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Forecasting incidence of dengue in Rajasthan, using time series analyses.
Indian J Public Health. 2012 Oct-Dec; 56(4):281-5.IJ

Abstract

AIM

To develop a prediction model for dengue fever/dengue haemorrhagic fever (DF/DHF) using time series data over the past decade in Rajasthan and to forecast monthly DF/DHF incidence for 2011.

MATERIALS AND METHODS

Seasonal autoregressive integrated moving average (SARIMA) model was used for statistical modeling.

RESULTS

During January 2001 to December 2010, the reported DF/DHF cases showed a cyclical pattern with seasonal variation. SARIMA (0,0,1) (0,1,1) 12 model had the lowest normalized Bayesian information criteria (BIC) of 9.426 and mean absolute percentage error (MAPE) of 263.361 and appeared to be the best model. The proportion of variance explained by the model was 54.3%. Adequacy of the model was established through Ljung-Box test (Q statistic 4.910 and P-value 0.996), which showed no significant correlation between residuals at different lag times. The forecast for the year 2011 showed a seasonal peak in the month of October with an estimated 546 cases.

CONCLUSION

Application of SARIMA model may be useful for forecast of cases and impending outbreaks of DF/DHF and other infectious diseases, which exhibit seasonal pattern.

Authors+Show Affiliations

Institute of Health Management Research, Jaipur, India.No affiliation info availableNo affiliation info availableNo affiliation info available

Pub Type(s)

Journal Article

Language

eng

PubMed ID

23354138

Citation

Bhatnagar, Sunil, et al. "Forecasting Incidence of Dengue in Rajasthan, Using Time Series Analyses." Indian Journal of Public Health, vol. 56, no. 4, 2012, pp. 281-5.
Bhatnagar S, Lal V, Gupta SD, et al. Forecasting incidence of dengue in Rajasthan, using time series analyses. Indian J Public Health. 2012;56(4):281-5.
Bhatnagar, S., Lal, V., Gupta, S. D., & Gupta, O. P. (2012). Forecasting incidence of dengue in Rajasthan, using time series analyses. Indian Journal of Public Health, 56(4), 281-5. https://doi.org/10.4103/0019-557X.106415
Bhatnagar S, et al. Forecasting Incidence of Dengue in Rajasthan, Using Time Series Analyses. Indian J Public Health. 2012 Oct-Dec;56(4):281-5. PubMed PMID: 23354138.
* Article titles in AMA citation format should be in sentence-case
TY - JOUR T1 - Forecasting incidence of dengue in Rajasthan, using time series analyses. AU - Bhatnagar,Sunil, AU - Lal,Vivek, AU - Gupta,Shiv D, AU - Gupta,Om P, PY - 2013/1/29/entrez PY - 2013/1/29/pubmed PY - 2013/4/10/medline SP - 281 EP - 5 JF - Indian journal of public health JO - Indian J Public Health VL - 56 IS - 4 N2 - AIM: To develop a prediction model for dengue fever/dengue haemorrhagic fever (DF/DHF) using time series data over the past decade in Rajasthan and to forecast monthly DF/DHF incidence for 2011. MATERIALS AND METHODS: Seasonal autoregressive integrated moving average (SARIMA) model was used for statistical modeling. RESULTS: During January 2001 to December 2010, the reported DF/DHF cases showed a cyclical pattern with seasonal variation. SARIMA (0,0,1) (0,1,1) 12 model had the lowest normalized Bayesian information criteria (BIC) of 9.426 and mean absolute percentage error (MAPE) of 263.361 and appeared to be the best model. The proportion of variance explained by the model was 54.3%. Adequacy of the model was established through Ljung-Box test (Q statistic 4.910 and P-value 0.996), which showed no significant correlation between residuals at different lag times. The forecast for the year 2011 showed a seasonal peak in the month of October with an estimated 546 cases. CONCLUSION: Application of SARIMA model may be useful for forecast of cases and impending outbreaks of DF/DHF and other infectious diseases, which exhibit seasonal pattern. SN - 0019-557X UR - https://www.unboundmedicine.com/medline/citation/23354138/Forecasting_incidence_of_dengue_in_Rajasthan_using_time_series_analyses_ L2 - http://www.ijph.in/article.asp?issn=0019-557X;year=2012;volume=56;issue=4;spage=281;epage=285;aulast=Bhatnagar DB - PRIME DP - Unbound Medicine ER -