The global COVID-19 pandemic began in December 2019 and spread rapidly around the world. Worldwide, more than 230 million people fell ill, 4.75 million cases were fatal. In addition to the threat to health, the pandemic resulted in social problems, an economic crisis and the transition of an ordinary life to a "new reality". Mathematical modeling is an effective tool for controlling the epidemic process of COVID-19 in specified territories. Modeling makes it possible to predict the future dynamics of the epidemic process and to identify the factors that affect the increase in incidence in the greatest way. The simulation results enable public health professionals to take effective evidence-based responses to contain the epidemic. The study aims to develop machine learning and compartment models of COVID-19 epidemic process and to investigate experimental results of simulation. The object of research is COVID-19 epidemic process and its dynamics in territory of Ukraine. The research subjects are methods and models of epidemic process simulation, which include machine learning methods and compartment models. To achieve this aim of the research, we have used machine learning forecasting methods and have built COVID-19 epidemic process linear regression model and COVID-19 epidemic process compartment model. Because of experiments with the developed models, the predictive dynamics of the epidemic process of COVID-19 for 30 days were obtained for confirmed cases, recovered and death. For ‘Confirmed’, ‘Recovered’ and ‘Death’ cases mean errors have almost 1.15, 0.037 and 1.39 percent deviant, respectively, with a linear regression model. For ‘Confirmed’, ‘Recovered’ and ‘Death’ cases mean errors have almost 3.29, 1.08, and 0.71 percent deviant, respectively, for the SIR model. Conclusions. At this stage in the development of the epidemic process of COVID-19, it is more expedient to use a linear model to predict the incidence rate, which has shown higher accuracy and efficiency, the reason for that lies on the fact that the used linear regression model for this research was implemented on merely 30 days (from fifteen days before 2nd of March) and not the whole dataset of COVID-19. Also, it is expected that if we try to forecast in longer time ranges, the linear regression model will lose precision. Alternatively, since SIR model is more comprised in including more factors, the model is expected to perform better in fore-casting longer time ranges.
Background: Limited studies have been conducted on access to COVID-19 vaccines and identifying the most appropriate health centres for performing vaccination in metropolitan areas. This study aimed to measure potential spatial access to COVID-19 vaccination centres in Mashhad, the second-most populous city in Iran.Methods: The age structure of the urban census tracts was integrated into the enhanced two-step floating catchment area model to improve accuracy. The model was developed based on three different scenarios: only public hospitals, only public healthcare centres, and the top 20% healthcare centres were employed as potential vaccination facilities. The weighted decision-matrix and analytic hierarchy process based on four criteria (i.e. service area, accessibility index, capacity of vaccination centres, and distance to main roads) were used to choose potential vaccination centres with the highest suitability for residents.Results: Our findings indicate that including the both public hospitals and public healthcare centres can provide high accessibility to vaccination in central parts of the urban areas. However, using only public healthcare centres for vaccination can provide higher accessibility to vaccination sites in the eastern and north-eastern parts of the study area. Therefore, a combination of public hospitals and public healthcare centres is recommended for efficient vaccination coverage.Conclusions: Measuring spatial access to COVID-19 vaccination centres can provide valuable insights for urban public health decision-makers. Our model, coupled with geographical information systems (GIS), provides more efficient vaccination coverage by identifying the most suitable healthcare centres, which is of special importance when only few centres are available.
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