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Hybrid modeling and forecasting of COVID-19: integrating SEAIQHRD and GPR for improved predictions.

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Bibliographic Details
Title:	Hybrid modeling and forecasting of COVID-19: integrating SEAIQHRD and GPR for improved predictions.
Authors:	Rao MA; Department of Mathematics & Statistics, Vignan's Foundation for Science, Technology & Research (Deemed to be University), Yadadri, Bhuvanagiri, Telangana, 508284, India., Jaradat EK; Department of Physics, Faculty of Science, Imam Mohammad Ibn Saud Islamic University (IMSIU), Riyadh, 11623, Saudi Arabia., Devi MP; Department of Mathematics & Statistics, Vignan's Foundation for Science, Technology & Research (Deemed to be University), Vadlamudi, Guntur, Andhra Pradesh, 522213, India., Dhandapani PB; Department of Mathematics, Sri Eshwar College of Engineering, Coimbatore, Tamil Nadu, 641 202, India., Nalule RM; Department of Mathematics, Busitema University, Tororo, 236, Uganda. rnalule.sci@busitema.ac.ug., Al-Hmoud M; Department of Physics, Faculty of Science, Imam Mohammad Ibn Saud Islamic University (IMSIU), Riyadh, 11623, Saudi Arabia.
Source:	BMC infectious diseases [BMC Infect Dis] 2026 Jan 10; Vol. 26 (1), pp. 210. Date of Electronic Publication: 2026 Jan 10.
Publication Type:	Journal Article
Journal Info:	Publisher: BioMed Central Country of Publication: England NLM ID: 100968551 Publication Model: Electronic Cited Medium: Internet ISSN: 1471-2334 (Electronic) Linking ISSN: 14712334 NLM ISO Abbreviation: BMC Infect Dis Subsets: MEDLINE
Database:	MEDLINE Ultimate

Description
ISSN:	1471-2334
DOI:	10.1186/s12879-025-12494-x