{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,10,23]],"date-time":"2026-10-23T20:42:21Z","timestamp":1792788141032,"version":"4.5.0"},"reference-count":81,"publisher":"Springer Science and Business Media LLC","issue":"1","license":[{"start":{"date-parts":[[2025,10,14]],"date-time":"2025-10-14T00:00:00Z","timestamp":1760400000000},"content-version":"tdm","delay-in-days":0,"URL":"https:\/\/creativecommons.org\/licenses\/by\/4.0"},{"start":{"date-parts":[[2025,10,14]],"date-time":"2025-10-14T00:00:00Z","timestamp":1760400000000},"content-version":"vor","delay-in-days":0,"URL":"https:\/\/creativecommons.org\/licenses\/by\/4.0"}],"funder":[{"name":"Pfizer & Valneva"}],"content-domain":{"domain":["link.springer.com"],"crossmark-restriction":false},"short-container-title":["Parasites Vectors"],"abstract":"<jats:title>Abstract<\/jats:title>\n                  <jats:sec>\n                    <jats:title>Background<\/jats:title>\n                    <jats:p>\n                      Lyme disease (LD) is a major public health concern in North America. The incidence of LD has increased in part due to the rapid expansion of\n                      <jats:italic>Ixodes scapularis<\/jats:italic>\n                      infected with\n                      <jats:italic>Borrelia burgdorferi<\/jats:italic>\n                      sensu lato (\n                      <jats:italic>Bb<\/jats:italic>\n                      ), the causative agent of LD. Understanding how environmental factors contribute to the spread of LD in humans remains a major challenge.\n                    <\/jats:p>\n                  <\/jats:sec>\n                  <jats:sec>\n                    <jats:title>Methods<\/jats:title>\n                    <jats:p>\n                      We aimed to measure the environmental associations and spatial dynamics of LD incidence across United States counties between 2010 and 2019 via a machine-learning (ML)-based model. We used LD incidence data in 1322 counties in 24 US states from the Centers for Disease Control and Prevention (CDC), categorized by four incidence classes (0\u20131,\u2009&gt;\u20091 to 10,\u2009&gt;\u200910 to 100,\u2009&gt;\u2009100 cases\/100,000 population). Explanatory variables of climate, habitat, land cover,\n                      <jats:italic>I. scapularis<\/jats:italic>\n                      presence, and distribution of tick hosts and\n                      <jats:italic>Bb<\/jats:italic>\n                      reservoirs were used to train the ML models.\n                    <\/jats:p>\n                  <\/jats:sec>\n                  <jats:sec>\n                    <jats:title>Results<\/jats:title>\n                    <jats:p>\n                      The performance of a random forest algorithm was high (area under the curve [AUC]\u2009=\u20090.89). As  expected, surveillance-dependent variables for adjacency to LD-endemic counties (gain ratio: 0.22) and presence of\n                      <jats:italic>I. scapularis<\/jats:italic>\n                      (gain ratio: 0.133) were identified as the top individual predictors of LD spread. However, the strongest overall contributions to the model were driven by vertebrate-related variables (\n                      <jats:italic>n<\/jats:italic>\n                      \u2009=\u20098) (ReliefF: 0.237), with landscape features for forest growth, canopy, and forest edge (length) also identified as strong (gain ratio\u2009&gt;\u20090.28) individual predictors. Climate predictors indicated the lowest LD incidence classes (&lt;\u200910 cases\/100,000 population) in warmer and drier counties and the highest LD incidence classes (&gt;\u200910 cases\/100,000 population) in the coldest and wettest counties.\n                    <\/jats:p>\n                  <\/jats:sec>\n                  <jats:sec>\n                    <jats:title>Conclusions<\/jats:title>\n                    <jats:p>\n                      Utilization of ML algorithms trained with variables impacting the circulation of\n                      <jats:italic>Bb<\/jats:italic>\n                      produced a comprehensive model of county-level LD incidence and captured the main factors acting on the spread of the pathogen. This represents an important step towards an integrated framework aimed at capturing LD incidence changes for preventive purposes.\n                    <\/jats:p>\n                  <\/jats:sec>\n                  <jats:sec>\n                    <jats:title>Graphical Abstract<\/jats:title>\n                  <\/jats:sec>","DOI":"10.1186\/s13071-025-07047-9","type":"journal-article","created":{"date-parts":[[2025,10,14]],"date-time":"2025-10-14T14:01:34Z","timestamp":1760450494000},"update-policy":"https:\/\/doi.org\/10.1007\/springer_crossmark_policy","source":"Crossref","is-referenced-by-count":5,"title":["Variables for habitat and vertebrate hosts of Ixodes scapularis are the best ecological predictors of the spatial spread of Lyme disease in the United States (2010\u20132019)"],"prefix":"10.1186","volume":"18","author":[{"given":"Patrick H.","family":"Kelly","sequence":"first","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Sarah","family":"Willis","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Alexander","family":"Davidson","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"James H.","family":"Stark","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Jennifer C.","family":"Mo\u0457si","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Agust\u00edn","family":"Estrada-Pe\u00f1a","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]}],"member":"297","published-online":{"date-parts":[[2025,10,14]]},"reference":[{"key":"7047_CR1","doi-asserted-by":"publisher","first-page":"616","DOI":"10.3201\/eid2702.202731","volume":"27","author":"KJ Kugeler","year":"2021","unstructured":"Kugeler KJ, Schwartz AM, Delorey MJ, Mead PS, Hinckley AF. Estimating the frequency of Lyme disease diagnoses, United States, 2010\u20132018. Emerg Infect Dis. 2021;27:616\u20139. https:\/\/doi.org\/10.3201\/eid2702.202731.","journal-title":"Emerg Infect Dis"},{"key":"7047_CR2","doi-asserted-by":"publisher","first-page":"849","DOI":"10.1186\/s12889-019-7219-x","volume":"19","author":"NH Ogden","year":"2019","unstructured":"Ogden NH, Bouchard C, Badcock J, Drebot MA, Elias SP, Hatchette TF, et al. What is the real number of Lyme disease cases in Canada? BMC Public Health. 2019;19:849. https:\/\/doi.org\/10.1186\/s12889-019-7219-x.","journal-title":"BMC Public Health"},{"key":"7047_CR3","doi-asserted-by":"publisher","first-page":"631","DOI":"10.1289\/ehp.1307799","volume":"122","author":"NH Ogden","year":"2014","unstructured":"Ogden NH, Radojevic M, Wu X, Duvvuri VR, Leighton PA, Wu J. Estimated effects of projected climate change on the basic reproductive number of the Lyme disease vector Ixodes scapularis. Environ Health Perspect. 2014;122:631\u20138. https:\/\/doi.org\/10.1289\/ehp.1307799.","journal-title":"Environ Health Perspect"},{"key":"7047_CR4","doi-asserted-by":"publisher","first-page":"426","DOI":"10.1093\/cid\/ciy510","volume":"68","author":"T Eddens","year":"2019","unstructured":"Eddens T, Kaplan DJ, Anderson AJM, Nowalk AJ, Campfield BT. Insights from the geographic spread of the Lyme disease epidemic. Clin Infect Dis. 2019;68:426\u201334. https:\/\/doi.org\/10.1093\/cid\/ciy510.","journal-title":"Clin Infect Dis"},{"key":"7047_CR5","doi-asserted-by":"publisher","first-page":"118","DOI":"10.15585\/mmwr.mm7306a1","volume":"73","author":"KJ Kugeler","year":"2024","unstructured":"Kugeler KJ, Earley A, Mead PS, Hinckley AF. Surveillance for Lyme disease after implementation of a revised case definition-United States, 2022. MMWR Morb Mortal Wkly Rep. 2024;73:118\u201323. https:\/\/doi.org\/10.15585\/mmwr.mm7306a1.","journal-title":"MMWR Morb Mortal Wkly Rep"},{"key":"7047_CR6","doi-asserted-by":"publisher","first-page":"2017","DOI":"10.3201\/eid2708.204763","volume":"27","author":"AR Marques","year":"2021","unstructured":"Marques AR, Strle F, Wormser GP. Comparison of Lyme disease in the United States and Europe. Emerg Infect Dis. 2021;27:2017\u201324. https:\/\/doi.org\/10.3201\/eid2708.204763.","journal-title":"Emerg Infect Dis"},{"key":"7047_CR7","doi-asserted-by":"publisher","first-page":"102230","DOI":"10.1016\/j.ttbdis.2023.102230","volume":"14","author":"HK Goethert","year":"2023","unstructured":"Goethert HK, Mather TN, O\u2019Callahan A, Telford Iii SR. Host-utilization differences between larval and nymphal deer ticks in northeastern US sites enzootic for Borrelia burgdorferi sensu stricto. Ticks Tick Borne Dis. 2023;14:102230. https:\/\/doi.org\/10.1016\/j.ttbdis.2023.102230.","journal-title":"Ticks Tick Borne Dis."},{"key":"7047_CR8","doi-asserted-by":"publisher","first-page":"2841","DOI":"10.1890\/07-1047.1","volume":"89","author":"K LoGiudice","year":"2008","unstructured":"LoGiudice K, Duerr ST, Newhouse MJ, Schmidt KA, Killilea ME, Ostfeld RS. Impact of host community composition on Lyme disease risk. Ecology. 2008;89:2841\u20139. https:\/\/doi.org\/10.1890\/07-1047.1.","journal-title":"Ecology"},{"key":"7047_CR9","doi-asserted-by":"publisher","DOI":"10.1128\/aem.00042-22","volume":"88","author":"HK Goethert","year":"2022","unstructured":"Goethert HK, Telford SR 3rd. Limited Capacity of Deer To Serve as Zooprophylactic Hosts for Borrelia burgdorferi in the Northeastern United States. Appl Environ Microbiol. 2022;88:e0004222. https:\/\/doi.org\/10.1128\/aem.00042-22.","journal-title":"Appl Environ Microbiol"},{"key":"7047_CR10","doi-asserted-by":"publisher","first-page":"250","DOI":"10.1093\/jme\/tjv199","volume":"53","author":"RJ Eisen","year":"2016","unstructured":"Eisen RJ, Eisen L, Ogden NH, Beard CB. Linkages of weather and climate with Ixodes scapularis and Ixodes pacificus (Acari: Ixodidae), enzootic transmission of Borrelia burgdorferi, and Lyme disease in North America. J Med Entomol. 2016;53:250\u201361. https:\/\/doi.org\/10.1093\/jme\/tjv199.","journal-title":"J Med Entomol"},{"key":"7047_CR11","doi-asserted-by":"publisher","first-page":"1546","DOI":"10.1093\/jme\/tjaa209","volume":"58","author":"MA Diuk-Wasser","year":"2021","unstructured":"Diuk-Wasser MA, VanAcker MC, Fernandez MP. Impact of land use changes and habitat fragmentation on the eco-epidemiology of tick-borne diseases. J Med Entomol. 2021;58:1546\u201364. https:\/\/doi.org\/10.1093\/jme\/tjaa209.","journal-title":"J Med Entomol"},{"key":"7047_CR12","doi-asserted-by":"publisher","first-page":"567","DOI":"10.1073\/pnas.0233733100","volume":"100","author":"K LoGiudice","year":"2003","unstructured":"LoGiudice K, Ostfeld RS, Schmidt KA, Keesing F. The ecology of infectious disease: effects of host diversity and community composition on Lyme disease risk. Proc Natl Acad Sci USA. 2003;100:567\u201371. https:\/\/doi.org\/10.1073\/pnas.0233733100.","journal-title":"Proc Natl Acad Sci USA"},{"key":"7047_CR13","doi-asserted-by":"publisher","first-page":"63","DOI":"10.1016\/j.ijpara.2005.08.016","volume":"36","author":"NH Ogden","year":"2006","unstructured":"Ogden NH, Maarouf A, Barker IK, Bigras-Poulin M, Lindsay LR, Morshed MG, et al. Climate change and the potential for range expansion of the Lyme disease vector Ixodes scapularis in Canada. Int J Parasitol. 2006;36:63\u201370. https:\/\/doi.org\/10.1016\/j.ijpara.2005.08.016.","journal-title":"Int J Parasitol"},{"key":"7047_CR14","doi-asserted-by":"publisher","DOI":"10.1016\/j.crpvbd.2023.100132","volume":"4","author":"S Westra","year":"2023","unstructured":"Westra S, Goldberg MS, Didan K. The association between the incidence of Lyme disease in the USA and indicators of greenness and land cover. Curr Res Parasitol Vect Borne Dis. 2023;4:100132. https:\/\/doi.org\/10.1016\/j.crpvbd.2023.100132.","journal-title":"Curr Res Parasitol Vect Borne Dis"},{"key":"7047_CR15","doi-asserted-by":"publisher","DOI":"10.1016\/j.ttbdis.2023.102233","volume":"14","author":"L Eisen","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;14:102233. https:\/\/doi.org\/10.1016\/j.ttbdis.2023.102233.","journal-title":"Ticks Tick Borne Dis"},{"key":"7047_CR16","doi-asserted-by":"publisher","DOI":"10.1128\/aem.02391-21","volume":"88","author":"HK Goethert","year":"2022","unstructured":"Goethert HK, Telford SR 3rd. Host Contributions to the Force of Borrelia burgdorferi and Babesia microti Transmission Differ at Edges of and within a Small Habitat Patch. Appl Environ Microbiol. 2022;88:e0239121. https:\/\/doi.org\/10.1128\/aem.02391-21.","journal-title":"Appl Environ Microbiol"},{"key":"7047_CR17","doi-asserted-by":"publisher","first-page":"e200319","DOI":"10.1001\/jamanetworkopen.2020.0319","volume":"3","author":"D Bisanzio","year":"2020","unstructured":"Bisanzio D, Fernandez MP, Martello E, Reithinger R, Diuk-Wasser MA. Current and future spatiotemporal patterns of Lyme disease reporting in the Northeastern United States. JAMA Netw Open. 2020;3:e200319. https:\/\/doi.org\/10.1001\/jamanetworkopen.2020.0319.","journal-title":"JAMA Netw Open"},{"key":"7047_CR18","doi-asserted-by":"publisher","first-page":"9817930","DOI":"10.1155\/2019\/9817930","volume":"2019","author":"D Wallace","year":"2019","unstructured":"Wallace D, Ratti V, Kodali A, Winter JM, Ayres MP, Chipman JW, et al. Effect of Rising Temperature on Lyme Disease: Ixodes scapularis Population Dynamics and Borrelia burgdorferi Transmission and Prevalence. Can J Infect Dis Med Microbiol. 2019;2019:9817930. https:\/\/doi.org\/10.1155\/2019\/9817930.","journal-title":"Can J Infect Dis Med Microbiol"},{"key":"7047_CR19","doi-asserted-by":"publisher","DOI":"10.1016\/j.ttbdis.2022.101925","volume":"13","author":"A Beck","year":"2022","unstructured":"Beck A, Bjork J, Biggerstaff B, Eisen L, Eisen R, Foster E, et al. Knowledge, attitudes, and behaviors regarding tick-borne disease prevention in Lyme disease-endemic areas of the Upper Midwest, United States. Ticks Tick Borne Dis. 2022;13:101925. https:\/\/doi.org\/10.1016\/j.ttbdis.2022.101925.","journal-title":"Ticks Tick Borne Dis"},{"key":"7047_CR20","doi-asserted-by":"publisher","DOI":"10.1016\/j.ttbdis.2020.101515","volume":"11","author":"GM Bron","year":"2020","unstructured":"Bron GM, Fernandez MDP, Larson SR, Maus A, Gustafson D, Tsao JI, et al. Context matters: contrasting behavioral and residential risk factors for Lyme disease between high-incidence states in the Northeastern and Midwestern United States. Ticks Tick-borne Dis. 2020;11:101515. https:\/\/doi.org\/10.1016\/j.ttbdis.2020.101515.","journal-title":"Ticks Tick-borne Dis"},{"key":"7047_CR21","doi-asserted-by":"publisher","first-page":"738","DOI":"10.1111\/gcb.15435","volume":"27","author":"LI Couper","year":"2021","unstructured":"Couper LI, MacDonald AJ, Mordecai EA. Impact of prior and projected climate change on US Lyme disease incidence. Glob Chang Biol. 2021;27:738\u201354. https:\/\/doi.org\/10.1111\/gcb.15435.","journal-title":"Glob Chang Biol"},{"key":"7047_CR22","doi-asserted-by":"publisher","first-page":"531","DOI":"10.1093\/aje\/kwg014","volume":"157","author":"S Subak","year":"2003","unstructured":"Subak S. Effects of climate on variability in Lyme disease incidence in the northeastern United States. Am J Epidemiol. 2003;157:531\u20138. https:\/\/doi.org\/10.1093\/aje\/kwg014.","journal-title":"Am J Epidemiol"},{"key":"7047_CR23","doi-asserted-by":"publisher","first-page":"E43","DOI":"10.9778\/cmajo.20120002","volume":"1","author":"AR Tuite","year":"2013","unstructured":"Tuite AR, Greer AL, Fisman DN. Effect of latitude on the rate of change in incidence of Lyme disease in the United States. CMAJ Open. 2013;1:E43\u20137. https:\/\/doi.org\/10.9778\/cmajo.20120002.","journal-title":"CMAJ Open"},{"key":"7047_CR24","doi-asserted-by":"publisher","first-page":"1152","DOI":"10.1289\/ehp.6052","volume":"111","author":"JS Brownstein","year":"2003","unstructured":"Brownstein JS, Holford TR, Fish D. A climate-based model predicts the spatial distribution of the Lyme disease vector Ixodes scapularis in the United States. Environ Health Perspect. 2003;111:1152\u20137. https:\/\/doi.org\/10.1289\/ehp.6052.","journal-title":"Environ Health Perspect"},{"key":"7047_CR25","doi-asserted-by":"publisher","first-page":"1176","DOI":"10.1093\/jme\/tjw076","volume":"53","author":"MB Hahn","year":"2016","unstructured":"Hahn MB, Jarnevich CS, Monaghan AJ, Eisen RJ. Modeling the geographic distribution of Ixodes scapularis and Ixodes pacificus (acari: ixodidae) in the contiguous United States. J Med Entomol. 2016;53:1176\u201391. https:\/\/doi.org\/10.1093\/jme\/tjw076.","journal-title":"J Med Entomol"},{"key":"7047_CR26","doi-asserted-by":"publisher","first-page":"1671","DOI":"10.4269\/ajtmh.17-0539","volume":"98","author":"TL Johnson","year":"2018","unstructured":"Johnson TL, Boegler KA, Clark RJ, Delorey MJ, Bjork JKH, Dorr FM, et al. An acarological risk model predicting the density and distribution of host-seeking Ixodes scapularis nymphs in Minnesota. Am J Trop Med Hyg. 2018;98:1671\u201382. https:\/\/doi.org\/10.4269\/ajtmh.17-0539.","journal-title":"Am J Trop Med Hyg"},{"key":"7047_CR27","doi-asserted-by":"publisher","first-page":"827","DOI":"10.1093\/jme\/tjae052","volume":"61","author":"AK Williams","year":"2024","unstructured":"Williams AK, Peterman WE, Pesapane R. Refining Ixodes scapularis (Acari: Ixodidae) distribution models: a comparison of current methods to an established protocol. J Med Entomol. 2024;61:827\u201344. https:\/\/doi.org\/10.1093\/jme\/tjae052.","journal-title":"J Med Entomol"},{"key":"7047_CR28","doi-asserted-by":"publisher","first-page":"1113024","DOI":"10.3389\/fpubh.2023.1113024","volume":"11","author":"E Fellin","year":"2023","unstructured":"Fellin E, Varin M, Millien V. Risky business: human-related data is lacking from Lyme disease risk models. Front Public Health. 2023;11:1113024. https:\/\/doi.org\/10.3389\/fpubh.2023.1113024.","journal-title":"Front Public Health"},{"key":"7047_CR29","doi-asserted-by":"publisher","first-page":"101","DOI":"10.4269\/ajtmh.24-0283","volume":"112","author":"H Goethert","year":"2025","unstructured":"Goethert H, O\u2019Callahan A, Johnson R, Roden-Reynolds P, Telford S. Minor Hosts Have a Major Impact on the Enzootic Transmission of Borrelia burgdorferi. Am J Trop Med Hyg. 2025;112:101\u201310. https:\/\/doi.org\/10.4269\/ajtmh.24-0283.","journal-title":"Am J Trop Med Hyg"},{"key":"7047_CR30","doi-asserted-by":"publisher","first-page":"320","DOI":"10.4269\/ajtmh.2012.11-0395","volume":"86","author":"MA Diuk-Wasser","year":"2012","unstructured":"Diuk-Wasser MA, Hoen AG, Cislo P, Brinkerhoff R, Hamer SA, Rowland M, et al. Human risk of infection with Borrelia burgdorferi, the Lyme disease agent, in eastern United States. Am J Trop Med Hyg. 2012;86:320\u20137. https:\/\/doi.org\/10.4269\/ajtmh.2012.11-0395.","journal-title":"Am J Trop Med Hyg"},{"key":"7047_CR31","doi-asserted-by":"publisher","first-page":"5","DOI":"10.3390\/ijerph15051048","volume":"15","author":"M McClure","year":"2018","unstructured":"McClure M, Diuk-Wasser M. Reconciling the Entomological Hazard and Disease Risk in the Lyme Disease System. Int J Environ Res Public Health. 2018;15:5. https:\/\/doi.org\/10.3390\/ijerph15051048.","journal-title":"Int J Environ Res Public Health"},{"key":"7047_CR32","unstructured":"United States Centers for Disease Control and Prevention (CDC). Lyme Disease Surveillance Data. 2023. https:\/\/www.cdc.gov\/lyme\/data-research\/facts-stats\/surveillance-data-1.html. Accessed 01 Aug 2023."},{"key":"7047_CR33","doi-asserted-by":"crossref","first-page":"261","DOI":"10.1093\/imammb\/16.3.261","volume":"16","author":"TC Porco","year":"1999","unstructured":"Porco TC. A mathematical model of the ecology of Lyme disease. Math Med Biol. 1999;16:261\u201396.","journal-title":"Math Med Biol"},{"key":"7047_CR34","doi-asserted-by":"publisher","first-page":"1328","DOI":"10.1093\/jme\/tjac049","volume":"59","author":"AC Fleshman","year":"2022","unstructured":"Fleshman AC, Foster E, Maes SE, Eisen RJ. Reported county-level distribution of seven human pathogens detected in host-seeking Ixodes scapularis and Ixodes pacificus (acari: ixodidae) in the contiguous United States. J Med Entomol. 2022;59:1328\u201335. https:\/\/doi.org\/10.1093\/jme\/tjac049.","journal-title":"J Med Entomol"},{"key":"7047_CR35","doi-asserted-by":"publisher","DOI":"10.1016\/j.ttbdis.2024.102316","volume":"15","author":"E Foster","year":"2024","unstructured":"Foster E, Holcomb KM, Eisen RJ. Density of host-seeking Ixodes scapularis nymphs by region, state, and county in the contiguous United States generated through national tick surveillance. Ticks Tick Borne Dis. 2024;15:102316. https:\/\/doi.org\/10.1016\/j.ttbdis.2024.102316.","journal-title":"Ticks Tick Borne Dis"},{"key":"7047_CR36","doi-asserted-by":"publisher","DOI":"10.1016\/j.ttbdis.2023.102250","volume":"14","author":"E Foster","year":"2023","unstructured":"Foster E, Maes SA, Holcomb KM, Eisen RJ. Prevalence of five human pathogens in host-seeking Ixodes scapularis and Ixodes pacificus by region, state, and county in the contiguous United States generated through national tick surveillance. Ticks Tick Borne Dis. 2023;14:102250. https:\/\/doi.org\/10.1016\/j.ttbdis.2023.102250.","journal-title":"Ticks Tick Borne Dis"},{"key":"7047_CR37","unstructured":"Walter Reed Biosystematics Unit (WRBU). VectorMap. https:\/\/vectormap.si.edu. Accessed 01 Aug 2023."},{"key":"7047_CR38","unstructured":"Global Biodiversity Information Facility (GBIF). GBIF Home Page. 2024. https:\/\/www.gbif.org\/. Accessed 01 Aug 2023."},{"key":"7047_CR39","doi-asserted-by":"publisher","first-page":"629","DOI":"10.1093\/jmedent\/35.5.629","volume":"35","author":"DT Dennis","year":"1998","unstructured":"Dennis DT, Nekomoto TS, Victor JC, Paul WS, Piesman J. Reported distribution of Ixodes scapularis and Ixodes pacificus (Acari: Ixodidae) in the United States. J Med Entomol. 1998;35:629\u201338. https:\/\/doi.org\/10.1093\/jmedent\/35.5.629.","journal-title":"J Med Entomol"},{"key":"7047_CR40","doi-asserted-by":"publisher","first-page":"349","DOI":"10.1093\/jme\/tjv237","volume":"53","author":"RJ Eisen","year":"2016","unstructured":"Eisen RJ, Eisen L, Beard CB. County-scale distribution of Ixodes scapularis and Ixodes pacificus (Acari: Ixodidae) in the continental United States. J Med Entomol. 2016;53:349\u201386. https:\/\/doi.org\/10.1093\/jme\/tjv237.","journal-title":"J Med Entomol"},{"key":"7047_CR41","doi-asserted-by":"crossref","first-page":"231","DOI":"10.14745\/ccdr.v44i10a01","volume":"44","author":"MP Nelder","year":"2018","unstructured":"Nelder MP, Wijayasri S, Russell CB, Johnson KO, Marchand-Austin A, Cronin K, et al. Climate change and Lyme disease: the continued rise of Lyme disease in Ontario, Canada: 2017. Can Commun Dis Rep. 2018;44:231.","journal-title":"Can Commun Dis Rep"},{"key":"7047_CR42","doi-asserted-by":"publisher","first-page":"1536","DOI":"10.1093\/jme\/tjaa220","volume":"58","author":"NH Ogden","year":"2021","unstructured":"Ogden NH, Beard BC, Ginsberg HS, Tsao JI. Possible effects of climate change on ixodid ticks and the pathogens they transmit: predictions and observations. J Med Entomol. 2021;58:1536\u201345. https:\/\/doi.org\/10.1093\/jme\/tjaa220.","journal-title":"J Med Entomol"},{"key":"7047_CR43","doi-asserted-by":"publisher","first-page":"eaat4858","DOI":"10.1126\/sciadv.aat4858","volume":"5","author":"MB Araujo","year":"2019","unstructured":"Araujo MB, Anderson RP, Marcia Barbosa A, Beale CM, Dormann CF, Early R, et al. Standards for distribution models in biodiversity assessments. Sci Adv. 2019;5:eaat4858. https:\/\/doi.org\/10.1126\/sciadv.aat4858.","journal-title":"Sci Adv"},{"key":"7047_CR44","doi-asserted-by":"publisher","first-page":"1433","DOI":"10.1111\/j.1365-2699.2011.02550.x","volume":"38","author":"A Guisan","year":"2011","unstructured":"Guisan A, Rahbek C. SESAM\u2014a new framework integrating macroecological and species distribution models for predicting spatio-temporal patterns of species assemblages. J Biogeogr. 2011;38:1433\u201344. https:\/\/doi.org\/10.1111\/j.1365-2699.2011.02550.x.","journal-title":"J Biogeogr"},{"key":"7047_CR45","doi-asserted-by":"publisher","unstructured":"Zellmer AJ, Claisse JT, Williams CM, Schwab S, Pondella D. Predicting optimal sites for ecosystemrestoration using stacked-species distribution modeling. Front Mar Sci. 2019;6. https:\/\/doi.org\/10.3389\/fmars.2019.00003.","DOI":"10.3389\/fmars.2019.00003"},{"key":"7047_CR46","doi-asserted-by":"crossref","unstructured":"Abatzoglou J, Dobrowski S, Parks S, and Hegewisch K. Terraclimate, a high-resolution global dataset of monthly climate and climatic water balance from 1958\u20132015. 2018 01 March 2023. Available from: https:\/\/www.climatologylab.org\/terraclimate.html.","DOI":"10.1038\/sdata.2017.191"},{"key":"7047_CR47","doi-asserted-by":"publisher","first-page":"302","DOI":"10.1186\/1756-3305-7-302","volume":"7","author":"A Estrada-Pe\u00f1a","year":"2014","unstructured":"Estrada-Pe\u00f1a A, Estrada-Sanchez A, de la Fuente J. A global set of Fourier-transformed remotely sensed covariates for the description of abiotic niche in epidemiological studies of tick vector species. Parasit Vectors. 2014;7:302. https:\/\/doi.org\/10.1186\/1756-3305-7-302.","journal-title":"Parasit Vectors"},{"key":"7047_CR48","unstructured":"North American Land Change Monitoring System (NALCMS). Annual National Land Cover Database (NLCD). 2024. https:\/\/www.mrlc.gov\/data\/north-american-land-change-monitoring-system. Accessed 04 Mar 2024."},{"key":"7047_CR49","unstructured":"2023. https:\/\/qgis.org\/. Accessed 01 Jul 2023."},{"key":"7047_CR50","doi-asserted-by":"publisher","first-page":"40","DOI":"10.1016\/j.cageo.2016.08.020","volume":"97","author":"L Busetto","year":"2016","unstructured":"Busetto L, Ranghetti L. An R package for automatic preprocessing of MODIS land products time series. Comput Geosci. 2016;97:40\u20138. https:\/\/doi.org\/10.1016\/j.cageo.2016.08.020.","journal-title":"Comput Geosci"},{"key":"7047_CR51","unstructured":"United States Geological Survey (USGS). Gap analysis project: species data download. https:\/\/www.usgs.gov\/programs\/gap-analysis-project\/science\/species-data-download. Accessed 01 Sep 2023."},{"key":"7047_CR52","doi-asserted-by":"publisher","first-page":"339","DOI":"10.7589\/0090-3558-26.3.339","volume":"26","author":"D Fish","year":"1990","unstructured":"Fish D, Daniels TJ. The role of medium-sized mammals as reservoirs of Borrelia burgdorferi in southern New York. J Wildl Dis. 1990;26:339\u201345. https:\/\/doi.org\/10.7589\/0090-3558-26.3.339.","journal-title":"J Wildl Dis"},{"key":"7047_CR53","first-page":"2349","volume":"14","author":"J Demsar","year":"2013","unstructured":"Demsar J, Curk T, Erjavec A, Gorup C, Hocevar T, Milutinovic M, et al. Orange: data mining toolbox in python. J Mach Learn Res. 2013;14:2349\u201353.","journal-title":"J Mach Learn Res"},{"key":"7047_CR54","doi-asserted-by":"publisher","first-page":"595","DOI":"10.1093\/jme\/tjae031","volume":"61","author":"HD Kiryluk","year":"2024","unstructured":"Kiryluk HD, Beard CB, Holcomb KM. The use of environmental data in descriptive and predictive models of vector-borne disease in North America. J Med Entomol. 2024;61:595\u2013602. https:\/\/doi.org\/10.1093\/jme\/tjae031.","journal-title":"J Med Entomol"},{"key":"7047_CR55","doi-asserted-by":"publisher","first-page":"538","DOI":"10.4269\/ajtmh.1994.51.538","volume":"51","author":"GE Glass","year":"1994","unstructured":"Glass GE, Amerasinghe FP, Morgan JM 3rd, Scott TW. Predicting Ixodes scapularis abundance on white-tailed deer using geographic information systems. Am J Trop Med Hyg. 1994;51:538\u201344. https:\/\/doi.org\/10.4269\/ajtmh.1994.51.538.","journal-title":"Am J Trop Med Hyg"},{"key":"7047_CR56","doi-asserted-by":"publisher","first-page":"289","DOI":"10.3201\/eid0803.010166","volume":"8","author":"M Guerra","year":"2002","unstructured":"Guerra M, Walker E, Jones C, Paskewitz S, Cortinas MR, Stancil A, et al. Predicting the risk of Lyme disease: habitat suitability for Ixodes scapularis in the north central United States. Emerg Infect Dis. 2002;8:289\u201397. https:\/\/doi.org\/10.3201\/eid0803.010166.","journal-title":"Emerg Infect Dis"},{"key":"7047_CR57","doi-asserted-by":"publisher","unstructured":"Ma Y, Kalantari Z, and Destouni G. Infectious Disease Sensitivity to Climate and Other Driver-Pressure Changes: Research Effort and Gaps for Lyme Disease and Cryptosporidiosis. Geohealth.2023;7:6. https:\/\/doi.org\/10.1029\/2022GH000760.","DOI":"10.1029\/2022GH000760"},{"key":"7047_CR58","doi-asserted-by":"publisher","first-page":"1455","DOI":"10.3201\/eid2108.141878","volume":"21","author":"KJ Kugeler","year":"2015","unstructured":"Kugeler KJ, Farley GM, Forrester JD, Mead PS. Geographic distribution and expansion of human Lyme disease, United States. Emerg Infect Dis. 2015;21:1455\u20137. https:\/\/doi.org\/10.3201\/eid2108.141878.","journal-title":"Emerg Infect Dis"},{"key":"7047_CR59","doi-asserted-by":"publisher","DOI":"10.1371\/journal.pbio.3001066","volume":"19","author":"HS Ginsberg","year":"2021","unstructured":"Ginsberg HS, Hickling GJ, Burke RL, Ogden NH, Beati L, LeBrun RA, et al. Why Lyme disease is common in the northern US, but rare in the south: the roles of host choice, host-seeking behavior, and tick density. PLoS Biol. 2021;19:e3001066. https:\/\/doi.org\/10.1371\/journal.pbio.3001066.","journal-title":"PLoS Biol"},{"key":"7047_CR60","doi-asserted-by":"publisher","first-page":"681","DOI":"10.1093\/jme\/tjx237","volume":"55","author":"MA Linske","year":"2018","unstructured":"Linske MA, Williams SC, Stafford KC, Ortega IM. Ixodes scapularis (Acari: Ixodidae) reservoir host diversity and abundance impacts on dilution of Borrelia burgdorferi (Spirochaetales: Spirochaetaceae) in residential and woodland habitats in Connecticut, United States. J Med Entomol. 2018;55:681\u201390. https:\/\/doi.org\/10.1093\/jme\/tjx237.","journal-title":"J Med Entomol"},{"key":"7047_CR61","doi-asserted-by":"crossref","first-page":"1266","DOI":"10.1603\/0046-225X-33.5.1266","volume":"33","author":"HS Ginsberg","year":"2004","unstructured":"Ginsberg HS, Zhioua E, Mitra S, Fischer J, Buckley PA, Verret F, et al. Woodland type and spatial distribution of nymphal Ixodes scapularis (Acari: Ixodidae). Environ Entomol. 2004;33:1266\u201373.","journal-title":"Environ Entomol"},{"key":"7047_CR62","doi-asserted-by":"publisher","DOI":"10.1016\/j.ttbdis.2019.101271","volume":"11","author":"HS Ginsberg","year":"2020","unstructured":"Ginsberg HS, Rulison EL, Miller JL, Pang G, Arsnoe IM, Hickling GJ, et al. Local abundance of Ixodes scapularis in forests: effects of environmental moisture, vegetation characteristics, and host abundance. Ticks Tick-borne Dis. 2020;11:101271. https:\/\/doi.org\/10.1016\/j.ttbdis.2019.101271.","journal-title":"Ticks Tick-borne Dis"},{"key":"7047_CR63","doi-asserted-by":"publisher","first-page":"487","DOI":"10.4081\/gh.2016.487","volume":"11","author":"N Asghar","year":"2016","unstructured":"Asghar N, Petersson M, Johansson M, Dinnetz P. Local landscape effects on population dynamics of Ixodes ricinus. Geospat Health. 2016;11:487. https:\/\/doi.org\/10.4081\/gh.2016.487.","journal-title":"Geospat Health"},{"key":"7047_CR64","doi-asserted-by":"publisher","first-page":"3","DOI":"10.1007\/s10980-025-02061-9","volume":"40","author":"AK Williams","year":"2025","unstructured":"Williams AK, Pesapane R, Zeiger BS, Peterman WE. Multiscale analysis of climate, habitat, and host relationships to predict blacklegged tick presence and abundance in Ohio, USA. Landscape Ecol. 2025;40:3. https:\/\/doi.org\/10.1007\/s10980-025-02061-9.","journal-title":"Landscape Ecol"},{"key":"7047_CR65","doi-asserted-by":"publisher","unstructured":"Estrada-Pe\u00f1a A, Alexander N, and Wint GRW. Perspectives on modelling the distribution of ticks forlarge areas: so far so good? Parasites & Vectors. 2016;9. https:\/\/doi.org\/10.1186\/s13071-016-1474-9.","DOI":"10.1186\/s13071-016-1474-9"},{"key":"7047_CR66","doi-asserted-by":"publisher","first-page":"94","DOI":"10.1016\/j.jag.2019.04.007","volume":"80","author":"D LeVine","year":"2019","unstructured":"LeVine D, Crews K. Time series harmonic regression analysis reveals seasonal vegetation productivity trends in semi-arid savannas. Int J Appl Earth Obs Geoinf. 2019;80:94\u2013101. https:\/\/doi.org\/10.1016\/j.jag.2019.04.007.","journal-title":"Int J Appl Earth Obs Geoinf"},{"key":"7047_CR67","doi-asserted-by":"publisher","unstructured":"Scharlemann JPW, Benz D, Hay SI, Purse BV, Tatem AJ, Wint GRW, et al. Global Data for Ecologyand Epidemiology: A Novel Algorithm for Temporal Fourier Processing MODIS Data. Plos One. 2008;3:1 https:\/\/doi.org\/10.1371\/journal.pone.0001408.","DOI":"10.1371\/journal.pone.0001408"},{"key":"7047_CR68","doi-asserted-by":"publisher","first-page":"29","DOI":"10.1016\/j.isprsjprs.2018.01.006","volume":"137","author":"BT Wilson","year":"2018","unstructured":"Wilson BT, Knight JF, McRoberts RE. Harmonic regression of Landsat time series for modeling attributes from national forest inventory data. ISPRS J Photogramm Remote Sens. 2018;137:29\u201346. https:\/\/doi.org\/10.1016\/j.isprsjprs.2018.01.006.","journal-title":"ISPRS J Photogramm Remote Sens"},{"key":"7047_CR69","doi-asserted-by":"publisher","DOI":"10.1016\/j.actatropica.2024.107268","volume":"256","author":"PH Kelly","year":"2024","unstructured":"Kelly PH, Tan Y, Yan Q, Shafquat M, Davidson A, Xu Q, et al. Borrelia burgdorferi sensu lato prevalence in Ixodes scapularis from Canada: a thirty-year summary and meta-analysis (1990\u20132020). Acta Trop. 2024;256:107268. https:\/\/doi.org\/10.1016\/j.actatropica.2024.107268.","journal-title":"Acta Trop"},{"key":"7047_CR70","doi-asserted-by":"publisher","first-page":"484","DOI":"10.1890\/15-0122","volume":"26","author":"T Levi","year":"2016","unstructured":"Levi T, Keesing F, Holt RD, Barfield M, Ostfeld RS. Quantifying dilution and amplification in a community of hosts for tick-borne pathogens. Ecol Appl. 2016;26:484\u201398. https:\/\/doi.org\/10.1890\/15-0122.","journal-title":"Ecol Appl"},{"key":"7047_CR71","doi-asserted-by":"publisher","first-page":"1665","DOI":"10.1098\/rstb.2013.0556","volume":"370","author":"T Levi","year":"2015","unstructured":"Levi T, Keesing F, Oggenfuss K, Ostfeld RS. Accelerated phenology of blacklegged ticks under climate warming. Philos Trans R Soc Lond B Biol Sci. 2015;370:1665. https:\/\/doi.org\/10.1098\/rstb.2013.0556.","journal-title":"Philos Trans R Soc Lond B Biol Sci"},{"key":"7047_CR72","doi-asserted-by":"publisher","first-page":"769","DOI":"10.1603\/Me13186","volume":"51","author":"KA Berger","year":"2014","unstructured":"Berger KA, Ginsberg HS, Gonzalez L, Mather TN. Relative humidity and activity patterns of Ixodes scapularis (acari: ixodidae). J Med Entomol. 2014;51:769\u201376. https:\/\/doi.org\/10.1603\/Me13186.","journal-title":"J Med Entomol"},{"key":"7047_CR73","doi-asserted-by":"publisher","first-page":"372","DOI":"10.1603\/0022-2585(2007)44[372:Doetsa]2.0.Co;2","volume":"44","author":"SE Rodgers","year":"2007","unstructured":"Rodgers SE, Zolnik CP, Mather TN. Duration of exposure to suboptimal atmospheric moisture affects nymphal blacklegged tick survival. J Med Entomol. 2007;44:372\u20135. https:\/\/doi.org\/10.1603\/0022-2585(2007)44[372:Doetsa]2.0.Co;2.","journal-title":"J Med Entomol"},{"key":"7047_CR74","doi-asserted-by":"publisher","first-page":"606","DOI":"10.1186\/s13071-016-1894-6","volume":"9","author":"JC Burtis","year":"2016","unstructured":"Burtis JC, Sullivan P, Levi T, Oggenfuss K, Fahey TJ, Ostfeld RS. The impact of temperature and precipitation on blacklegged tick activity and Lyme disease incidence in endemic and emerging regions. Parasit Vectors. 2016;9:606. https:\/\/doi.org\/10.1186\/s13071-016-1894-6.","journal-title":"Parasit Vectors"},{"key":"7047_CR75","doi-asserted-by":"publisher","first-page":"2021","DOI":"10.1093\/jme\/tjab086","volume":"58","author":"CA Lippi","year":"2021","unstructured":"Lippi CA, Ryan SJ, White AL, Gaff HD, Carlson CJ. Trends and opportunities in tick-borne disease geography. J Med Entomol. 2021;58:2021\u20139. https:\/\/doi.org\/10.1093\/jme\/tjab086.","journal-title":"J Med Entomol"},{"key":"7047_CR76","doi-asserted-by":"publisher","first-page":"217","DOI":"10.1186\/s12911-023-02315-z","volume":"23","author":"S Boligarla","year":"2023","unstructured":"Boligarla S, Laison EKE, Li J, Mahadevan R, Ng A, Lin Y, et al. Leveraging machine learning approaches for predicting potential Lyme disease cases and incidence rates in the United States using Twitter. BMC Med Inform Decis Mak. 2023;23:217. https:\/\/doi.org\/10.1186\/s12911-023-02315-z.","journal-title":"BMC Med Inform Decis Mak"},{"key":"7047_CR77","doi-asserted-by":"publisher","unstructured":"Sulyok M, Richter H, Sulyok Z, Kapit\u00e1ny-F\u00f6v\u00e9ny M, and Walker MD. Predicting tick-borneencephalitis using Google Trends. Ticks and Tick-Borne Diseases. 2020;11:1. https:\/\/doi.org\/10.1016\/j.ttbdis.2019.101306.","DOI":"10.1016\/j.ttbdis.2019.101306"},{"key":"7047_CR78","doi-asserted-by":"publisher","DOI":"10.1371\/journal.pone.0260122","volume":"16","author":"FC Curriero","year":"2021","unstructured":"Curriero FC, Wychgram C, Rebman AW, Corrigan AE, Kvit A, Shields T, et al. The lyme and tickborne disease dashboard: a map-based resource to promote public health awareness and research collaboration. PLoS ONE. 2021;16:e0260122. https:\/\/doi.org\/10.1371\/journal.pone.0260122.","journal-title":"PLoS ONE"},{"key":"7047_CR79","doi-asserted-by":"publisher","first-page":"26","DOI":"10.2807\/1560-7917.ES.2023.28.26.2200666","volume":"28","author":"GRW Wint","year":"2023","unstructured":"Wint GRW, Balenghien T, Berriatua E, Braks M, Marsboom C, Medlock J, et al. VectorNet: collaborative mapping of arthropod disease vectors in Europe and surrounding areas since 2010. Euro Surveill. 2023;28:26. https:\/\/doi.org\/10.2807\/1560-7917.ES.2023.28.26.2200666.","journal-title":"Euro Surveill"},{"key":"7047_CR80","doi-asserted-by":"publisher","DOI":"10.2196\/43790","volume":"9","author":"SP Maxwell","year":"2023","unstructured":"Maxwell SP, Brooks C, Kim D, McNeely CL, Cho S, Thomas KC. Improving surveillance of human tick-borne disease risks: spatial analysis using multimodal databases. JMIR Public Health Surveill. 2023;9:e43790. https:\/\/doi.org\/10.2196\/43790.","journal-title":"JMIR Public Health Surveill"},{"key":"7047_CR81","doi-asserted-by":"publisher","first-page":"453","DOI":"10.1007\/s10393-023-01669-0","volume":"20","author":"AM Urcuqui-Bustamante","year":"2023","unstructured":"Urcuqui-Bustamante AM, Leahy JE, Sponarski C, Gardner AM. Collaborative modeling of the tick-borne disease social-ecological system: a conceptual framework. EcoHealth. 2023;20:453\u201367. https:\/\/doi.org\/10.1007\/s10393-023-01669-0.","journal-title":"EcoHealth"}],"container-title":["Parasites &amp; Vectors"],"original-title":[],"language":"en","link":[{"URL":"https:\/\/link.springer.com\/content\/pdf\/10.1186\/s13071-025-07047-9.pdf","content-type":"application\/pdf","content-version":"vor","intended-application":"text-mining"},{"URL":"https:\/\/link.springer.com\/article\/10.1186\/s13071-025-07047-9\/fulltext.html","content-type":"text\/html","content-version":"vor","intended-application":"text-mining"},{"URL":"https:\/\/link.springer.com\/content\/pdf\/10.1186\/s13071-025-07047-9.pdf","content-type":"application\/pdf","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2025,10,14]],"date-time":"2025-10-14T14:01:43Z","timestamp":1760450503000},"score":1,"resource":{"primary":{"URL":"https:\/\/parasitesandvectors.biomedcentral.com\/articles\/10.1186\/s13071-025-07047-9"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2025,10,14]]},"references-count":81,"journal-issue":{"issue":"1","published-online":{"date-parts":[[2025,12]]}},"alternative-id":["7047"],"URL":"https:\/\/doi.org\/10.1186\/s13071-025-07047-9","relation":{},"ISSN":["1756-3305"],"issn-type":[{"value":"1756-3305","type":"electronic"}],"subject":[],"published":{"date-parts":[[2025,10,14]]},"assertion":[{"value":"9 June 2025","order":1,"name":"received","label":"Received","group":{"name":"ArticleHistory","label":"Article History"}},{"value":"6 September 2025","order":2,"name":"accepted","label":"Accepted","group":{"name":"ArticleHistory","label":"Article History"}},{"value":"14 October 2025","order":3,"name":"first_online","label":"First Online","group":{"name":"ArticleHistory","label":"Article History"}},{"order":1,"name":"Ethics","group":{"name":"EthicsHeading","label":"Declarations"}},{"value":"Not applicable.","order":2,"name":"Ethics","group":{"name":"EthicsHeading","label":"Ethics approval and consent to participate"}},{"value":"Not applicable.","order":3,"name":"Ethics","group":{"name":"EthicsHeading","label":"Consent for publication"}},{"value":"This analysis was supported and jointly funded by Valneva and Pfizer as part of their co-development of a Lyme disease vaccine. Patrick H. Kelly, Sarah Willis, Alexander Davidson, James H. Stark, and Jennifer C. Mo\u0457si are employees of Pfizer and may hold shares and\/or stock options in the company. Agust\u00edn Estrada-Pe\u00f1a is a paid consultant for Pfizer in connection with the development of this manuscript.","order":4,"name":"Ethics","group":{"name":"EthicsHeading","label":"Competing interests"}}],"article-number":"410"}}