scholarly journals A Scalable Forecasting Framework to Predict COVID-19 Hospital Bed Occupancy

2022 ◽  
Author(s):  
Jakob Heins ◽  
Jan Schoenfelder ◽  
Steffen Heider ◽  
Axel R. Heller ◽  
Jens O. Brunner
Keyword(s):  
Monte Carlo Simulation ◽  
Monte Carlo ◽  
Intensive Care ◽  
Intensive Care Units ◽  
Bed Occupancy ◽  
Short Term ◽  
Ministry Of Health ◽  
Hospital Bed

We present a scalable forecasting framework with a Monte Carlo simulation to forecast the short-term bed occupancy of patients with confirmed and suspected COVID-19 in intensive care units and regular wards. Our forecasts were a central part of the official weekly reports of the Bavarian State Ministry of Health and Care from May 2020 to March 2021.

Parameters of bed occupancy of intensive care units in Hungary

2011 ◽  
Vol 152 (24) ◽  
pp. 946-950 ◽  
Author(s):  
Miklós Gresz
Keyword(s):  
Health Care ◽  
Intensive Care Unit ◽  
Intensive Care ◽  
Health Insurance ◽  
Intensive Care Units ◽  
Emergency Care ◽  
Present Author ◽  
Bed Occupancy ◽  
National Network ◽  
Bed Capacity

According to the Semmelweis Plan for Saving Health Care, ”the capacity of the national network of intensive care units in Hungary is one but not the only bottleneck of emergency care at present”. Author shows on the basis of data reported to the health insurance that not on a single calendar day more than 75% of beds in intensive care units were occupied. There were about 15 to 20 thousand sick days which could be considered unnecessary because patients occupying these beds were discharged to their homes directly from the intensive care unit. The data indicate that on the whole bed capacity is not low, only in some institutions insufficient. Thus, in order to improve emergency care in Hungary, the rearrangement of existing beds, rather than an increase of bed capacity is needed. Orv. Hetil., 2011, 152, 946–950.

scholarly journals Burden, Predictors and Short-Term Outcomes of Traumatic Brain Injury Among Patients Admitted to Ugandan Intensive Care Units

2020 ◽  
Vol 8 (4) ◽  
pp. 77
Author(s):  
Agnes Wabule ◽  
Kavuma Arthur Mwanje ◽  
Daniel Obua ◽  
Janat Tumukunde ◽  
Jane Nakibuuka ◽  
...  
Keyword(s):  
Traumatic Brain Injury ◽  
Intensive Care ◽  
Brain Injury ◽  
Intensive Care Units ◽  
Short Term

scholarly journals Psychiatric intensive care and low secure units: where are we now?

2008 ◽  
Vol 32 (12) ◽  
pp. 441-443 ◽  
Author(s):  
M. Dominic Beer
Keyword(s):  
Mental Health ◽  
Intensive Care ◽  
Health Services ◽  
Intensive Care Units ◽  
Short Term ◽  
Term Care ◽  
Policy Makers ◽  
General Mental Health ◽  
Department Of Health ◽  
Patient Groups

SummaryThe last decade has seen clinicians and policy makers develop psychiatric intensive care units and low secure units from the so-called ‘special care wards’ of the 1980s and 1990s. Psychiatric intensive care units are for short-term care, while low secure units are for care for up to about 2 years. Department of Health standards have been set for these units. A national survey has shown that there are two main patient groups in the low secure units: patients on forensic sections coming down from medium secure units and those on civil sections who are transferred from general psychiatric facilities. Recent clinical opinion has emphasised the important role both psychiatric intensive care units and low secure units play in providing a bridge between forensic and general mental health services.

Prognostic score of short-term survival in HIV-infected patients admitted to medical intensive care units

2001 ◽  
Vol 12 (4) ◽  
pp. 239-244 ◽  
Author(s):  
M. Bonarek ◽  
P. Morlat ◽  
G. Chêne ◽  
D. Rapin ◽  
G. Hilbert ◽  
...  
Keyword(s):  
Intensive Care ◽  
Intensive Care Units ◽  
Prognostic Score ◽  
Short Term ◽  
Term Survival ◽  
Medical Intensive Care ◽  
Short Term Survival

Short-term host discrimination in the parasitoid wasp Trissolcus basalis Wollaston (Hymenoptera : Scelionidae)

1999 ◽  
Vol 47 (1) ◽  
pp. 19 ◽  
Author(s):  
S. A. Field ◽  
M. A. Keller
Keyword(s):  
Monte Carlo Simulation ◽  
Monte Carlo ◽  
Simulation Model ◽  
Parasitoid Wasp ◽  
Chemical Components ◽  
The Novel ◽  
Host Discrimination ◽  
Trissolcus Basalis ◽  
Short Term ◽  
Monte Carlo Simulation Model

The ability of host discrimination allows insect parasitoids to avoid superparasitism (oviposition in a previously attacked host). However, superparasitism can sometimes be adaptive, so attempts to identify host discrimination must be made under appropriate ecological conditions. We tested the ability of the parasitoid wasp Trissolcus basalis to discriminate between self- and conspecific-parasitised hosts (conspecific discrimination) under ecologically realistic conditions, in which conspecific discrimination should be adaptive. Data were analysed using a Monte Carlo simulation model that permitted testing of several different ways in which conspecific discrimination could be achieved. We obtained the novel result that females avoided self-superparasitism on a patch consisting of a mixture of self- and conspecific-parasitised hosts, but that this avoidance was not due to true conspecific discrimination. Instead, it was due to short- term discrimination between newly and previously parasitised hosts. Two likely mechanisms for such discrimination are proposed: a short-lived host-derived volatile; and the presence of two or more chemical components in the marking pheromone.

A theoretical model of jump diffusion-mean reversion

2017 ◽  
Vol 23 (3) ◽  
pp. 537-554
Author(s):  
Anindya Chakrabarty ◽  
Zongwei Luo ◽  
Rameshwar Dubey ◽  
Shan Jiang
Keyword(s):  
Monte Carlo Simulation ◽  
Monte Carlo ◽  
Theoretical Model ◽  
Interest Rate ◽  
Mean Reversion ◽  
Jump Diffusion ◽  
Fixed Income ◽  
Short Term ◽  
Content Type ◽  
Better Than

Purpose The purpose of this paper is to develop a theoretical model of a jump diffusion-mean reversion constant proportion portfolio insurance strategy under the presence of transaction cost and stochastic floor as opposed to the deterministic floor used in the previous literatures. Design/methodology/approach The paper adopts Merton’s jump diffusion (JD) model to simulate the price path followed by risky assets and the CIR mean reversion model to simulate the path followed by the short-term interest rate. The floor of the CPPI strategy is linked to the stochastic process driving the value of a fixed income instrument whose yield follows the CIR mean reversion model. The developed model is benchmarked against CNX-NIFTY 50 and is back tested during the extreme regimes in the Indian market using the scenario-based Monte Carlo simulation technique. Findings Back testing the algorithm using Monte Carlo simulation across the crisis and recovery phases of the 2008 recession regime revealed that the portfolio performs better than the risky markets during the crisis by hedging the downside risk effectively and performs better than the fixed income instruments during the growth phase by leveraging on the upside potential. This makes it a value-enhancing proposition for the risk-averse investors. Originality/value The study modifies the CPPI algorithm by re-defining the floor of the algorithm to be a stochastic mean reverting process which is guided by the movement of the short-term interest rate in the economy. This development is more relevant for two reasons: first, the short-term interest rate changes with time, and hence the constant yield during each rebalancing steps is not practically feasible; second, the historical literatures have revealed that the short-term interest rate tends to move opposite to that of the equity market. Thereby, during the bear run the floor will increase at a higher rate, whereas the growth of the floor will stagnate during the bull phase which aids the model to capitalize on the upward potential during the growth phase and to cut down on the exposure during the crisis phase.

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