Abstract
With recent rises of sophisticated and dangerous epidemics, there is a growing need for a system that could predict disease severity with high accuracy. In this paper, we address the problem of forecasting the magnitude of dengue in a short term period, i.e. one week ahead. We consider inputs as both statistics of historical cases and biological factors affecting the dengue virus, including the temperature, population and mosquito density. We propose a two-phase model simulating the disease transmission process, which are the local outbreak and then province transmission. The locality phase estimates the number of potential cases in each province independently in the following week. Then, in the transmission phase, an artificial neural network is used to predict the mobility of the dengue virus across provinces. Our proposed method obtains a higher accuracy than the conventional models of time series, linear regression, and ARIMA. Moreover, this provides the first research results about dengue prediction in Vietnam.
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References
Alvin, C.R., William, F.C.: Methods of Multivariate Analysis, 3rd edn. Wiley, New York (2012)
Benyun, S., et al.: Inferring Plasmodium vivax transmission networks from tempo-spatial surveillance data. PLOS Neglected Trop. Dis. 8(2), e2682 (2014)
Center for Developing Information Technology and Geographic Information System (DITAGIS). http://www.ditagis.hcmut.edu.vn/
Chai, T., Draxler, R.R.: Root mean square error (RMSE) or mean absolute error (MAE)? – Arguments against avoiding RMSE in the literature. Geoscientific Model Dev. 7, 1247–1250 (2014)
David, L.S., et al.: Ross, Macdonald, and a theory for the dynamics and control of mosquito-transmitted pathogens. PLoS Pathog. 8(4), e1002588 (2012)
David, L.S., McKenzie, F.E.: Statics and dynamics of malaria infection in Anopheles Mosquitos. Malaria J. 3, 13 (2004)
Dayama, P., Kameshwaran, S.: Predicting the dengue incidence in Singapore using univariate time series models. In: AMIA Annual Symposium Proceedings, pp. 285–292 (2013)
Edson, Z.M., Elisângela, A.S.S: Predicting the number of cases of dengue infection in Ribeirão Preto, São Paulo state, Brazil using a SARIMA model. Cadernos de Saúde Pública Reports in Public Health, Rio de Janeiro, pp. 1809–1818 (2011)
Felissa, R.L., Jerry, D.D.: Emerging Infectious Diseases, Trends and Issues, 2nd edn. Springer, New York (2007)
General Statistics Office of Vietnam. http://www.gso.gov.vn/
IRI/LDEO Climate Data Library. http://iridl.ldeo.columbia.edu/
Karim, M.N., et al.: Climatic factors influencing Dengue cases in Dhaka City: a model for Dengue prediction. Indian J. Med. Res. 136(1), 32–39 (2012)
Liu-Helmersson, J., et al.: Vectorial capacity of Aedes Aegypti: effects of temperature and implications for global Dengue epidemic potential. PLoS ONE 9(3), e89783 (2014)
Louis, L.: Impact of daily temperature fluctuations on Dengue virus transmission by Aedes Aegypti. Proc. Natl. Acad. Sci. 108(18), 7460–7465 (2011)
Michael, C.W., et al.: A computer system for forecasting malaria epidemic risk using remotely sensed environmental data. In: Proceedings of the 2012 International Congress on Environmental Modelling and Software Managing Resources of a Limited Planet, pp. 482–489 (2012)
Michael, N.: Neural Networks and Deep Learning. Determination Press (2015)
Pasteur Institute at Ho Chi Minh City, Vietnam. http://www.pasteurhcm.gov.vn/
Robert, H.S., David, S.S.: Time Series Analysis and Its Applications. Springer, New York (2011)
Søren, B., Murat, K.: Time Series Analysis and Forecasting by Example, 1st edn. Wiley, Hoboken (2011)
Vietnam National Centre for Hydro meteorological Forecasting (NCHMF). http://www.nchmf.gov.vn/
Wongkoon, S., Jaroensutasinee, M., Jaroensutasinee, K.: Development of temporal modeling for prediction of Dengue infection in Northeastern Thailand. Asian Pac. J. Trop. Med. 5(3), 249–252 (2012)
Acknowledgements
This work is funded by Vietnam National University at Ho Chi Minh City (VNU-HCMC) under the grant number B2015-42-02. We would also like to thank the anonymous reviewers for their constructive comments that help to make the final version of this paper.
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Dinh, T.Q., Le, H.V., Cao, T.H., Luong, Q.C., Diep, H.T. (2016). Forecasting the Magnitude of Dengue in Southern Vietnam. In: Nguyen, N.T., Trawiński, B., Fujita, H., Hong, TP. (eds) Intelligent Information and Database Systems. ACIIDS 2016. Lecture Notes in Computer Science(), vol 9621. Springer, Berlin, Heidelberg. https://doi.org/10.1007/978-3-662-49381-6_53
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DOI: https://doi.org/10.1007/978-3-662-49381-6_53
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