Logo-ehsj
Int J Epidemiol Res. 2019;6(3): 132-143.
doi: 10.15171/ijer.2019.24
  Abstract View: 476
  PDF Download: 482

Review Article

A Review of Epidemic Forecasting Using Artificial Neural Networks

Philemon Manliura Datilo 1,2, Zuhaimy Ismail 1* ORCID logo, Jayeola Dare 3 ORCID logo

1 Department of Mathematical Sciences, Universiti Teknologi Malaysia, Johor, Malaysia
2 Department of Information Technology, Modibbo Adama University of Technology, Yola School of Management and Information Technology, Adamawa State, Nigeria
3 Adekunle Ajasin University, Department of Mathematical Sciences, Faculty of Science, Ondo State, Nigeria
*Corresponding Author: Zuhaimy Ismail, Tel: +60 14-310 1974; Email:, Email: zuhaimyi@yahoo.com

Abstract

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 analysed.

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.

First Name
 
Last Name
 
Email Address
 
Comments
 
Security code


Abstract View: 477

Your browser does not support the canvas element.


PDF Download: 482

Your browser does not support the canvas element.

Submitted: 03 Jul 2018
Accepted: 20 Jul 2019
ePublished: 25 Sep 2019
EndNote EndNote

(Enw Format - Win & Mac)

BibTeX BibTeX

(Bib Format - Win & Mac)

Bookends Bookends

(Ris Format - Mac only)

EasyBib EasyBib

(Ris Format - Win & Mac)

Medlars Medlars

(Txt Format - Win & Mac)

Mendeley Web Mendeley Web
Mendeley Mendeley

(Ris Format - Win & Mac)

Papers Papers

(Ris Format - Win & Mac)

ProCite ProCite

(Ris Format - Win & Mac)

Reference Manager Reference Manager

(Ris Format - Win only)

Refworks Refworks

(Refworks Format - Win & Mac)

Zotero Zotero

(Ris Format - Firefox Plugin)