Document Type : Review article


1 Department of Mathematical Sciences, Universiti Teknologi Malaysia, Johor, Malaysia /Department of Information Technology, Modibbo Adama University of Technology, Yola School of Management and Information Technology, Adamawa State, Nigeria

2 Department of Mathematical Sciences, Universiti Teknologi Malaysia, Johor, Malaysia

3 Adekunle Ajasin University, Department of Mathematical Sciences, Faculty of Science, Ondo State, Nigeria


Background and aims: Since accurate forecasts help inform decisions for preventive health-care
intervention and epidemic control, this goal can only be achieved by making use of appropriate
techniques and methodologies. As much as forecast precision is important, methods and model
selection procedures are critical to forecast precision. This study aimed at providing an overview of
the selection of the right artificial neural network (ANN) methodology for the epidemic forecasts. It is
necessary for forecasters to apply the right tools for the epidemic forecasts with high precision.
Methods: It involved sampling and survey of epidemic forecasts based on ANN. A comparison of
performance using ANN forecast and other methods was reviewed. Hybrids of a neural network with
other classical methods or meta-heuristics that improved performance of epidemic forecasts were
Results: Implementing hybrid ANN using data transformation techniques based on improved
algorithms, combining forecast models, and using technological platforms enhance the learning and
generalization of ANN in forecasting epidemics.
Conclusion: The selection of forecasting tool is critical to the precision of epidemic forecast; hence, a
working guide for the choice of appropriate tools will help reduce inconsistency and imprecision in
forecasting epidemic size in populations. ANN hybrids that combined other algorithms and models,
data transformation and technology should be used for an epidemic forecast.


1. Wu JT, Cowling BJ. Real-time forecasting of infectious disease epidemics. Hong Kong Med J. 2018;24 Suppl 6(5):26-9. 
2. Bharambe AA, Kalbande DR. Techniques and approaches for disease outbreak prediction: A survey. Proceedings of the ACM Symposium on Women in Research 2016. Indore, India: ACM, 2016. p. 100-2. 
3. Tabataba FS, Chakraborty P, Ramakrishnan N, Venkatramanan S, Chen J, Lewis B, et al. A framework for evaluating epidemic forecasts. BMC Infect Dis. 2017;17(1):345. doi: 10.1186/ s12879-017-2365-1. 
4. Satish S, Smitha GR. Epidemic Disease Detection and Forecasting: A Survey. International Journal of Advance Research, Ideas and Innovations in Technology. 2017;3(2):384- 6. 
5. Razi MA, Athappilly K. A comparative predictive analysis of neural networks (NNs), nonlinear regression and classification and regression tree (CART) models. Expert Syst Appl. 2005;29(1):65-74. doi: 10.1016/j.eswa.2005.01.006. 
6. Paliwal M, Kumar UA. Neural networks and statistical techniques: A review of applications. Expert Syst Appl. 2009;36(1):2-17. doi: 10.1016/j.eswa.2007.10.005. 
7. Trtica-Majnaric L, Zekic-Susac M, Sarlija N, Vitale B. Prediction of influenza vaccination outcome by neural networks and logistic regression. J Biomed Inform. 2010;43(5):774-81. doi: 10.1016/j.jbi.2010.04.011. 
8. Eftekhar B, Mohammad K, Ardebili HE, Ghodsi M, Ketabchi E. Comparison of artificial neural network and logistic regression models for prediction of mortality in head trauma based on initial clinical data. BMC Med Inform Decis Mak. 2005;5(1):3. doi: 10.1186/1472-6947-5-3. 
9. Turabieh H. A Hybrid ANN-GWO Algorithm for Prediction of Heart Disease. Am J Oper Res. 2016;6(2):136-46. doi: 10.4236/ajor.2016.62016. 
10. Zhang G, Patuwo BE, Hu MY. Forecasting with artificial neural networks: The state of the art. Int J Forecast. 1998;14(1):35-62. 
11. Güresen E, Kayakutu G. Artificial Neural Networks: Definition, Properties and Misuses. In: Flores JA, ed. Focus on Artificial Neural Network. Nova Science Publishers; 2011. 
12. Amato F, López A, Peña-Méndez EM, Vanhara P, Hampl A, Havel J. Artificial neural networks in medical diagnosis. J Appl Biomed. 2013;11(2):47-58. doi: 10.2478/v10136-012- 0031-x.
13. Saberian F, Zamani A. Prediction of Seasonal Influenza Epidemics in Tehran Using Artificial Neural Networks. In: The 22nd Iranian Conference on Electrical Engineering; 2014. 
14. Subers BE. Epidemic Prediction The future of disease through mathematical modeling. Arcadia University; 2011. p. 1-8. 
15. Ahrens W, Pigeot I. Handbook of epidemiology. 2nd ed. New York: Springer Science +Business Media; 2014. p. 2489. 
16. Canzani E, Lechner U. Insights from Modeling Epidemics of Infectious Diseases-A Literature Review. In: ISCRAM; 2015. p. 8. 
17. Dybowski R, Gant V. Clinical applications of artificial neural networks. Cambridge University Press; 2007. p. 380. 
18. Sree Hari Rao V, Naresh Kumar M. Estimation of the parameters of an infectious disease model using neural networks. Nonlinear Anal Real World Appl. 2010;11(3):1810-8. doi: 10.1016/j.nonrwa.2009.04.006. 
19. Antoniou GE, Mentzelopoulou S. Neural Networks: An Application to the Epidemics. In: Proceeding of Neural and Statistical Computations; 1995. p. 18-21. 
20. Samarasinghe S, Waidyarathne KP. Artificial neural networks to identify naturally existing disease severity status. Neural Computing and Applications. 2014;25(5):1031-1041. 
21. Rajalakshmi V, Anandha Mala GS. Software aided diagnosing of diseases using RBF based neural networks [RADD]. Biomed Res. 2016;27(3):718-24. 
22. Sharma S. Role of Data Mining Techniques in Human Disease Diagnosis Abstract. Int J Comput Technol. 2016;3(2):184-8. 
23. Faisal T, Taib MN, Ibrahim F. Neural network diagnostic system for dengue patients risk classification. J Med Syst. 2012;36(2):661-76. doi: 10.1007/s10916-010-9532-x. 
24. Hwang S, Clarite DS, Elijorde FI, Gerardo BD, Byun Y. A web-based analysis for dengue tracking and prediction using artificial neural network. Adv Sci Technol Lett. 2016;122:160- 4. doi: 10.14257/astl.2016.122.32. 
25. Cetiner BG, Sari M, Aburas HM. Recognition of dengue disease patterns using artificial neural networks. In: 5th International Advanced Technologies Symposium. Karabük University, Turkey; 2009. p. 13-6. 
26. Jabbar MA, Deekshatulu BL, Chandra P. Classification of heart disease using artificial neural network and feature subset selection. Global Journal of Computer Science and Technology Neural & Artificial Intelligence. 2013;13(3):4-8. 
27. Er O, Yumusak N, Temurtas F. Chest diseases diagnosis using artificial neural networks. Expert Syst Appl. 2010;37(12):7648- 55. doi: 10.1016/j.eswa.2010.04.078. 
28. Ghosh D, Guha R. Use of genetic algorithm and neural network approaches for risk factor selection: A case study of West Nile virus dynamics in an urban environment. Comput Environ Urban Syst. 2010;34(3):189-203. doi: 10.1016/j. compenvurbsys.2010.02.007. 
29. Elveren E, Yumusak N. Tuberculosis disease diagnosis using artificial neural network trained with genetic algorithm. J Med Syst. 2011;35(3):329-32. doi: 10.1007/s10916-009-9369-3. 
30. Priya E, Srinivasan S. Automated Identification of Tuberculosis Objects in Digital Images Using Neural Network and Neuro Fuzzy Inference Systems. J Med Imaging Heal Informatics [Internet]. 2015;5(3):506–12. Available from: http://openurl. 7018&volume=5&issue=3&spage=506
31. Muller PS, Meenakshi Sundaram S, Nirmala M, Nagarajan E. Application of computational technique in design of classifier for early detection of gestational diabetes mellitus. Appl Math Sci. 2015;9(67):3327-36. doi: 10.12988/ams.2015.54319. 
32. Biswas SK. An ANN Based Pattern Classification Algorithm for Diagnosis of Swine Flu. Journal of Intelligent Computing. 2014;5(1):16-30. 33. 
33. Sanoob MU, Madhu A, Ajesh KR, Varghese SM. Artificial neural network for diagnosis of pancreatic cancer. Int J Cybern Inform. 2016;5(2):41-9. doi: 10.5121/ijci.2016.5205. 
34. Breu F, Guggenbichler S, Wollmann J. Utilization of Neural Network for Disease Forecasting. Vasa [Internet]. 2008; Available from: A65RM03P4874243N.pdf. 
35. Baridam B, Irozuru C. The Prediction of Prevalence and Spread of HIV/AIDS Using Artificial Neural Network–the Case of Rivers State in the Niger Delta, Nigeria. Int J Comput Appl. 2012;44(2):42-5. 
36. Prieto A, Prieto B, Ortigosa EM, Ros E, Pelayo F, Ortega J, et al. Neural networks: An overview of early research, current frameworks and new challenges. Neurocomputing. 2016;214:242-68. doi: 10.1016/j.neucom.2016.06.014. 
37. Priddy KL, Keller PE. Artificial Neural Network: An Introduction. SPIE Publications; 2005. 
38. Pasini A. Artificial neural networks for small dataset analysis. J Thorac Dis. 2015;7(5):953-60. doi: 10.3978/j.issn.2072- 1439.2015.04.61. 
39. Livingstone D. A Practical Guide to Scientific Data Analysis. UK: John Wiley & Sons, Ltd; 2009. doi: 10.1002/9780470017913. 
40. Aburas HM, Cetiner BG, Sari M. Dengue confirmed-cases prediction: A neural network model. Expert Syst Appl. 2010;37(6):4256-60. doi: 10.1016/j.eswa.2009.11.077. 
41. Husin NA, Mustapha N, Sulaiman MN, Yaakob R. A hybrid model using genetic algorithm and neural network for predicting dengue outbreak. In: 2012 4th Conference on Data Mining and Optimization (DMO). Langkawi, Malaysia: IEEE; 2012. p. 23-7. doi: 10.1109/DMO.2012.6329793. 
42. Maier HR, Dandy GC, May RJ, Fernando T. Efficient selection of inputs for artificial neural network models. MODSIM 2005 Int Congr Model Simul; 2005;170–6. Available from: http:// 
43. Karlik B, Vehbi Olgac A. Performance analysis of various activation functions in generalized MLP architectures of neural networks. International Journal of Artificial Intelligence and Expert Systems. 2010;1(4):111-22. 
44. Isa IS, Saad Z, Omar S, Osman MK, Ahmad KA, Mat Sakim HA. Suitable MLP network activation functions for breast cancer and thyroid disease detection. 2010 Second Int Conf Comput Intell Model Simul. Tuban, Indonesia: IEEE; 2010. p. 39-44. doi: 10.1109/CIMSiM.2010.93. 4
45. Ittiyavirah SP, Jones SA, Siddarth P. Analysis of different activation functions using Backpropagation Neural Networks. Journal of Theoretical and Applied Information Technology. 2013;47(3):1344-48. 
46. Baptista D, Morgado-Dias F. Artificial Neural Networks: a Review of Training Tools. In: 10th Portuguese Conference on Automatic Control. Funchal, Portugal: Controlo; 2012. p. 292- 7. 
47. Basheer IA, Hajmeer M. Artificial neural networks: fundamentals, computing, design, and application. J Microbiol Methods. 2000;43(1):3-31. doi: 10.1016/S0167- 7012(00)00201-3. 
48. Reynaldi A, Lukas S, Margaretha H. Backpropagation and Levenberg-Marquardt algorithm for training finite element neural network. Proc - UKSim-AMSS 6th Eur Model Symp EMS 2012. Valetta, Malta: IEEE; 2012. p. 89-94. doi: 10.1109/ EMS.2012.56. 
49. Lawrence S, Giles CL, Tsoi AC. Lessons in neural network training: Overfitting may be harder than expected. In: Proceedng of the Fourteenth National Conference on Artifical Intelligence; 1997. p. 540–5. 
50. Karegowda AG, Manjunath AS, Jayaram MA. Application of genetic algorithm optimized neural network connection weights for medical diagnosis of pima Indians diabetes. International Journal on Soft Computing. 2011;2(2):15-23. 
51. Yaghini M, Khoshraftar MM, Fallahi M. A hybrid algorithm for artificial neural network training. Eng Appl Artif Intell. 2013;26(1):293-301. doi: 10.1016/j.engappai.2012.01.023. 
52. Ghiassi M, Saidane H. A dynamic architecture for artificial neural networks. Neurocomputing. 2005;63:397-413. doi: 10.1016/j.neucom.2004.03.014. 
53. Makridakis S. Accuracy measures: theoretical and practical concerns. Int J Forecast. 1993;9(4):527-9. doi: 10.1016/0169- 2070(93)90079-3. 
54. Yokuma JT, Armstrong JS. Beyond accuracy: Comparison of criteria used to select forecasting methods. Int J Forecast. 1995;11(4):591-7. doi: 10.1016/0169-2070(95)00615-X. 
55. Armstrong JS. Findings from evidence-based forecasting: Methods for reducing forecast error. Int J Forecast. 2006;22(3):583-98. doi: 10.1016/j.ijforecast.2006.04.006. 
56. Shcherbakov MV, Brebels A, Shcherbakova NL, Tyukov AP, Janovsky TA, Kamaev VA. A survey of forecast error measures. World Appl Sci J. 2013;24(24):171-6. doi: 10.5829/idosi. wasj.2013.24.itmies.80032. 
57. Hyndman RJ, Koehler AB. Another look at measures of forecast accuracy. Int J Forecast. 2006;22(4):679-88. doi: 10.1016/j. ijforecast.2006.03.001. 
58. Kamalanand K, Jawahar PM. Prediction of human immunodeficiency virus-1 viral load from CD4 cell count using artificial neural networks. J Med Imaging Health Inform. 2015;5(3):641-6. doi: 10.1166/jmihi.2015.1430. 
59. Wang Y, Li J, Gu J, Zhou Z, Wang Z. Artificial neural networks for infectious diarrhea prediction using meteorological factors in Shanghai (China). Appl Soft Comput. 2015;35:280-90. doi: 10.1016/j.asoc.2015.05.047. 
60. Soemsap T, Wongthanavasu WS. Forecasting Number of Dengue Patients. In: Proceedings of the International Electrical Engineering Congress; 2014. p. 1-4. 
61. Nedjah N, Abraham A, Mourelle LM. Hybrid artificial neural network. Neural Comput Appl. 2007;16(3):207-8. doi: 10.1007/s00521-007-0083-0. 
62. Gutiérrez PA, Martínez CH. Hybrid Artificial Neural Networks: Models, Algorith and Data. Torremolinos-Málaga, Spain: Advances in Computational Intelligence: 11th International Work-Conference on Artificial Neural Networks, IWANN; 2011. p. 177-84.
63. Zakharov AA, Olennikov EA, Payusova TI, Silnov DS. Cloud service for data analysis in medical information systems using artificial neural networks. Int J Appl Eng Res. 2016;11(4):2917- 20. 
64. Elijorde FI, Clarite DS, Gerardo BD, Byun Y. Tracking and Prediction of Dengue Outbreak Using Cloud-Based Services and Artificial Neural Network. International Journal of Multimedia and Ubiquitous Engineering. 2016;11(5):355-66. doi: 10.14257/ijmue.2016.11.5.33. 
65. Makridakis S, Hibon M. The M3-Competition: results, conclusions and implications. Int J Forecast. 2000;16(4):451- 76. 
66. Crone SF, Hibon M, Nikolopoulos K. Advances in forecasting with neural networks? Empirical evidence from the NN3 competition on time series prediction. Int J Forecast. 2011;27(3):635-60. doi: 10.1016/j.ijforecast.2011.04.001. 
67. Armstrong JS. Principles of Forecasting: A Handbook for Researchers and Practitioners. New York: Kluwer Academic Publishers; 2002.
68. Yao X. Evolving artificial neural networks. Proc IEEE. 1999;87(9):1423-47. 
69. Jia W, Zhao D, Shen T, Su C, Hu C, Zhao Y. A new optimized GA-RBF neural network algorithm. Comput Intell Neurosci. 2014;2014:982045. doi: 10.1155/2014/982045. 
70. Vishwakarma MDD. Genetic algorithm based weights optimization of artificial neural network. International Journal of Advanced Research in Electrical, Electronics and Instrumentation Engineering. 2012;1(3):206-11. 
71. Askarzadeh A, Rezazadeh A. Artificial neural network training using a new efficient optimization algorithm. Appl Soft Comput. 2013;13(2):1206-13. doi: 10.1016/j.asoc.2012.10.023. 
72. Khan K, Sahai A. A comparison of BA, GA, PSO, BP and LM for training feed forward neural networks in e-learning context. Intell Syst Appl. 2012;7:23-9. doi: 10.5815/ijisa.2012.07.03. 
73. Kanjamapornkul K, Mathew J, Tanasugarn L, Kijsirikul B. Forecasting of Ebola Outbreak Cases 2014 with Hilbert Huang Transform and Artificial Neural Network model (HHT-ANN). 2014. 
74. Arifianto A, Barmawi AM, Wibowo AT. Malaria incidence forecasting from incidence record and weather pattern using polynomial neural network. International Journal of Future Computer and Communication. 2014;3(1):60. doi: 10.7763/ ijfcc.2014.v3.268. 
75. Yu L, Zhou L, Tan L, Jiang H, Wang Y, Wei S, et al. Application of a new hybrid model with seasonal auto-regressive integrated moving average (ARIMA) and nonlinear auto-regressive neural network (NARNN) in forecasting incidence cases of HFMD in Shenzhen, China. PLoS One. 2014;9(6):e98241. doi: 10.1371/ journal.pone.0098241. 
76. Belciug S, Gorunescu F. A hybrid neural network/genetic algorithm applied to breast cancer detection and recurrence. Expert Systems. 2013;30(3):243-54. doi: 10.1111/j.1468- 0394.2012.00635.x. 
77. Gan R, Chen X, Yan Y, Huang D. Application of a hybrid method combining grey model and back propagation artificial neural networks to forecast hepatitis B in china. Comput Math Methods Med. 2015;2015:328273. doi: 10.1155/2015/328273. 
78. Zhou L, Xia J, Yu L, Wang Y, Shi Y, Cai S, et al. Using a hybrid model to forecast the prevalence of Schistosomiasis in humans. Int J Environ Res Public Health. 2016;13(4):355. doi: 10.3390/ijerph13040355. 
79. Yan W, Xu Y, Yang X, Zhou Y. A hybrid model for short-term bacillary dysentery prediction in Yichang City, China. Jpn J Infect Dis. 2010;63(4):264-70. 
80. Cao S, Wang F, Tam W, Tse LA, Kim JH, Liu J, et al. A hybrid seasonal prediction model for tuberculosis incidence in China. BMC Med Inform Decis Mak. 2013;13:56. doi: 10.1186/1472- 6947-13-56. 
81. Zhang X, Liu Y, Yang M, Zhang T, Young AA, Li X. Comparative study of four time series methods in forecasting typhoid fever incidence in China. PLoS One. 2013;8(5):e63116. doi: 10.1371/journal.pone.0063116. 
82. Rismala R, Liong TH, Ardiyanti A. Prediction of malaria incidence in Banggai regency using evolving neural network. Proc 2013 Int Conf Technol Informatics, Manag Eng Environ TIME-E 2013. Bandung, Indonesia: IEEE; 2013. p. 89-94. doi: 10.1109/TIME-E.2013.6611970. 
83. Samira K, Ahmad J. A New Artificial Intelligence Method for Prediction of Diabetes Type 2. Bull la Soc R des Sci Liege. 2016;85:376–91. 
84. Azeez A, Obaromi D, Odeyemi A, Ndege J. Seasonality and Trend Forecasting of Tuberculosis Prevalence Data in Eastern Cape , South Africa , Using a Hybrid Model. Int J Environ Res Public Health. 2016;13(8):E757. doi: 10.3390/ ijerph13080757. 
85. Wei W, Jiang J, Liang H, Gao L, Liang B, Huang J, Zang N, et al. Application of a Combined Model with Autoregressive Integrated Moving Average (ARIMA) and Generalized Regression Neural Network ( GRNN ) in Forecasting Hepatitis Incidence in Heng County, China. PLoS One. 2016;11(6):e0156768. doi: 10.1371/journal.pone.0156768 
86. Kupusinac A, Stokic E, Kovacevic I. Hybrid EANN-EA System for the Primary Estimation of Cardiometabolic Risk. J Med Syst. 2016;40(6):138. doi: 10.1007/s10916-016-0498-1.