indicators, but the minimal outpatient sentinel surveillance system needed for reliable trend estimation remains unknown.MethodsWe used a stochastic, compartmental transmission model to evaluate the performance of various surveillance indicators at reliably triggering an alarm in response to, but not before, a step increase in transmission of SARS-CoV-2. The surveillance indicators included hospital admissions, hospital occupancy, and sentinel cases with varying levels of sampling effort capturing 5, 10, 20, 50, or 100% of incident mild cases. We tested 3 levels of transmission increase, 3 population sizes, and conditions of either simultaneous transmission increase or lagged increase in the older population. We compared the indicators’ performance at triggering alarm soon after, but not prior, to the transmission increase.ResultsCompared to surveillance based on hospital admissions, outpatient sentinel surveillance that captured at least 20% of incident mild cases could trigger an alarm 2 to 5 days earlier for a mild increase in transmission and 6 days earlier for a moderate or strong increase. Sentinel surveillance triggered fewer false alarms and averted more deaths per day spent in mitigation. When transmission increase in older populations lagged the increase in younger populations by 14 days, sentinel surveillance extended its lead time over hospital admissions by an additional 2 days.ConclusionsSentinel surveillance of mild symptomatic cases can provide more timely and reliable information on changes in transmission to inform decision-makers in an epidemic like COVID-19.

IMPLEMENTATION OF AN ANKLE FRACTURE FIXATION PATHWAY TO REDUCE HOSPITAL BED OCCUPANCY

Ankle fractures are among the most common types of fractures. If surgery is not performed within 12 to 24 hours, ankle swelling is likely to develop and delay the operative fixation. This leads to patients staying longer in the ward waiting and increased hospital occupancy. This prolonged stay has significant financial implication as well as it is frustrating for both patients and health care professionals.The aim was to formulate a pathway for the ankle fracture patients coming to the emergency department, outpatients and planned for operative intervention. To identify whether pre-operative hospital admissions of stable ankle fracture patients are reduced with the implementation of the pathway. We formulated an ankle fracture fixation pathway, which was approved for use in December 2020. A retrospective analysis of 6 months hospital admissions of ankle fracture patients in the period between January to June 2020. The duration from admission to the actual surgery was collected to review if some admissions could have been avoided and patients brought directly on the surgery day.A total of 23 patients were included. Mean age was 60.5 years and SD was 17years. 94% of patients were females. 10 patients were appropriately discharged.7 Patients were appropriately admitted. 6 Patients were unnecessarily admitted. These 6 patients were admitted on presentation to ED. Retrospective analysis of this audit showed that this cohort of patients met the safe discharge criteria and could have been discharged. Duration of unnecessary stay ranged from 1 to 11 days (21 days in total). Total saving could have been £6300.Standards were met in 74% of cases. Preoperative hospital admission could be reduced with the proposed pathway. It is a valuable tool to be used and should be implemented to reduce unnecessary hospital admissions.

Resources availability and COVID-19 mortality among US counties.

The COVID-19 was declared a pandemic by WHO on 03/2020 has claimed millions of lives worldwide. The US leads all countries in COVID-19-related deaths. Individual level (preexisting conditions and demographics) and county-level (availability of resources) factors have been attributed to increased risk of COVID-19-related deaths. This study builds on previous studies to assess the relationship between county-level resources and COVID-19 mortality among 2,438 US counties. We merged 2019 data from AHA, AHRF, and USA FACTS. The dependent variable was the total number of COVID-19-related deaths. Independent variables included county-level resources: (1) hospital staffing levels (FTE RNs, hospitalists, and intensivists) per 10,000 population; (2) hospital capacity (occupancy rate, proportion of teaching hospitals, and number of airborne infection control rooms per 10,000 population); and (3) macroeconomic resources [per capita income and location (urban/rural)]. We controlled for population 65+, racial/ethnic minority, and COVID-19 deaths per 1,000 population. A negative binomial regression was used. Hospital staffing per 10,000 population {FTE RN [IRR = 0.997; CI (0.995-0.999)], FTE hospitalists [IRR = 0.936; CI (0.897-0.978)], and FTE intensivists [IRR = 0.606; CI (0.516-0.712)]} was associated with lower COVID-19-related deaths. Hospital occupancy rate, proportion of teaching hospitals, and total number of airborne infection control rooms per 10,000 population were positively associated with COVID-19-related deaths. Per capita income and being in an urban county were positively associated with COVID-19-related deaths. Finally, the proportion of 65+, racial/ethnic minorities, and the number of cases were positively associated with COVID-19-related deaths. Our findings suggest that focusing on maintaining adequate hospital staffing could improve COVID-19 mortality.

RECALMIN IV. Evolution in the activity of internal medicine units of the National Health System (2008–2021)

AimsThis work aims to analyze the structure, activity, and outcomes of internal medicine units and departments (IMU) of the Spanish National Health System (SNHS) and to analyze the challenges for the specialty and propose policies for improvement. It also aims to compare the results from the 2021 RECALMIN survey with IMU surveys from previous years (2008, 2015, 2017, 2019). MethodsThis work is a cross-sectional, descriptive study of IMUs in acute care general hospitals of the SNHS that compares data from 2020 with previous studies. The study variables were collected through an ad hoc questionnaire. ResultsBetween 2014 and 2020, hospital occupancy and discharges by IMU increased (annual mean of 4% and 3.8%, respectively), as did hospital cross-consultation and initial consultation rates (2.1% in both cases). E-consultations increased notably in 2020. Risk-adjusted mortality and length of hospital stay did not show significant changes from 2013−2020. Progress in the implementation of good practices and systematic care for complex chronic patients was limited. A consistent finding in RECALMIN surveys was the variability among IMUs in terms of resources and activity, though no statistically significant differences were found in regard to outcomes. ConclusionsThere is considerable room for improvement in the operation of IMUs. The reduction in unjustified variability in clinical practice and inequities in health outcomes are a challenge for IMU managers and the Spanish Society of Internal Medicine.

Quantifying the effect of delaying the second COVID-19 vaccine dose in England: a mathematical modelling study

The UK was the first country to start national COVID-19 vaccination programmes, initially administering doses 3 weeks apart. However, early evidence of high vaccine effectiveness after the first dose and the emergence of the SARS-CoV-2 alpha variant prompted the UK to extend the interval between doses to 12 weeks. In this study, we aimed to quantify the effect of delaying the second vaccine dose in England. We used a previously described model of SARS-CoV-2 transmission, calibrated to COVID-19 surveillance data from England, including hospital admissions, hospital occupancy, seroprevalence data, and population-level PCR testing data, using a Bayesian evidence-synthesis framework. We modelled and compared the epidemic trajectory in the counterfactual scenario in which vaccine doses were administered 3 weeks apart against the real reported vaccine roll-out schedule of 12 weeks. We estimated and compared the resulting numbers of daily infections, hospital admissions, and deaths. In sensitivity analyses, we investigated scenarios spanning a range of vaccine effectiveness and waning assumptions. In the period from Dec 8, 2020, to Sept 13, 2021, the number of individuals who received a first vaccine dose was higher under the 12-week strategy than the 3-week strategy. For this period, we estimated that delaying the interval between the first and second COVID-19 vaccine doses from 3 to 12 weeks averted a median (calculated as the median of the posterior sample) of 58 000 COVID-19 hospital admissions (291 000 cumulative hospitalisations [95% credible interval 275 000-319 000] under the 3-week strategy vs 233 000 [229 000-238 000] under the 12-week strategy) and 10 100 deaths (64 800 deaths [60 200-68 900] vs 54 700 [52 800-55 600]). Similarly, we estimated that the 3-week strategy would have resulted in more infections compared with the 12-week strategy. Across all sensitivity analyses the 3-week strategy resulted in a greater number of hospital admissions. In results by age group, the 12-week strategy led to more hospitalisations and deaths in older people in spring 2021, but fewer following the emergence of the delta variant during summer 2021. England's delayed-second-dose vaccination strategy was informed by early real-world data on vaccine effectiveness in the context of limited vaccine supplies in a growing epidemic. Our study shows that rapidly providing partial (single-dose) vaccine-induced protection to a larger proportion of the population was successful in reducing the burden of COVID-19 hospitalisations and deaths overall. UK National Institute for Health Research; UK Medical Research Council; Community Jameel; Wellcome Trust; UK Foreign, Commonwealth and Development Office; Australian National Health and Medical Research Council; and EU.

Effective pandemic policy design through feedback does not need accurate predictions.

The COVID-19 pandemic has had an enormous toll on human health and well-being and led to major social and economic disruptions. Public health interventions in response to burgeoning case numbers and hospitalizations have repeatedly bent down the epidemic curve, effectively creating a feedback control system. Worst case scenarios have been avoided in many places through this responsive feedback. We aim to formalize and illustrate how to incorporate principles of feedback control into pandemic projections and decision-making, and ultimately shift the focus from prediction to the design of interventions. Starting with an epidemiological model for COVID-19, we illustrate how feedback control can be incorporated into pandemic management using a simple design that couples recent changes in case numbers or hospital occupancy with explicit policy restrictions. We demonstrate robust ability to control a pandemic using a design that responds to hospital cases, despite simulating large uncertainty in reproduction number R0 (range: 1.04-5.18) and average time to hospital admission (range: 4-28 days). We show that shorter delays, responding to case counts versus hospital measured infections, reduce both the cumulative case count and the average level of interventions. Finally, we show that feedback is robust to changing compliance to public health directives and to systemic changes associated with variants of concern and with the introduction of a vaccination program. The negative impact of a pandemic on human health and societal disruption can be reduced by coupling models of disease propagation with models of the decision-making process. In contrast to highly varying open-loop projections, incorporating feedback explicitly in the decision-making process is more reflective of the real-world challenge facing public health decision makers. Using feedback principles, effective control strategies can be designed even if the pandemic characteristics are highly uncertain, encouraging earlier and smaller actions that reduce both case counts and the extent of interventions.

A multistate model and its standalone tool to predict hospital and ICU occupancy by patients with COVID-19

ObjectiveThis study aims to build a multistate model and describe a predictive tool for estimating the daily number of intensive care unit (ICU) and hospital beds occupied by patients with coronavirus 2019 disease (COVID-19). Material and methodsThe estimation is based on the simulation of patient trajectories using a multistate model where the transition probabilities between states are estimated via competing risks and cure models. The input to the tool includes the dates of COVID-19 diagnosis, admission to hospital, admission to ICU, discharge from ICU and discharge from hospital or death of positive cases from a selected initial date to the current moment. Our tool is validated using 98,496 cases positive for severe acute respiratory coronavirus 2 extracted from the Aragón Healthcare Records Database from July 1, 2020 to February 28, 2021. ResultsThe tool demonstrates good performance for the 7- and 14-days forecasts using the actual positive cases, and shows good accuracy among three scenarios corresponding to different stages of the pandemic: 1) up-scenario, 2) peak-scenario and 3) down-scenario. Long term predictions (two months) also show good accuracy, while those using Holt-Winters positive case estimates revealed acceptable accuracy to day 14 onwards, with relative errors of 8.8%. DiscussionIn the era of the COVID-19 pandemic, hospitals must evolve in a dynamic way. Our prediction tool is designed to predict hospital occupancy to improve healthcare resource management without information about clinical history of patients. ConclusionsOur easy-to-use and freely accessible tool (https://github.com/peterman65) shows good performance and accuracy for forecasting the daily number of hospital and ICU beds required for patients with COVID-19.

Modelling the impact of the Omicron BA.5 subvariant in New Zealand.

New Zealand experienced a wave of the Omicron variant of SARS-CoV-2 in early 2022, which occurred against a backdrop of high two-dose vaccination rates, ongoing roll-out of boosters and paediatric doses, and negligible levels of prior infection. New Omicron subvariants have subsequently emerged with a significant growth advantage over the previously dominant BA.2. We investigated a mathematical model that included waning of vaccine-derived and infection-derived immunity, as well as the impact of the BA.5 subvariant which began spreading in New Zealand in May 2022. The model was used to provide scenarios to the New Zealand Government with differing levels of BA.5 growth advantage, helping to inform policy response and healthcare system preparedness during the winter period. In all scenarios investigated, the projected peak in new infections during the BA.5 wave was smaller than in the first Omicron wave in March 2022. However, results indicated that the peak hospital occupancy was likely to be higher than in March 2022, primarily due to a shift in the age distribution of infections to older groups. We compare model results with subsequent epidemiological data and show that the model provided a good projection of cases, hospitalizations and deaths during the BA.5 wave.

Generating simple classification rules to predict local surges in COVID-19 hospitalizations

Low rates of vaccination, emergence of novel variants of SARS-CoV-2, and increasing transmission relating to seasonal changes and relaxation of mitigation measures leave many US communities at risk for surges of COVID-19 that might strain hospital capacity, as in previous waves. The trajectories of COVID-19 hospitalizations differ across communities depending on their age distributions, vaccination coverage, cumulative incidence, and adoption of risk mitigating behaviors. Yet, existing predictive models of COVID-19 hospitalizations are almost exclusively focused on national- and state-level predictions. This leaves local policymakers in urgent need of tools that can provide early warnings about the possibility that COVID-19 hospitalizations may rise to levels that exceed local capacity. In this work, we develop a framework to generate simple classification rules to predict whether COVID-19 hospitalization will exceed the local hospitalization capacity within a 4- or 8-week period if no additional mitigating strategies are implemented during this time. This framework uses a simulation model of SARS-CoV-2 transmission and COVID-19 hospitalizations in the US to train classification decision trees that are robust to changes in the data-generating process and future uncertainties. These generated classification rules use real-time data related to hospital occupancy and new hospitalizations associated with COVID-19, and when available, genomic surveillance of SARS-CoV-2. We show that these classification rules present reasonable accuracy, sensitivity, and specificity (all ≥ 80%) in predicting local surges in hospitalizations under numerous simulated scenarios, which capture substantial uncertainties over the future trajectories of COVID-19. Our proposed classification rules are simple, visual, and straightforward to use in practice by local decision makers without the need to perform numerical computations.