{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2025,3,17]],"date-time":"2025-03-17T19:40:03Z","timestamp":1742240403790,"version":"3.40.1"},"reference-count":19,"publisher":"American Veterinary Medical Association (AVMA)","issue":"S1","license":[{"start":{"date-parts":[[2025,3,1]],"date-time":"2025-03-01T00:00:00Z","timestamp":1740787200000},"content-version":"unspecified","delay-in-days":0,"URL":"http:\/\/creativecommons.org\/licenses\/by-nc\/4.0\/"}],"content-domain":{"domain":[],"crossmark-restriction":false},"short-container-title":["ajvr"],"published-print":{"date-parts":[[2025,3,1]]},"abstract":"<jats:title>Abstract<\/jats:title>\n<jats:sec id=\"sc_a1\">\n<jats:title>Objective<\/jats:title>\n<jats:p>Lyme disease is a vector-borne emerging zoonosis in Ontario driven by human population growth and climate change. Lyme disease is also a prime example of the One Health concept. While little can be done to immediately reverse climate change and population growth, public health must resort to health communication as its best option for disease control until an effective vaccine becomes available. Disease surveillance enabling precision public health has an important role in this respect: one of the goals of disease surveillance is to forecast the future burden of disease to inform those who need to know. The goal of this study was to forecast the burden of Lyme disease using automated machine learning and statistical learning approaches.<\/jats:p>\n<\/jats:sec>\n<jats:sec id=\"sc_a2\">\n<jats:title>Methods<\/jats:title>\n<jats:p>Lyme disease reports were retrieved from Ontario\u2019s integrated Public Health Information System surveillance system from January 2005 to December 2023. The reports from January 2005 to December 2021 were used as training data, and reports from January 2022 to December 2023 served as validation data. Forecasts from a seasonal autoregressive integrated moving-average model were used as a benchmark for forecasts from a feed-forward single-layer neural network machine learning algorithm.<\/jats:p>\n<\/jats:sec>\n<jats:sec id=\"sc_a3\">\n<jats:title>Results<\/jats:title>\n<jats:p>The Lyme disease burden in Ontario is predicted to increase dramatically. Neither the neural network nor the seasonal autoregressive integrated moving-average model proved to be generally more accurate.<\/jats:p>\n<\/jats:sec>\n<jats:sec id=\"sc_a4\">\n<jats:title>Conclusions<\/jats:title>\n<jats:p>The increasing burden of human Lyme disease is concerning to public health, further indicating ecosystem changes and challenges for canine health.<\/jats:p>\n<\/jats:sec>\n<jats:sec id=\"sc_a5\">\n<jats:title>Clinical Relevance<\/jats:title>\n<jats:p>Human Lyme disease surveillance provides useful information to veterinarians.<\/jats:p>\n<\/jats:sec>","DOI":"10.2460\/ajvr.24.10.0300","type":"journal-article","created":{"date-parts":[[2025,2,11]],"date-time":"2025-02-11T17:07:00Z","timestamp":1739293620000},"page":"S80-S83","source":"Crossref","is-referenced-by-count":0,"title":["Of Lyme disease and machine learning in a One Health world"],"prefix":"10.2460","volume":"86","author":[{"ORCID":"https:\/\/orcid.org\/0000-0003-3537-0629","authenticated-orcid":true,"given":"Olaf","family":"Berke","sequence":"first","affiliation":[{"name":"Department of Population Medicine, Ontario Veterinary College, University of Guelph, Guelph, ON, Canada"},{"name":"Centre for Public Health and Zoonoses, University of Guelph, Guelph, ON, Canada"},{"name":"One Health Institute, University of Guelph, Guelph, ON, Canada"},{"name":"Centre for Advancing Responsible and Ethical Artificial Intelligence, University of Guelph, Guelph, ON, Canada"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Sarah T.","family":"Chan","sequence":"additional","affiliation":[{"name":"Centre for Data Management, Innovation and Analytics, Public Health Agency of Canada, Ottawa, ON, Canada"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"ORCID":"https:\/\/orcid.org\/0000-0002-4069-9771","authenticated-orcid":true,"given":"Armin","family":"Orang","sequence":"additional","affiliation":[{"name":"Department of Population Medicine, Ontario Veterinary College, University of Guelph, Guelph, ON, Canada"}],"role":[{"role":"author","vocabulary":"crossref"}]}],"member":"1272","reference":[{"issue":"10","key":"B1","series-title":"Can Commun Dis Rep","first-page":"231","article-title":"The continued rise of Lyme disease in Ontario, Canada: 2017","volume":"44","author":"Nelder MP","year":"2018","unstructured":"Nelder MP, Wijayasri S, Russell CB, et al. The continued rise of Lyme disease in Ontario, Canada: 2017. Can Commun Dis Rep. 2018;44(10):231\u2013236. doi:10.14745\/ccdr.v44i10a01"},{"key":"B2","series-title":"Ethics Med Public Health","first-page":"100728","article-title":"Epidemics and pandemics: is human overpopulation the elephant in the room?","volume":"19","author":"Spernovasilis N","year":"2021","unstructured":"Spernovasilis N, Markaki I, Papadakis M, Tsioutis C, Markaki L. Epidemics and pandemics: is human overpopulation the elephant in the room? Ethics Med Public Health. 2021;19:100728. doi:10.1016\/j.jemep.2021.100728"},{"issue":"6","key":"B3","series-title":"Ticks Tick Borne Dis","first-page":"102233","article-title":"Changes in the geographic distribution of the blacklegged tick, Ixodes scapularis, in the United States","volume":"14","author":"Eisen L","year":"2023","unstructured":"Eisen L, Eisen RJ. Changes in the geographic distribution of the blacklegged tick, Ixodes scapularis, in the United States. Ticks Tick Borne Dis. 2023;14(6):102233. doi:10.1016\/j.ttbdis.2023.102233"},{"article-title":"A brief history of Lyme disease in Connecticut","year":"2019","key":"B4","unstructured":"A brief history of Lyme disease in Connecticut. Connecticut State Department of Public Health. Last modified July 1, 2019. Accessed October 15, 2024. https:\/\/portal.ct.gov\/dph\/epidemiology-and-emerging-infections\/a-brief-history-of-lyme-disease-in-connecticut"},{"article-title":"Tick lifecycles","year":"2024","key":"B5","unstructured":"Tick lifecycles. CDC. Last modified October 11, 2024. Accessed October 15, 2024. https:\/\/www.cdc.gov\/ticks\/about\/tick-lifecycles.html"},{"issue":"8","key":"B6","series-title":"Emerg Infect Dis","first-page":"2017","article-title":"Comparison of Lyme disease in the United States and Europe","volume":"27","author":"Marques AR","year":"2021","unstructured":"Marques AR, Strle F, Wormser GP. Comparison of Lyme disease in the United States and Europe. Emerg Infect Dis. 2021;27(8):2017\u20132024. doi:10.3201\/eid2708.204763"},{"key":"B7","series-title":"UN Chronicle","article-title":"Population and climate change: decent living for all without compromising climate mitigation","author":"Muttarak R","year":"2024","unstructured":"Muttarak R. Population and climate change: decent living for all without compromising climate mitigation. UN Chronicle. April 8, 2024. Accessed January 31, 2025. https:\/\/www.un.org\/en\/population-climate-change-decent-living-all-without-compromising-climate-mitigation"},{"issue":"6701","key":"B8","series-title":"Science","first-page":"1170","article-title":"Canine sentinels and our shared exposome","volume":"384","author":"Sexton C","year":"2024","unstructured":"Sexton C, Ruple A. Canine sentinels and our shared exposome. Science. 2024;384(6701):1170\u20131172. doi:10.1126\/science.adl0426"},{"issue":"1","key":"B9","series-title":"Vet Ital","first-page":"23","article-title":"Human and animal sentinels for shared health risks","volume":"45","author":"Rabinowitz P","year":"2009","unstructured":"Rabinowitz P, Scotch M, Conti L. Human and animal sentinels for shared health risks. Vet Ital. 2009;45(1):23\u201324."},{"issue":"4","key":"B10","series-title":"Bull World Health Organ","first-page":"327","article-title":"Use of time-series analysis in infectious disease surveillance","volume":"76","author":"Allard R","year":"1998","unstructured":"Allard R. Use of time-series analysis in infectious disease surveillance. Bull World Health Organ. 1998;76(4):327\u2013333."},{"key":"B11","series-title":"Lancet Reg Health Southeast Asia","first-page":"100452","article-title":"Rabies control in Bangladesh and prediction of human rabies cases by 2030: a One Health approach","volume":"27","author":"Ghosh S","year":"2024","unstructured":"Ghosh S, Hasan MN, Nath ND, et al. Rabies control in Bangladesh and prediction of human rabies cases by 2030: a One Health approach. Lancet Reg Health Southeast Asia. 2024;27:100452. doi:10.1016\/j.lansea.2024.100452"},{"issue":"6","key":"B12","series-title":"Can Commun Dis Rep","first-page":"192","article-title":"Good times bad times: automated forecasting of seasonal cryptosporidiosis in Ontario using machine learning","volume":"46","author":"Berke O","year":"2020","unstructured":"Berke O, Trotz-Williams L, de Montigny S. Good times bad times: automated forecasting of seasonal cryptosporidiosis in Ontario using machine learning. Can Commun Dis Rep. 2020;46(6):192\u2013197. doi:10.14745\/ccdr.v46i06a07"},{"issue":"7","key":"B13","series-title":"Viruses","first-page":"1367","article-title":"Time-series analysis for the number of foot and mouth disease outbreak episodes in cattle farms in Thailand using data from 2010\u20132020","volume":"14","author":"Punyapornwithaya V","year":"2022","unstructured":"Punyapornwithaya V, Mishra P, Sansamur C, et al. Time-series analysis for the number of foot and mouth disease outbreak episodes in cattle farms in Thailand using data from 2010\u20132020. 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