A Seasonal Autoregressive Integrated Moving Average (SARIMA) forecasting model to predict the epidemic trends of scrub typhus in China

• Published in: PloS one Vol. 20; no. 6; p. e0325905
• Main Authors: Peng, Pei-Ying, Duan, Hui-Ying, Xu, Lei, Sun, Ji-Qin, Ma, Li-Juan, Zu, Ya, Yan, Ting-Liang
• Format: Journal Article
• Language: English
• Published: United States Public Library of Science 23.06.2025; Public Library of Science (PLoS)
• Subjects: Analysis; Autoregressive moving-average models; Biology and Life Sciences; China; China - epidemiology; Communicable diseases; Data collection; Disease control; Distribution; Engineering and Technology; Epidemic models; Epidemics; Epidemiological Models; Epidemiology; Female; Forecasting; Forecasting - methods; Forecasting models; Forecasting techniques; Forecasts and trends; Health surveillance; Humans; Infectious diseases; Male; Medicine and Health Sciences; Models, Statistical; Morbidity; Moving averages; Orientia tsutsugamushi; People and Places; Physical Sciences; Prediction models; Research and Analysis Methods; Scrub typhus; Scrub Typhus - epidemiology; Seasonal variations; Seasons; Software; Time series; Trends; Typhus; White noise; China; United States > US

Scrub typhus is a deadly infectious disease that is frequently underdiagnosed. Forecasting the emergence of infectious diseases using epidemiological models has emerged as a crucial instrument for comprehending the dynamics of their occurrence. This research aimed to investigate epidemic traits and...