{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,11,4]],"date-time":"2026-11-04T01:18:47Z","timestamp":1793755127066,"version":"4.6.1"},"reference-count":50,"publisher":"Elsevier BV","license":[{"start":{"date-parts":[[2022,12,1]],"date-time":"2022-12-01T00:00:00Z","timestamp":1669852800000},"content-version":"tdm","delay-in-days":0,"URL":"https:\/\/www.elsevier.com\/tdm\/userlicense\/1.0\/"},{"start":{"date-parts":[[2022,12,1]],"date-time":"2022-12-01T00:00:00Z","timestamp":1669852800000},"content-version":"tdm","delay-in-days":0,"URL":"https:\/\/www.elsevier.com\/legal\/tdmrep-license"},{"start":{"date-parts":[[2022,9,30]],"date-time":"2022-09-30T00:00:00Z","timestamp":1664496000000},"content-version":"vor","delay-in-days":0,"URL":"http:\/\/creativecommons.org\/licenses\/by-nc-nd\/4.0\/"}],"funder":[{"DOI":"10.13039\/100007588","name":"Washington State University","doi-asserted-by":"publisher","id":[{"id":"10.13039\/100007588","id-type":"DOI","asserted-by":"publisher"}]},{"DOI":"10.13039\/100011661","name":"Pacific Northwest National Laboratory","doi-asserted-by":"publisher","id":[{"id":"10.13039\/100011661","id-type":"DOI","asserted-by":"publisher"}]},{"DOI":"10.13039\/100000774","name":"Defense Threat Reduction Agency","doi-asserted-by":"publisher","award":["CB11029"],"award-info":[{"award-number":["CB11029"]}],"id":[{"id":"10.13039\/100000774","id-type":"DOI","asserted-by":"publisher"}]}],"content-domain":{"domain":["clinicalkey.com","clinicalkey.com.au","clinicalkey.jp","elsevier.com","sciencedirect.com"],"crossmark-restriction":true},"short-container-title":["One Health"],"published-print":{"date-parts":[[2022,12]]},"DOI":"10.1016\/j.onehlt.2022.100439","type":"journal-article","created":{"date-parts":[[2022,10,1]],"date-time":"2022-10-01T01:00:58Z","timestamp":1664586058000},"page":"100439","update-policy":"https:\/\/doi.org\/10.1016\/elsevier_cm_policy","source":"Crossref","is-referenced-by-count":76,"special_numbering":"C","title":["Predicting infectious disease for biopreparedness and response: A systematic review of machine learning and deep learning approaches"],"prefix":"10.1016","volume":"15","author":[{"given":"Ravikiran","family":"Keshavamurthy","sequence":"first","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Samuel","family":"Dixon","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Karl T.","family":"Pazdernik","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Lauren E.","family":"Charles","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]}],"member":"78","reference":[{"key":"10.1016\/j.onehlt.2022.100439_bb0005","doi-asserted-by":"crossref","first-page":"63","DOI":"10.1007\/s00430-002-0122-5","article-title":"Emerging and re-emerging infectious diseases","volume":"191","author":"Feldmann","year":"2002","journal-title":"Med. Microbiol. Immunol."},{"key":"10.1016\/j.onehlt.2022.100439_bb0010","doi-asserted-by":"crossref","first-page":"2045","DOI":"10.1098\/rstb.2010.0387","article-title":"How to make predictions about future infectious disease risks","volume":"366","author":"Woolhouse","year":"2011","journal-title":"Philos. Trans. Royal Soc. B: Biol. Sci."},{"issue":"2015","key":"10.1016\/j.onehlt.2022.100439_bb0015","article-title":"Modeling infectious disease dynamics in the complex landscape of global health","volume":"347","author":"Heesterbeek","year":"1979","journal-title":"Science"},{"key":"10.1016\/j.onehlt.2022.100439_bb0020","doi-asserted-by":"crossref","DOI":"10.1371\/journal.pone.0139701","article-title":"Using social media for actionable disease surveillance and outbreak management: a systematic literature review","volume":"10","author":"Charles-Smith","year":"2015","journal-title":"PLoS One"},{"key":"10.1016\/j.onehlt.2022.100439_bb0025","doi-asserted-by":"crossref","first-page":"783","DOI":"10.3390\/pathogens10070783","article-title":"Digital biosurveillance for zoonotic disease detection in Kenya","volume":"10","author":"Keshavamurthy","year":"2021","journal-title":"Pathogens"},{"key":"10.1016\/j.onehlt.2022.100439_bb0030","first-page":"e41","article-title":"Development and dissemination of infectious disease dynamic transmission models during the COVID-19 pandemic: what can we learn from other pathogens and how can we move forward?, lancet digit","volume":"3","author":"Becker","year":"2021","journal-title":"Health."},{"key":"10.1016\/j.onehlt.2022.100439_bb0035","first-page":"366","article-title":"Some studies in machine learning using the game of checkers. II\u2014recent Progress","author":"Samuel","year":"1988","journal-title":"Comp. Games I."},{"key":"10.1016\/j.onehlt.2022.100439_bb0040","doi-asserted-by":"crossref","first-page":"436","DOI":"10.1038\/nature14539","article-title":"Deep learning","volume":"521","author":"Lecun","year":"2015","journal-title":"Nature"},{"issue":"2015","key":"10.1016\/j.onehlt.2022.100439_bb0045","first-page":"255","article-title":"Machine learning: trends, perspectives, and prospects","volume":"349","author":"Jordan","year":"1979","journal-title":"Science"},{"key":"10.1016\/j.onehlt.2022.100439_bb0050","first-page":"2222","article-title":"What is machine learning? A primer for the epidemiologist","volume":"188","author":"Bi","year":"2019","journal-title":"Am. J. Epidemiol."},{"key":"10.1016\/j.onehlt.2022.100439_bb0055","doi-asserted-by":"crossref","first-page":"477","DOI":"10.1038\/nrmicro1845","article-title":"Mathematical models of infectious disease transmission","volume":"6","author":"Grassly","year":"2008","journal-title":"Nat. Rev. Microbiol."},{"key":"10.1016\/j.onehlt.2022.100439_bb0060","doi-asserted-by":"crossref","first-page":"2045","DOI":"10.1098\/rstb.2010.0387","article-title":"How to make predictions about future infectious disease risks","volume":"366","author":"Woolhouse","year":"2011","journal-title":"Philos. Trans. Royal Soc. B: Biol. Sci."},{"key":"10.1016\/j.onehlt.2022.100439_bb0065","doi-asserted-by":"crossref","DOI":"10.1136\/bmjopen-2020-037860","article-title":"Predicting population health with machine learning: a scoping review","volume":"10","author":"Morgenstern","year":"2020","journal-title":"BMJ Open"},{"key":"10.1016\/j.onehlt.2022.100439_bb0070","doi-asserted-by":"crossref","first-page":"233","DOI":"10.1038\/nmeth.4642","article-title":"Points of significance: statistics versus machine learning","volume":"15","author":"Bzdok","year":"2018","journal-title":"Nat. Methods"},{"key":"10.1016\/j.onehlt.2022.100439_bb0075","doi-asserted-by":"crossref","first-page":"1596","DOI":"10.3390\/ijerph15081596","article-title":"Predicting infectious disease using deep learning and big data","volume":"15","author":"Chae","year":"2018","journal-title":"Int. J. Environ. Res. Public Health"},{"key":"10.1016\/j.onehlt.2022.100439_bb0080","doi-asserted-by":"crossref","DOI":"10.1016\/j.chaos.2020.109864","article-title":"Time series forecasting of COVID-19 transmission in Canada using LSTM networks","volume":"135","author":"Chimmula","year":"2020","journal-title":"Chaos, Solitons Fractals"},{"key":"10.1016\/j.onehlt.2022.100439_bb0085","doi-asserted-by":"crossref","first-page":"44","DOI":"10.1016\/j.idh.2018.10.002","article-title":"Artificial intelligence for infectious disease big data analytics","volume":"24","author":"Wong","year":"2019","journal-title":"Infect. Dis. Health."},{"key":"10.1016\/j.onehlt.2022.100439_bb0090","doi-asserted-by":"crossref","DOI":"10.1016\/j.heliyon.2021.e07371","article-title":"The roles of machine learning methods in limiting the spread of deadly diseases: a systematic review","volume":"7","author":"Alfred","year":"2021","journal-title":"Heliyon."},{"key":"10.1016\/j.onehlt.2022.100439_bb0095","doi-asserted-by":"crossref","DOI":"10.3390\/pathogens11020185","article-title":"A comparison of infectious disease forecasting methods across locations, diseases, and time","volume":"11","author":"Dixon","year":"2022","journal-title":"Pathogens."},{"key":"10.1016\/j.onehlt.2022.100439_bb0100","doi-asserted-by":"crossref","first-page":"276","DOI":"10.1186\/1471-2105-15-276","article-title":"Comparison of ARIMA and random Forest time series models for prediction of avian influenza H5N1 outbreaks","volume":"15","author":"Kane","year":"2014","journal-title":"BMC Bioinform."},{"key":"10.1016\/j.onehlt.2022.100439_bb0105","doi-asserted-by":"crossref","DOI":"10.1038\/s41598-020-66650-1","article-title":"Predicting dengue importation into Europe, using machine learning and model-agnostic methods","volume":"10","author":"Salami","year":"2020","journal-title":"Sci. Rep."},{"key":"10.1016\/j.onehlt.2022.100439_bb0110","doi-asserted-by":"crossref","DOI":"10.1186\/1297-9716-44-42","article-title":"A global model of avian influenza prediction in wild birds: the importance of northern regions","volume":"44","author":"Herrick","year":"2013","journal-title":"Vet. Res."},{"key":"10.1016\/j.onehlt.2022.100439_bb0115","article-title":"Applications and comparisons of four time series models in epidemiological surveillance data","volume":"9","author":"Zhang","year":"2014","journal-title":"PLoS One"},{"key":"10.1016\/j.onehlt.2022.100439_bb0120","doi-asserted-by":"crossref","first-page":"641253","DOI":"10.3389\/fpubh.2021.641253","article-title":"COVID-19 dynamic monitoring and real-time spatio-temporal forecasting","volume":"9","author":"da Silva","year":"2021","journal-title":"Front. Public Health"},{"key":"10.1016\/j.onehlt.2022.100439_bb0125","doi-asserted-by":"crossref","first-page":"1","DOI":"10.1186\/s13104-020-4889-5","article-title":"A comparative study on predicting influenza outbreaks using different feature spaces: application of influenza-like illness data from early warning alert and response system in Syria","volume":"13","author":"Darwish","year":"2020","journal-title":"BMC Res. Notes."},{"key":"10.1016\/j.onehlt.2022.100439_bb0130","doi-asserted-by":"crossref","first-page":"4204","DOI":"10.3390\/ijerph17124204","article-title":"Artificial neural network modeling of novel coronavirus (COVID-19) incidence rates across the continental United States","volume":"17","author":"Mollalo","year":"2020","journal-title":"Int. J. Environ. Res. Public Health"},{"key":"10.1016\/j.onehlt.2022.100439_bb0135","doi-asserted-by":"crossref","first-page":"1","DOI":"10.1371\/journal.pntd.0008924","article-title":"Enhancing fine-grained intra-urban dengue forecasting by integrating spatial interactions of human movements between urban regions","volume":"14","author":"Liu","year":"2020","journal-title":"PLoS Negl. Trop. Dis."},{"key":"10.1016\/j.onehlt.2022.100439_bb0140","doi-asserted-by":"crossref","first-page":"20200691","DOI":"10.1098\/rsif.2020.0691","article-title":"Predicting dengue outbreaks at neighbourhood level using human mobility in urban areas","volume":"17","author":"Bomfim","year":"2020","journal-title":"J. R. Soc. Interface"},{"key":"10.1016\/j.onehlt.2022.100439_bb0145","doi-asserted-by":"crossref","DOI":"10.1016\/j.compbiomed.2020.103859","article-title":"LSTM based prediction of malaria abundances using big data","volume":"124","author":"Santosh","year":"2020","journal-title":"Comput. Biol. Med."},{"key":"10.1016\/j.onehlt.2022.100439_bb0150","doi-asserted-by":"crossref","first-page":"S375","DOI":"10.1093\/infdis\/jiw400","article-title":"Big data for infectious disease surveillance and modeling","volume":"214","author":"Bansal","year":"2016","journal-title":"J. Infect. Dis."},{"key":"10.1016\/j.onehlt.2022.100439_bb0155","doi-asserted-by":"crossref","first-page":"e20","DOI":"10.1016\/S2214-109X(14)70356-0","article-title":"Role of big data in the early detection of Ebola and other emerging infectious diseases","volume":"3","author":"Milinovich","year":"2015","journal-title":"Lancet Glob. Health"},{"key":"10.1016\/j.onehlt.2022.100439_bb0160","doi-asserted-by":"crossref","first-page":"332","DOI":"10.1136\/bmj.b2535","article-title":"Preferred reporting items for systematic reviews and meta-analyses: the PRISMA statement","volume":"339","author":"Moher","year":"2009","journal-title":"BMJ."},{"key":"10.1016\/j.onehlt.2022.100439_bb0165","doi-asserted-by":"crossref","DOI":"10.1371\/journal.pone.0138237","article-title":"The role of Google scholar in evidence reviews and its applicability to Grey literature searching","volume":"10","author":"Haddaway","year":"2015","journal-title":"PLoS One"},{"key":"10.1016\/j.onehlt.2022.100439_bb0170","doi-asserted-by":"crossref","first-page":"6","DOI":"10.1089\/bsp.2011.0088","article-title":"Public health surveillance and infectious disease detection","volume":"10","author":"Morse","year":"2012","journal-title":"Biosecurity Bioterrorism."},{"key":"10.1016\/j.onehlt.2022.100439_bb0175","doi-asserted-by":"crossref","DOI":"10.1371\/journal.pone.0091989","article-title":"Disease prediction models and operational readiness","volume":"9","author":"Corley","year":"2014","journal-title":"PLoS One"},{"key":"10.1016\/j.onehlt.2022.100439_bb0180","doi-asserted-by":"crossref","first-page":"295","DOI":"10.1038\/s42256-020-0184-3","article-title":"Artificial intelligence cooperation to support the global response to COVID-19","volume":"2","author":"Luengo-Oroz","year":"2020","journal-title":"Nat. Mach. Intellig."},{"key":"10.1016\/j.onehlt.2022.100439_bb0185","first-page":"1","article-title":"Global hotspots and correlates of emerging zoonotic diseases, Nature","volume":"8","author":"Allen","year":"2017","journal-title":"Communications"},{"key":"10.1016\/j.onehlt.2022.100439_bb0190","first-page":"1","article-title":"Global hotspots and correlates of emerging zoonotic diseases, Nature","volume":"8","author":"Allen","year":"2017","journal-title":"Communications"},{"key":"10.1016\/j.onehlt.2022.100439_bb0195","doi-asserted-by":"crossref","first-page":"e246","DOI":"10.1016\/S2214-109X(17)30025-6","article-title":"Establishing the Africa Centres for disease control and prevention: responding to Africa\u2019s health threats","volume":"5","author":"Nkengasong","year":"2017","journal-title":"Lancet Glob. Health"},{"key":"10.1016\/j.onehlt.2022.100439_bb0200","doi-asserted-by":"crossref","first-page":"1435","DOI":"10.3389\/fpubh.2021.710961","article-title":"A roadmap for building data science capacity for health discovery and innovation in Africa","volume":"9","author":"Beyene","year":"2021","journal-title":"Front. Public Health"},{"key":"10.1016\/j.onehlt.2022.100439_bb0205","doi-asserted-by":"crossref","first-page":"6","DOI":"10.3389\/fdgth.2020.00006","article-title":"Artificial intelligence for healthcare in Africa","volume":"2","author":"Owoyemi","year":"2020","journal-title":"Front. Digit. Health."},{"key":"10.1016\/j.onehlt.2022.100439_bb0210","series-title":"The boosting approach to machine learning: an overview","first-page":"141","author":"Schapire","year":"2003"},{"key":"10.1016\/j.onehlt.2022.100439_bb0215","series-title":"Proceedings of the 22nd ACM SIGKDD International Conference on Knowledge Discovery and Data Mining","first-page":"785","article-title":"XGBoost: A scalable tree boosting system","author":"Chen","year":"2016"},{"key":"10.1016\/j.onehlt.2022.100439_bb0220","series-title":"Tree-Based Methods","first-page":"327","author":"James","year":"2021"},{"key":"10.1016\/j.onehlt.2022.100439_bb0225","doi-asserted-by":"crossref","first-page":"1011","DOI":"10.1038\/nbt0908-1011","article-title":"What are decision trees?","volume":"26","author":"Kingsford","year":"2008","journal-title":"Nat. Biotechnol."},{"key":"10.1016\/j.onehlt.2022.100439_bb0230","doi-asserted-by":"crossref","first-page":"2627","DOI":"10.1016\/S1352-2310(97)00447-0","article-title":"Artificial neural networks (the multilayer perceptron)\u2014a review of applications in the atmospheric sciences","volume":"32","author":"Gardner","year":"1998","journal-title":"Atmos. Environ."},{"key":"10.1016\/j.onehlt.2022.100439_bb0235","series-title":"The Power of Depth for Feedforward Neural Networks Ohad Shamir","first-page":"1","volume":"49","author":"Eldan","year":"2016"},{"key":"10.1016\/j.onehlt.2022.100439_bb0240","doi-asserted-by":"crossref","first-page":"1","DOI":"10.1038\/s41598-018-24271-9","article-title":"Recurrent neural networks for multivariate time series with missing values","volume":"8","author":"Che","year":"2018","journal-title":"Sci. Rep."},{"key":"10.1016\/j.onehlt.2022.100439_bb0245","series-title":"Advances in Machine Learning Modeling Reviewing Hybrid and Ensemble Methods, Lecture Notes in Networks and Systems","first-page":"215","volume":"101","author":"Ardabili","year":"2020"},{"key":"10.1016\/j.onehlt.2022.100439_bb0250","doi-asserted-by":"crossref","first-page":"1348","DOI":"10.3390\/pathogens10101348","article-title":"Operational considerations in global health modeling","volume":"10","author":"Broadway","year":"2021","journal-title":"Pathogens"}],"container-title":["One Health"],"original-title":[],"language":"en","link":[{"URL":"https:\/\/api.elsevier.com\/content\/article\/PII:S2352771422000714?httpAccept=text\/xml","content-type":"text\/xml","content-version":"vor","intended-application":"text-mining"},{"URL":"https:\/\/api.elsevier.com\/content\/article\/PII:S2352771422000714?httpAccept=text\/plain","content-type":"text\/plain","content-version":"vor","intended-application":"text-mining"}],"deposited":{"date-parts":[[2026,5,15]],"date-time":"2026-05-15T21:08:03Z","timestamp":1778879283000},"score":1,"resource":{"primary":{"URL":"https:\/\/linkinghub.elsevier.com\/retrieve\/pii\/S2352771422000714"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2022,12]]},"references-count":50,"alternative-id":["S2352771422000714"],"URL":"https:\/\/doi.org\/10.1016\/j.onehlt.2022.100439","relation":{"has-preprint":[{"id-type":"doi","id":"10.1101\/2022.06.30.22277117","asserted-by":"object"}]},"ISSN":["2352-7714"],"issn-type":[{"value":"2352-7714","type":"print"}],"subject":[],"published":{"date-parts":[[2022,12]]},"assertion":[{"value":"Elsevier","name":"publisher","label":"This article is maintained by"},{"value":"Predicting infectious disease for biopreparedness and response: A systematic review of machine learning and deep learning approaches","name":"articletitle","label":"Article Title"},{"value":"One Health","name":"journaltitle","label":"Journal Title"},{"value":"https:\/\/doi.org\/10.1016\/j.onehlt.2022.100439","name":"articlelink","label":"CrossRef DOI link to publisher maintained version"},{"value":"article","name":"content_type","label":"Content Type"},{"value":"\u00a9 2022 Battelle Memorial Institute. Published by Elsevier B.V.","name":"copyright","label":"Copyright"}],"article-number":"100439"}}