Preditive Models And Health Sciences: A Brief Analysis

  • Jair Sales Paulino, Msc 1. Master Program in Regional Sustainable Development (PRODER), Science and Technology Center, Universidade Federal do Cariri (UFCA), Juazeiro do Norte, Ceará, Brasil.
  • Crístenes Sanches Lucena Gomes, Fellow School of Medicine, Universidade Federal do Cariri (UFCA), Barbalha, Ceará, Brasil.
  • Jucier Gonçalves Júnior School of Medicine, Universidade Federal do Cariri (UFCA), Barbalha, Ceará, Brasil.
  • Micaelle Nayara Dias Rodrigues, Post Graduate Post graduation in Human Resources Management, Faculdade de Juazeiro do Norte (FJN), Juazeiro do Norte, Ceará, Brasil.
  • Ana Leice Silva Souza, Post Graduate Pós-Graduação em Consultoria e Auditoria em Gestão Financeira
  • João Vitor Cândido Pimentel, Fellow School of Medicine, Universidade Federal do Cariri (UFCA), Barbalha, Ceará, Brasil
  • Kelvin Alexandre de Oliveira Brito, Fellow Universidade Regional do Cariri (URCA), Juazeiro do Norte, Ceará. Brasil
  • Samuel Flavio Lima Saboia, Fellow Universidade Regional do Cariri (URCA), Juazeiro do Norte, Ceará. Brasil
  • Paulo Renato Alves Firmino, PhD Master Program in Regional Sustainable Development (PRODER), Science and Technology Center, Universidade Federal do Cariri (UFCA), Juazeiro do Norte, Ceará, Brasil.


Background: Predictive Models are an important tool in event predicting and health planning. Despite this, there are few works focusing this area. Thus, the analysis of the real benefits of these models in Health Sciences is necessary to be performed.

Results: Predictive techniques largely evolved in second half of XX century. The development of AR, MA, ARMA, ARIMA and SARIMA models, through Box-Jenkins methodology, constitute a robust conjunct of mechanisms able to help in solution of epidemiological modeling problems, mainly in Health Sciences, once it allows to evaluate individual characteristics of living beings and its correlation with pathologies in the same space-time. Nevertheless, AR, MA and ARMA does not have tendency in seasonality, which weakens the analysis.

Conclusions: To predict the natural history of endemic/epidemic and its health-disease processes in a determined population is a sine que non condition to its adequate management in Public Health context and in adoption of affirmative measures concerning health promotion. Thus, the predictive models, with emphasis in ARIMA, SARIMA, Artificial Neural Networks and Formalism of Copulas are alternatives that can be feasible.


1. Lima-Costa MF, Barreto SM. Tipos de estudos epidemiológicos: conceitos básicos e aplicações na área do envelhecimento. Epidemiol. Serv. Saúde, 2003; 12(4):189-201.

2. Barakat RMR. Epidemiology of Schistosomiasis in Egypt: Travel through Time: Review. Journal of Advanced Research, v.4, p.425-432, 2013.

3. Bassanezi MSCB. Uma trágica primavera. A epidemia de gripe de 1918 no Estado de São Paulo, Brasil. In: BAENINGER, R.; DEDECCA, S. C. (Org.). Processos migratórios no estado de São Paulo: estudos temáticos. Campinas: Núcleo de Estudos de População - Nepo/Unicamp, 2013. 628p.

4. Costa LMC, Merchan-Hamann E. Pandemias de influenza e a estrutura sanitária brasileira: breve histórico e caracterização dos cenários. Rev Pan-Amaz Saude, 2016; 7(1):11-25.

5. Pereira BB, Limongi JE. Epidemiologia de desfechos na saúde humana relacionados à poluição atmosférica no Brasil: uma revisão sistemática. Cad. Saúde Colet., 2015; 2: 91-100.

6. Atkinson RW, Mills IC, WAlton HÁ, Anderson HR. Fine particle components and health – a systematic review and meta-analysis of epidemiological time series studies of daily mortality and hospital admissions. J Expo Sci Environ Epidemiol. 2015; 25: 208-214.

7. Morettin PA, Toloi CMC. Análise de séries temporais. 2 ed. São Paulo: Blucher, 2006, 546 p.

8. 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):1-11.

9. Dritsaki C. Forecast of Sarima Models: Αn Application to Unemployment Rates of Greece. Am J Appl Math Stat, 2016; 4(5):136-148.

10. Kiurski JS, Oros IB, Kecic VS, Kovacevic IM, Aksentijevic SM. The temporal variation of indoor pollutants in photocopying shop. Stoch Environ Res Risk Assess 2016; 30(4): 1289-1300.

11. Molla MR, Nuruzzaman SM, Hossain MS, Rana S. Performance Assessment of SARIMA Model with Holt –Winter’s Trend and Additive Seasonality Smoothing Method on Forecasting Electricity Production of Australia an Empirical Study. Global Journal of Researches in Engineering, 2016;16(2):6-11.

12. Azeez A, Obaromi D, Odeyemi A, Ndege J, Muntabayi R. 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): 757.

13. Shahdoust M, Sadeghifar M, Poorolajal J, Javanrooh N, Amini P. Predicting Hepatitis B Monthly Incidence Rates Using Weighted Markov Chains and Time Series Methods. J Res Health Sci. 2015; 15(1): 28-31.

14. Sitepu MS et al. Temporal patterns and a disease forecasting model of dengue hemorrhagic fever in Jakarta based on 10 years of surveillance data. Southeast Asian J Trop Med Public Health. 2013; 44(2): 206-17.

15. Brooks C. Introductory econometrics for finance – 2nd edn. 2008. Cambridge University Press.

16. Firmino PRA, Mattos Neto PSG, Ferreira TAE. Correcting and combining time series forecasters. Neural Netw. 2014; 50:1-11.

17. Cryer JD, Chan KS. Time series analysis with applications in R. 2ed. New York: Springer, 2008.

18. Latorre MRDO, Cardoso MRA. Análise de séries temporais em epidemiologia: uma introdução sobre os aspectos metodológicos. Rev. Bras. Epidemiol. 2001; 4(3): 145-152.

19. Lai D. Monitoring the SARS Epidemic in China: A Time Series Analysis. Journal of Data Science. 2005; 3: 279-293.

20. Permanasari AE, Hidayah I, Bustoni I. A. SARIMA (Seasonal ARIMA) implementation on time series to forecast the number of Malaria incidence. In: International Conference on Information Technology and Electrical Engineering, 2013.

21. Xing W et. al.. Hand, foot, and mouth disease in China, 2008-12: an epidemiological study. Lancet Infect. Dis. 2014; 14:308-318.

22. Tempia S et al. Mortality associated with seasonal and pandemic Influenza Among Pregnant and Nonpregnant Women of Childbearing Age in a High-HIV-Prevalence Setting-South Africa, 1999-2009. Clin Infect Dis., 2015; 61(7): 1063-70.

23. Box G, Jenkins GM. Time series analysis: forecasting and control. San Francisco: Holden-Day, 1976.

24. Liu LM, Hudak GB. Forecasting and time series analysis using the SCA statistical system. Chicago: Scientific Computing Associates, 1994.

25. Søreide K, Thorsen K, Søreide J.A. Predicting outcomes in patients with perforated gastroduodenal ulcers: artificial neural network modelling indicates a highly complex disease. Eur J Trauma Emerg Surg; 2015; 41: 91.

26. Cucchetti A et. al. Artificial neural network is superior to meld in predicting mortality of patients with end-stage liver disease. Gut. 2007; 56: 253-258.

27. Oliveira RTA, Assis TFOA, Firmino, PRA, Ferreira, TAE. Copulas based
time series combined forecasters. Information Sciences. 2017; 376: 110-124.

28. Permanasari AE, Rambli DRA, Dominic PDD. Performance of univariate
forecasting on seasonal diseases: the case of tuberculosis. In Software Tools
and Algorithms for Biological Systems. New York: Springer; 2011:171–179.

29. Ying Z, Ping X, Lauren EM, Erline EM, Hui L, Yuan H, Min Z, Meng-jie W, Min K, Qiong W, Jia-yuan L. Comparison of Breast Cancer Risk Predictive Models and Screening Strategies for Chinese Women. Journal of Women’s Health. 2017. 26:3.

30. Puddu PE, Menotti A. Artificial neural network versus multiple logistic function to predict 25-year coronary heart disease mortality in the Seven Countries Study. Eur J Cardiovasc Prev Rehabil 2009; 16: 583-591.

31. Puddu PE, Menotti A. Artificial neural networks versus proportional hazards Cox models to predict 45-year all-cause mortality in the Italian Rural Areas of the Seven Countries Study. BMC Medical Research Methodology. 2012, 12:100.

32. Center for Disease Control and Prevention (CDC). Introduction to Epidemiology, 2016. Acess in 17 January 2017. Available:

33. Martinez EZ, Silva EA, Fabbro ALD. A SARIMA forecasting model to predict the number of cases of dengue in Campinas, State of São Paulo, Brazil. Rev. Soc. Bras. Med. Trop. 2011; 4: 436-440.
How to Cite
PAULINO, Jair Sales et al. Preditive Models And Health Sciences: A Brief Analysis. International Archives of Medicine, [S.l.], v. 10, july 2017. ISSN 1755-7682. Available at: <>. Date accessed: 28 july 2017. doi: