Modeling and Forecasting of COVID-19 Dynamics in Krasnoyarsk Krai Residents Using a Single Healthcare Institution as an Example
DOI:
https://doi.org/10.52575/2687-0932-2024-51-3-643-656Keywords:
COVID-19, Regression model, Exponential models, Prophet library, LSTM, Machine learning, ForecastingAbstract
Making informed decisions in healthcare requires information about the spread of epidemics. While most existing models for forecasting COVID-19 spread focus on national or regional levels, this study proposes a solution for prediction at the level of individual healthcare institutions (LHIs). The aim of this research is to develop and evaluate the accuracy of models that predict the dynamics of key local indicators of COVID-19 spread within a single LHI, ultimately aiding in optimizing the allocation of medical resources. This research employs methods of regression analysis, exponential smoothing, long short-term memory (LSTM), XGBoost decision trees, and the Prophet model. Data on COVID-19 cases from the Krasnoyarsk Regional Hospital from March 2020 to December 2023 was utilized for model construction. The investigated models allow for assessing the dynamics of coronavirus infection within a single LHI, enabling the implementation of load balancing and resource allocation technologies both within the LHI and across other LHIs.
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