Abstract
Changing weather patterns affect the incidence of respiratory tract infections, which causes huge economic burden for healthcare services. Early warning for the infection may help healthcare service providers to prepare for an epidemic on time. The purpose of the current research is to explore the relationship between respiratory tract infection episodes and climatic factors and to predict the number of daily episodes in different weather zones of active weather stations in Bangladesh. Prescription data collected from clinics are integrated with climatic factors of the nearest weather stations, and the integrated dataset is used to predict the daily respiratory tract infection episodes. We apply panel generalized linear models and show that the number of episodes increases to a greater extent for increasing magnitude of rolling standard deviation of relative humidity and rolling mean of wind speed. A 7-day-ahead forecast of number of episodes based on rolling window models of regression tree, random forest, support vector regression, and deep neural network is estimated to know the severity of epidemic for healthcare planning. A further 1-day-ahead confirmation forecast is produced to assess the necessity of healthcare service plan adopted based on a 7-day-ahead forecast. Root mean squared forecast errors computed for both 7-day-ahead and 1-day-ahead forecasts from these models provide qualitatively similar results, except for three weather stations where an unusually high number of episodes are observed because of extreme climate and high level of air pollution.
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References
Alencar, A.P.: Seasonality of hospitalizations due to respiratory diseases: modelling serial correlation all we need is poisson. J. Appl. Stat. 45(10), 1813–1822 (2018)
Althouse, B.M., Flasche, S., Thiem, V.D., Hashizume, M., Ariyoshi, K., Anh, D.D., Rodgers, G.L., Klugman, K.P., Hu, H., Yoshida, L.M., et al.: Seasonality of respiratory viruses causing hospitalizations for acute respiratory infections in children in Nha Trang, Vietnam. Int. J. Infect. Dis. 75, 18–25 (2018)
Araújo, F.H.D., Santana, A.M., Neto, P.D.A.S.: Using machine learning to support healthcare professionals in making preauthorisation decisions. Int. J. Med. Inform. 94, 1–7 (2016)
Blangiardo, M., Finazzi, F., Cameletti, M.: Two-stage bayesian model to evaluate the effect of air pollution on chronic respiratory diseases using drug prescriptions. Spat. Spatio-temporal Epidemiol. 18, 1–12 (2016)
Cai, M., Pipattanasomporn, M., Rahman, S.: Day-ahead building-level load forecasts using deep learning versus traditional time-series techniques. Appl. Energy 236, 1078–1088 (2019)
Choi, Y., Ahn, H., Chen, J.J.: Regression trees for analysis of count data with extra Poisson variation. Comput. Stat. Data Anal. 49(3), 893–915 (2005)
Eccles, R.: An explanation for the seasonality of acute upper respiratory tract viral infections. Acta Otolaryngol. 122(2), 183–191 (2002)
Erbas, B., Hyndman, R.J.: Sensitivity of the estimated air pollution-respiratory admissions relationship to statistical model choice. Int. J. Environ. Health Res. 15(6), 437–448 (2005)
Fang, K., Jiang, Y., Song, M.: Customer profitability forecasting using big data analytics: A case study of the insurance industry. Comput. Ind. Eng. 101, 554–564 (2016)
Gurley, E.S., Salje, H., Homaira, N., Ram, P.K., Haque, R., Petri Jr., W.A., Bresee, J., Moss, W.J., Luby, S.P., Breysse, P., et al.: Seasonal concentrations and determinants of indoor particulate matter in a low-income community in Dhaka, Bangladesh. Environ. Res. 121, 11–16 (2013)
He, W.: Load forecasting via deep neural networks. Procedia Comput. Sci. 122, 308–314 (2017)
Lahouar, A., Slama, J.B.H.: Day-ahead load forecast using random forest and expert input selection. Energy Convers. Manag. 103(12), 1040–1051 (2015)
Li, Y., Peterson, M.E., Campbell, H., Nair, H.: Association of seasonal viral acute respiratory infection with pneumococcal disease: a systematic review of population-based studies. BMJ Open 8(4), e019743 (2018)
Liu, Y., Liu, J., Chen, F., Shamsi, B.H., Wang, Q., Jiao, F., Qiao, Y., Shi, Y.: Impact of meteorological factors on lower respiratory tract infections in children. J. Int. Med. Res. 44(1), 30–41 (2016)
Lu, C.J., Lee, T.S., Chiu, C.C.: Financial time series forecasting using independent component analysis and support vector regression. Decision Support Syst. 47(2), 115–125 (2009)
Mirsaeidi, M., Motahari, H., Taghizadeh Khamesi, M., Sharifi, A., Campos, M., Schraufnagel, D.E.: Climate change and respiratory infections. Ann. Am. Thorac. Soc. 13(8), 1223–1230 (2016)
Moineddin, R., Nie, J.X., Domb, G., Leong, A.M., Upshur, R.E.: Seasonality of primary care utilization for respiratory diseases in Ontario: a time-series analysis. BMC Health Serv. Res. 8(1), 160 (2008)
Moniz, N., Branco, P., Torgo, L.: Resampling strategies for imbalanced time series forecasting. Int. J. Data Sci. Anal. 3(3), 161–181 (2017)
Oviedo, S., Contreras, I., Quirós, C., Giménez, M., Conget, I., Vehi, J.: Risk-based postprandial hypoglycemia forecasting using supervised learning. Int. J. Med. Inform. 126, 1–8 (2019)
Rana, M.M., Sulaiman, N., Sivertsen, B., Khan, M.F., Nasreen, S.: Trends in atmospheric particulate matter in Dhaka, Bangladesh, and the vicinity. Environ. Sci. Pollut. Res. 23(17), 17393–17403 (2016)
Seong, S.J., Park, S.J., Park, T.H., Shin, C.U., Park, D.S., Kim, J.M., Cha, J.W.: Epidemic respiratory disease prediction using ensemble method. In: International Conference on Future Information & Communication Engineering vol 10, pp 253–256 (2018)
Shi, T., McAllister, D.A., O’Brien, K.L., Simoes, E.A., Madhi, S.A., Gessner, B.D., Polack, F.P., Balsells, E., Acacio, S., Aguayo, C., et al.: Global, regional, and national disease burden estimates of acute lower respiratory infections due to respiratory syncytial virus in young children in 2015: a systematic review and modelling study. The Lancet 390(10098), 946–958 (2017)
Spathis, D., Vlamos, P.: Diagnosing asthma and chronic obstructive pulmonary disease with machine learning. Health Inform. J. 25(3), 811–827 (2019)
Voyant, C., Notton, G., Kalogirou, S., Nivet, M.L., Paoli, C., Motte, F., Fouilloy, A.: Machine learning methods for solar radiation forecasting: A review. Renew. Energy 105, 569–582 (2017)
Wang, J., Duggasani, A.: Forecasting hotel reservations with long short-term memory-based recurrent neural networks. Int. J. Data Sci. Anal. 9, 77–94 (2020)
WHO: Country profiles of environmental burden of disease, World Health Organization (2009). www.who.int/quantifyingehimpacts/national/countryprofile
WHO: Ambient (outdoor) air quality and health. World Health Organization (2018). https://www.who.int/en/news-room/fact-sheets/detail/
Xu, Z., Etzel, R.A., Su, H., Huang, C., Guo, Y., Tong, S.: Impact of ambient temperature on children’s health: a systematic review. Environ. Res. 117(12), 120–131 (2012)
Xu, Z., Hu, W., Tong, S.: Temperature variability and childhood pneumonia: an ecological study. Environ. Health 13(51), 1–8 (2014)
Zhang, H., Triche, E., Leaderer, B.: Model for the analysis of binary time series of respiratory symptoms. Am. J. Epidemiol. 151(12), 1206–1215 (2000)
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Khan, A.R., Hasan, K.T., Islam, T. et al. Forecasting respiratory tract infection episodes from prescription data for healthcare service planning. Int J Data Sci Anal 11, 169–180 (2021). https://doi.org/10.1007/s41060-020-00235-z
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DOI: https://doi.org/10.1007/s41060-020-00235-z