Department of Social Medicine and Public Health, Faculty of Public Health, Medical University of Plovdiv, Plovdiv, Bulgaria.
Folia Med (Plovdiv). 2020 Sep 30;62(3):509-514. doi: 10.3897/folmed.62.e49812.
Epidemiological forecasting facilitates scientifically sound solutions to upcoming theoretical and practical issues, in the development of public health management, in particular of infectious diseases.
To critically analyze the most recent scientific advances in the biosocial nature and methodology of epidemiological forecasting to present a real-life example of pertussis, a disease with shifting epidemiology.
For the prediction of pertussis morbidity the autoregressive integrated moving average (ARIMA) the model was established by utilizing the method of time series analysis to construct a model of overall morbidity using Time series modeller in SPSS v.25. To model pertussis morbidity we obtained official data from the Ministry of Health and the National Center for Infectious and Parasitic Diseases, since the beginning of disease registration from 1903 until 2018. We also analyzed the shifting epidemiology of pertussis.
The proper identification procedures we applied indicated ARIMA (3,0,0) model to best fit our original time series of the annual whooping cough morbidity for the 1921-2018 period. The model predicts better morbidity in a one-step forecast. The incidence rate is expected to be stable at about 1.35 per 100,000 in the next three years, which is close to the 2016 level and lower than those in 2017-2018.
The ARIMA (3,0,0) model in our study is an adequate tool for presenting the pertussis morbidity trend and is suitable to forecast near-future disease dynamics, with acceptable error tolerance.
流行病学预测有助于在公共卫生管理的发展中，为即将出现的理论和实践问题提供科学合理的解决方案，尤其是在传染病方面。
批判性分析流行病学预测的生物社会性质和方法的最新科学进展，以展示具有不断变化的流行病学特征的百日咳疾病的实际案例。
为了预测百日咳发病率，我们利用时间序列分析方法建立了自回归综合移动平均（ARIMA）模型，通过在 SPSS v.25 中的 Time series modeller 构建了总体发病率模型。为了模拟百日咳发病率，我们从 1903 年开始获得了卫生部和国家传染病和寄生虫病中心的官方数据，直到 2018 年。我们还分析了百日咳的不断变化的流行病学。
我们应用的适当识别程序表明，ARIMA（3,0,0）模型最适合拟合我们的原始年度百日咳发病率时间序列，适用于 1921-2018 年期间。该模型在一步预测中能更好地预测发病率。预计在未来三年，发病率将稳定在每 10 万人约 1.35 左右，接近 2016 年的水平，低于 2017-2018 年的水平。
在我们的研究中，ARIMA（3,0,0）模型是展示百日咳发病率趋势的合适工具，适用于预测近期疾病动态，具有可接受的误差容忍度。
