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Short-Term Forecasting of Monkeypox Cases Using a Novel Filtering and Combining Technique

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...

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Autores principales: Iftikhar, Hasnain, Khan, Murad, Khan, Mohammed Saad, Khan, Mehak
Formato: Online Artículo Texto
Lenguaje:English
Publicado: MDPI 2023
Materias:
Acceso en línea:https://www.ncbi.nlm.nih.gov/pmc/articles/PMC10252741/
https://www.ncbi.nlm.nih.gov/pubmed/37296775
http://dx.doi.org/10.3390/diagnostics13111923
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author Iftikhar, Hasnain
Khan, Murad
Khan, Mohammed Saad
Khan, Mehak
author_facet Iftikhar, Hasnain
Khan, Murad
Khan, Mohammed Saad
Khan, Mehak
author_sort Iftikhar, Hasnain
collection PubMed
description 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’s 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.
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spelling pubmed-102527412023-06-10 Short-Term Forecasting of Monkeypox Cases Using a Novel Filtering and Combining Technique Iftikhar, Hasnain Khan, Murad Khan, Mohammed Saad Khan, Mehak Diagnostics (Basel) Article 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’s 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. MDPI 2023-05-31 /pmc/articles/PMC10252741/ /pubmed/37296775 http://dx.doi.org/10.3390/diagnostics13111923 Text en © 2023 by the authors. https://creativecommons.org/licenses/by/4.0/Licensee MDPI, Basel, Switzerland. This article is an open access article distributed under the terms and conditions of the Creative Commons Attribution (CC BY) license (https://creativecommons.org/licenses/by/4.0/).
spellingShingle Article
Iftikhar, Hasnain
Khan, Murad
Khan, Mohammed Saad
Khan, Mehak
Short-Term Forecasting of Monkeypox Cases Using a Novel Filtering and Combining Technique
title Short-Term Forecasting of Monkeypox Cases Using a Novel Filtering and Combining Technique
title_full Short-Term Forecasting of Monkeypox Cases Using a Novel Filtering and Combining Technique
title_fullStr Short-Term Forecasting of Monkeypox Cases Using a Novel Filtering and Combining Technique
title_full_unstemmed Short-Term Forecasting of Monkeypox Cases Using a Novel Filtering and Combining Technique
title_short Short-Term Forecasting of Monkeypox Cases Using a Novel Filtering and Combining Technique
title_sort short-term forecasting of monkeypox cases using a novel filtering and combining technique
topic Article
url https://www.ncbi.nlm.nih.gov/pmc/articles/PMC10252741/
https://www.ncbi.nlm.nih.gov/pubmed/37296775
http://dx.doi.org/10.3390/diagnostics13111923
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