[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.

Development of demand forecasting tool for natural resources recouping from municipal solid waste.

Sustainable waste management requires an integrated planning and design strategy for reliable forecasting of waste generation, collection, recycling, treatment and disposal for the successful development of future residential precincts. The success of the future development and management of waste relies to a high extent on the accuracy of the prediction and on a comprehensive understanding of the overall waste management systems. This study defies the traditional concepts of waste, in which waste was considered as the last phase of production and services, by putting forward the new concept of waste as an intermediate phase of production and services. The study aims to develop a demand forecasting tool called 'zero waste index' (ZWI) for measuring the natural resources recouped from municipal solid waste. The ZWI (ZWI demand forecasting tool) quantifies the amount of virgin materials recovered from solid waste and subsequently reduces extraction of natural resources. In addition, the tool estimates the potential amount of energy, water and emissions avoided or saved by the improved waste management system. The ZWI is tested in a case study of waste management systems in two developed cities: Adelaide (Australia) and Stockholm (Sweden). The ZWI of waste management systems in Adelaide and Stockholm is 0.33 and 0.17 respectively. The study also enumerates per capita energy savings of 2.9 GJ and 2.83 GJ, greenhouse gas emissions reductions of 0.39 tonnes (CO2e) and 0.33 tonnes (CO2e), as well as water savings of 2.8 kL and 0.92 kL in Adelaide and Stockholm respectively.

A meteorological-based forecasting model for predicting minimal infection rates in Culex pipiens-restuans complex using Québec's West Nile virus integrated surveillance system.

Background: The ministère de la Santé et des Services sociaux (MSSS) du Québec (Québec's health authority) has expressed an interest in the development of an early warning tool to identify seasonal human outbreaks of West Nile virus infection in order to modulate public health interventions. The objective of this study was to determine if a user-friendly meteorological-based forecasting tool could be used to predict minimal infection rates for the Culex pipiens-restuans complex-a proxy of human risk-ahead of mosquito season. Methods: Annual minimal infection rate (number of positive pools/number of mosquitoes) was calculated for 856 mosquito traps set from 2003 to 2006 and 2013 to 2018 throughout the south of Québec's. Coefficient of determination (R2) were estimated using the validation dataset (one third of the database by random selection) with generalized estimation equations, which were prior fitted backwards with polynomial terms using the training dataset (two thirds of the database), in order to minimize the Bayesian information criteria. Mean temperatures and precipitation were grouped at five temporal scales (by month, by season and by 4, 6 and 10-months groupings). Results: Mean temperatures and cumulative precipitation from the previous months of March (R2=0.37), May (R2=0.36), December (R2=0.35) and the autumn season (R2=0.38) accounted for ~40% of Cx. pipiens-restuans annual minimal infection rates variations. Including the "year of sampling" variable in all regression models increased the predictive abilities (R2 between 0.42 and 0.57). Conclusion: All regression models explored have too weak predictive abilities to be useful as a public health tool. Other factors implicated in the epidemiology of the West Nile virus need to be incorporated in a meteorological-based early warning model for it to be useful to the provincial health authorities.

PharmValCalc: A calculator tool to forecast population health pharmacist impact.

OBJECTIVE: (1) Describe the development of a population health pharmacist (PHP) value calculator to forecast pharmacist staffing, care quality impact, and ROI; (2) exemplify PHP value through ACO stakeholder perspectives; and (3) discuss the use of the pharmacist value calculator to engage pharmacy, clinical, administrative, and financial leaders in discussions to initiate or expand pharmacist integration within population health (PH) initiatives. TOOL DESCRIPTION: The role of the pharmacist in population health (PH) is evolving as healthcare payment moves towards population-based, value-driven care. However, a challenge remains to identify the optimal use of the pharmacist in PH initiatives to maximize quality and cost performance. PharmValCalc was developed to demonstrate the value proposition for PHP interventions. PharmValCalc can be used to forecast PHP impact to: (1) reduce preventable, medication-related 30-day all cause hospital readmissions and emergency department (ED) visits for elderly patients, and (2) improve medication-related quality performance for uncontrolled patients with diabetes and hypertension. PharmValCalc forecasts the required PHP full-time equivalents (FTE), care quality performance goal improvement, and return on investment (ROI). PRACTICE INNOVATION: While other pharmacist impact calculators have been developed, PharmValCalc is uniquely designed for the 4 common PHP interventions listed above. In addition, provider executives verified that the estimated calculator outputs for each outcome (i.e., PHP FTE, care quality goal performance, and ROI) are within acceptable ranges to justify new or expanded PHP interventions in different ACO settings. CONCLUSION: PharmValCalc is a pragmatic tool for pharmacists and pharmacy leaders in value-based organizations to use when planning the initiation or expansion of PHP interventions with executive-level medical or administrative decision-makers.