Microsimulation Models for Simulating Pathways of Patients with Mental Diseases

Predicting demand for care is necessary to provide sufficient capacities in hospitals and enable better planning to cope with challenges like changes in the structure of the population. Emphasis in this work is put on the analysis of the data and on the parametrization of a simulation model for the paths of patients with mental diseases through the health care system. Survival analysis and model selection methods are used for this purpose. Data on patients and their treatments is analyzed with methods of survival analysis. Different methods for modelling the survival and hazard function are presented and compared. Hereby, the focus is on the cox model. It is used to model the hazard function and can be extended to analyze multiple events. With the use of model selection methods the significant parameters are determined. Only these are included in the simulation model. These methods shall help to raise the quality of the parametrization and therefore the whole simulation model. In the microsimulation model, every patient has a particular set of parameters and can be in one of several predefined, exclusive states. The events are implemented as state changes. The probabilities for the events are calculated using the hazard functions. These are estimated with several extensions of the cox model. The considered events in the simulation are readmissions to hospital and contacts to ambulant psychiatrists. The simulation runs for a predefined time span and the sequence of events of each patient is tracked. After the simulation, the individual paths of the patients as well as aggregated quantities such as the overall numbers of certain events are analyzed. Simulations for different populations are performed. The results for various scenarios are presented. Scenarios with and without contacts to a psychiatrist are considered as well as different maximum numbers of admissions. Also, the subpopulations are compared. For example, differences in the results for diagnosis groups are encountered. These simulations shall lead to an improvement of the prediction of the pathways of the patients and therefore help to evaluate interventions like treatment changes the health care system or the utilization of the capacities in hospitals

Microsimulation Models for Simulating Pathways of Patients with Mental Diseases

Predicting demand for care is necessary to provide sufficient capacities in hospitals and enable better planning to cope with challenges like changes in the structure of the population. Emphasis in this work is put on the analysis of the data and on the parametrization of a simulation model for the paths of patients with mental diseases through the health care system. Survival analysis and model selection methods are used for this purpose. Data on patients and their treatments is analyzed with methods of survival analysis. Different methods for modelling the survival and hazard function are presented and compared. Hereby, the focus is on the cox model. It is used to model the hazard function and can be extended to analyze multiple events. With the use of model selection methods the significant parameters are determined. Only these are included in the simulation model. These methods shall help to raise the quality of the parametrization and therefore the whole simulation model. In the microsimulation model, every patient has a particular set of parameters and can be in one of several predefined, exclusive states. The events are implemented as state changes. The probabilities for the events are calculated using the hazard functions. These are estimated with several extensions of the cox model. The considered events in the simulation are readmissions to hospital and contacts to ambulant psychiatrists. The simulation runs for a predefined time span and the sequence of events of each patient is tracked. After the simulation, the individual paths of the patients as well as aggregated quantities such as the overall numbers of certain events are analyzed. Simulations for different populations are performed. The results for various scenarios are presented. Scenarios with and without contacts to a psychiatrist are considered as well as different maximum numbers of admissions. Also, the subpopulations are compared. For example, differences in the results for diagnosis groups are encountered. These simulations shall lead to an improvement of the prediction of the pathways of the patients and therefore help to evaluate interventions like treatment changes the health care system or the utilization of the capacities in hospitals

Methods for integrated simulation: 10 concepts to integrate

This note summarises the current status of the work of EUROSIMs ans ASIMs Technical Committees "Data Driven System Simulation" - with main emphasis on Big Data integration in simulation. This overview suggests ten developed concepts and methods which should be considered, implemented and documented in modern simulation studies with Big Data

Targeted COVID-19 Vaccination (TAV-COVID) Considering Limited Vaccination Capacities—An Agent-Based Modeling Evaluation

(1) Background: The Austrian supply of COVID-19 vaccine is limited for now. We aim to provide evidence-based guidance to the authorities in order to minimize COVID-19-related hospitalizations and deaths in Austria. (2) Methods: We used a dynamic agent-based population model to compare different vaccination strategies targeted to the elderly (65 ≥ years), middle aged (45–64 years), younger (15–44 years), vulnerable (risk of severe disease due to comorbidities), and healthcare workers (HCW). First, outcomes were optimized for an initially available vaccine batch for 200,000 individuals. Second, stepwise optimization was performed deriving a prioritization sequence for 2.45 million individuals, maximizing the reduction in total hospitalizations and deaths compared to no vaccination. We considered sterilizing and non-sterilizing immunity, assuming a 70% effectiveness. (3) Results: Maximum reduction of hospitalizations and deaths was achieved by starting vaccination with the elderly and vulnerable followed by middle-aged, HCW, and younger individuals. Optimizations for vaccinating 2.45 million individuals yielded the same prioritization and avoided approximately one third of deaths and hospitalizations. Starting vaccination with HCW leads to slightly smaller reductions but maximizes occupational safety. (4) Conclusion: To minimize COVID-19-related hospitalizations and deaths, our study shows that elderly and vulnerable persons should be prioritized for vaccination until further vaccines are available