• Logo
  • HamaraJournals

A Protocol of Scoping Review of Peak Point Prediction Methods for Epidemic Diseases: Applicable to Coronavirus 2019 Prediction

Elham Nazari, Zahra Ebnehoseini, Hamed Tabesh



Introduction: Given, widespread COVID-19 across the world a comprehensive literature review can be used to forecast COVID-19 peak in the countries. The present protocol study aimed to explore epidemic peak prediction models in communicable diseases.

Material and Methods: This protocol study was conducted based on Arksey and O'Malley's. This framework encompasses purpose and hypothesis, modeling, model achievements aspects. A systematic search of English in PubMed was conducted to identify relevant studies. In the pilot step, two reviewers independently extracted the variables from 10 eligible studies to develop a primary list of variables and a data extraction form. In the second step, all eligible studies were assessed by researchers. In the third step, two data extraction forms were combined. The data were extracted and categories were created based on frequency. Qualitative and quantitative methods were used to synthesize the extracted data.

Results: The current study were focused on forecasting the epidemic peak time that is a worlds’ concern issue. The results of current scoping review on prediction methods for epidemic disease can provide foundational knowledge, and have important value for the prediction model studies of COVID-19.

Conclusion: Our findings will help researchers by a summary of evidence to present new ideas and further research especially for studies were focused on COVID-19. Our results can improve the understanding of prediction methods for COVID-19.


Centers for Disease Control and Prevention. Lesson 1: Introduction to epidemiology. In: Centers for Disease Control and Prevention. Principles of epidemiology in public health practice: An introduction to applied epidemiology and biostatistics. 3rd ed. U.S. Department of Health and Human Services; 2006.

Myers MF, Rogers DJ, Cox J, Flahault A, Hay SI. Forecasting disease risk for increased epidemic preparedness in public health. Adv Parasitol. 2000; 47: 309-30. PMID: 10997211 DOI: 10.1016/s0065-308x(00)47013-2

Quinn SC, Kumar S. Health inequalities and infectious disease epidemics: A challenge for global health security. Biosecur Bioterror. 2014; 12(5): 263-73. PMID: 25254915 DOI: 10.1089/bsp.2014.0032

Mcquilkin PA, Udhayashankar K, Niescierenko M, Maranda L. Health-care access during the ebola virus epidemic in Liberia. Am J Trop Med Hyg. 2017 Sep;97(3):931-6. PMID: 28722621 DOI: 10.4269/ajtmh.16-0702

Rull M, Kickbusch I, Lauer H. Policy debate| international responses to global epidemics: Ebola and beyond. International Development Policy. 2015; 6(2): 1-33.

Riou J, Poletto C, Boëlle PY. Improving early epidemiological assessment of emerging aedes-transmitted epidemics using historical data. PLoS Negl Trop Dis. 2018; 12(6): e0006526. PMID: 29864129 DOI: 10.1371/journal.pntd.0006526

Moss R, Zarebski A, Dawson P, McCaw JM. Forecasting influenza outbreak dynamics in melbourne from internet search query surveillance data. Influenza Other Respir Viruses. 2016; 10(4): 314-23. PMID: 26859411 DOI: 10.1111/irv.12376

Tuite AR, Fisman DN. The idea model: A single equation approach to the ebola forecasting challenge. Epidemics. 2018; 22: 71-7. PMID: 27717616 DOI: 10.1016/j.epidem.2016.09.001

Asher J. Forecasting ebola with a regression transmission model. Epidemics. 2018; 22: 50-5. PMID: 28342787 DOI: 10.1016/j.epidem.2017.02.009

Bioglio L, Génois M, Vestergaard CL, Poletto C, Barrat A, Colizza V. Recalibrating disease parameters for increasing realism in modeling epidemics in closed settings. BMC Infect Dis. 2016; 16(1): 676. PMID: 27842507 DOI: 10.1186/s12879-016-2003-3

Bolzoni L, Bonacini E, Soresina C, Groppi M. Time-optimal control strategies in SIR epidemic models. Math Biosci. 2017; 292: 86-96. PMID: 28801246 DOI: 10.1016/j.mbs.2017.07.011

Li J, Li W, Jin Z. The epidemic model based on the approximation for third-order motifs on networks. Math Biosci. 2018; 297: 12-26. PMID: 29330075 DOI: 10.1016/j.mbs.2018.01.002

Ben-Nun M, Riley P, Turtle J, Bacon DP, Riley S. Forecasting national and regional influenza-like illness for the USA. PLoS Comput Biol. 2019; 15(5): e1007013. PMID: 31120881 DOI: 10.1371/journal.pcbi.1007013

Zhang Y, Bambrick H, Mengersen K, Tong S, Feng L, Zhang L, et al. Resurgence of pertussis infections in Shandong, China: Space-time cluster and trend analysis. Am J Trop Med Hyg. 2019; 100(6): 1342-54. PMID: 30994096 DOI: 10.4269/ajtmh.19-0013

Chowell G, Tariq A, Hyman JM. A novel sub-epidemic modeling framework for short-term forecasting epidemic waves. BMC Med. 2019; 17(1): 164. PMID: 31438953 DOI: 10.1186/s12916-019-1406-6

Arksey H, O'Malley L. Scoping studies: Towards a methodological framework. International Journal of Social Research Methodology. 2005; 8(1): 19-32.

Colquhoun HL, Levac D, O'Brien KK, Straus S, Tricco AC, Perrier L, et al. Scoping reviews: Time for clarity in definition, methods, and reporting. J Clin Epidemiol. 2014; 67(12): 1291-4. PMID: 25034198 DOI: 10.1016/j.jclinepi.2014.03.013

Nazari E, Pour R, Tabesh H. Comprehensive overview of decision-fusion technique in healthcare: A scoping review protocol. Frontiers in Health Informatics. 2018; 7: 7.

Levac D, Colquhoun H, O'Brien, K.K. Scoping studies: Advancing the methodology. Implementation Science. 2010; 5: 69.

DOI: http://dx.doi.org/10.30699/fhi.v10i1.275


  • There are currently no refbacks.