Skip to main content Accessibility help
×
Home

Predicting the incidence of hand, foot and mouth disease in Sichuan province, China using the ARIMA model

  • L. LIU (a1) (a2), R. S. LUAN (a1), F. YIN (a1), X. P. ZHU (a2) and Q. LÜ (a2)...

Summary

Hand, foot and mouth disease (HFMD) is an infectious disease caused by enteroviruses, which usually occurs in children aged <5 years. In China, the HFMD situation is worsening, with increasing number of cases nationwide. Therefore, monitoring and predicting HFMD incidence are urgently needed to make control measures more effective. In this study, we applied an autoregressive integrated moving average (ARIMA) model to forecast HFMD incidence in Sichuan province, China. HFMD infection data from January 2010 to June 2014 were used to fit the ARIMA model. The coefficient of determination (R 2), normalized Bayesian Information Criterion (BIC) and mean absolute percentage of error (MAPE) were used to evaluate the goodness-of-fit of the constructed models. The fitted ARIMA model was applied to forecast the incidence of HMFD from April to June 2014. The goodness-of-fit test generated the optimum general multiplicative seasonal ARIMA (1,0,1) × (0,1,0)12 model (R 2 = 0·692, MAPE = 15·982, BIC = 5·265), which also showed non-significant autocorrelations in the residuals of the model (P = 0·893). The forecast incidence values of the ARIMA (1,0,1) × (0,1,0)12 model from July to December 2014 were 4103–9987, which were proximate forecasts. The ARIMA model could be applied to forecast HMFD incidence trend and provide support for HMFD prevention and control. Further observations should be carried out continually into the time sequence, and the parameters of the models could be adjusted because HMFD incidence will not be absolutely stationary in the future.

  • View HTML
    • Send article to Kindle

      To send this article to your Kindle, first ensure no-reply@cambridge.org is added to your Approved Personal Document E-mail List under your Personal Document Settings on the Manage Your Content and Devices page of your Amazon account. Then enter the ‘name’ part of your Kindle email address below. Find out more about sending to your Kindle. Find out more about sending to your Kindle.

      Note you can select to send to either the @free.kindle.com or @kindle.com variations. ‘@free.kindle.com’ emails are free but can only be sent to your device when it is connected to wi-fi. ‘@kindle.com’ emails can be delivered even when you are not connected to wi-fi, but note that service fees apply.

      Find out more about the Kindle Personal Document Service.

      Predicting the incidence of hand, foot and mouth disease in Sichuan province, China using the ARIMA model
      Available formats
      ×

      Send article to Dropbox

      To send this article to your Dropbox account, please select one or more formats and confirm that you agree to abide by our usage policies. If this is the first time you use this feature, you will be asked to authorise Cambridge Core to connect with your <service> account. Find out more about sending content to Dropbox.

      Predicting the incidence of hand, foot and mouth disease in Sichuan province, China using the ARIMA model
      Available formats
      ×

      Send article to Google Drive

      To send this article to your Google Drive account, please select one or more formats and confirm that you agree to abide by our usage policies. If this is the first time you use this feature, you will be asked to authorise Cambridge Core to connect with your <service> account. Find out more about sending content to Google Drive.

      Predicting the incidence of hand, foot and mouth disease in Sichuan province, China using the ARIMA model
      Available formats
      ×

Copyright

Corresponding author

* Author for correspondence: Mr L. Liu, Sichuan Center for Disease Control and Prevention, West China School of Public Health, No. 6 Zhongxue Road, Chengdu, Sichuan 610041, People's Republic of China, 610041. (Email: sheva_liulei@126.com)

References

Hide All
1. Seong, JK, et al. Risk factors for neurologic complications of hand, foot and mouth disease in the Republic of Korea, 2009. Journal of Korean Medical Science 2013; 28: 120127.
2. Ji, H, et al. Seroepidemiology of human enterovirus71 and coxsackievirus A16 in Jiangsu province, China. Virology Journal 2012; 9: 248256.
3. Peng, JP, et al. Sensitive and rapid detection of viruses associated with hand foot and mouth disease using multiplexed MALDI-TOF analysis. Journal of Clinical Virology 2013; 56: 170174.
4. Guan, P, Huang, DS, Zhou, BS. Forecasting model for the incidence of hepatitis A based on artificial neural network. World Journal of Gastroenterology 2004; 10: 35793582.
5. Wang, YJ, et al. Applying linear regression statistical method to predict the epidemic of hemorrhagic fever with renal syndrome. Chinese Journal of Vector Biology and Control 2006; 17: 333334.
6. Clement, J, et al. Relating increasing hantavirus incidences to the changing climate: the mast connection. International Journal of Health Geographics 2009; 8: 1.
7. Guo, LC, et al. Appling grey swing model to predict the incidence trend of hemorrhagic fever with renal syndrome in Shenyang. Journal of China Medical University 2008; 37: 839842.
8. Wu, ZM, et al. Prediction for incidence of hemorrhagic fever with renal syndrome with back propagation artificial neural network model. Chinese Journal of Vector Biology and Control 2006; 17: 223226.
9. Huang, XX, et al. Prediction of monthly hand foot and mouth disease incidence in China by using autoregressive integrated moving average model. Disease Surveillance 2013; 28: 396399.
10. Akhtar, S, Rozi, S. An autoregressive integrated moving average model for short-term prediction of hepatitis C virus seropositivity among male volunteer blood donors in Karachi, Pakistan. World Journal of Gastroenterology 2009; 15: 16071612.
11. Wentong, Z. The Course of Statistical Analysis with SPSS. Beijing, China: Hope Electronic Press, 2002, pp. 250289.
12. Chatfield, C. The Analysis of Time Series: Theory and Practice. London: Chapman and Hall.
13. Jenkins, GW, Reinsel, GC, Box, GEP. Time Series Analysis, 3rd edn. South Winsdor, New South Wales, Australia: Holden Day.
14. Bowerman, BL, O'Connell, R. Forecasting and Time Series: An Applied Approach. Boston: South-Western College Publications.
15. Chang, ZR, et al. Epidemiological features of hand foot and mouth disease in China, 2008–2009. China Journal of Epidemiology 2011; 32: 676680.
16. Kuhn, L, Davidson, LL, Durkin, MS. Use of Poisson regression and time series analysis for detecting changes over time in rates of child injury following a prevention program. American Journal of Epidemiology 1994; 140: 943955.
17. Liang, W, et al. Prediction of malaria incidence in malaria epidemic area with time series models. Journal of the Fourth Military Medical University 2004; 25: 507510.
18. Silawan, T, et al. Temporal patterns and forecast of dengue infection in northeastern Thailand. The Southeast Asian Journal of Tropical Medicine and Public Health 2008; 39: 9098.
19. Wong, J, Chan, A, Chiang, YH. Time series forecasts of the construction labor market in Hong Kong: the Box-Jenkins approach. Construction Management and Economics 2005; 23: 979991.
20. Mumbare, SS, et al. Trends in average living children at the time of terminal contraception: a time series analysis over 27 years using ARIMA (p,d,q) nonseasonal model. Indian Journal of Community Medicine 2014; 39: 223228.
21. Earnest, A, et al. Using autoregressive integrated moving average (ARIMA) models to predict and monitor the number of beds occupied during a SARS outbreak in a tertiary hospital in Singapore. BMC Health Services Research 2005; 5: 36.
22. Li, XJ, et al. A time series model in incidence forecasting of hemorrhagic fever with renal syndrome. Journal of Shandong University (Health Sciences) 2008; 46: 547549.
23. Hii, YL, Rockl, J, Ng, N. Short term effects of weather on hand, foot and mouth disease. PLoS ONE 2011; 2: 16.
24. Wang, JF, et al. Hand, foot and mouth disease: spatiotemporal transmission and climate. International Journal of Health Geographics 2011; 10: 2534.
25. Zou, XN, et al. Etiologic and epidemiologic analysis of hand foot and mouth disease in Guangzhou city: a review of 4,753 cases. Brazilian Journal of Infectious Diseases 2012; 16: 457465.
26. Onozuka, D, Hashizume, M. The influence of temperature and humidity on the incidence of hand, foot, and mouth disease in Japan. Science of the Total Environment 2011; 410: 119125.
27. Zhu, SB, Xiang, FL. Multiple linear regression analysis of hand, foot and mouth diseases and the meteorological factors. Zhejiang Preventive Medicine 2013; 3: 3739.
28. Feng, GS, Yu, SC, Hu, YH. Application of panel data model in the study of the relationship between reported hand-foot-mouth morbidity and temperature. Chinese Preventive Medicine 2013; 12: 910913.

Keywords

Metrics

Full text views

Total number of HTML views: 0
Total number of PDF views: 0 *
Loading metrics...

Abstract views

Total abstract views: 0 *
Loading metrics...

* Views captured on Cambridge Core between <date>. This data will be updated every 24 hours.

Usage data cannot currently be displayed