Predicting Dengue in the Philippines Using an Artificial Neural Network
Main Article Content
Keywords
dengue, Philippines, artificial neural network, climate change
Abstract
Dengue fever is an infectious disease caused by Flavivirus transmitted by Aedes mosquito. This disease predominantly occurs in the tropical and subtropical regions. With no specific treatment, the most effective way to prevent dengue is vector control. The dependence of Aedes mosquito population on meteorological variables make prediction of dengue infection possible using conventional statistical and epidemiologic models. However, with increasing average global temperature, the predictability of these models may be lessened employing the need for artificial neural network. This study uses artificial neural network to predict dengue incidence in the entire Philippines with humidity, rainfall, and temperature as independent variables. All generated predictive models have mean squared logarithmic error of less than 0.04.
References
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[30] Benedum CM, Seidahmed OME, Eltahir EAB, Markuzon N. “Statistical modeling of the effect of rainfall flushing on dengue transmission in Singapore,” PLoS Negl Trop Dis, 2018; 12(12): e0006935.
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[33] Ebi K, Nealon J. “Dengue in a changing climate,” Environmental Research, 151 (2016): 115-123.
[2] Undurraga E, Edillo F, Erasmo JN, Alera MT, Yoon IK, Largo F, Shepard D. “Disease Burden of Dengue in the Philippines: Adjusting for Underreporting by Comparing Active and Passive Dengue Surveillance in Punta Princesa, Cebu City,” Am J Trop Med Hyg, 2017 Apr 5; 96(4): 887–898.
[3] Gubler DJ. “Dengue and dengue hemorrhagic fever,” Clin Microbiol Rev, 1998; 11(3):480–496.
[4] Gubler DJ. “Epidemic dengue/dengue hemorrhagic fever as a public health, social and economic problem in the 21st century,” Trends Microbiol, 2002; 10(2):100-103.
[5] Rigau-Pérez JG, Clark GG, Gubler DJ, Reiter P, Sanders EJ, Vorndam AV. Dengue and dengue haemorrhagic fever, Lancet. 1998; 352(9132):971-977.
[6] Singh M, Chakraborty A, Kumar S, Kumar A. “The epidemiology of dengue viral infection in developing countries: A systematic review,” J Health Res Rev, 2017; 4(3):104-107.
[7] Nelson MJ, Self LS, Pant CP, Slim U. “Diurnal periodicity of attraction to human bait of Aedes aegypti in Jakarta, Indonesia,” J Med Entomol, 1978; 14: 504-10.
[8] Sheppard PM, Maedonald WW, Tonk RJ, Grab B. “The dynamics of an adult population of Aedes aegypti in relation to DHF in Bangkok,” J Animal Ecology, 1969; 38: 661-702.
[9] Naish S, Dale P, Mackenzie JS, McBride J, Mengersen K, Tong S. “Climate change and dengue: a critical and systematic review of quantitative modelling approaches,” BMC Infect Dis, 2014; 14(1):167.
[10] Ehelepola NDB, Ariyaratne K, Buddhadasa WMNP, Ratnayake S, Wickramasinghe M, Promprou S, et al. “A study of the correlation between dengue and weather in Kandy City, Sri Lanka (2003 -2012) and lessons learned,” Infect Dis Poverty, 2015;4:42.
[11] Kalra NL, Kaul SM, Rastogi RM. “Prevalence of Aedes aegypti and Aedes albopictus vectors of DF/DHF in North, North-East and Central India,” Dengue Bulletin, 1997; 21: 84–92.
[12] Descloux E, et al. “Climate-based models for understanding and forecasting dengue epidemics,” PLoS Negl Trop Dis, 2012; 6(2): e1470.
[13] Focks DA, Haile DG, Daniels E, Mount GA. “Dynamic life table model for Aedes aegypti (Diptera: Culicidae): analysis of the literature and model development,” J Med Entomol, 1993; 30: 1003-1017.
[14] Focks DA, Haile DG, Daniels E, Mount GA. “Dynamic life table model for Aedes aegypti (Diptera: Culicidae): simulation results and validation,” J Med Entomol, 1993; 30: 1018-1028.
[15] Focks DA, Haile DG, Daniels E, Keesling JE. “A simulation model of the epidemiology of urban dengue fever: literature analysis, model development, preliminary validation, and samples of simulation results,” Am J Trop Med Hyg, 1995; 53: 489-506.
[16] Hopp MJ, Foley JA. “Global-scale relationships between climate and the Dengue fever vector, Aedes aegypti,” Clim Change, 2001; 48: 441-463.
[17] Focks DA, Brenner RJ, Hayes J, Daniels E. “Transmission thresholds for for dengue in terms of Aedes aegypti pupae per person with discussion of their utility in source reduction efforts,” Am J Trop Med Hyg, 2002; 62: 11-18.
[18] Schnoor JL. “The IPCC fourth assessment,” Environ Sci Technol, 2007; 41: 1503.
[19] Chumpu R, Khamsemanan N, Nattee C. “The association between dengue incidences and provincial-level weather variables in Thailand from 2001 to 2014,” PLoS ONE, 2019; 14(12): e0226945.
[20] Choi Y, et al. “Effects of weather factors on dengue fever incidence and implications for interventions in Cambodia,” BMC Public Health, 2016; 16(1):241.
[21] Chuang T-W, Chaves LF, Chen P-J. “Effects of local and regional climatic fluctuations on dengue outbreaks in southern Taiwan,” PLoS ONE, 2017; 12(6): e0178698.
[22] Xuan LTT, Hau PV, Thu DT, Toan DTT. “Estimates of meteorological variability in association with dengue cases in a coastal city in northern Vietnam: an ecological study,” Glob Health Action, 2014, 7: 23119.
[23] Carvajal T, et al. “Machine learning methods reveal the temporal pattern of dengue incidence using meteorological factors in metropolitan Manila, Philippines,” BMC Infectious Diseases, (2018) 18:183.
[24] Jain R, Sontisirikit S, Iamsirithaworn S, Prendinger H. “Prediction of dengue outbreaks based on disease surveillance, meteorological and socio-economic data,” BMC Infectious Diseases, (2019) 19:272.
[25] Bakar AA, Kefli Z, Abdullah S, Sahani M. “Predictive models for dengue outbreak using multiple rulebase classifiers,” 2011 International Conference on Electrical Engineering and Informatics (ICEEI), Bandung, Indonesia, 17-19 July 2011. DOI: 10.1109/ICEEI.2011.6021830.
[26] Seposo XT, Dang TN, Honda Y. “Exploring the effects of high temperature on mortality in four cities in the Philippines using various heat wave definitions in different mortality subgroups,” Glob Health Action, 2017; 10:1368969.
[27] Alzahrani R, Parker A. “Neuromorphic Circuits With Neural Modulation Enhancing the Information Content of Neural Signaling,” International Conference on Neuromorphic Systems 2020 DOI: 10.1145/3407197.3407204.
[28] Nair V, Hinton G. "Rectified Linear Units Improve Restricted Boltzmann Machines", 27th International Conference on International Conference on Machine Learning, ICML'10, USA: Omnipress, pp. 807-814.
[29] Iguchi J, Seposo X, Honda Y. “Meteorological factors affecting dengue incidence in Davao, Philippines,” BMC Public Health, (2018) 18:629.
[30] Benedum CM, Seidahmed OME, Eltahir EAB, Markuzon N. “Statistical modeling of the effect of rainfall flushing on dengue transmission in Singapore,” PLoS Negl Trop Dis, 2018; 12(12): e0006935.
[31] Shang C-S, et al. “The Role of Imported Cases and Favorable Meteorological Conditions in the Onset of Dengue Epidemics,” PLoS Negl Trop Dis, 2010; 4(8): e775.
[32] Liu-Helmersson J, Quam M, Wilder-Smith A, Stenlund H, Ebi K, Massad E, Rocklov J. “Climate Change and Aedes Vectors: 21st Century projections for dengue transmission in Europe,” EbioMedicine, 7 (2016): 267-277.
[33] Ebi K, Nealon J. “Dengue in a changing climate,” Environmental Research, 151 (2016): 115-123.
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Published
Oct 24, 2023
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How to Cite
Predicting Dengue in the Philippines Using an Artificial Neural Network. (2023). Journal of Applied Interdisciplinary Research, Special Issue, 80-89. https://doi.org/10.25929/e3bk-a370