APPLYING TIME SERIES MODELS FOR PREDICTING THE RATE OF INFLUENZA FLU IN HUNG YEN PROVINCE

  • Luong Xuan Hong Hung Yen University of Technology and Education
  • Pham Thi Anh Huong Hung Yen University of Technology and Education
  • Nguyen Van Chien Hanoi University of Science and Technology
  • Dam Quang Thinh Hung Yen University of Technology and Education
  • Thi-Thu-Huyen Tran Hung Yen University of Technology and Education
  • Nguyen Van Hau Hung Yen University of Technology and Education
Keywords: time series models, Auto regression model, moving average model, Integrated Autoregression model, influenza flu

Abstract

Hung Yen is a province located in the center of the Red River Delta, North Vietnam is also a province heavily affected by infectious diseases. In recent times, many scientists have studied and applied time series models to predict the rate and the number of infectious diseases, thereby helping the health-care organizations to prevent the spread of deadly infectious disease outbreaks (e.g. dengue, diarrhea, influenza, etc.). In this paper, we investigate and apply three time series models: Auto regression (AR), moving average (MA), and Integrated Autoregression model. Autoregressive Integrated Moving Average (ARIMA). Experiments show that the ARIMA model gives better results.

References

Bộ Y tế (2011), Hướng dẫn chẩn đoán và điều trị cúm mùa, Ban hành kèm theo Quyết định số 2078/QĐ-BYT ngày 23 tháng 6 năm 2011.

Bộ Y tế (2019), Hướng dẫn chẩn đoán, điều trị sốt xuất huyết Dengue, Ban hành kèm theo Quyết định số 3705/QĐ-BYT ngày 22 tháng 8 năm 2019.

Eckstein D, Künzel V, Schäfer L, Winges M. GLOBAL CLIMATE RISK INDEX 2020. Who Suffers Most from Extreme Weather Events? Weather-Related Loss Events in 2018 and 1999 to 2018. 2019.

V. Sharma, Malaria, Outbreak prediction model using machine learning. International Journal of Advanced Research in Computer Engineering and Technology.

Sirisena P., Noordeen F., Kurukulasuriya H., Romesh T.A., Fernando L. Effect of climatic factors and population density on the distribution of Dengue in Sri Lanka: a gis based evaluation for prediction of outbreaks. PLoS ONE, 2017, 12(1), pp. 1–14.

Heinrichs B., Eickhoff S.B. Your evidence? Machine learning algorithms for medical diagnosis and prediction. Hum. Brain Mapp, 2020, 41(6), pp. 1435–1444.

Li Q., Cao W., Ren H., Ji Z., Jiang H. Spatiotemporal responses of Dengue fever transmission to the road network in an urban area. Acta Trop, 2018, 183, pp. 8–13. http://www.sciencedirect.com/science/article/pii/S0001706X17311294.

Published
2022-03-31
How to Cite
Luong Xuan Hong, Pham Thi Anh Huong, Nguyen Van Chien, Dam Quang Thinh, Thi-Thu-Huyen Tran, & Nguyen Van Hau. (2022). APPLYING TIME SERIES MODELS FOR PREDICTING THE RATE OF INFLUENZA FLU IN HUNG YEN PROVINCE. UTEHY Journal of Science and Technology, 33, 7-13. Retrieved from http://jst.utehy.edu.vn/index.php/jst/article/view/515