{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,10,13]],"date-time":"2026-10-13T01:31:59Z","timestamp":1791855119895,"version":"4.3.4"},"reference-count":42,"publisher":"Elsevier BV","license":[{"start":{"date-parts":[[2026,6,1]],"date-time":"2026-06-01T00:00:00Z","timestamp":1780272000000},"content-version":"tdm","delay-in-days":0,"URL":"https:\/\/www.elsevier.com\/tdm\/userlicense\/1.0\/"},{"start":{"date-parts":[[2026,6,1]],"date-time":"2026-06-01T00:00:00Z","timestamp":1780272000000},"content-version":"tdm","delay-in-days":0,"URL":"https:\/\/www.elsevier.com\/legal\/tdmrep-license"},{"start":{"date-parts":[[2026,4,8]],"date-time":"2026-04-08T00:00:00Z","timestamp":1775606400000},"content-version":"vor","delay-in-days":0,"URL":"http:\/\/creativecommons.org\/licenses\/by-nc-nd\/4.0\/"}],"content-domain":{"domain":["clinicalkey.com","clinicalkey.com.au","clinicalkey.es","clinicalkey.fr","clinicalkey.jp","elsevier.com","sciencedirect.com"],"crossmark-restriction":true},"short-container-title":["New Microbes and New Infections"],"published-print":{"date-parts":[[2026,6]]},"DOI":"10.1016\/j.nmni.2026.101751","type":"journal-article","created":{"date-parts":[[2026,4,10]],"date-time":"2026-04-10T08:24:31Z","timestamp":1775809471000},"page":"101751","update-policy":"https:\/\/doi.org\/10.1016\/elsevier_cm_policy","source":"Crossref","is-referenced-by-count":1,"special_numbering":"C","title":["Artificial intelligence at the frontlines: Emerging infectious and parasitic diseases in the digital era"],"prefix":"10.1016","volume":"71","author":[{"ORCID":"https:\/\/orcid.org\/0000-0003-4330-3047","authenticated-orcid":false,"given":"Dina S.","family":"Nasr","sequence":"first","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Nour Bader","family":"Alraee","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Sham Wathek Arabi","family":"Katbi","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Najwa Mahmoud","family":"Kouli","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Naziha Ismail","family":"Asaad","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Mariam M.","family":"Ismail","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Shifan","family":"Khanday","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]}],"member":"78","reference":[{"issue":"4","key":"10.1016\/j.nmni.2026.101751_bib1","first-page":"xiii","article-title":"Emerging and reemerging infectious diseases: global overview","volume":"33","author":"Zumla","year":"2019","journal-title":"Infect Dis Clin"},{"issue":"13","key":"10.1016\/j.nmni.2026.101751_bib2","doi-asserted-by":"crossref","first-page":"1946","DOI":"10.3390\/healthcare11131946","article-title":"Antimicrobial resistance: a growing serious threat for global public health","volume":"11","author":"Salam","year":"2023","journal-title":"Healthcare (Basel)"},{"issue":"1 Suppl 1","key":"10.1016\/j.nmni.2026.101751_bib3","doi-asserted-by":"crossref","DOI":"10.1590\/1678-4685-gmb-2020-0355","article-title":"Zoonotic spillover: understanding basic aspects for better prevention","volume":"44","author":"Ellwanger","year":"2021","journal-title":"Genet Mol Biol"},{"key":"10.1016\/j.nmni.2026.101751_bib4","doi-asserted-by":"crossref","DOI":"10.3389\/fpubh.2025.1609615","article-title":"Artificial intelligence in early warning systems for infectious disease surveillance: a systematic review","volume":"13","author":"Villanueva-Miranda","year":"2025","journal-title":"Front Public Health"},{"key":"10.1016\/j.nmni.2026.101751_bib5","article-title":"Artificial intelligence in infection surveillance: data integration, applications and future directions","author":"Li","year":"2025","journal-title":"Biomed J"},{"key":"10.1016\/j.nmni.2026.101751_bib6","doi-asserted-by":"crossref","DOI":"10.3389\/frai.2025.1645467","article-title":"AI-driven epidemic intelligence: the future of outbreak detection and response","volume":"8","author":"Kaur","year":"2025","journal-title":"Front Artif Intell"},{"issue":"10","key":"10.1016\/j.nmni.2026.101751_bib7","doi-asserted-by":"crossref","DOI":"10.1098\/rsos.250536","article-title":"Application of remote sensing and geospatial technologies in predicting vector-borne disease outbreaks","volume":"12","author":"Abbasi","year":"2025","journal-title":"R Soc Open Sci"},{"key":"10.1016\/j.nmni.2026.101751_bib8","doi-asserted-by":"crossref","DOI":"10.1016\/j.clwat.2025.100189","article-title":"AI-based wastewater treatment for a circular economy and sustainable management of PFAS, heavy metals, microplastics, and antibiotics","volume":"5","author":"Parsoya","year":"2026","journal-title":"Cleaner Water"},{"key":"10.1016\/j.nmni.2026.101751_bib9","doi-asserted-by":"crossref","DOI":"10.1016\/j.jbi.2021.103751","article-title":"Machine learning research towards combating COVID-19: virus detection, spread prevention, and medical assistance","volume":"117","author":"Shahid","year":"2021","journal-title":"J Biomed Inf"},{"issue":"44","key":"10.1016\/j.nmni.2026.101751_bib10","doi-asserted-by":"crossref","first-page":"1396","DOI":"10.46234\/ccdcw2025.234","article-title":"Developing machine learning prediction model for daily influenza reported cases using multichannel surveillance data\u2014Hubei Province, China, 2023\u20132025","volume":"7","author":"Zhang","year":"2025","journal-title":"China CDC Wkly"},{"issue":"1","key":"10.1016\/j.nmni.2026.101751_bib11","doi-asserted-by":"crossref","DOI":"10.1038\/s41598-025-97072-6","article-title":"Spatio-temporal modelling and prediction of malaria incidence in Mozambique using climatic indicators from 2001 to 2018","volume":"15","author":"Armando","year":"2025","journal-title":"Sci Rep"},{"issue":"1","key":"10.1016\/j.nmni.2026.101751_bib12","doi-asserted-by":"crossref","first-page":"7","DOI":"10.1186\/s42522-026-00194-8","article-title":"Geospatial modelling for zoonotic disease hotspot identification within a one health framework: a systematic review","volume":"8","author":"Nyengere","year":"2026","journal-title":"One Health Outlook"},{"issue":"6","key":"10.1016\/j.nmni.2026.101751_bib13","doi-asserted-by":"crossref","first-page":"717","DOI":"10.1002\/mlf2.70055","article-title":"Community-level wastewater surveillance with machine learning methods to assess underreporting of COVID-19 case counts","volume":"4","author":"Szeto","year":"2025","journal-title":"mLife"},{"key":"10.1016\/j.nmni.2026.101751_bib14","article-title":"Revolutionizing infectious disease surveillance: multi-omics technologies and AI-driven integration","volume":"3","author":"Aswini","year":"2025","journal-title":"Decod Infect Transm"},{"issue":"2","key":"10.1016\/j.nmni.2026.101751_bib15","doi-asserted-by":"crossref","DOI":"10.1001\/jamanetworkopen.2023.0191","article-title":"Using haplotype-based artificial intelligence to evaluate SARS-CoV-2 novel variants and mutations","volume":"6","author":"Zhao","year":"2023","journal-title":"JAMA Netw Open"},{"issue":"5","key":"10.1016\/j.nmni.2026.101751_bib16","doi-asserted-by":"crossref","DOI":"10.1128\/mbio.01768-23","article-title":"Whole-genome surveillance identifies markers of Plasmodium falciparum drug resistance and novel genomic regions under selection in Mozambique","volume":"14","author":"Coonahan","year":"2023","journal-title":"mBio"},{"issue":"1","key":"10.1016\/j.nmni.2026.101751_bib17","doi-asserted-by":"crossref","first-page":"5","DOI":"10.1007\/s44163-023-00049-5","article-title":"Evaluation of artificial intelligence techniques in disease diagnosis and prediction","volume":"3","author":"Ghaffar","year":"2023","journal-title":"Discov Artif Intell"},{"issue":"1","key":"10.1016\/j.nmni.2026.101751_bib18","doi-asserted-by":"crossref","first-page":"159","DOI":"10.3390\/diagnostics13010159","article-title":"A review of recent advances in deep learning models for chest disease detection using radiography","volume":"13","author":"Ait","year":"2023","journal-title":"Diagnostics (Basel)"},{"issue":"1","key":"10.1016\/j.nmni.2026.101751_bib19","doi-asserted-by":"crossref","first-page":"11","DOI":"10.3390\/idr18010011","article-title":"Diagnostic accuracy of utilizing artificial intelligence for malaria diagnostic: a systematic review and meta-analysis","volume":"18","author":"Faratisha","year":"2026","journal-title":"Infect Dis Rep"},{"issue":"1","key":"10.1016\/j.nmni.2026.101751_bib20","doi-asserted-by":"crossref","first-page":"689","DOI":"10.1186\/s12909-023-04698-z","article-title":"Revolutionizing healthcare: the role of artificial intelligence in clinical practice","volume":"23","author":"Alowais","year":"2023","journal-title":"BMC Med Educ"},{"issue":"Suppl 1","key":"10.1016\/j.nmni.2026.101751_bib21","article-title":"Deep CNN models for predicting COVID-19 in CT and X-ray images","volume":"8","author":"Chaddad","year":"2021","journal-title":"J Med Imaging (Bellingham)"},{"issue":"9","key":"10.1016\/j.nmni.2026.101751_bib22","article-title":"Observer performance evaluation of a deep learning model for multilabel classification of active tuberculosis lung zone-wise manifestations","volume":"15","author":"Devasia","year":"2023","journal-title":"Cureus"},{"key":"10.1016\/j.nmni.2026.101751_bib23","doi-asserted-by":"crossref","DOI":"10.7717\/peerj.9674","article-title":"Convolutional neural networks to automate the screening of malaria in low-resource countries","volume":"8","author":"Zhao","year":"2020","journal-title":"PeerJ"},{"issue":"3","key":"10.1016\/j.nmni.2026.101751_bib24","doi-asserted-by":"crossref","first-page":"374","DOI":"10.3390\/diagnostics15030374","article-title":"Multi-classification of skin lesion images including mpox disease using transformer-based deep learning architectures","volume":"15","author":"Vuran","year":"2025","journal-title":"Diagnostics (Basel)"},{"key":"10.1016\/j.nmni.2026.101751_bib25","doi-asserted-by":"crossref","first-page":"102327","DOI":"10.1109\/ACCESS.2021.3097559","article-title":"Cough sound detection and diagnosis using artificial intelligence techniques: challenges and opportunities","volume":"9","author":"Alqudaihi","year":"2021","journal-title":"IEEE Access"},{"key":"10.1016\/j.nmni.2026.101751_bib26","doi-asserted-by":"crossref","first-page":"130","DOI":"10.53771\/ijbpsa.2025.9.2.0048","article-title":"Artificial intelligence for forecasting climate-driven vector-borne disease outbreaks","volume":"9","author":"Egbuna","year":"2025","journal-title":"Int J Biol Pharm Sci Arch"},{"issue":"1","key":"10.1016\/j.nmni.2026.101751_bib27","doi-asserted-by":"crossref","first-page":"280","DOI":"10.1186\/s13690-025-01780-4","article-title":"Machine learning-based prediction of household sanitation facility access in Sub-Saharan Africa: insights from DHS data (2012-2024)","volume":"83","author":"Yitageasu","year":"2025","journal-title":"Arch Public Health"},{"issue":"10","key":"10.1016\/j.nmni.2026.101751_bib28","doi-asserted-by":"crossref","DOI":"10.1371\/journal.pcbi.1004513","article-title":"Combining search, social media, and traditional data sources to improve influenza surveillance","volume":"11","author":"Santillana","year":"2015","journal-title":"PLoS Comput Biol"},{"issue":"5","key":"10.1016\/j.nmni.2026.101751_bib29","doi-asserted-by":"crossref","DOI":"10.1371\/journal.pone.0299386","article-title":"Predicting malaria outbreak in the Gambia using machine learning techniques","volume":"19","author":"Khan","year":"2024","journal-title":"PLoS One"},{"key":"10.1016\/j.nmni.2026.101751_bib30","article-title":"A climatic suitability indicator to support Leishmania infantum surveillance in Europe: a modelling study","volume":"43","author":"Carvalho","year":"2024","journal-title":"Lancet Reg Health Eur"},{"issue":"6","key":"10.1016\/j.nmni.2026.101751_bib31","doi-asserted-by":"crossref","DOI":"10.1371\/journal.pntd.0010517","article-title":"Machine learning-based risk factor analysis and prevalence prediction of intestinal parasitic infections using epidemiological survey data","volume":"16","author":"Zafar","year":"2022","journal-title":"PLoS Neglected Trop Dis"},{"key":"10.1016\/j.nmni.2026.101751_bib32","doi-asserted-by":"crossref","DOI":"10.3389\/fimmu.2025.1567116","article-title":"Artificial intelligence in vaccine research and development: an umbrella review","volume":"16","author":"El Arab","year":"2025","journal-title":"Front Immunol"},{"issue":"4","key":"10.1016\/j.nmni.2026.101751_bib33","doi-asserted-by":"crossref","first-page":"688","DOI":"10.1016\/j.cell.2020.01.021","article-title":"A deep learning approach to antibiotic discovery","volume":"180","author":"Stokes","year":"2020","journal-title":"Cell"},{"key":"10.1016\/j.nmni.2026.101751_bib34","doi-asserted-by":"crossref","first-page":"1526","DOI":"10.3389\/fphar.2019.01526","article-title":"DeepMalaria: artificial intelligence driven discovery of potent antiplasmodials","volume":"10","author":"Keshavarzi","year":"2020","journal-title":"Front Pharmacol"},{"issue":"20","key":"10.1016\/j.nmni.2026.101751_bib35","doi-asserted-by":"crossref","first-page":"3168","DOI":"10.1111\/jcmm.17940","article-title":"Antimalarial drug discovery against malaria parasites through haplopine modification: an advanced computational approach","volume":"27","author":"Akash","year":"2023","journal-title":"J Cell Mol Med"},{"issue":"8","key":"10.1016\/j.nmni.2026.101751_bib36","doi-asserted-by":"crossref","first-page":"1019","DOI":"10.3390\/biology14081019","article-title":"VaccineDesigner: a web-based tool for streamlined multi-epitope vaccine design","volume":"14","author":"Trygoniaris","year":"2025","journal-title":"Biology"},{"key":"10.1016\/j.nmni.2026.101751_bib37","doi-asserted-by":"crossref","DOI":"10.3389\/fphar.2025.1498662","article-title":"Protein structure prediction via deep learning: an in-depth review","volume":"16","author":"Meng","year":"2025","journal-title":"Front Pharmacol"},{"issue":"12","key":"10.1016\/j.nmni.2026.101751_bib38","doi-asserted-by":"crossref","first-page":"e667","DOI":"10.1016\/S2589-7500(20)30192-8","article-title":"Artificial intelligence in COVID-19 drug repurposing","volume":"2","author":"Zhou","year":"2020","journal-title":"Lancet Digit Health"},{"issue":"1","key":"10.1016\/j.nmni.2026.101751_bib39","doi-asserted-by":"crossref","first-page":"183","DOI":"10.18295\/2075-0528.2828","article-title":"Artificial intelligence in infectious diseases: hopes and hurdles","volume":"25","author":"Al-Lawati","year":"2025","journal-title":"Sultan Qaboos Univ Med J"},{"issue":"3","key":"10.1016\/j.nmni.2026.101751_bib40","doi-asserted-by":"crossref","first-page":"306","DOI":"10.3390\/healthcare14030306","article-title":"Federated learning in healthcare ethics: a systematic review of privacy-preserving and equitable medical AI","volume":"14","author":"Mir","year":"2026","journal-title":"Healthcare (Basel)"},{"issue":"10","key":"10.1016\/j.nmni.2026.101751_bib41","first-page":"228","article-title":"Artificial intelligence in infectious disease clinical practice: an overview of gaps, opportunities, and limitations","volume":"9","author":"Sarantopoulos","year":"2024","journal-title":"Trop Med Infect Dis"},{"issue":"20","key":"10.1016\/j.nmni.2026.101751_bib42","doi-asserted-by":"crossref","first-page":"2602","DOI":"10.3390\/diagnostics15202602","article-title":"Artificial intelligence in infectious disease diagnostic technologies","volume":"15","author":"Dong","year":"2025","journal-title":"Diagnostics (Basel)"}],"container-title":["New Microbes and New Infections"],"original-title":[],"language":"en","link":[{"URL":"https:\/\/api.elsevier.com\/content\/article\/PII:S2052297526000557?httpAccept=text\/xml","content-type":"text\/xml","content-version":"vor","intended-application":"text-mining"},{"URL":"https:\/\/api.elsevier.com\/content\/article\/PII:S2052297526000557?httpAccept=text\/plain","content-type":"text\/plain","content-version":"vor","intended-application":"text-mining"}],"deposited":{"date-parts":[[2026,5,25]],"date-time":"2026-05-25T13:58:57Z","timestamp":1779717537000},"score":1,"resource":{"primary":{"URL":"https:\/\/linkinghub.elsevier.com\/retrieve\/pii\/S2052297526000557"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2026,6]]},"references-count":42,"alternative-id":["S2052297526000557"],"URL":"https:\/\/doi.org\/10.1016\/j.nmni.2026.101751","relation":{},"ISSN":["2052-2975"],"issn-type":[{"value":"2052-2975","type":"print"}],"subject":[],"published":{"date-parts":[[2026,6]]},"assertion":[{"value":"Elsevier","name":"publisher","label":"This article is maintained by"},{"value":"Artificial intelligence at the frontlines: Emerging infectious and parasitic diseases in the digital era","name":"articletitle","label":"Article Title"},{"value":"New Microbes and New Infections","name":"journaltitle","label":"Journal Title"},{"value":"https:\/\/doi.org\/10.1016\/j.nmni.2026.101751","name":"articlelink","label":"CrossRef DOI link to publisher maintained version"},{"value":"article","name":"content_type","label":"Content Type"},{"value":"\u00a9 2026 The Authors. Published by Elsevier Ltd.","name":"copyright","label":"Copyright"}],"article-number":"101751"}}