{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,3,26]],"date-time":"2026-03-26T19:05:56Z","timestamp":1774551956559,"version":"3.50.1"},"reference-count":57,"publisher":"MDPI AG","issue":"4","license":[{"start":{"date-parts":[[2025,4,10]],"date-time":"2025-04-10T00:00:00Z","timestamp":1744243200000},"content-version":"vor","delay-in-days":0,"URL":"https:\/\/creativecommons.org\/licenses\/by\/4.0\/"}],"content-domain":{"domain":[],"crossmark-restriction":false},"short-container-title":["Symmetry"],"abstract":"<jats:p>Artificial intelligence (AI) plays a crucial role in modern healthcare by enhancing disease modeling and outbreak prediction. In this study, we develop an epidemiological model for the Marburg virus, integrating vaccination and treatment strategies while considering vaccine efficacy and treatment failure. The model exhibits mathematical symmetry in its equilibrium analysis, ensuring a balanced assessment of disease dynamics across human and bat reservoir populations. We compute the Marburg-free and endemic equilibrium points, derive the secondary infection threshold, and conduct sensitivity analysis using the PRCC method to identify key disease transmission parameters that are important for disease control. To validate the theory, we optimized a deep neural network (DNN) via grid search and employed it for dynamic analysis, which also validates the cutting-edge application of AI in healthcare. We also compare AI-based predictions with traditional numerical solutions for reproduction number for humans R0h&gt;1 and R0h&lt;1 for validation and efficacy of the AI approach. The results demonstrate the model\u2019s stability, efficacy, and predictive power, emphasizing the synergy between AI and mathematical epidemiology. This study provides valuable insights for public health interventions and effective disease control strategies by leveraging AI-driven simulations, highlighting AI\u2019s potential to revolutionize and enhance early detection and tailor treatment strategies.<\/jats:p>","DOI":"10.3390\/sym17040578","type":"journal-article","created":{"date-parts":[[2025,4,10]],"date-time":"2025-04-10T08:46:20Z","timestamp":1744274780000},"page":"578","update-policy":"https:\/\/doi.org\/10.3390\/mdpi_crossmark_policy","source":"Crossref","is-referenced-by-count":4,"title":["Artificial Neural Network-Based Approach for Dynamic Analysis and Modeling of Marburg Virus Epidemics for Health Care"],"prefix":"10.3390","volume":"17","author":[{"given":"Noreen","family":"Mustafa","sequence":"first","affiliation":[{"name":"Abdus Salam School of Mathematical Sciences, GC University, Lahore 54600, Pakistan"}]},{"ORCID":"https:\/\/orcid.org\/0000-0001-8642-0660","authenticated-orcid":false,"given":"Jamshaid Ul","family":"Rahman","sequence":"additional","affiliation":[{"name":"Abdus Salam School of Mathematical Sciences, GC University, Lahore 54600, Pakistan"}]},{"ORCID":"https:\/\/orcid.org\/0000-0002-5228-1073","authenticated-orcid":false,"given":"Umar","family":"Ishtiaq","sequence":"additional","affiliation":[{"name":"Office of Research, Innovation and Commercialization, University of Management and Technology, Lahore 54770, Pakistan"}]},{"ORCID":"https:\/\/orcid.org\/0000-0001-8042-1806","authenticated-orcid":false,"given":"Ioan-Lucia","family":"Popa","sequence":"additional","affiliation":[{"name":"Department of Computing, Mathematics and Electronics, \u201c1 Decembrie 1918\u201d University of Alba Iulia, 510009 Alba Iulia, Romania"},{"name":"Faculty of Mathematics and Computer Science, Transilvania University of Brasov, Iuliu Maniu Street 50, 500091 Brasov, Romania"}]}],"member":"1968","published-online":{"date-parts":[[2025,4,10]]},"reference":[{"key":"ref_1","unstructured":"World Health Organization (2023). 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