icumonitoring.ch: a platform for short-term forecasting of intensive care unit occupancy during the COVID-19 epidemic in Switzerland.

In Switzerland, the COVID-19 epidemic is progressively slowing down owing to “social distancing” measures introduced by the Federal Council on 16 March 2020. However, the gradual ease of these measures may initiate a second epidemic wave, the length and intensity of which are difficult to anticipate. In this context, hospitals must prepare for a potential increase in intensive care unit (ICU) admissions of patients with acute respiratory distress syndrome. Here, we introduce icumonitoring.ch, a platform providing hospital-level projections for ICU occupancy. We combined current data on the number of beds and ventilators with canton-level projections of COVID-19 cases from two S-E-I-R models. We disaggregated epidemic projection in each hospital in Switzerland for the number of COVID-19 cases, hospitalisations, hospitalisations in ICU, and ventilators in use. The platform is updated every 3-4 days and can incorporate projections from other modelling teams to inform decision makers with a range of epidemic scenarios for future hospital occupancy.

Time-dependent risk of COVID-19 death with overwhelmed health-care capacity in Japan, 2020-2022.

BACKGROUND: It has been descriptively argued that the case fatality risk (CFR) of coronavirus disease (COVID-19) is elevated when medical services are overwhelmed. The relationship between CFR and pressure on health-care services should thus be epidemiologically explored to account for potential epidemiological biases. The purpose of the present study was to estimate the age-dependent CFR in Tokyo and Osaka over time, investigating the impact of caseload demand on the risk of death. METHODS: We estimated the time-dependent CFR, accounting for time delay from diagnosis to death. To this end, we first determined the time distribution from diagnosis to death, allowing variations in the delay over time. We then assessed the age-dependent CFR in Tokyo and Osaka. In Osaka, the risk of intensive care unit (ICU) admission was also estimated. RESULTS: The CFR was highest among individuals aged 80 years and older and during the first epidemic wave from February to June 2020, estimated as 25.4% (95% confidence interval [CI] 21.1 to 29.6) and 27.9% (95% CI 20.6 to 36.1) in Tokyo and Osaka, respectively. During the fourth wave of infection (caused by the Alpha variant) in Osaka the CFR among the 70s and ≥ 80s age groups was, respectively, 2.3 and 1.5 times greater than in Tokyo. Conversely, despite the surge in hospitalizations, the risk of ICU admission among those aged 80 and older in Osaka decreased. Such time-dependent variation in the CFR was not seen among younger patients < 70 years old. With the Omicron variant, the CFR among the 80s and older in Tokyo and Osaka was 3.2% (95% CI 3.0 to 3.5) and 2.9% (95% CI 2.7 to 3.1), respectively. CONCLUSION: We found that without substantial control, the CFR can increase when a surge in cases occurs with an identifiable elevation in risk-especially among older people. Because active treatment options including admission to ICU cannot be offered to the elderly with an overwhelmed medical service, the CFR value can potentially double compared with that in other areas of health care under less pressure.

Dynamic assessment of surge capacity in a large hospital network during COVID-19 pandemic.

BACKGROUND: The COVID-19 pandemic has provided an unprecedented scenario to deepen knowledge of surge capacity (SC), assessment of which remains a challenge. This study reports a large-scale experience of a multi-hospital network, with the aim of evaluating the characteristics of different hospitals involved in the response and of measuring a real-time SC based on two complementary modalities (actual, base) referring to the intensive care units (ICU). METHODS: Data analysis referred to two consecutive pandemic waves (March-December 2020). Regarding SC, two different levels of analysis are considered: single hospital category (referring to a six-level categorization based on the number of hospital beds) and multi-hospital wide (referring to the response of the entire hospital network). RESULTS: During the period of 114 days, the analysis revealed a key role of the biggest hospitals (>Category-4) in terms of involvement in the pandemic response. In terms of SC, Category-4 hospitals showed the highest mean SC values, irrespective of the calculation method and level of analysis. At the multi-hospital level, the analysis revealed an overall ICU-SC (base) of 84.4% and an ICU-SC (actual) of 106.5%. CONCLUSIONS: The results provide benchmarks to better understand ICU hospital response capacity, highlighting the need for a more flexible approach to SC definition.

Go To Travel campaign and the geographic spread of COVID-19 in Japan.

BACKGROUND: In 2020, the Japanese government implemented first of two Go To Travel campaigns to promote the tourism sector as well as eating and drinking establishments, especially in remote areas. The present study aimed to explore the relationship between enhanced travel and geographic propagation of COVID-19 across Japan, focusing on the second campaign with nationwide large-scale economic boost in 2020. METHODS: We carried out an interrupted time-series analysis to identify the possible cause-outcome relationship between the Go To Travel campaign and the spread of infection to nonurban areas in Japan. Specifically, we counted the number of prefectures that experienced a weekly incidence of three, five, and seven COVID-19 cases or more per 100,000 population, and we compared the rate of change before and after the campaign. RESULTS: Three threshold values and three different models identified an increasing number of prefectures above the threshold, indicating that the inter-prefectural spread intensified following the launch of the second Go To Travel campaign from October 1st, 2020. The simplest model that accounted for an increase in the rate of change only provided the best fit. We estimated that 0.24 (95% confidence interval 0.15 to 0.34) additional prefectures newly exceeded five COVID-19 cases per 100,000 population per week during the second campaign. CONCLUSIONS: The enhanced movement resulting from the Go To Travel campaign facilitated spatial spread of COVID-19 from urban to nonurban locations, where health-care capacity may have been limited.

Navigating hospitals safely through the COVID-19 epidemic tide: Predicting case load for adjusting bed capacity.

BACKGROUND: The pressures exerted by the coronavirus disease 2019 (COVID-19) pandemic pose an unprecedented demand on healthcare services. Hospitals become rapidly overwhelmed when patients requiring life-saving support outpace available capacities. OBJECTIVE: We describe methods used by a university hospital to forecast case loads and time to peak incidence. METHODS: We developed a set of models to forecast incidence among the hospital catchment population and to describe the COVID-19 patient hospital-care pathway. The first forecast utilized data from antecedent allopatric epidemics and parameterized the care-pathway model according to expert opinion (ie, the static model). Once sufficient local data were available, trends for the time-dependent effective reproduction number were fitted, and the care pathway was reparameterized using hazards for real patient admission, referrals, and discharge (ie, the dynamic model). RESULTS: The static model, deployed before the epidemic, exaggerated the bed occupancy for general wards (116 forecasted vs 66 observed), ICUs (47 forecasted vs 34 observed), and predicted the peak too late: general ward forecast April 9 and observed April 8 and ICU forecast April 19 and observed April 8. After April 5, the dynamic model could be run daily, and its precision improved with increasing availability of empirical local data. CONCLUSIONS: The models provided data-based guidance for the preparation and allocation of critical resources of a university hospital well in advance of the epidemic surge, despite overestimating the service demand. Overestimates should resolve when the population contact pattern before and during restrictions can be taken into account, but for now they may provide an acceptable safety margin for preparing during times of uncertainty.

Changes in the Number of Physicians and Hospital Bed Capacity in Europe.

OBJECTIVES: Our aim was to examine the numbers of practicing physicians and total numbers of hospital beds in European Organisation for Economic Co-operation and Development countries. METHODS: Data analyzed were derived from the "Organisation for Economic Co-operation and Development Health Statistics 2020" database between 1980 and 2018. The selected countries were compared according to the type of healthcare system and geographical location by parametric and nonparametric tests. RESULTS: In 1980, Bismarck-type systems showed an average number of physicians of 2.3 persons/1000 population; in Beveridge-type systems, it was 1.7 persons. By 2018, it leveled out reaching 3.9 persons in both healthcare system types. In 1980, average physician number/1000 was 2.5 persons in Eastern Europe; in Western Europe, it was 1.9 persons. By 2018 this proportion changed with Western Europe having the higher number (3.7 persons; 3.9 persons). In 1980, average number of hospital beds/1000 population was 9.6 in Bismarck-type systems whereas in Beveridge-type systems it was 8.8. By 2018, it decreased to 5.6 in Bismarck-type systems (-42%) and to 3.1 in Beveridge-type systems (-65%). In 1980, the average number of hospital beds/1000 population in Eastern Europe was 10.3; in Western Europe, it was 8.5. By 2018, the difference between the 2 regions did not change. CONCLUSIONS: Although the number of physicians was 33% higher in 1980 in Eastern Europe than in Western Europe, by 2018 the number of physicians was 5% higher in Western Europe. In general, regardless of the healthcare system and geographical location, the proportion of physicians per 1000 population has improved due to a larger decrease in the number of hospital beds.

The association of strained ICU capacity with hospital patient racial and ethnic composition and federal relief during the COVID-19 pandemic.

OBJECTIVE: To identify the association between strained intensive care unit (ICU) capacity during the COVID-19 pandemic and hospital racial and ethnic patient composition, federal pandemic relief, and other hospital characteristics. DATA SOURCES: We used government data on hospital capacity during the pandemic and Provider Relief Fund (PRF) allocations, Medicare claims and enrollment data, hospital cost reports, and Social Vulnerability Index data. STUDY DESIGN: We conducted cross-sectional bivariate analyses relating strained capacity and PRF award per hospital bed with hospital patient composition and other characteristics, with and without adjustment for hospital referral region (HRR). DATA COLLECTION: We linked PRF data to CMS Certification Numbers based on hospital name and location. We used measures of racial and ethnic composition generated from Medicare claims and enrollment data. Our sample period includes the weeks of September 18, 2020 through November 5, 2021, and we restricted our analysis to short-term, general hospitals with at least one intensive care unit (ICU) bed. We defined "ICU strain share" as the proportion of ICU days occurring while a given hospital had an ICU occupancy rate ≥ 90%. PRINCIPAL FINDINGS: After adjusting for HRR, hospitals in the top tercile of Black patient shares had higher ICU strain shares than did hospitals in the bottom tercile (30% vs. 22%, p < 0.05) and received greater PRF amounts per bed ($118,864 vs. $92,407, p < 0.05). Having high versus low ICU occupancy relative to pre-pandemic capacity was associated with a modest increase in PRF amounts per bed after adjusting for HRR ($107,319 vs. $96,627, p < 0.05), but there were no statistically significant differences when comparing hospitals with high versus low ICU occupancy relative to contemporaneous capacity. CONCLUSIONS: Hospitals with large Black patient shares experienced greater strain during the pandemic. Although these hospitals received more federal relief, funding was not targeted overall toward hospitals with high ICU occupancy rates.

Association Between Caseload Surge and COVID-19 Survival in 558 U.S. Hospitals, March to August 2020.

BACKGROUND: Several U.S. hospitals had surges in COVID-19 caseload, but their effect on COVID-19 survival rates remains unclear, especially independent of temporal changes in survival. OBJECTIVE: To determine the association between hospitals' severity-weighted COVID-19 caseload and COVID-19 mortality risk and identify effect modifiers of this relationship. DESIGN: Retrospective cohort study. (ClinicalTrials.gov: NCT04688372). SETTING: 558 U.S. hospitals in the Premier Healthcare Database. PARTICIPANTS: Adult COVID-19-coded inpatients admitted from March to August 2020 with discharge dispositions by October 2020. MEASUREMENTS: Each hospital-month was stratified by percentile rank on a surge index (a severity-weighted measure of COVID-19 caseload relative to pre-COVID-19 bed capacity). The effect of surge index on risk-adjusted odds ratio (aOR) of in-hospital mortality or discharge to hospice was calculated using hierarchical modeling; interaction by surge attributes was assessed. RESULTS: Of 144 116 inpatients with COVID-19 at 558 U.S. hospitals, 78 144 (54.2%) were admitted to hospitals in the top surge index decile. Overall, 25 344 (17.6%) died; crude COVID-19 mortality decreased over time across all surge index strata. However, compared with nonsurging (<50th surge index percentile) hospital-months, aORs in the 50th to 75th, 75th to 90th, 90th to 95th, 95th to 99th, and greater than 99th percentiles were 1.11 (95% CI, 1.01 to 1.23), 1.24 (CI, 1.12 to 1.38), 1.42 (CI, 1.27 to 1.60), 1.59 (CI, 1.41 to 1.80), and 2.00 (CI, 1.69 to 2.38), respectively. The surge index was associated with mortality across ward, intensive care unit, and intubated patients. The surge-mortality relationship was stronger in June to August than in March to May (slope difference, 0.10 [CI, 0.033 to 0.16]) despite greater corticosteroid use and more judicious intubation during later and higher-surging months. Nearly 1 in 4 COVID-19 deaths (5868 [CI, 3584 to 8171]; 23.2%) was potentially attributable to hospitals strained by surging caseload. LIMITATION: Residual confounding. CONCLUSION: Despite improvements in COVID-19 survival between March and August 2020, surges in hospital COVID-19 caseload remained detrimental to survival and potentially eroded benefits gained from emerging treatments. Bolstering preventive measures and supporting surging hospitals will save many lives. PRIMARY FUNDING SOURCE: Intramural Research Program of the National Institutes of Health Clinical Center, the National Institute of Allergy and Infectious Diseases, and the National Cancer Institute.

A pragmatic method to compare international critical care beds: Implications to pandemic preparedness and non-pandemic planning.

BACKGROUND: The current method for assessing critical care (CCU) bed numbers between countries is unreliable. METHODS: A pragmatic method is presented using a logarithmic relationship between CCU beds per 1000 deaths and deaths per 1000 population, both of which are readily available. The method relies on the importance of the nearness to death effect, and on the effect of population size. RESULTS: The method was tested using CCU bed numbers from 65 countries. A series of logarithmic relationships can be seen. High versus low countries can be distinguished by adjusting all countries to a common crude mortality rate. Hence at 9.5 deaths per 1000 population 'high' CCU bed countries average of around 30 CCU beds per 1000 deaths, while 'very low' countries only average 3 CCU beds per 1000 deaths. The United Kingdom falls among countries with low critical care provision with an average of 8 CCU beds per 1000 deaths, and during the COVID-19 epidemic UK industry intervened to rapidly manufacture various types of ventilators to avoid a catastrophe. CCU bed numbers in India are around 8.1 per 1000 deaths, which places it in the low category. However, such beds are inequitably distributed with the poorest states all in the 'very low' category. In India only around 50% of CCU beds have a ventilator. CONCLUSION: A feasible region is defined for the optimum number of CCU beds.

Spatial Analysis of COVID-19 cases and intensive care beds in the State of Ceará, Brazil. / Análise Espacial dos Casos de COVID-19 e leitos de terapia intensiva no estado do Ceará, Brasil.

The geographical distribution of COVID-19 through Geographic Information Systems resources is hardly explored. We aimed to analyze the distribution of COVID-19 cases and the exclusive intensive care beds in the state of Ceará, Brazil. This is an ecological study with the geographic distribution of the case detection coefficient in 184 municipalities. Maps of crude and estimated values (global and local Bayesian method) were developed, calculating the Moran index and using BoxMap and MoranMap. Intensive care beds were distributed through geolocalized points. In total, 3,000 cases and 459 beds were studied. The highest rates were found in the capital Fortaleza, the Metropolitan Region (MR), and the south of this region. A positive spatial autocorrelation has been identified in the local Bayesian rate (I = 0.66). The distribution of beds superimposed on the BoxMap shows clusters with a High-High pattern of number of beds (capital, MR, northwestern part). However, a similar pattern is found in the far east or transition areas with insufficient beds. The MoranMap shows clusters statistically significant in the state. COVID-19 interiorization in Ceará requires contingency measures geared to the distribution of specific intensive care beds for COVID-19 cases in order to meet the demand.

A distribuição geográfica da COVID-19 por meio de recursos de Sistemas de Informação Geográfica é pouco explorada. O objetivo foi analisar a distribuição de casos da COVID-19 e de leitos de terapia intensiva exclusivos para a doença no estado do Ceará, Brasil. Estudo ecológico, com distribuição geográfica do coeficiente de detecção de casos da doença em 184 municípios. Construíram-se mapas dos valores brutos e estimados (método bayesiano global e local), com cálculo do índice de Moran e utilização do "BoxMap" e "MoranMap" Os leitos foram distribuídos por meio de pontos geolocalizados. Estudaram-se 3.000 casos e 459 leitos. As maiores taxas encontram-se na capital Fortaleza, região metropolitana (RM) e ao sul dessa região. Há autocorrelação espacial positiva na taxa bayesiana local (I = 0,66). A distribuição dos leitos de terapia intensiva sobreposta ao "BoxMap" evidenciou aglomerados com padrão Alto-Alto apresentando número de leitos (capital, RM, porção noroeste); porém, há o mesmo padrão (extremo leste) e em áreas de transição com insuficiência de leito. O "MoranMap" evidenciou "clusters" estatisticamente significativos no estado. A interiorização da COVID-19 no Ceará demanda medidas de contingência voltadas à distribuição dos leitos de terapia intensiva específicos para casos de COVID19 para atender à demanda.

Surge capacity and capability of intensive care units across a large healthcare system: An operational blueprint for regional integration.

OBJECTIVE: Many hospitals were unprepared for the surge of patients associated with the spread of coronavirus disease 2019 (COVID-19) pandemic. We describe the processes to develop and implement a surge plan framework for resource allocation, staffing, and standardized management in response to the COVID-19 pandemic across a large integrated regional healthcare system. SETTING: A large academic medical center in the Cleveland metropolitan area, with a network of 10 regional hospitals throughout Northeastern Ohio with a daily capacity of more than 500 intensive care unit (ICU) beds. RESULTS: At the beginning of the pandemic, an equitable delivery of healthcare services across the healthcare system was developed. This distribution of resources was implemented with the potential needs and resources of the individual ICUs in mind, and epidemiologic predictions of virus transmissibility. We describe the processes to develop and implement a surge plan framework for resource allocation, staffing, and standardized management in response to the COVID-19 pandemic across a large integrated regional healthcare system. We also describe an additional level of surge capacity, which is available to well-integrated institutions called "extension of capacity." This refers to the ability to immediately have access to the beds and resources within a hospital system with minimal administrative burden. CONCLUSIONS: Large integrated hospital systems may have an advantage over individual hospitals because they can shift supplies among regional partners, which may lead to faster mobilization of resources, rather than depending on local and national governments. The pandemic response of our healthcare system highlights these benefits.

Retrospective modelling of hospital bed capacities associated with the administration of remdesivir during the first wave of COVID-19 in a German metropolitan city.

OBJECTIVES: Internationally, healthcare systems are confronted by an ever-increasing scarcity of medical resources due to the ongoing novel coronavirus disease 2019 (COVID-19) pandemic. The aim of this study was to investigate the impact of remdesivir on the demand of hospital bed capacities for hospitalized COVID-19 patients and to evaluate the potentially created capacities for treating additional COVID-19 patients or elective treatments at the hospital. METHODS: An epidemiological model was developed that utilized the population of Cologne (Germany) during the first COVID-19 wave (first hospitalized patient-30 September 2020) to compare two scenarios: no administration of remdesivir (A) and the administration of remdesivir according to the EMA label (B). The results of the Adaptive COVID-19 Treatment Trial were used to evaluate the potential impact of remdesivir on hospital capacity. RESULTS: With the first recorded patient on 2 March 2020, a total of 576 COVID-19 hospitalized patients were detected during the first wave in Cologne. Comparing both scenarios (A versus B) of the model, the administration of remdesivir increased the number of discharges from 259 to 293 (+5.8%) and fewer patients needed ICU admission [214 versus 178 (-6.3%)]. In addition, the model estimated 20 fewer deaths (scenario B). Based on a reduced length of stay, 31.4 hospital beds (57.0 versus 25.6) could have been freed by administering remdesivir to eligible patients. This would have allowed either the treatment of an additional 730 COVID-19 patients or 660 elective treatments. CONCLUSIONS: In our model, remdesivir administration profoundly contributed to free hospital capacities in the metropolitan city Cologne in Germany.

A country-level analysis comparing hospital capacity and utilisation during the first COVID-19 wave across Europe.

BACKGROUND: The exponential increase in SARS-CoV-2 infections during the first wave of the pandemic created an extraordinary overload and demand on hospitals, especially intensive care units (ICUs), across Europe. European countries have implemented different measures to address the surge ICU capacity, but little is known about the extent. The aim of this paper is to compare the rates of hospitalised COVID-19 patients in acute and ICU care and the levels of national surge capacity for intensive care beds across 16 European countries and Lombardy region during the first wave of the pandemic (28 February to 31 July). METHODS: For this country level analysis, we used data on SARS-CoV-2 cases, current and/or cumulative hospitalised COVID-19 patients and current and/or cumulative COVID-19 patients in ICU care. To analyse whether capacities were exceeded, we also retrieved information on the numbers of hospital beds, and on (surge) capacity of ICU beds during the first wave of the COVID-19 pandemic from the COVID-19 Health System Response Monitor (HSRM). Treatment days and mean length of hospital stay were calculated to assess hospital utilisation. RESULTS: Hospital and ICU capacity varied widely across countries. Our results show that utilisation of acute care bed capacity by patients with COVID-19 did not exceed 38.3% in any studied country. However, the Netherlands, Sweden, and Lombardy would not have been able to treat all patients with COVID-19 requiring intensive care during the first wave without an ICU surge capacity. Indicators of hospital utilisation were not consistently related to the number of SARS-CoV-2 infections. The mean number of hospital days associated with one SARS-CoV-2 case ranged from 1.3 (Norway) to 11.8 (France). CONCLUSION: In many countries, the increase in ICU capacity was important to accommodate the high demand for intensive care during the first COVID-19 wave.

A demand-capacity mismatch between rehabilitation need and service provision as a result of the COVID-19 pandemic? Early clinical observations from a large teaching hospital in London.

In this short report the authors characterise inpatient bed occupancy and predicted rehabilitation need of patients cared for in two acute hospitals of a large London NHS Trust during the first wave of the COVID-19 pandemic, including 394 people with confirmed COVID-19. Data were captured on a single day (17th April 2020) from the two Trust hospitals to inform discharge planning in line with national COVID-19 Hospital Discharge Service policy guidance. Our data suggests that the proportion of COVID-19 patients predicted to require rehabilitation upon hospital discharge may be greater than the estimates described in the national COVID-19 Hospital Discharge Service policy guidance; posing the question is there a demand-capacity mismatch between rehabilitation need and service provision as a result of the COVID-19 pandemic?

Lessons learned from the rapid development of a statewide simulation model for predicting COVID-19's impact on healthcare resources and capacity.

The first case of COVID-19 was detected in North Carolina (NC) on March 3, 2020. By the end of April, the number of confirmed cases had soared to over 10,000. NC health systems faced intense strain to support surging intensive care unit admissions and avert hospital capacity and resource saturation. Forecasting techniques can be used to provide public health decision makers with reliable data needed to better prepare for and respond to public health crises. Hospitalization forecasts in particular play an important role in informing pandemic planning and resource allocation. These forecasts are only relevant, however, when they are accurate, made available quickly, and updated frequently. To support the pressing need for reliable COVID-19 data, RTI adapted a previously developed geospatially explicit healthcare facility network model to predict COVID-19's impact on healthcare resources and capacity in NC. The model adaptation was an iterative process requiring constant evolution to meet stakeholder needs and inform epidemic progression in NC. Here we describe key steps taken, challenges faced, and lessons learned from adapting and implementing our COVID-19 model and coordinating with university, state, and federal partners to combat the COVID-19 epidemic in NC.

Increasing ICU capacity to accommodate higher demand during the COVID-19 pandemic.

OBJECTIVES: To describe the short term ability of Australian intensive care units (ICUs) to increase capacity in response to heightened demand caused by the COVID-19 pandemic. DESIGN: Survey of ICU directors or delegated senior clinicians (disseminated 30 August 2021), supplemented by Australian and New Zealand Intensive Care Society (ANZICS) registry data. SETTING: All 194 public and private Australian ICUs. MAIN OUTCOME MEASURES: Numbers of currently available and potentially available ICU beds in case of a surge; available levels of ICU-relevant equipment and staff. RESULTS: All 194 ICUs responded to the survey. The total number of currently open staffed ICU beds was 2183. This was 195 fewer (8.2%) than in 2020; the decline was greater for rural/regional (18%) and private ICUs (18%). The reported maximal ICU bed capacity (5623) included 813 additional physical ICU bed spaces and 2627 in surge areas outside ICUs. The number of available ventilators (7196) exceeded the maximum number of ICU beds. The reported number of available additional nursing staff would facilitate the immediate opening of 383 additional physical ICU beds (47%), but not the additional bed spaces outside ICUs. CONCLUSIONS: The number of currently available staffed ICU beds is lower than in 2020. Equipment shortfalls have been remediated, with sufficient ventilators to equip every ICU bed. ICU capacity can be increased in response to demand, but is constrained by the availability of appropriately trained staff. Fewer than half the potentially additional physical ICU beds could be opened with currently available staff numbers while maintaining pre-pandemic models of care.

[Bed capacity management in times of the COVID-19 pandemic : A simulation-based prognosis of normal and intensive care beds using the descriptive data of the University Hospital Augsburg]. / Bettenkapazitätssteuerung in Zeiten der COVID-19-Pandemie : Eine simulationsbasierte Prognose der Normal- und Intensivstationsbetten anhand der deskriptiven Daten des Universitätsklinikums Augsburg.

BACKGROUND: Following the regional outbreak in China, severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) has spread all over the world, presenting the healthcare systems with huge challenges worldwide. In Germany the coronavirus diseases 2019 (COVID-19) pandemic has resulted in a slowly growing demand for health care with a sudden occurrence of regional hotspots. This leads to an unpredictable situation for many hospitals, leaving the question of how many bed resources are needed to cope with the surge of COVID-19 patients. OBJECTIVE: In this study we created a simulation-based prognostic tool that provides the management of the University Hospital of Augsburg and the civil protection services with the necessary information to plan and guide the disaster response to the ongoing pandemic. Especially the number of beds needed on isolation wards and intensive care units (ICU) are the biggest concerns. The focus should lie not only on the confirmed cases as the patients with suspected COVID-19 are in need of the same resources. MATERIAL AND METHODS: For the input we used the latest information provided by governmental institutions about the spreading of the disease, with a special focus on the growth rate of the cumulative number of cases. Due to the dynamics of the current situation, these data can be highly variable. To minimize the influence of this variance, we designed distribution functions for the parameters growth rate, length of stay in hospital and the proportion of infected people who need to be hospitalized in our area of responsibility. Using this input, we started a Monte Carlo simulation with 10,000 runs to predict the range of the number of hospital beds needed within the coming days and compared it with the available resources. RESULTS: Since 2 February 2020 a total of 306 patients were treated with suspected or confirmed COVID-19 at this university hospital. Of these 84 needed treatment on the ICU. With the help of several simulation-based forecasts, the required ICU and normal bed capacity at Augsburg University Hospital and the Augsburg ambulance service in the period from 28 March 2020 to 8 June 2020 could be predicted with a high degree of reliability. Simulations that were run before the impact of the restrictions in daily life showed that we would have run out of ICU bed capacity within approximately 1 month. CONCLUSION: Our simulation-based prognosis of the health care capacities needed helps the management of the hospital and the civil protection service to make reasonable decisions and adapt the disaster response to the realistic needs. At the same time the forecasts create the possibility to plan the strategic response days and weeks in advance. The tool presented in this study is, as far as we know, the only one accounting not only for confirmed COVID-19 cases but also for suspected COVID-19 patients. Additionally, the few input parameters used are easy to access and can be easily adapted to other healthcare systems.