{"doi":"10.3390/life14070783","title":"Towards Improved XAI-Based Epidemiological Research into the Next Potential Pandemic","abstract":"<jats:p>By applying AI techniques to a variety of pandemic-relevant data, artificial intelligence (AI) has substantially supported the control of the spread of the SARS-CoV-2 virus. Along with this, epidemiological machine learning studies of SARS-CoV-2 have been frequently published. While these models can be perceived as precise and policy-relevant to guide governments towards optimal containment policies, their black box nature can hamper building trust and relying confidently on the prescriptions proposed. This paper focuses on interpretable AI-based epidemiological models in the context of the recent SARS-CoV-2 pandemic. We systematically review existing studies, which jointly incorporate AI, SARS-CoV-2 epidemiology, and explainable AI approaches (XAI). First, we propose a conceptual framework by synthesizing the main methodological features of the existing AI pipelines of SARS-CoV-2. Upon the proposed conceptual framework and by analyzing the selected epidemiological studies, we reflect on current research gaps in epidemiological AI toolboxes and how to fill these gaps to generate enhanced policy support in the next potential pandemic.</jats:p>","journal":"Life","year":2024,"id":608904,"datarank":0.3172241150670741,"base_score":2.0794415416798357,"endowment":2.0794415416798357,"self_citation_contribution":0.31191623125197543,"citation_network_contribution":0.00530788381509868,"self_endowment_contribution":0.31191623125197543,"citer_contribution":0.00530788381509868,"corpus_percentile":null,"corpus_rank":null,"citation_count":7,"citer_count":5,"citers_with_citation_signal":1,"citers_with_endowment":1,"datacite_reuse_total":0,"is_dataset":false,"is_dataset_confidence":null,"is_data_producer":false,"deposit_databanks":null,"is_oa":false,"file_count":0,"downloads":0,"has_version_chain":false,"published_date":null,"fair_score":null,"fair_percentile":null,"algorithm_id":"datarank_citation_only_1hop_v6","ranking_scope":"data_only","authors":[{"id":1515572,"name":"Maria A. Wimmer","orcid":"0000-0002-8460-1027","position":1,"is_corresponding":false},{"id":51740,"name":"Hamed Khalili","orcid":"0000-0002-7956-6941","position":0,"is_corresponding":false}],"reference_count":0,"raw_metadata":{"has_enrichment":true,"resolved":true,"title":"Towards Improved XAI-Based Epidemiological Research into the Next Potential Pandemic","abstract":"<jats:p>By applying AI techniques to a variety of pandemic-relevant data, artificial intelligence (AI) has substantially supported the control of the spread of the SARS-CoV-2 virus. Along with this, epidemiological machine learning studies of SARS-CoV-2 have been frequently published. While these models can be perceived as precise and policy-relevant to guide governments towards optimal containment policies, their black box nature can hamper building trust and relying confidently on the prescriptions proposed. This paper focuses on interpretable AI-based epidemiological models in the context of the recent SARS-CoV-2 pandemic. We systematically review existing studies, which jointly incorporate AI, SARS-CoV-2 epidemiology, and explainable AI approaches (XAI). First, we propose a conceptual framework by synthesizing the main methodological features of the existing AI pipelines of SARS-CoV-2. Upon the proposed conceptual framework and by analyzing the selected epidemiological studies, we reflect on current research gaps in epidemiological AI toolboxes and how to fill these gaps to generate enhanced policy support in the next potential pandemic.</jats:p>","is_dataset_classified":null,"base_score":2.0794415416798357,"endowment":2.0794415416798357,"datacite_reuse_total":0,"file_count":0,"downloads":0,"views":0,"has_version_chain":false,"is_dataset":false,"is_oa":false,"pmid":"39063538","pmcid":"PMC11278356","openalex_id":"https://openalex.org/W4399892387","authors":[],"funders":[{"funder_name":"Ministry of Science and Health of Rhineland-Palatinate, Germany","grant_id":"7208-0008#2021/0003-1501 15401","title":null}],"total_grants":1,"fwci":1.704,"citation_percentile":0.85840297,"influential_citations":0,"citation_trend":[{"year":2024,"count":1},{"year":2025,"count":4},{"year":2026,"count":2}],"oa_status":"gold","license":"cc-by","oa_locations":[{"url":"https://www.mdpi.com/2075-1729/14/7/783/pdf?version=1718960202","host_type":"journal"},{"url":"https://www.mdpi.com/2075-1729/14/7/783/pdf?version=1718960202","host_type":"publisher"},{"url":"https://www.mdpi.com/2075-1729/14/7/783/pdf","host_type":"publisher"},{"url":"https://doi.org/10.3390/life14070783","host_type":"journal"},{"url":"https://pubmed.ncbi.nlm.nih.gov/39063538","host_type":"repository"},{"url":"https://www.ncbi.nlm.nih.gov/pmc/articles/11278356","host_type":"repository"},{"url":"https://doaj.org/article/267e2389da8e4feb90a9caeff350e768","host_type":"repository"},{"url":"https://pmc.ncbi.nlm.nih.gov/articles/PMC11278356/pdf/life-14-00783.pdf","host_type":"repository"},{"url":"https://europepmc.org/articles/PMC11278356","host_type":"Europe_PMC"},{"url":"https://europepmc.org/articles/PMC11278356?pdf=render","host_type":"Europe_PMC"}],"fields_of_study":["Explainable Artificial Intelligence (XAI)","COVID-19 diagnosis using AI","Anomaly Detection Techniques and Applications"],"mesh_terms":[],"keywords":["Pandemic","Context (archaeology)","Computer science","Data science","Epidemiology","Coronavirus disease 2019 (COVID-19)","Artificial intelligence","Operations research","Medicine","Geography","Infectious disease (medical specialty)","Engineering","Disease","AI","Non-pharmaceutical Interventions","Interpretable Machine Learning","Explainable Deep Learning","Explainable Machine Learning","Sars-cov-2","Interpretable Deep Learning"],"sdg_mappings":[{"sdg_number":0,"sdg_label":"Good health and well-being"}],"linked_datasets":[],"clinical_trials":[],"software_tools":[],"database_accessions":[],"source":"live","citation_network_status":"fetched"},"created_at":"2026-07-31T00:07:57.940865Z","pmid":null,"pmcid":null,"fwci":null,"citation_percentile":null,"influential_citations":0,"oa_status":null,"license":null,"views":0,"total_file_size_bytes":0,"version_count":0,"fair_f":null,"fair_a":null,"fair_i":null,"fair_r":null,"fair_zscore":null,"fair_rationale":null,"fair_model":null,"fair_agent_version":null,"fair_fulltext_source":null,"fair_has_llm":null,"fair_computed_at":null,"clinical_trials":[],"software_tools":[],"db_accessions":[],"linked_datasets":[],"topics":[]}