Modeling the spread of infections during an epidemiological outbreak using an improved mathematical model

• Published in: Chaos, Solitons & Fractals: X Solitons & Fractals: X, 2024-06, Vol.12, p.100111, Article 100111
• Main Author: Elmunim, Nouf Abd
• Format: Article
• Language: English
• Subjects: Covid-19; M-HW; Modelling; Pandemic
• ISSN: 2590-0544; 2590-0544
• DOI: 10.1016/j.csfx.2024.100111

•An infectious disease outbreak causes significant economic, social, and political disruption, not to mention a large number of deaths. Pandemics occur periodically worldwide, and there is nothing more devastating. An accurate forecasting model is therefore essential to estimate the effect of the pandemic and plan accordingly. This research aims to provide a solution that could help the world predict the number of cases during pandemics and prepare to accommodate subsequent cases.•The mathematical time series multiplicative Holt–Winter (M-HW) model was improved to forecast the future number of infected cases. The model selected the best smooth constant parameter that gives the best forecasting results. The model was applied to the coronavirus 19 (COVID-19) data, where COVID-19 is the recent pandemic that affected all nations worldwide since 2019. Data from COVID-19 was used during February 2022 and 2023 In Saudi Arabia the M-HW model forecast the number of cases of COVID-19 in two different periods in Saudi Arabia. and had satisfactory forecasting performance.•For the daily confirmed cases in February 2023 and February 2022, the M-HW model showed excellent accuracy of 99.51 % to 99.66 % respectively. The MAPE values in February 2023 range from 0.015 to 1.07, while in February 2022 they range from 0.032 to 2.269. In addition, the RMSE in February 2023 was 0.35, while in February 2022 was 6.88.•An improved mathematical model was developed to forecast the number of infected cases in the study. Data from COVID-19 was used in the model during February 2022 and 2023 in Saudi Arabia. The model proved to be accurate and highly efficient. Thus, this model could be useful to forecast the number of cases in different regions in case of a pandemic, so that help to prepare accordingly. Pandemics occur periodically worldwide. An accurate forecasting model is therefore essential to estimate the effect of the pandemic and plan accordingly. This research aims to provide a solution that could help the world predict the number of infection cases during pandemics and prepare to accommodate subsequent cases. The mathematical Multiplicative Holt–Winter (M-HW) model was improved regarding the data used to provide an accurate forecast. The model was applied to the Coronavirus (COVID-19) data, where COVID-19 is the recent pandemic that affected all nations worldwide since 2019. Two different periods in Saudi Arabia were modelled to estimate COVID-19 cases. Based on the daily confi