{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,10,19]],"date-time":"2026-10-19T16:31:20Z","timestamp":1792427480391,"version":"4.5.0"},"reference-count":51,"publisher":"Public Library of Science (PLoS)","issue":"5","license":[{"start":{"date-parts":[[2024,5,1]],"date-time":"2024-05-01T00:00:00Z","timestamp":1714521600000},"content-version":"vor","delay-in-days":0,"URL":"http:\/\/creativecommons.org\/licenses\/by\/4.0\/"}],"content-domain":{"domain":["www.plosone.org"],"crossmark-restriction":false},"short-container-title":["PLoS ONE"],"abstract":"<jats:p>This study integrates advanced machine learning techniques, namely Artificial Neural Networks, Long Short-Term Memory, and Gated Recurrent Unit models, to forecast monkeypox outbreaks in Canada, Spain, the USA, and Portugal. The research focuses on the effectiveness of these models in predicting the spread and severity of cases using data from June 3 to December 31, 2022, and evaluates them against test data from January 1 to February 7, 2023. The study highlights the potential of neural networks in epidemiology, especially concerning recent monkeypox outbreaks. It provides a comparative analysis of the models, emphasizing their capabilities in public health strategies. The research identifies optimal model configurations and underscores the efficiency of the Levenberg-Marquardt algorithm in training. The findings suggest that ANN models, particularly those with optimized Root Mean Squared Error, Mean Absolute Percentage Error, and the Coefficient of Determination values, are effective in infectious disease forecasting and can significantly enhance public health responses.<\/jats:p>","DOI":"10.1371\/journal.pone.0300216","type":"journal-article","created":{"date-parts":[[2024,5,1]],"date-time":"2024-05-01T17:24:26Z","timestamp":1714584266000},"page":"e0300216","update-policy":"https:\/\/doi.org\/10.1371\/journal.pone.corrections_policy","source":"Crossref","is-referenced-by-count":15,"title":["Machine learning in epidemiology: Neural networks forecasting of monkeypox cases"],"prefix":"10.1371","volume":"19","author":[{"ORCID":"https:\/\/orcid.org\/0000-0003-3367-192X","authenticated-orcid":true,"given":"Lulah","family":"Alnaji","sequence":"first","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]}],"member":"340","published-online":{"date-parts":[[2024,5,1]]},"reference":[{"issue":"19","key":"pone.0300216.ref001","doi-asserted-by":"crossref","first-page":"1783","DOI":"10.1056\/NEJMra2208860","article-title":"Monkeypox","volume":"387","author":"A Gessain","year":"2022","journal-title":"New England Journal of Medicine"},{"issue":"7","key":"pone.0300216.ref002","doi-asserted-by":"crossref","first-page":"721","DOI":"10.1097\/INF.0000000000002074","article-title":"Emergence of Monkeypox\u2014West and Central Africa, 1970\u20132017","volume":"37","author":"RJ Leggiadro","year":"2018","journal-title":"The Pediatric Infectious Disease Journal"},{"issue":"10","key":"pone.0300216.ref003","doi-asserted-by":"crossref","first-page":"1539","DOI":"10.3201\/eid1610.100713","article-title":"Human monkeypox outbreak caused by novel virus belonging to Congo Basin clade, Sudan, 2005","volume":"16","author":"P Formenty","year":"2010","journal-title":"Emerging infectious diseases"},{"key":"pone.0300216.ref004","article-title":"Human monkeypox: an emerging zoonotic disease","author":"S Parker","year":"2007","journal-title":"Future Medicine"},{"issue":"10367","key":"pone.0300216.ref005","doi-asserted-by":"crossref","first-page":"1953","DOI":"10.1016\/S0140-6736(22)02187-0","article-title":"Human monkeypox virus infection in women and non-binary individuals during the 2022 outbreaks: a global case series","volume":"400","author":"JP Thornhill","year":"2022","journal-title":"The Lancet"},{"issue":"23","key":"pone.0300216.ref006","first-page":"537","article-title":"Multistate outbreak of monkeypox\u2013Illinois, Indiana, and Wisconsin, 2003","volume":"52","author":"Centers for Disease Control and Prevention (CDC and others)","year":"2003","journal-title":"MMWR. 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Image Data Collection and Implementation of Deep Learning-Based Model in Detecting Monkeypox Disease Using Modified VGG16. arXiv preprint arXiv:2206.01862. 2022."},{"key":"pone.0300216.ref013","doi-asserted-by":"crossref","first-page":"1","DOI":"10.1016\/j.psep.2020.05.029","article-title":"Forecasting the prevalence of COVID-19 outbreak in Egypt using nonlinear autoregressive artificial neural networks","volume":"141","author":"AI Saba","year":"2020","journal-title":"Process Safety and Environmental Protection"},{"key":"pone.0300216.ref014","first-page":"2787","article-title":"Artificial neural networks for prediction of Covid-19 in Saudi Arabia","volume":"66","author":"NN Hamadneh","year":"2021","journal-title":"Computational Materials Science"},{"key":"pone.0300216.ref015","doi-asserted-by":"crossref","first-page":"1","DOI":"10.1016\/j.asoc.2018.02.004","article-title":"Optimal forecast combination based on neural networks for time series forecasting","volume":"66","author":"L Wang","year":"2018","journal-title":"Applied Soft Computing"},{"key":"pone.0300216.ref016","doi-asserted-by":"crossref","first-page":"e222","DOI":"10.1017\/S0950268820002174","article-title":"Predictions of coronavirus COVID-19 distinct cases in Pakistan through an artificial neural network","volume":"148","author":"I Ahmad","year":"2020","journal-title":"Epidemiology & Infection"},{"key":"pone.0300216.ref017","doi-asserted-by":"crossref","first-page":"2901","DOI":"10.1007\/s10916-011-9768-0","article-title":"Prediction of breast cancer using artificial neural networks","volume":"36","author":"I Saritas","year":"2012","journal-title":"Journal of Medical Systems"},{"issue":"2","key":"pone.0300216.ref018","doi-asserted-by":"crossref","first-page":"943","DOI":"10.3390\/app11030943","article-title":"Comparison of Dengue Predictive Models Developed Using Artificial Neural Network and Discriminant Analysis with Small Dataset","volume":"11","author":"P Silitonga","year":"2021","journal-title":"Applied Sciences"},{"issue":"1","key":"pone.0300216.ref019","doi-asserted-by":"crossref","first-page":"5","DOI":"10.3390\/tropicalmed3010005","article-title":"Application of Artificial Neural Networks for Dengue Fever Outbreak Predictions in the Northwest Coast of Yucatan, Mexico and San Juan, Puerto Rico","volume":"3","author":"AE Laureano-Rosario","year":"2018","journal-title":"Tropical Medicine and Infectious Disease"},{"key":"pone.0300216.ref020","doi-asserted-by":"crossref","first-page":"687","DOI":"10.1016\/j.matcom.2021.01.022","article-title":"Using a genetic algorithm to fit parameters of a COVID-19 SEIR model for US states","volume":"185","author":"P Yarsky","year":"2021","journal-title":"Mathematics and Computers in Simulation"},{"key":"pone.0300216.ref021","unstructured":"Kingma DP, Ba J. Adam: A method for stochastic optimization. arXiv preprint arXiv:1412.6980. 2014."},{"issue":"2","key":"pone.0300216.ref022","doi-asserted-by":"crossref","first-page":"164","DOI":"10.1090\/qam\/10666","article-title":"A method for the solution of certain non-linear problems in least squares","volume":"2","author":"Kenneth Levenberg","year":"1944","journal-title":"Quarterly of Applied Mathematics"},{"key":"pone.0300216.ref023","unstructured":"Roser M, Ortiz-Ospina E, Ritchie H. Our World in Data. University of Oxford. 2013. Available from: https:\/\/ourworldindata.org\/"},{"issue":"6088","key":"pone.0300216.ref024","doi-asserted-by":"crossref","first-page":"533","DOI":"10.1038\/323533a0","article-title":"Learning representations by back-propagating errors","volume":"323","author":"DE Rumelhart","year":"1986","journal-title":"Nature"},{"key":"pone.0300216.ref025","doi-asserted-by":"crossref","unstructured":"Cho K, Van Merri\u00ebnboer B, Bahdanau D, Bengio Y. Learning phrase representations using RNN encoder-decoder for statistical machine translation. arXiv preprint arXiv:1406.1078. 2014.","DOI":"10.3115\/v1\/D14-1179"},{"key":"pone.0300216.ref026","doi-asserted-by":"crossref","unstructured":"Cho K, Van Merri\u00ebnboer B, Bahdanau D, Bengio Y. On the properties of neural machine translation: Encoder-decoder approaches. arXiv preprint arXiv:1409.1259. 2014.","DOI":"10.3115\/v1\/W14-4012"},{"issue":"8","key":"pone.0300216.ref027","doi-asserted-by":"crossref","first-page":"1735","DOI":"10.1162\/neco.1997.9.8.1735","article-title":"Long short-term memory","volume":"9","author":"S Hochreiter","year":"1997","journal-title":"Neural computation"},{"key":"pone.0300216.ref028","unstructured":"Geirhos R, Janssen DHJ, Sch\u00fctt HH, Rauber J, Bethge M, Wichmann FA. Comparing deep neural networks against humans: object recognition when the signal gets weaker. arXiv preprint arXiv:1706.06969. 2017."},{"key":"pone.0300216.ref029","doi-asserted-by":"crossref","first-page":"497","DOI":"10.1016\/j.energy.2013.01.036","article-title":"Parametric analysis and optimization of entropy generation in unsteady MHD flow over a stretching rotating disk using artificial neural network and particle swarm optimization algorithm","volume":"55","author":"M Rashidi","year":"2013","journal-title":"Energy"},{"key":"pone.0300216.ref030","doi-asserted-by":"crossref","first-page":"470","DOI":"10.1016\/j.neucom.2013.05.023","article-title":"A new hybrid artificial neural networks for rainfall\u2013runoff process modeling","volume":"121","author":"S Asadi","year":"2013","journal-title":"Neurocomputing"},{"key":"pone.0300216.ref031","doi-asserted-by":"crossref","first-page":"1007","DOI":"10.1016\/j.egypro.2015.07.832","article-title":"River Flow Model Using Artificial Neural Networks","volume":"74","author":"I Aichouri","year":"2015","journal-title":"Energy Procedia"},{"key":"pone.0300216.ref032","doi-asserted-by":"crossref","DOI":"10.1093\/oso\/9780198538493.001.0001","volume-title":"Neural networks for pattern recognition","author":"CM Bishop","year":"1995"},{"key":"pone.0300216.ref033","volume-title":"Deep learning","author":"I Goodfellow","year":"2016"},{"issue":"4","key":"pone.0300216.ref034","doi-asserted-by":"crossref","first-page":"115","DOI":"10.1007\/BF02478259","article-title":"A logical calculus of the ideas immanent in nervous activity","volume":"5","author":"WS McCulloch","year":"1943","journal-title":"The bulletin of mathematical biophysics"},{"issue":"6","key":"pone.0300216.ref035","doi-asserted-by":"crossref","first-page":"386","DOI":"10.1037\/h0042519","article-title":"The perceptron: a probabilistic model for information storage and organization in the brain","volume":"65","author":"F Rosenblatt","year":"1958","journal-title":"Psychological review"},{"key":"pone.0300216.ref036","volume-title":"The organization of behavior; a neuropsychological theory","author":"DO Hebb","year":"1949"},{"issue":"8","key":"pone.0300216.ref037","doi-asserted-by":"crossref","first-page":"2554","DOI":"10.1073\/pnas.79.8.2554","article-title":"Neural networks and physical systems with emergent collective computational abilities","volume":"79","author":"JJ Hopfield","year":"1982","journal-title":"Proceedings of the national academy of sciences"},{"issue":"1","key":"pone.0300216.ref038","doi-asserted-by":"crossref","first-page":"13971","DOI":"10.1038\/s41598-019-50262-5","article-title":"A novel hybrid model for predicting blast-induced ground vibration based on k-nearest neighbors and particle swarm optimization","volume":"9","author":"Xuan-Nam Bui","year":"2019","journal-title":"Scientific Reports"},{"issue":"1","key":"pone.0300216.ref039","first-page":"1","article-title":"Forecasting incidence of hand, foot and mouth disease using BP neural networks in Jiangsu province, China","volume":"19","author":"W Liu","year":"2019","journal-title":"BMC infectious diseases"},{"key":"pone.0300216.ref040","doi-asserted-by":"crossref","first-page":"115403","DOI":"10.1016\/j.enconman.2022.115403","article-title":"On the problem formulation for parameter extraction of the photovoltaic model: Novel integration of hybrid evolutionary algorithm and Levenberg Marquardt based on adaptive damping parameter formula","volume":"256","author":"Hussein Mohammed Ridha","year":"2022","journal-title":"Energy Conversion and Management"},{"key":"pone.0300216.ref041","volume-title":"Practical methods of optimization","author":"R Fletcher","year":"2013"},{"issue":"5","key":"pone.0300216.ref042","doi-asserted-by":"crossref","first-page":"578","DOI":"10.1049\/ip-gtd:20020462","article-title":"Artificial neural network based peak load forecasting using Levenberg\u2013Marquardt and quasi-Newton methods","volume":"149","author":"LM Saini","year":"2002","journal-title":"IEE Proceedings-Generation, Transmission and Distribution"},{"key":"pone.0300216.ref043","unstructured":"Chung J, Gulcehre C, Cho K, Bengio Y. Empirical evaluation of gated recurrent neural networks on sequence modeling. arXiv preprint arXiv:1412.3555. 2014."},{"issue":"2","key":"pone.0300216.ref044","first-page":"200462","article-title":"Predicting the performance of a desulfurizing bio-filter using an artificial neural network (ANN) model","volume":"26","author":"K Salehi","year":"2021","journal-title":"Environmental Engineering Research"},{"issue":"6","key":"pone.0300216.ref045","first-page":"2461","article-title":"Investigation of Robustness of Hybrid Artificial Neural Network with Artificial Bee Colony and Firefly Algorithm in Predicting COVID-19 New Cases: Case Study of Iran","volume":"36","author":"SS Moazeni","year":"2022","journal-title":"Stochastic Environmental Research and Risk Assessment"},{"key":"pone.0300216.ref046","doi-asserted-by":"crossref","first-page":"89","DOI":"10.1016\/B978-0-12-821285-1.00021-X","volume-title":"Handbook of Hydroinformatics","author":"A Seraj","year":"2023"},{"key":"pone.0300216.ref047","doi-asserted-by":"crossref","first-page":"104452","DOI":"10.1016\/j.ijmultiphaseflow.2023.104452","article-title":"An artificial neural network model for the prediction of entrained droplet fraction in annular gas-liquid two-phase flow in vertical pipes","volume":"164","author":"AM Aliyu","year":"2023","journal-title":"International Journal of Multiphase Flow"},{"key":"pone.0300216.ref048","doi-asserted-by":"crossref","first-page":"124952","DOI":"10.1016\/j.fuel.2022.124952","article-title":"Optimized machine learning models for natural fractures prediction using conventional well logs","volume":"326","author":"S Tabasi","year":"2022","journal-title":"Fuel"},{"key":"pone.0300216.ref049","doi-asserted-by":"crossref","first-page":"547","DOI":"10.1016\/j.fuel.2017.06.131","article-title":"Improved predictions of wellhead choke liquid critical-flow rates: modelling based on hybrid neural network training learning based optimization","volume":"207","author":"A Choubineh","year":"2017","journal-title":"Fuel"},{"key":"pone.0300216.ref050","doi-asserted-by":"crossref","first-page":"120860","DOI":"10.1016\/j.saa.2022.120860","article-title":"Application of NIRs coupled with PLS and ANN modelling to predict average droplet size in oil-in-water emulsions prepared with different microfluidic devices","volume":"270","author":"AJ Tu\u0161ek","year":"2022","journal-title":"Spectrochimica Acta Part A: Molecular and Biomolecular Spectroscopy"},{"key":"pone.0300216.ref051","doi-asserted-by":"crossref","first-page":"6551","DOI":"10.1016\/j.egyr.2022.04.073","article-title":"Data driven models to predict pore pressure using drilling and petrophysical data","volume":"8","author":"F Jafarizadeh","year":"2022","journal-title":"Energy Reports"}],"container-title":["PLOS ONE"],"original-title":[],"language":"en","link":[{"URL":"https:\/\/dx.plos.org\/10.1371\/journal.pone.0300216","content-type":"unspecified","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2024,5,1]],"date-time":"2024-05-01T17:25:08Z","timestamp":1714584308000},"score":1,"resource":{"primary":{"URL":"https:\/\/dx.plos.org\/10.1371\/journal.pone.0300216"}},"subtitle":[],"editor":[{"given":"Mihajlo","family":"Jakovljevic","sequence":"first","affiliation":[],"role":[{"vocabulary":"crossref","role":"editor"}]}],"short-title":[],"issued":{"date-parts":[[2024,5,1]]},"references-count":51,"journal-issue":{"issue":"5","published-online":{"date-parts":[[2024,5,1]]}},"URL":"https:\/\/doi.org\/10.1371\/journal.pone.0300216","relation":{},"ISSN":["1932-6203"],"issn-type":[{"value":"1932-6203","type":"electronic"}],"subject":[],"published":{"date-parts":[[2024,5,1]]}}}