Logo-ehsj
Int J Epidemiol Res. 2019;6(3): 132-143. doi: 10.15171/ijer.2019.24

Review Article

A Review of Epidemic Forecasting Using Artificial Neural Networks

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

Cited by CrossRef: 34

1- Ni Z, Wang F, Jan N. Quality Assessment of Vocational Education Teaching Reform Based on Deep Learning. Computational and Mathematical Methods in Medicine. 2022;2022:1 [Crossref]
2- Swaraj A, Verma K, Kaur A, Singh G, Kumar A, Melo de Sales L. Implementation of stacking based ARIMA model for prediction of Covid-19 cases in India. Journal of Biomedical Informatics. 2021;121:103887 [Crossref]
3- Kafieh R, Saeedizadeh N, Arian R, Amini Z, Serej N, Vaezi A, Javanmard S. Isfahan and Covid-19: Deep spatiotemporal representation. Chaos, Solitons & Fractals. 2020;141:110339 [Crossref]
4- Petropoulos F, Makridakis S, Stylianou N. COVID-19: Forecasting confirmed cases and deaths with a simple time series model. International Journal of Forecasting. 2022;38(2):439 [Crossref]
5- Souza Jr. G, Braga M, Rodrigues L, Fernandes R, Ramos R, Carneiro A, Brito S, Dolácio C, Tavares Jr. I, Noronha F, Pinheiro R, Diniz H, Botelho M, Vallinoto A, Rocha J. Boletim COVID-PA: relatos sobre projeções baseadas em inteligência artificial no enfrentamento da pandemia de COVID-19 no estado do Pará. Epidemiol Serv Saúde. 2021;30(4) [Crossref]
6- Banerjee M, Ghosh S. Behavioral and game-theoretic modeling of dengue epidemic: Comment on “Mathematical models for dengue fever epidemiology: A 10-year systematic review” by M. Aguiar et al.. Physics of Life Reviews. 2022;43:20 [Crossref]
7- Biazzo I, Braunstein A, Dall’Asta L, Mazza F. A Bayesian generative neural network framework for epidemic inference problems. Sci Rep. 2022;12(1) [Crossref]
8- Aguiar M, Anam V, Blyuss K, Estadilla C, Guerrero B, Knopoff D, Kooi B, Mateus L, Srivastav A, Steindorf V, Stollenwerk N. Prescriptive, descriptive or predictive models: What approach should be taken when empirical data is limited? Reply to comments on “Mathematical models for Dengue fever epidemiology: A 10-year systematic review”. Physics of Life Reviews. 2023;46:56 [Crossref]
9- Puleio A. Recurrent neural network ensemble, a new instrument for the prediction of infectious diseases. Eur Phys J Plus. 2021;136(3) [Crossref]
10- Panja M, Chakraborty T, Nadim S, Ghosh I, Kumar U, Liu N. An ensemble neural network approach to forecast Dengue outbreak based on climatic condition. Chaos, Solitons & Fractals. 2023;167:113124 [Crossref]
11- Alba S, Rood E, Mecatti F, Ross J, Dodd P, Chang S, Potgieter M, Bertarelli G, Henry N, LeGrand K, Trouleau W, Shaweno D, MacPherson P, Qin Z, Mergenthaler C, Giardina F, Augustijn E, Baloch A, Latif A. TB Hackathon: Development and Comparison of Five Models to Predict Subnational Tuberculosis Prevalence in Pakistan. TropicalMed. 2022;7(1):13 [Crossref]
12- Tong Y, Xiong S, He X, Yang S, Wang Z, Tao R, Liu R, Zhu B. RoeNet: Predicting discontinuity of hyperbolic systems from continuous data. Numerical Meth Engineering. 2023; [Crossref]
13- Mohammadi F, Pourzamani H, Karimi H, Mohammadi M, Mohammadi M, Ardalan N, Khoshravesh R, Pooresmaeil H, Shahabi S, Sabahi M, Sadat miryonesi F, Najafi M, Yavari Z, Mohammadi F, Teiri H, Jannati M. Artificial neural network and logistic regression modelling to characterize COVID-19 infected patients in local areas of Iran. Biomedical Journal. 2021;44(3):304 [Crossref]
14- Stergiou K, Minopoulos G, Memos V, Stergiou C, Koidou M, Psannis K. A Machine Learning-Based Model for Epidemic Forecasting and Faster Drug Discovery. Applied Sciences. 2022;12(21):10766 [Crossref]
15- Cortés-Martínez K, Estrada-Esquivel H, Martínez-Rebollar A, Hernández-Pérez Y, Ortiz-Hernández J. The State of the Art of Data Mining Algorithms for Predicting the COVID-19 Pandemic. Axioms. 2022;11(5):242 [Crossref]
16- Youssef A, Alfarraj O, Alkhalaf M, Hassanein A. A supervised biosensor-based non-variant structuring approach for analyzing infectious disease data. Measurement. 2022;193:110903 [Crossref]
17- Rath S. Trends in using IoT with machine learning in smart health assessment. ijhs. 2022;:3335 [Crossref]
18- Cogollo M, González-Parra G, Arenas A. Modeling and Forecasting Cases of RSV Using Artificial Neural Networks. Mathematics. 2021;9(22):2958 [Crossref]
19- Zagrouba R, Adnan Khan M, Adnan Khan A, Aamer Saleem M, Faheem Mushtaq M, Rehman A, Farhan Khan M. Modelling and Simulation of COVID-19 Outbreak Prediction Using Supervised Machine Learning. 2021;66(3):2397 [Crossref]
20- Hernández-Casas S, Beltrán-Morales L, Vargas-López V, Vergara-Solana F, Seijo J. Price Forecast for Mexican Red Spiny Lobster (Panulirus spp.) Using Artificial Neural Networks (ANNs). Applied Sciences. 2022;12(12):6044 [Crossref]
21- Aldahiri A, Alrashed B, Hussain W. Trends in Using IoT with Machine Learning in Health Prediction System. Forecasting. 2021;3(1):181 [Crossref]
22- Pal R, Sekh A, Kar S, Prasad D. Neural Network Based Country Wise Risk Prediction of COVID-19. Applied Sciences. 2020;10(18):6448 [Crossref]
23- Punyapornwithaya V, Arjkumpa O, Buamithup N, Kuatako N, Klaharn K, Sansamur C, Jampachaisri K. Forecasting of daily new lumpy skin disease cases in Thailand at different stages of the epidemic using fuzzy logic time series, NNAR, and ARIMA methods. Preventive Veterinary Medicine. 2023;217:105964 [Crossref]
24- Weera W, Botmart T, Zuhra S, Sabir Z, Asif Zahoor Raja M, Ben Said S. A Neural Study of the Fractional Heroin Epidemic Model. 2023;74(2):4453 [Crossref]

Chairperson:

Reza Imani   

Associate Professor of Infectious Diseases, Department of Infectious Diseases, Social Determinants of Health Research Center, Shahrekord University of Medical Sciences, Shahrekord, Iran. 

 Email: eimani@skums.ac.ir

 

Editor-in-Chief: 

Soleiman Kheiri  

Professor of Biostatistics, Department of Epidemiology and Biostatistics, School of Health, Shahrekord University of Medical Sciences, Shahrekord, Iran. 

 Email: kheiri@skums.ac.ir

Indexing & Abstracting

 

 

 

 

 

 

 

Follower of

 


COPE

 Powered by