Simulation and forecasting of the influenza epidemic process using seasonal autoregressive integrated moving average model

Abstract

Today's global COVID-19 pandemic has affected the spread of influenza. COVID-19 and influenza are respiratory infections and have several similar symptoms. They are, however, caused by various viruses; there are also some differences in the categories of people most at risk of severe forms of these diseases. The strategies for their treatment are also different. Mathematical modeling is an effective tool for controlling the epidemic process of influenza in specified territories. The results of modeling and forecasts obtained with the help of simulation models make it possible to develop timely justified anti-epidemic measures to reduce the dynamics of the incidence of influenza. The study aims to develop a seasonal autoregressive integrated moving average (SARIMA) model for influenza epidemic process simulation and to investigate the experimental results of the simulation. The work is targeted at the influenza epidemic process and its dynamic in the territory of Ukraine. The subjects of the research are methods and models of epidemic process simulation, which include machine learning methods, in particular the SARIMA model. To achieve the aim of the research, we have used methods of forecasting and have built the influenza epidemic process SARIMA model. Because of experiments with the developed model, the predictive dynamics of the epidemic process of influenza for 10 weeks were obtained. Such a forecast can be used by persons making decisions on the implementation of anti-epidemic and deterrent measures if the forecast exceeds the epidemic thresholds of morbidity. Conclusions. The paper describes experimental research on the application of the SARIMA model to the epidemic process of influenza simulation. Models have been verified by influenza morbidity in the Kharkiv region (Ukraine) in epidemic seasons for the time ranges as follows: 2017-18, 2018-19, 2019-20, and 2020-21. Data were provided by the Kharkiv Regional Centers for Disease Control and Prevention of the Ministry of Health of Ukraine. The forecasting results show a downward trend in the dynamics of the epidemic process of influenza in the Kharkiv region. It is due to the introduction of anti-epidemic measures aimed at combating COVID-19. Activities such as wearing masks, social distancing, and lockdown also contribute to reducing seasonal influenza epidemics.

Trends, Projections, and Regional Disparities of Maternal Mortality in Africa (1990–2030): An ARIMA Forecasting Approach

“…Moreover, the ARIMA model has demonstrated utility in predicting the trajectory of infectious diseases, such as influenza [ 36 ]. A Seasonal ARIMA (SARIMA) model was adopted in one study to simulate and project the influenza epidemic dynamics in Ukraine, facilitating the planning of efficient anti-epidemic measures.…”

“…This has been exemplified in several contexts: it has been used to forecast measles immunization coverage, providing policymakers with valuable information to allocate resources effectively [ 35 ]. It was instrumental in simulating and forecasting the influenza epidemic process in Ukraine [ 36 ], thereby contributing to the timely development of anti-epidemic measures, and it successfully predicted the potential impact of COVID-19 vaccination on reducing maternal deaths in Mexico [ 37 ]. In this study, we leverage the ARIMA model, following these precedents, to analyze trends, projections, and regional disparities of maternal mortality in Africa from 1990 to 2030.…”