{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,9,18]],"date-time":"2026-09-18T00:02:41Z","timestamp":1789689761929,"version":"4.0.1"},"reference-count":44,"publisher":"MDPI AG","issue":"11","license":[{"start":{"date-parts":[[2023,5,31]],"date-time":"2023-05-31T00:00:00Z","timestamp":1685491200000},"content-version":"vor","delay-in-days":0,"URL":"https:\/\/creativecommons.org\/licenses\/by\/4.0\/"}],"content-domain":{"domain":[],"crossmark-restriction":false},"short-container-title":["Diagnostics"],"abstract":"<jats:p>In the modern world, new technologies such as artificial intelligence, machine learning, and big data are essential to support healthcare surveillance systems, especially for monitoring confirmed cases of monkeypox. The statistics of infected and uninfected people worldwide contribute to the growing number of publicly available datasets that can be used to predict early-stage confirmed cases of monkeypox through machine-learning models. Thus, this paper proposes a novel filtering and combination technique for accurate short-term forecasts of infected monkeypox cases. To this end, we first filter the original time series of the cumulative confirmed cases into two new subseries: the long-term trend series and residual series, using the two proposed and one benchmark filter. Then, we predict the filtered subseries using five standard machine learning models and all their possible combination models. Hence, we combine individual forecasting models directly to obtain a final forecast for newly infected cases one day ahead. Four mean errors and a statistical test are performed to verify the proposed methodology\u2019s performance. The experimental results show the efficiency and accuracy of the proposed forecasting methodology. To prove the superiority of the proposed approach, four different time series and five different machine learning models were included as benchmarks. The results of this comparison confirmed the dominance of the proposed method. Finally, based on the best combination model, we achieved a forecast of fourteen days (two weeks). This can help to understand the spread and lead to an understanding of the risk, which can be utilized to prevent further spread and enable timely and effective treatment.<\/jats:p>","DOI":"10.3390\/diagnostics13111923","type":"journal-article","created":{"date-parts":[[2023,5,31]],"date-time":"2023-05-31T04:41:49Z","timestamp":1685508109000},"page":"1923","update-policy":"https:\/\/doi.org\/10.3390\/mdpi_crossmark_policy","source":"Crossref","is-referenced-by-count":39,"title":["Short-Term Forecasting of Monkeypox Cases Using a Novel Filtering and Combining Technique"],"prefix":"10.3390","volume":"13","author":[{"ORCID":"https:\/\/orcid.org\/0000-0002-8533-5410","authenticated-orcid":false,"given":"Hasnain","family":"Iftikhar","sequence":"first","affiliation":[{"name":"Department of Mathematics, City University of Science and Information Technology, Peshawar 25000, Pakistan"},{"name":"Department of Statistics, Quaid-i-Azam University, Islamabad 45320, Pakistan"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"ORCID":"https:\/\/orcid.org\/0000-0002-4265-4446","authenticated-orcid":false,"given":"Murad","family":"Khan","sequence":"additional","affiliation":[{"name":"Department of Statistics, Abdul Wali Khan University Mardan, Mardan 23200, Pakistan"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"ORCID":"https:\/\/orcid.org\/0009-0008-8653-0876","authenticated-orcid":false,"given":"Mohammed Saad","family":"Khan","sequence":"additional","affiliation":[{"name":"Faculty of Computer Sciences and Engineering, Ghulam Ishaq Khan Institute of Engineering Sciences and Technology, Topi, Swabi 23640, Pakistan"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"ORCID":"https:\/\/orcid.org\/0000-0001-8959-6872","authenticated-orcid":false,"given":"Mehak","family":"Khan","sequence":"additional","affiliation":[{"name":"Department of Computer Science, AI Lab, Oslo Metropolitan University, P.O. Box 4 St. Olavs Plass, 0130 Oslo, Norway"}],"role":[{"vocabulary":"crossref","role":"author"}]}],"member":"1968","published-online":{"date-parts":[[2023,5,31]]},"reference":[{"key":"ref_1","unstructured":"World Health Organization (2023, February 17). COVID-19. Available online: https:\/\/covid19.who.int\/."},{"key":"ref_2","doi-asserted-by":"crossref","first-page":"101133","DOI":"10.3389\/fmed.2022.1011335","article-title":"Ocular manifestations of recent viral pandemics: A literature review","volume":"9","author":"Taha","year":"2022","journal-title":"Front. Med."},{"key":"ref_3","doi-asserted-by":"crossref","first-page":"e49","DOI":"10.1016\/j.bja.2022.06.007","article-title":"Monkeypox and its pandemic potential: What the anaesthetist should know","volume":"129","author":"Chowdhury","year":"2022","journal-title":"Br. J. Anaesth."},{"key":"ref_4","doi-asserted-by":"crossref","first-page":"1","DOI":"10.1128\/br.37.1.1-18.1973","article-title":"Monkeypox virus","volume":"37","author":"Cho","year":"1973","journal-title":"Bacteriol. Rev."},{"key":"ref_5","first-page":"599","article-title":"Isolation and properties of the causal agent of a new variola-like disease (monkeypox) in man","volume":"46","author":"Marennikova","year":"1972","journal-title":"Bull. World Health Organ."},{"key":"ref_6","doi-asserted-by":"crossref","unstructured":"Bunge, E.M., Hoet, B., Chen, L., Lienert, F., Weidenthaler, H., Baer, L.R., and Steffen, R. (2022). The changing epidemiology of human monkeypox\u2014A potential threat? A systematic review. PLoS Negl. Trop. Dis., 16.","DOI":"10.1371\/journal.pntd.0010141"},{"key":"ref_7","doi-asserted-by":"crossref","first-page":"2919","DOI":"10.1128\/JCM.40.8.2919-2921.2002","article-title":"Outbreaks of disease suspected of being due to human monkeypox virus infection in the Democratic Republic of Congo in 2001","volume":"40","author":"Meyer","year":"2002","journal-title":"J. Clin. Microbiol."},{"key":"ref_8","doi-asserted-by":"crossref","first-page":"2200422","DOI":"10.2807\/1560-7917.ES.2022.27.22.2200422","article-title":"Community transmission of monkeypox in the United Kingdom, April to May 2022","volume":"27","author":"Vivancos","year":"2022","journal-title":"Eurosurveillance"},{"key":"ref_9","unstructured":"Mathieu, E., Spooner, F., Dattani, S., Ritchie, H., and Roser, M. (2023, February 10). \u201cMpox (monkeypox)\u201d. Published Online at OurWorldInData.org. Available online: https:\/\/ourworldindata.org\/monkeypox."},{"key":"ref_10","doi-asserted-by":"crossref","unstructured":"Mao, L., Ying, J., Selekon, B., Gonofio, E., Wang, X., Nakoune, E., Wong, G., and Berthet, N. (2022). Development and Characterization of Recombinase-Based Isothermal Amplification Assays (RPA\/RAA) for the Rapid Detection of Monkeypox Virus. Viruses, 14.","DOI":"10.3390\/v14102112"},{"key":"ref_11","unstructured":"World Health Organization (2023, May 11). Monkeypox. Available online: https:\/\/www.who.int\/publications\/m\/item\/multi-country-outbreak-of-mpox--external-situation-report--22---11-may-2023\/."},{"key":"ref_12","doi-asserted-by":"crossref","first-page":"4034","DOI":"10.1002\/jmv.27884","article-title":"Monkeypox: A potential global threat?","volume":"94","author":"Yang","year":"2022","journal-title":"J. Med. Virol."},{"key":"ref_13","doi-asserted-by":"crossref","unstructured":"Iftikhar, H., and Rind, M. (2020). Forecasting daily COVID-19 confirmed, deaths and recovered cases using univariate time series models: A case of Pakistan study. MedRxiv.","DOI":"10.1101\/2020.09.20.20198150"},{"key":"ref_14","doi-asserted-by":"crossref","first-page":"2847","DOI":"10.3934\/mbe.2023134","article-title":"Predictive modeling of the COVID-19 data using a new version of the flexible Weibull model and machine leaning techniques","volume":"20","author":"Bantan","year":"2023","journal-title":"Math. Biosci. Eng."},{"key":"ref_15","doi-asserted-by":"crossref","unstructured":"Ahmad, Z., Almaspoor, Z., Khan, F., and El-Morshedy, M. (2022). On predictive modeling using a new flexible Weibull distribution and machine learning approach: Analyzing the COVID-19 data. Mathematics, 10.","DOI":"10.3390\/math10111792"},{"key":"ref_16","doi-asserted-by":"crossref","first-page":"6001876","DOI":"10.1155\/2022\/6001876","article-title":"COVID-19 pandemic data modeling in Pakistan using time-series SIR","volume":"2022","author":"Taimoor","year":"2022","journal-title":"Comput. Math. Methods Med."},{"key":"ref_17","doi-asserted-by":"crossref","first-page":"3135","DOI":"10.1007\/s00521-021-06548-9","article-title":"A novel approach based on combining deep learning models with statistical methods for COVID-19 time series forecasting","volume":"34","author":"Abbasimehr","year":"2022","journal-title":"Neural Comput. Appl."},{"key":"ref_18","doi-asserted-by":"crossref","unstructured":"Alshanbari, H.M., Iftikhar, H., Khan, F., Rind, M., Ahmad, Z., and El-Bagoury, A.A.A.H. (2023). On the Implementation of the Artificial Neural Network Approach for Forecasting Different Healthcare Events. Diagnostics, 13.","DOI":"10.3390\/diagnostics13071310"},{"key":"ref_19","doi-asserted-by":"crossref","unstructured":"Iftikhar, H., Khan, M., Khan, Z., Khan, F., Alshanbari, H.M., and Ahmad, Z. (2023). A Comparative Analysis of Machine Learning Models: A Case Study in Predicting Chronic Kidney Disease. Sustainability, 15.","DOI":"10.3390\/su15032754"},{"key":"ref_20","doi-asserted-by":"crossref","first-page":"7021","DOI":"10.1007\/s11227-020-03560-z","article-title":"Bidirectional LSTM-RNN-based hybrid deep learning frameworks for univariate time series classification","volume":"77","author":"Khan","year":"2021","journal-title":"J. Supercomput."},{"key":"ref_21","doi-asserted-by":"crossref","first-page":"177","DOI":"10.1007\/s00779-020-01447-7","article-title":"End-to-end multivariate time series classification via hybrid deep learning architectures","volume":"27","author":"Khan","year":"2023","journal-title":"Pers. Ubiquitous Comput."},{"key":"ref_22","doi-asserted-by":"crossref","first-page":"1995","DOI":"10.1007\/s11063-021-10484-z","article-title":"Attention-based deep gated fully convolutional end-to-end architectures for time series classification","volume":"53","author":"Khan","year":"2021","journal-title":"Neural Process. Lett."},{"key":"ref_23","doi-asserted-by":"crossref","unstructured":"Patel, M., Surti, M., and Adnan, M. (2022). Artificial intelligence (AI) in Monkeypox infection prevention. J. Biomol. Struct. Dyn., 1\u20135.","DOI":"10.1080\/07391102.2022.2134214"},{"key":"ref_24","doi-asserted-by":"crossref","unstructured":"Qureshi, M., Khan, S., Bantan, R.A., Daniyal, M., Elgarhy, M., Marzo, R.R., and Lin, Y. (2022). Modeling and Forecasting Monkeypox Cases Using Stochastic Models. J. Clin. Med., 11.","DOI":"10.3390\/jcm11216555"},{"key":"ref_25","doi-asserted-by":"crossref","unstructured":"Dada, E.G., Oyewola, D.O., Joseph, S.B., Emebo, O., and Oluwagbemi, O.O. (2022). Ensemble Machine Learning for Monkeypox Transmission Time Series Forecasting. Appl. Sci., 12.","DOI":"10.3390\/app122312128"},{"key":"ref_26","doi-asserted-by":"crossref","first-page":"127","DOI":"10.3390\/forecast5010005","article-title":"Forecasting the Monkeypox Outbreak Using ARIMA, Prophet, NeuralProphet, and LSTM Models in the United States","volume":"5","author":"Long","year":"2023","journal-title":"Forecasting"},{"key":"ref_27","doi-asserted-by":"crossref","unstructured":"Eid, M.M., El-Kenawy, E.S.M., Khodadadi, N., Mirjalili, S., Khodadadi, E., Abotaleb, M., Alharbi, A.H., Abdelhamid, A.A., Ibrahim, A., and  Amer, G.M. (2022). Meta-heuristic optimization of LSTM-based deep network for boosting the prediction of monkeypox cases. Mathematics, 10.","DOI":"10.3390\/math10203845"},{"key":"ref_28","doi-asserted-by":"crossref","unstructured":"Priyadarshini, I., Mohanty, P., Kumar, R., and Taniar, D. (2023). Monkeypox Outbreak Analysis: An Extensive Study Using Machine Learning Models and Time Series Analysis. Computers, 12.","DOI":"10.3390\/computers12020036"},{"key":"ref_29","doi-asserted-by":"crossref","first-page":"256","DOI":"10.1016\/j.jinf.2022.12.021","article-title":"Predicting Monkeypox incidence: Fear is not over!","volume":"86","author":"Khan","year":"2022","journal-title":"J. Infect."},{"key":"ref_30","unstructured":"Mohbey, K.K., Meena, G., Kumar, S., and Lokesh, K. (2022). A CNN-LSTM-based hybrid deep learning approach to detect sentiment polarities on Monkeypox tweets. arXiv."},{"key":"ref_31","doi-asserted-by":"crossref","unstructured":"Bleichrodt, A., Dahal, S., Maloney, K., Casanova, L., Luo, R., and Chowell, G. (2023). Real-time forecasting the trajectory of monkeypox outbreaks at the national and global levels, July\u2013October 2022. BMC Med., 21.","DOI":"10.1186\/s12916-022-02725-2"},{"key":"ref_32","doi-asserted-by":"crossref","first-page":"e28382","DOI":"10.1002\/jmv.28382","article-title":"Association between public attention and monkeypox epidemic: A global lag-correlation analysis","volume":"95","author":"Yan","year":"2023","journal-title":"J. Med. Virol."},{"key":"ref_33","doi-asserted-by":"crossref","first-page":"141","DOI":"10.1080\/15435075.2016.1251925","article-title":"Non-linear autoregressive neural network approach for inside air temperature prediction of a pillar cooler","volume":"14","author":"Islam","year":"2017","journal-title":"Int. J. Green Energy"},{"key":"ref_34","doi-asserted-by":"crossref","first-page":"272","DOI":"10.1016\/j.jclepro.2018.07.164","article-title":"Application of support vector machine models for forecasting solar and wind energy resources: A review","volume":"199","author":"Zendehboudi","year":"2018","journal-title":"J. Clean. Prod."},{"key":"ref_35","doi-asserted-by":"crossref","unstructured":"Breiman, L. (2017). Classification and Regression Trees, Routledge.","DOI":"10.1201\/9781315139470"},{"key":"ref_36","doi-asserted-by":"crossref","first-page":"150984","DOI":"10.1109\/ACCESS.2021.3126545","article-title":"Electricity spot prices forecasting based on ensemble learning","volume":"9","author":"Bibi","year":"2021","journal-title":"IEEE Access"},{"key":"ref_37","doi-asserted-by":"crossref","first-page":"427","DOI":"10.1016\/j.resourpol.2017.05.007","article-title":"Forecasting copper prices by decision tree learning","volume":"52","author":"Liu","year":"2017","journal-title":"Resour. Policy"},{"key":"ref_38","first-page":"31","article-title":"Decision tree for the weather forecasting","volume":"76","author":"Kumar","year":"2013","journal-title":"Int. J. Comput. Appl."},{"key":"ref_39","doi-asserted-by":"crossref","first-page":"501","DOI":"10.1016\/j.ejor.2003.08.037","article-title":"Neural network forecasting for seasonal and trend time series","volume":"160","author":"Zhang","year":"2005","journal-title":"European J. Oper. Res."},{"key":"ref_40","doi-asserted-by":"crossref","unstructured":"Shah, I., Iftikhar, H., Ali, S., and Wang, D. (2019). Short-term electricity demand forecasting using components estimation technique. Energies, 12.","DOI":"10.3390\/en12132532"},{"key":"ref_41","doi-asserted-by":"crossref","first-page":"163","DOI":"10.3390\/forecast2020009","article-title":"Modeling and forecasting medium-term electricity consumption using component estimation technique","volume":"2","author":"Shah","year":"2020","journal-title":"Forecasting"},{"key":"ref_42","doi-asserted-by":"crossref","first-page":"3581037","DOI":"10.1155\/2022\/3581037","article-title":"Modeling and forecasting electricity demand and prices: A comparison of alternative approaches","volume":"2022","author":"Shah","year":"2022","journal-title":"J. Math."},{"key":"ref_43","doi-asserted-by":"crossref","unstructured":"Iftikhar, H., Bibi, N., Canas Rodrigues, P., and L\u00f3pez-Gonzales, J.L. (2023). Multiple Novel Decomposition Techniques for Time Series Forecasting: Application to Monthly Forecasting of Electricity Consumption in Pakistan. Energies, 16.","DOI":"10.3390\/en16062579"},{"key":"ref_44","doi-asserted-by":"crossref","first-page":"134","DOI":"10.1198\/073500102753410444","article-title":"Comparing predictive accuracy","volume":"20","author":"Diebold","year":"2002","journal-title":"J. Bus. Econ. Stat."}],"container-title":["Diagnostics"],"original-title":[],"language":"en","link":[{"URL":"https:\/\/www.mdpi.com\/2075-4418\/13\/11\/1923\/pdf","content-type":"unspecified","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2025,10,10]],"date-time":"2025-10-10T19:45:58Z","timestamp":1760125558000},"score":1,"resource":{"primary":{"URL":"https:\/\/www.mdpi.com\/2075-4418\/13\/11\/1923"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2023,5,31]]},"references-count":44,"journal-issue":{"issue":"11","published-online":{"date-parts":[[2023,6]]}},"alternative-id":["diagnostics13111923"],"URL":"https:\/\/doi.org\/10.3390\/diagnostics13111923","relation":{},"ISSN":["2075-4418"],"issn-type":[{"value":"2075-4418","type":"electronic"}],"subject":[],"published":{"date-parts":[[2023,5,31]]}}}