AbstractContinued spread of chronic wasting disease (CWD) through wild cervid herds negatively impacts populations, erodes wildlife conservation, drains resource dollars, and challenges wildlife management agencies. Risk factors for CWD have been investigated at state scales, but a regional model to predict locations of new infections can guide increasingly efficient surveillance efforts.

摘要慢性消耗性疾病（CWD）通过野生鹿群的持续传播对种群产生负面影响，侵蚀野生动物保护，消耗资源美元，并对野生动物管理机构提出挑战。CWD的风险因素已经在州范围内进行了调查，但预测新感染地点的区域模型可以指导越来越有效的监测工作。

We predicted CWD incidence by county using CWD surveillance data depicting white-tailed deer (Odocoileus virginianus) in 16 eastern and midwestern US states. We predicted the binary outcome of CWD-status using four machine learning models, utilized five-fold cross-validation and grid search to pinpoint the best model, then compared model predictions against the subsequent year of surveillance data.

我们使用描述美国东部和中西部16个州的白尾鹿（Odocoileus virginianus）的CWD监测数据预测了各县的CWD发病率。我们使用四个机器学习模型预测了CWD状态的二元结果，利用五重交叉验证和网格搜索来确定最佳模型，然后将模型预测与随后一年的监测数据进行了比较。

Cross validation revealed that the Light Boosting Gradient model was the most reliable predictor given the regional data. The predictive model could be helpful for surveillance planning. Predictions of false positives emphasize areas that warrant targeted CWD surveillance because of similar conditions with counties known to harbor CWD.

交叉验证表明，鉴于区域数据，光增强梯度模型是最可靠的预测因子。预测模型可能有助于监测规划。假阳性预测强调了需要有针对性的CWD监测的地区，因为与已知存在CWD的县的情况相似。

However, disagreements in positives and negatives between the CWD Prediction Web App predictions and the on-the-ground surveillance data one year later underscore the need for state wildlife agency professionals to use a layered modeling approach to ensure robust surveillance planning. The CWD Prediction Web App is at https://cwd-predict.streamlit.app/..

然而，CWD预测网络应用程序预测与一年后的地面监测数据之间在正面和负面方面存在分歧，这突显了国家野生动物管理局专业人员需要使用分层建模方法来确保稳健的监测规划。CWD预测Web应用程序位于https://cwd-predict.streamlit.app/..

IntroductionChronic wasting disease (CWD) is a transmissible spongiform encephalopathy that infects members of the Cervidae family1. The disease stems from the misfolding of prion proteins, leading to neurodegeneration, weight loss, altered behavior, and eventual death2. Since first detected in the 1960s, CWD continues to spread through wild and captive cervids across North America3.

引言慢性消耗性疾病（CWD）是一种传染性海绵状脑病，感染Cervidae家族成员1。该疾病源于朊病毒蛋白的错误折叠，导致神经变性，体重减轻，行为改变和最终死亡2。自20世纪60年代首次发现以来，CWD继续通过北美的野生和圈养鹿传播3。

To date, 34 United States (US) state wildlife agencies and four Canadian provincial wildlife agencies have detected CWD in at least one wild cervid herd3.Wildlife agencies in North America have established surveillance programs to detect CWD in wild cervid populations4. Such programs focus on identifying locations most likely to harbor CWD and provide the best opportunity to manage the disease while prevalence is low5; however, these programs constitute an enormous monetary and human resource cost to agencies6.

迄今为止，34个美国（美国）州野生动物机构和四个加拿大省级野生动物机构已经在至少一个野生鹿群中检测到CWD。北美的野生动物机构已经建立了监测计划，以检测野生鹿群中的CWD。这些计划的重点是确定最有可能窝藏CWD的地点，并在患病率较低的情况下提供管理疾病的最佳机会5；然而，这些计划对机构构成了巨大的货币和人力资源成本6。

Accordingly, post hoc evaluation of existing surveillance data has focused on pinpointing variables in association with the emergence and spread of CWD to further inform the next year of surveillance7.Anthropogenic factors such as transport and captivity5,8, 9 of cervids and natural movements8 of cervids can contribute to initial introduction of CWD.

因此，对现有监测数据的事后评估侧重于确定与CWD的出现和传播相关的变量，以进一步为下一年的监测提供信息7。人为因素，如cervids的运输和捕获5,8,9以及cervids的自然运动8可能有助于CWD的初步引入。

Persistence of prions in the environment10, soil types11, baiting and feeding12, forest cover13, water14, cervid density15, and natural movements8 contribute to disease spread. Authority for non-imperiled terrestrial wildlife, including most deer species, resides with state and provincial governments16,17; as a result, management and surveillance efforts for CWD are highly variable between jurisdictions.Important and complex questions are driving rapid development, refinement, and use of technology in ecology18,19.

朊病毒在环境中的持久性10，土壤类型11，诱饵和饲养12，森林覆盖13，水14，cervid密度15和自然运动8有助于疾病传播。包括大多数鹿类在内的非濒危陆地野生动物管理局由州和省政府负责16,17；因此，CWD的管理和监督工作在不同司法管辖区之间差异很大。重要而复杂的问题正在推动生态学技术的快速发展，完善和使用18,19。

Among these technologi.

在这些技术中。

Table 1 Definitions of variables in the Orthogonal Dataset, borrowed with permission33.Full size tableFigure 1The known status of chronic wasting disease (CWD) in wild white-tailed deer by county in the 2019–20 season according to the results of surveillance testing by US state wildlife agencies33. CWD Detected represents counties where governing wildlife officials confirmed at least one CWD-positive case in wild, white-tailed deer in the 2019–20 season.

表1正交数据集中变量的定义，借用了许可33。全尺寸表图1根据美国国家野生动物管理局33的监测测试结果，2019-20赛季各县野生白尾鹿慢性消耗性疾病（CWD）的已知状态33。检测到的CWD代表了管理野生动物官员在2019-20年季节在野生白尾鹿中确认至少一例CWD阳性病例的县。

CWD Not Detected represents counties where governing wildlife officials conducted CWD testing in 2019–20 in wild, white-tailed deer, but did not confirm CWD in any subject. Not Considered represents counties that did not exist in the Pooled Dataset33. Map was created in QGIS (version 3.32.2-Lima)60.Full size imageThe Balanced Orthogonal Dataset consisted of a subset of 158 counties depicting conditions in the 2019–20 season-year.

未检测到的CWD代表了2019-20年野生动物管理官员在野生白尾鹿中进行CWD测试的县，但没有确认任何主题的CWD。未考虑代表汇总数据集中不存在的县33。地图是在QGIS（版本3.32.2-Lima）60中创建的。全尺寸图像平衡正交数据集由158个县的子集组成，描绘了2019-20赛季的情况。

Of the 158 counties, 50% (79/158) represented CWD-positive counties and 50% (79/158) represented randomly selected CWD-non detect counties. All counties in the Balanced Orthogonal Dataset contained values for hunter harvest (although that value could have been zero). [Note that of the 85 total positive counties available in the Orthogonal Dataset, six counties in the US state of Mississippi were excluded from the Balanced Dataset due to missing Total_harvest values.] The Training Dataset consisted of 126 (80%) records randomly selected from the Balanced Orthogonal Dataset while the Testing Dataset consisted of the remaining 32 (20%) records of the Balanced Orthogonal Dataset.

在158个县中，50%（79/158）代表CWD阳性县，50%（79/158）代表随机选择的CWD未检测县。平衡正交数据集中的所有县都包含猎人收获的值（尽管该值可能为零）。[请注意，在正交数据集中可用的85个总阳性县中，由于缺少total\u harvest值，美国密西西比州的6个县被排除在平衡数据集中。]训练数据集由126个（80%）记录组成从平衡正交数据集中随机选择，而测试数据集由平衡正交数据集的其余32个（20%）记录组成。

Summary statistics for each variable in the Pooled, Orthogonal, Balanced Orthogonal, Training, and Testing Datasets are provided in the Supplement.The Balanced Orthogonal Dataset set contained non-linear data and outliers,.

补充资料中提供了汇总，正交，平衡正交，训练和测试数据集中每个变量的汇总统计数据。平衡正交数据集包含非线性数据和异常值，。

Table 2 The confusion matrix of the best Light Gradient Boosting (LGB) model when CWD Prediction Web App predictions were compared against on-the-ground surveillance in white-tailed deer in the season-year 2020–21.Full size tableThe CWD Prediction Web App had 70 TPs for the 2020–21 season-year, 66 of which constituted counties already known to be CWD-positive in white-tailed deer from the 2019–20 surveillance data.

表2最佳光梯度增强（LGB）模型的混淆矩阵，当CWD预测网络应用程序的预测与2020-21赛季白尾鹿的地面监测进行比较时。全尺寸表CWD预测网络应用程序在2020-21赛季有70个TP，其中66个构成了2019-20年监测数据中已知白尾鹿CWD阳性的县。

The remaining four TPs depicted counties that indeed turned positive in white-tailed deer for the first time in the 2020–21 season-year, just as the model predicted (Dakota county, Minnesota; Shawano, Washington, and Wood counties, Wisconsin). The CWD Prediction Web App had 325 FPs relative to surveillance data from the 2020–21 season-year.The CWD Prediction Web App had 946 TNs for the 2020–21 season-year.

剩下的四个TPs描述了在2020-21个季节年份，白尾鹿的数量确实首次呈阳性的县，正如模型预测的那样（明尼苏达州达科塔县；华盛顿州沙瓦诺县和威斯康星州伍德县）。相对于2020-21赛季的监测数据，CWD预测Web应用程序的FPs为325 FPs。CWD预测Web应用程序在2020-21赛季有946个TNs。

The CWD Prediction Web App had 15 FNs for the 2020–21 season-year, 13 of which were counties the CWD Prediction Web App knew were positive from the 2019–20 but incorrectly assigned to be negative in the 2020–21 season-year. The remaining two counties (Wyandot county, Ohio; Lauderdale county, Tennessee) were negative in 2019–20 and detected a positive in 2020–21, but the CWD Prediction Web App did not successfully predict that transition in CWD-status.

CWD预测网络应用程序在2020-21赛季有15个FN，其中13个是CWD预测网络应用程序知道的2019-20赛季为正值的县，但在2020-21赛季被错误地分配为负值。其余两个县（俄亥俄州怀安多县；田纳西州劳德代尔县）在2019-20年呈阴性，2020-21年呈阳性，但CWD预测网络应用程序未能成功预测CWD状态的转变。

The CWD Prediction Web App is at https://cwd-predict.streamlit.app/. The code is available at https://github.com/sohel10/lgbm.DiscussionDespite the governing autonomy of management agencies, free-ranging wildlife spans jurisdictional boundaries. Consequently, wildlife agencies across North America would benefit from cooperative efforts designed to understand shared risk factors of disease.

CWD预测Web应用程序位于https://cwd-predict.streamlit.app/.代码位于https://github.com/sohel10/lgbm.DiscussionDespite管理机构的管理自主权，自由放养的野生动物跨越了管辖范围。因此，北美各地的野生动物机构将受益于旨在了解疾病共同风险因素的合作努力。

Our study was the first to use regional data that represent a single species exposed to diverse management goals, herd dyn.

我们的研究是第一个使用区域数据的研究，这些数据代表了暴露于不同管理目标的单一物种，即群体dyn。

Data availability

数据可用性

The data are publicly available at https://doi.org/10.7298/7txw-2681.2. The CWD Prediction Web App is at https://cwd-predict.streamlit.app/.

这些数据可以在https://doi.org/10.7298/7txw-2681.2.CWD预测Web应用程序位于https://cwd-predict.streamlit.app/.

ReferencesWilliams, E. & Young, S. Chronic wasting disease of captive mule deer: a spongiform encephalopathy. J. Wildl. Dis. 16, 89–96 (1980).Article

参考文献Williams，E。＆Young，S。圈养骡鹿的慢性消耗性疾病：海绵状脑病。J、 怀尔德。Dis。16，89-96（1980）。文章

CAS

中科院

Google Scholar

谷歌学者

Poggiolini, I., Saverioni, D. & Parchi, P. Prion protein misfolding, strains, and neurotoxicity: An update from studies on mammalian prions. Int. J. Cell Biol. 2013, 24. https://doi.org/10.1155/2013/910314 (2013).Article

Poggiolini，I.，Saverioni，D。＆Parchi，P。朊病毒蛋白质错误折叠，菌株和神经毒性：哺乳动物朊病毒研究的最新进展。国际细胞生物学杂志。2013年，24。https://doi.org/10.1155/2013/910314（2013年）。文章

Google Scholar

谷歌学者

United States Geological Survey (USGS). Distribution of chronic wasting disease in North America. https://www.usgs.gov/media/images/distribution-chronic-wasting-disease-north-america-0. (2024).Association of Fish and Wildlife Agencies (AFWA). Best management practices for surveillance, management, and control of chronic wasting disease.

美国地质调查局（USGS）。北美慢性消耗性疾病的分布。https://www.usgs.gov/media/images/distribution-chronic-wasting-disease-north-america-0.（2024年）。鱼类和野生动物管理机构协会（AFWA）。监测、管理和控制慢性消耗性疾病的最佳管理实践。

fishwildlife.org/application/files/1315/7054/8052/AFWA_CWD_BMP_First_Supplement_FINAL.pdf. (Washington, DC, USA, 2018).Schuler, K., Hollingshead, N., Kelly, J., Applegate, R., & Yoest, C. Risk-based surveillance for chronic wasting disease in Tennessee. Tennessee Wildlife Resources Agency (TWRA) Wildlife Technical Report 18–4 (2018).Chiavacci, S.

fishwildies.org/application/files/1315/7054/8052/AFWA\u CWD\u BMP\u First\u Supplement\u FINAL.pdf。（华盛顿特区，美国，2018）。Schuler，K.，Hollingshead，N.，Kelly，J.，Applegate，R.，＆Yoest，C.田纳西州慢性消耗性疾病的基于风险的监测。田纳西州野生动物资源局（TWRA）野生动物技术报告18-4（2018）。基亚瓦奇，S。

J. The economic costs of chronic wasting disease in the United States. PLoS One 17(12), e0278366. https://doi.org/10.1371/journal.pone.0278366 (2022).Article .

J、 美国慢性消耗性疾病的经济成本。《公共科学图书馆·综合》17（12），e0278366。https://doi.org/10.1371/journal.pone.0278366（2022年）。文章。

CAS

中科院

PubMed

PubMed

PubMed Central

公共医学中心

Google Scholar

谷歌学者

Hanley, B. et al. Informing surveillance through the characterization of outbreak potential of chronic wasting disease in white-tailed deer. Ecolo. Model. 471, 110054. https://doi.org/10.1016/j.ecolmodel.2022.110054 (2022).Article

Hanley，B.等人，通过描述白尾鹿慢性消耗性疾病的爆发潜力，为监测提供信息。埃科洛。型号。471110054。https://doi.org/10.1016/j.ecolmodel.2022.110054（2022年）。文章

Google Scholar

谷歌学者

Miller, M., & Fischer, J. The first five (or more) decades of chronic wasting disease: Lessons for the five decades to come. Transactions of the North American wildlife and natural resources conference. Vol. 81 110–120 (2016).Miller, M. & Williams, E. Chronic wasting disease of cervids.

Miller，M.，＆Fischer，J。慢性消耗性疾病的前五十年（或更长时间）：未来五十年的教训。北美野生动物和自然资源会议汇刊。第81卷110–120（2016）。Miller，M。＆Williams，E。鹿的慢性消耗性疾病。

Curr. Top. Microbiol. Immunol. 284, 193–214 (2004).CAS .

货币。顶部。微生物。免疫。284193-214（2004）。CAS。

PubMed

PubMed

Google Scholar

谷歌学者

Miller, M., Williams, E., Hobbs, N. & Wolfe, L. Environmental sources of prions transmission in mule deer. Emerg. Infect. Dis. 10(6), 1003–1006 (2004).Article

Miller，M.，Williams，E.，Hobbs，N。＆Wolfe，L。骡鹿朊病毒传播的环境来源。紧急感染。Dis。10（6），1003–1006（2004）。文章

PubMed

PubMed

PubMed Central

公共医学中心

Google Scholar

谷歌学者

Johnson, C., Pederson, J., Chappell, R., McKenzie, D. & Aiken, J. Oral transmissibility of prion disease is enhanced by binding to soil particles. PLoS Pathog. 3(7), 0874–0881 (2007).Article

Johnson，C.，Pederson，J.，Chappell，R.，McKenzie，D。＆Aiken，J。朊病毒病的口腔传播性通过与土壤颗粒的结合而增强。PLoS Pathog。3（7），0874–0881（2007）。文章

CAS

中科院

Google Scholar

谷歌学者

New York State Interagency. CWD Risk Minimization Plan. https://extapps.dec.ny.gov/docs/wildlife_pdf/cwdpreventionplan2018.pdf (2018).Ruiz, M., Kelly, A., Brown, W., Novakofski, J. & Mateus-Pinilla, N. Influence of landscape factors and management decisions on spatial and temporal patterns of the transmission of chronic wasting disease in white-tailed deer.

纽约州跨机构。CWD风险最小化计划。https://extapps.dec.ny.gov/docs/wildlife_pdf/cwdpreventionplan2018.pdf（2018年）。Ruiz，M.，Kelly，A.，Brown，W.，Novakofski，J。＆Mateus Pinilla，N。景观因素和管理决策对白尾鹿慢性消耗性疾病传播时空模式的影响。

Geospat. Health 8(1), 215–227 (2013).Article .

Geospat公司。健康8（1），215-227（2013）。文章。

Google Scholar

谷歌学者

Nichols, T. et al. Detection of protease-resistant cervid prion protein in water from a CWD-endemic area. Prion 3(3), 171–183 (2009).Article

Nichols，T.等人。检测CWD流行地区水中的蛋白酶抗性鹿朊病毒蛋白。朊病毒3（3），171-183（2009）。文章

CAS

中科院

PubMed

PubMed

PubMed Central

公共医学中心

Google Scholar

谷歌学者

Storm, D. et al. Deer density and disease prevalence influence transmission of chronic wasting disease in white-tailed deer. Ecosphere 4, 1–14 (2013).Article

Storm，D。等人。鹿密度和疾病流行率影响白尾鹿慢性消耗性疾病的传播。生态圈4,1-14（2013）。文章

Google Scholar

谷歌学者

Baier, L. E. Federalism, preemption, and the nationalization of American wildlife management: the dynamic balance between state and federal authority. (Rowman & Littlefield, Lanham, Maryland, USA, 2022).

拜尔（Baier，L.E.）《联邦制、先发制人和美国野生动物管理的国有化：州政府和联邦政府之间的动态平衡》。（Rowman＆Littlefield，美国马里兰州兰厄姆，2022）。

Google Scholar

谷歌学者

Hilton, C. D. & Ballard, J. R. Wildlife regulations that affect veterinarians in the United States. in Fowler's Zoo and Wild Animal Medicine Current Therapy, Vol. 10. 43-46. https://doi.org/10.1016/B978-0-323-82852-9.00008-3 (2023).Recknagel, F. Applications of machine learning to ecological modelling.

希尔顿（Hilton，C.D.）和巴拉德（Ballard，J.R.）。影响美国兽医的野生动物法规。《福勒动物园与野生动物医学》第10卷。43年至46年。https://doi.org/10.1016/B978-0-323-82852-9.00008-3（2023年）。Recknagel，F。机器学习在生态建模中的应用。

Ecol. Model. 146(1–3), 303–310 (2001).Article .

Ecol公司。型号。146（1-3），303-310（2001）。文章。

Google Scholar

谷歌学者

Allan, B. et al. Futurecasting ecological research: The rise of technoecology. Ecosphere 9(5), e02163. https://doi.org/10.1002/ecs2.2163 (2018).Article

Allan，B.等人，《未来生态研究：技术生态学的兴起》。生态圈9（5），e02163。https://doi.org/10.1002/ecs2.2163（2018年）。文章

Google Scholar

谷歌学者

Ahmed, M., Ishikawa, F., & Sugiyama, M. Testing machine learning code using polyhedral region. In Proceedings of the 28th ACM Joint Meeting on European Software Engineering Conference and Symposium on the Foundations of Software Engineering, 1533–1536 (2020).Dietterich, T. Machine learning in ecosystem informatics and sustainability.

Ahmed，M.，Ishikawa，F.，＆Sugiyama，M.使用多面体区域测试机器学习代码。在第28届ACM欧洲软件工程会议联合会议论文集和软件工程基础研讨会上，1533-1536（2020）。Dieterich，T。生态系统信息学和可持续性中的机器学习。

In International Joint Conference on Artificial Intelligence (IJCAI) (Pasadena, CA, USA, 2009).Tuia, D. et al. Perspectives in machine learning for wildlife conservation. Nat. Commun. 13, 792–807 (2022).Article .

国际人工智能联合会议（IJCAI）（美国加利福尼亚州帕萨迪纳，2009年）。Tuia，D.等人，《机器学习对野生动物保护的展望》。国家公社。13792-807（2022）。文章。

CAS

中科院

PubMed

PubMed

PubMed Central

公共医学中心

Google Scholar

谷歌学者

Nakagawa, S. et al. Rapid literature mapping on the recent use of machine learning for wildlife imagery. Peer Community J. https://doi.org/10.24072/pcjournal.261 (2023).Article

Nakagawa，S.等人。关于最近将机器学习用于野生动物图像的快速文献映射。同行社区J。https://doi.org/10.24072/pcjournal.261（2023年）。文章

Google Scholar

谷歌学者

Besson, M. et al. Towards the fully automated monitoring of ecological communities. Ecol. Lett. 25, 2753–2775 (2022).Article

Besson，M.等人致力于生态群落的全自动监测。Ecol公司。利特。252753-2775（2022）。文章

PubMed

PubMed

PubMed Central

公共医学中心

Google Scholar

谷歌学者

Robles-Fernández, Á., Santiago-Alarcon, D. & Lira-Noriega, A. Wildlife susceptibility to infectious diseases at global scales. Proc. Natl. Acad. Sci. 119(35), e2122851119. https://doi.org/10.1073/pnas.2122851119 (2022).Article

罗伯斯·费尔南德斯。，Santiago Alarcon，D。和Lira Noriega，A。野生动物在全球范围内对传染病的易感性。程序。纳特尔。阿卡德。科学。119（35），e2122851119。https://doi.org/10.1073/pnas.2122851119（2022年）。文章

CAS

中科院

PubMed

PubMed

PubMed Central

公共医学中心

Google Scholar

谷歌学者

Pillai, N., Ramkumar, M. & Nanduri, B. Artificial intelligence models for zoonotic pathogens: A survey. Microorganisms 10(10), 1911–1931 (2022).Article

Pillai，N.，Ramkumar，M。＆Nanduri，B。人畜共患病原体的人工智能模型：一项调查。微生物10（10），1911-1931（2022）。文章

PubMed

PubMed

PubMed Central

公共医学中心

Google Scholar

谷歌学者

Breiman, L. Random forests. Mach. Learn. 45, 5–32 (2001).Article

Breiman，L。随机森林。马赫。学习。45，5-32（2001）。文章

Google Scholar

谷歌学者

Quinlan, J. Induction of decision trees. Mach. Learn. 1(1), 81–106 (1986).Article

Quinlan，J。决策树的归纳。马赫。学习。1（1），81-106（1986）。文章

Google Scholar

谷歌学者

Friedman, J. Greedy function approximation: A gradient boosting machine. Ann. Stat. 29(5), 1189–1232 (2001).Article

弗里德曼，J。贪婪函数近似：梯度提升机。《美国统计年鉴》29（5），1189–1232（2001）。文章

MathSciNet

MathSciNet

Google Scholar

谷歌学者

Ke, G., et al. LightGBM: A highly efficient gradient boosting decision tree. NIPS'17: Proceedings of the 31st International Conference on Neural Information Processing Systems 3149–3157 (2017).Staartjes, V. & Kernback, J. Foundations of machine learning-based clinical predictions modeling: Part III—model evaluation and other points of significance.

Ke，G。等人。LightGBM：一种高效的梯度提升决策树。NIPS'17：第31届神经信息处理系统国际会议论文集3149-3157（2017）。Staartjes，V。＆Kernback，J。基于机器学习的临床预测建模基础：第三部分模型评估和其他重要点。

In Machine learning in clinical neuroscience. Acta Neurochirurgica Supplement Vol. 134 (eds Staartjes, V. et al.) (Springer, 2022). https://doi.org/10.1007/978-3-030-85292-4_4.Chapter .

临床神经科学中的机器学习。神经科学学报补编第134卷（eds Staartjes，V。等人）（Springer，2022）。https://doi.org/10.1007/978-3-030-85292-4_4.Chapter。

Google Scholar

谷歌学者

R Statistical Computing (R). R package: Confusion matrix. https://search.r-project.org/CRAN/refmans/qwraps2/html/confusion_matrix.html#:~:text=sensitivity%20%3D%20TP%20%2F%20(TP%20%2B,TN%20%2F%20(TN%20%2B%20FN). (2023).Schuler, K., et al. North American wildlife agency CWD testing and ancillary data (2000–2022) [Dataset].

R统计计算（R）。R包：混淆矩阵。https://search.r-project.org/CRAN/refmans/qwraps2/html/confusion_matrix.html#:~：文本=灵敏度%20%3D%20TP%20%2F%20（TP%20%2B，TN%20%2F%20（TN%20%2B%20FN）。（2023年）。Schuler，K.等人，《北美野生动物管理局CWD测试和辅助数据（2000-2022）》[数据集]。

Cornell University Library eCommons Repository (2024). https://doi.org/10.7298/7txw-2681.2Michigan Department of Natural Resources (MIDNR). CWD and Cervidae regulations in North America NEW. https://michigan.gov/dnr/managing-resources/wildlife/cwd/hunters/cwd-and-cervidae-regulations-in-north-america.

康奈尔大学图书馆eCommons Repository（2024）。https://doi.org/10.7298/7txw-2681.2Michigan自然资源部（MIDNR）。北美新的CWD和Cervidae法规。https://michigan.gov/dnr/managing-resources/wildlife/cwd/hunters/cwd-and-cervidae-regulations-in-north-america.

(2022).Green, M. L., Manjerovic, M. B., Mateus-Pinilla, N. & Novakofski, J. Genetic assignment tests reveal dispersal of white-tailed deer: Implication for chronic wasting disease. J. Mammal. 95(3), 646–654 (2014).Article .

（2022年）。Green，M.L.，Manjerovic，M.B.，Mateus Pinilla，N。和Novakofski，J。基因分配测试揭示了白尾鹿的扩散：对慢性消耗性疾病的影响。J、 哺乳动物。95（3），646-654（2014）。文章。

Google Scholar

谷歌学者

Conner, M. M. et al. The relationship between harvest management and chronic wasting disease prevalence trends in western mule deer (Odocoileus Hemionus) herds. J. Wildl. Dis. 57(4), 831–843 (2021).Article

Conner，M.M.等人。收获管理与西部骡鹿群慢性消耗性疾病流行趋势之间的关系。J、 怀尔德。Dis。57（4），831-843（2021）。文章

PubMed

PubMed

Google Scholar

谷歌学者

Kuznetsova, A., McKenzie, D., Banser, P., Siddique, T. & Aiken, J. Potential role of soil properties in the spread of CWD in western Canada. Prion 8(1), 92–99 (2014).Article

Kuznetsova，A.，McKenzie，D.，Banser，P.，Siddique，T。＆Aiken，J。土壤性质在加拿大西部CWD传播中的潜在作用。朊病毒8（1），92-99（2014）。文章

CAS

中科院

PubMed

PubMed

PubMed Central

公共医学中心

Google Scholar

谷歌学者

Walter, W., Walsh, D., Farnsworth, M., Winkelman, D. & Miller, M. Soil clay content underlies prion infection odds. Nat. Commun. 2, 200–206 (2011).Article

Walter，W.，Walsh，D.，Farnsworth，M.，Winkelman，D。＆Miller，M。土壤粘土含量是朊病毒感染几率的基础。国家公社。2200–206（2011）。文章

Google Scholar

谷歌学者

Dorak, S. et al. Clay content and pH: Soil characteristic associations with the persistent presence of chronic wasting disease in northern Illinois. Sci. Rep. 7(1), 18062–18072 (2017).Article

Dorak，S.等人，《粘土含量和pH值：伊利诺伊州北部土壤特征与慢性消耗性疾病持续存在的关联》。科学。代表7（1），18062–18072（2017）。文章

PubMed

PubMed

PubMed Central

公共医学中心

Google Scholar

谷歌学者

Wyckoff, A. et al. Clay components in soil dictate environmental stability and bioavailability of cervid prions in mice. Front. Microbiol. 7, 1–11 (2016).Article

土壤中的粘土成分决定了cervid朊病毒在小鼠体内的环境稳定性和生物利用度。正面。微生物。7,1-11（2016）。文章

Google Scholar

谷歌学者

Booth, C., Lichtenberg, S., Chappell, R. & Pedersen, J. Chemical inactivation of prions is altered by binding to the soil mineral montmorillonite. ACS Infect. Dis. 7(4), 859–870 (2021).Article

Booth，C.，Lichtenberg，S.，Chappell，R。＆Pedersen，J。朊病毒的化学失活通过与土壤矿物蒙脱石的结合而改变。ACS感染。Dis。7（4），859-870（2021）。文章

CAS

中科院

PubMed

PubMed

Google Scholar

谷歌学者

Ibe, O. C. Laws of large numbers. Basic concepts in probability. In Markov processes for stochastic modeling 2nd edn (Elsevier, 2013). https://doi.org/10.1016/C2012-0-06106-6.Chapter

Ibe，O.C.大数定律。概率论的基本概念。在马尔可夫过程中进行随机建模第二版（Elsevier，2013）。https://doi.org/10.1016/C2012-0-06106-6.Chapter

Google Scholar

谷歌学者

Cunningham, C., Peery, R., Dao, A., McKenzie, D. & Coltman, D. Predicting the spread-risk potential of chronic wasting disease to sympatric ungulate species. Prion 14(1), 56–66 (2020).Article

Cunningham，C.，Peery，R.，Dao，A.，McKenzie，D。＆Coltman，D。预测慢性消耗性疾病向同域有蹄类物种的传播风险潜力。朊病毒14（1），56-66（2020）。文章

Google Scholar

谷歌学者

Tidd, J. Trophy-hunting business owner admits to illegally importing deer to Kansas (Accessed 10 August 2018); https://www.kansas.com/sports/outdoors/article198543619.htmlFitzgerald, R. They smuggled deer to Forrest County, feds say. But that wasn't the only problem (Accessed 10 August 2018); https://www.sunherald.com/news/local/crime/article173323226.htmlOrgan, J., et al.

Tidd，J.Trophy狩猎企业主承认向堪萨斯州非法进口鹿（2018年8月10日访问）；https://www.kansas.com/sports/outdoors/article198543619.htmlFitzgerald联邦调查局称，他们将鹿走私到福雷斯特县。但这不是唯一的问题（2018年8月10日访问）；https://www.sunherald.com/news/local/crime/article173323226.htmlOrgan，J。等人。

The North American Model of Wildlife Conservation. The Wildlife Society Technical Review 12–04. The Wildlife Society. (Bethesda, Maryland, USA, 2012).Centers for Disease Control (CDC). Chronic wasting disease (occurrence) (Accessed 15 April 2024); https://www.cdc.gov/prions/cwd/occurrence.htmlBooth, J.

北美野生动物保护模式。野生动物协会技术评论12-04。野生动物协会。（美国马里兰州贝塞斯达，2012年）。疾病控制中心（CDC）。慢性消耗性疾病（发生）（2024年4月15日访问）；https://www.cdc.gov/prions/cwd/occurrence.htmlBooth，J。

G. et al. Sample size for estimating disease prevalence in free-ranging wildlife: A Bayesian modeling approach. J. Agric. Biol. Environ. Stat. https://doi.org/10.1007/s13253-023-00578-7 (2023).Article .

G、 等。估计自由放养野生动物疾病流行率的样本量：贝叶斯建模方法。J、 农业。生物环境。统计。https://doi.org/10.1007/s13253-023-00578-7（2023年）。文章。

Google Scholar

谷歌学者

Schuler, K., Jenks, J., Klaver, R., Jennelle, C. & Bowyer, R. Chronic wasting disease detection and mortality sources in a semi-protected deer population. Wildl. Biol. 1, 1–7 (2018).Article

Schuler，K.，Jenks，J.，Klaver，R.，Jennelle，C。＆Bowyer，R。半保护鹿种群中慢性消耗性疾病的检测和死亡率来源。怀尔德。生物学1,1-7（2018）。文章

Google Scholar

谷歌学者

United States Department of Agriculture and Animal and Plant Health Inspection Service Veterinary Services. Chronic wasting disease program standards. www.aphis.usda.gov/sites/default/files/cwd-program-standards.pdf (2019).Walter, W. D. et al. Predicting the odds of chronic wasting disease with Habitat Risk software.

美国农业部动植物卫生检查局兽医服务。慢性消耗性疾病计划标准。www.aphis.usda.gov/sites/default/files/cwd-program-standards.pdf（2019）。Walter，W.D.等人。使用栖息地风险软件预测慢性消耗性疾病的几率。

Spat. Spatio-temporal Epidemiol. 49, 100650. https://doi.org/10.1016/j.sste.2024.100650 (2024).Article .

争吵。时空流行病。49100650。https://doi.org/10.1016/j.sste.2024.100650（2024年）。文章。

Google Scholar

谷歌学者

Winter, S. N., Kirchgessner, M. S., Frimpong, E. A. & Escobar, L. E. A landscape epidemiological approach for predicting chronic wasting disease: A case study in Virginia, US. Front. Vet. Sci. https://doi.org/10.3389/fvets.2021.698767 (2021).Article

Winter，S.N.，Kirchgessner，M.S.，Frimpong，E.A。和Escobar，L.E。预测慢性消耗性疾病的景观流行病学方法：美国弗吉尼亚州的案例研究。兽医。科学。https://doi.org/10.3389/fvets.2021.698767（2021年）。文章

PubMed

PubMed

PubMed Central

公共医学中心

Google Scholar

谷歌学者

Hefley, T., Hooten, M., Russell, R., Walsh, D. & Powell, J. When mechanism matters: Bayesian forecasting using models of ecological diffusion. Ecol. Lett. 20(5), 640–650 (2017).Article

Hefley，T.，Hooten，M.，Russell，R.，Walsh，D。＆Powell，J。当机制重要时：使用生态扩散模型进行贝叶斯预测。Ecol公司。利特。20（5），640-650（2017）。文章

PubMed

PubMed

Google Scholar

谷歌学者

Todman, L., Bush, A. & Hood, A. “Small data” for big insights in ecology. Trends Ecol. Evol. 38(7), 615–622 (2023).Article

托德曼（Todman，L.）、布什（Bush，A.）和胡德（Hood），A.“小数据”为生态学提供了深刻见解。趋势Ecol。进化。38（7），615-622（2023）。文章

PubMed

PubMed

Google Scholar

谷歌学者

Albon, C. Machine learning with Python cookbook (O’Reilly Media Inc., 2018).

Albon，C。使用Python cookbook进行机器学习（O'Reilly Media Inc.，2018）。

Google Scholar

谷歌学者

Shalev-Shwartz, S. & Ben-David, S. Understanding machine learning: From theory to algorithms (Cambridge University Press, 2014).Book

Shalev Shwartz，S。和Ben David，S。理解机器学习：从理论到算法（剑桥大学出版社，2014）。书籍

Google Scholar

谷歌学者

Pedregosa, F. et al. Scikit-learn: Machine learning in Python. J. Mach. Learn. Res. 12, 2825–2830 (2011).MathSciNet

Pedregosa，F.等人，《Scikit learn：Python中的机器学习》。J、 马赫。学习。第122825-2830号决议（2011年）。数学网

Google Scholar

谷歌学者

Hastie, T., Tibshirani, R. & Friedman, J. The elements of statistical learning: Data mining, inference and prediction 2nd edn. (Springer, 2009). https://doi.org/10.1007/978-0-387-84858-7.Book

Hastie，T.，Tibshirani，R。＆Friedman，J。统计学习的要素：数据挖掘，推理和预测第二版。（斯普林格，2009）。https://doi.org/10.1007/978-0-387-84858-7.Book

Google Scholar

谷歌学者

Pudjihartono, N., Fadason, T., Kempa-Liehr, A. & O’Sullivan, J. A review of feature selection methods for machine learning-based disease risk prediction. Front. Bioinform. 2, 1–17 (2022).Article

Pudjihartono，N.，Fadason，T.，Kempa Liehr，A。＆O'Sullivan，J。基于机器学习的疾病风险预测的特征选择方法综述。正面。生物信息。2，1-17（2022）。文章

Google Scholar

谷歌学者

QGIS.org, QGIS Geographic Information System. QGIS Association. http://www.qgis.org (2024).Download referencesAcknowledgementsWe thank W. Kozlowski, J. Peaslee, M. Cosgrove, J. Dennison, T. DeRosia, C. Faller, D. Howlett, M. Keightley, E. Kessel, J. Kessler, E. Larson, M. McCord, A. Nolder, D.

QGIS.org，QGIS地理信息系统。QGIS协会。http://www.qgis.org（2024年）。下载参考文献致谢我们感谢W.Kozlowski，J.Peaslee，M.Cosgrove，J.Dennison，T.DeRosia，C.Faller，D.Howlett，M.Keightley，E.Kessel，J.Kessler，E.Larson，M.McCord，A.Nolder，D。

Stanfield, S. Stura, J. Sweaney, G. Timko, B. Wallingford, B. Wojcik, C. Yoest, A. J. Riggs, and 5 anonymous individuals, Arkansas Game and Fish Commission Wildlife Management Division staff and F23AF01862-00, Georgia Wildlife Resources Division, Indiana Department of Natural Resources Division of Fish and Wildlife, Iowa DNR Wildlife Bureau staff, Minnesota DNR staff, Mississippi Department of Wildlife, Fisheries, and Parks staff, New York State Department of Environmental Conservation Wildlife Health Unit staff, the North Carolina Wildlife Resources Commission staff, Ohio DNR Division of Wildlife staff.

Stanfield、S.Stura、J.Sweeney、G.Timko、B.Wallingford、B.Wojcik、C.Yoest、A.J.Riggs和5名匿名人士、阿肯色州狩猎和鱼类委员会野生动物管理司工作人员和F23AF01862-00、佐治亚州野生动物资源司、印第安纳州自然资源部鱼类和野生动物司、爱荷华州DNR野生动物局工作人员、明尼苏达州DNR工作人员、密西西比州野生动物、渔业和公园部工作人员、纽约州环境保护部野生动物健康股工作人员、北卡罗来纳州野生动物资源委员会工作人员、俄亥俄州DNR野生动物司工作人员。

Data collection was funded in part by Arkansas’s Wildlife Restoration funds, ‘State Wildlife Health’; Florida’s State Game Trust Fund Deer Management Program; Georgia’s Wildlife and Sport Fish Restoration Program; Indiana DNR and Fish and Wildlife F18AF00484, W38R05 White-tailed Deer Management, F20AF10029-00, Monitoring Wildlife Populations and Health W-51-R-01, F21AF02467-01, Monitoring Wildlife Populations and Health W-51-R-02; Iowa’s Fish and Wildlife Trust Fund and U.

数据收集部分由阿肯色州野生动物恢复基金“州立野生动物健康”资助；佛罗里达州游戏信托基金鹿管理计划；乔治亚州野生动物和运动鱼类恢复计划；印第安纳州DNR和鱼类及野生动物F18AF00484，W38R05白尾鹿管理，F20AF10029-00，监测野生动物种群和健康W-51-R-01，F21AF02467-01，监测野生动物种群和健康W-51-R-02；爱荷华州鱼类和野生动物信托基金和美国。

S. Fish and Wildlife Service Wildlife and Sport Fish Restoration Program; Maryland’s award of the U. S. Fish and Wildlife Service Wildlife and Sport Fish Restoration Program, W 61-R-29; Minnesota DNR; New York’s Wildlife Health Unit and New York’s award for Federal Aid Wildlife Restoration Grant W-178-R; North Carolina’s award for Federal Aid in Wildlife Restoration; Tennessee’s award for the Wil.

S、 鱼类和野生动物服务野生动物和运动鱼类恢复计划；马里兰州美国鱼类和野生动物管理局野生动物和运动鱼类恢复计划奖，W 61-R-29；明尼苏达州DNR；纽约野生动物健康部门和纽约联邦援助野生动物恢复奖W-178-R；北卡罗莱纳州野生动物恢复联邦援助奖；田纳西州Wil奖。

PubMed Google ScholarBrenda J. HanleyView author publicationsYou can also search for this author in

PubMed Google ScholarBrenda J.HanleyView作者出版物您也可以在

PubMed Google ScholarCorey I. MitchellView author publicationsYou can also search for this author in

PubMed Google ScholarCorey I.MitchellView作者出版物您也可以在

PubMed Google ScholarRachel C. AbbottView author publicationsYou can also search for this author in

PubMed Google ScholarRachel C.AbbottView作者出版物您也可以在

PubMed Google ScholarNicholas A. HollingsheadView author publicationsYou can also search for this author in

PubMed Google ScholarNicholas A.HollingsheadView作者出版物您也可以在

PubMed Google ScholarJames G. BoothView author publicationsYou can also search for this author in

PubMed谷歌学者James G.BoothView作者出版物您也可以在

PubMed Google ScholarJoe GuinnessView author publicationsYou can also search for this author in

PubMed Google ScholarJoe GuinnessView作者出版物您也可以在

PubMed Google ScholarChristopher S. JennelleView author publicationsYou can also search for this author in

PubMed Google ScholarChristopher S.JennelleView作者出版物您也可以在

PubMed Google ScholarFlorian H. HodelView author publicationsYou can also search for this author in

PubMed谷歌学者Florian H.HodelView作者出版物您也可以在

PubMed Google ScholarCarlos Gonzalez-CrespoView author publicationsYou can also search for this author in

PubMed Google ScholarCarlos Gonzalez CrespoView作者出版物您也可以在

PubMed Google ScholarChristopher R. MiddaughView author publicationsYou can also search for this author in

PubMed Google ScholarChristopher R.MiddaughView作者出版物您也可以在

PubMed Google ScholarJennifer R. BallardView author publicationsYou can also search for this author in

PubMed谷歌学者Jennifer R.BallardView作者出版物您也可以在

PubMed Google ScholarBambi ClemonsView author publicationsYou can also search for this author in

PubMed Google ScholarBambi ClemonsView作者出版物您也可以在

PubMed Google ScholarCharlie H. KillmasterView author publicationsYou can also search for this author in

PubMed Google ScholarCharlie H.KillmasterView作者出版物您也可以在

PubMed Google ScholarTyler M. HarmsView author publicationsYou can also search for this author in

PubMed Google ScholarTyler M.HarmsView作者出版物您也可以在

PubMed Google ScholarJoe N. CaudellView author publicationsYou can also search for this author in

PubMed Google ScholarJoe N.CaudellView作者出版物您也可以在

PubMed Google ScholarKathryn M. Benavidez WestrichView author publicationsYou can also search for this author in

PubMed Google ScholarKathryn M.Benavidez WestrichView作者出版物您也可以在

PubMed Google ScholarEmily McCallenView author publicationsYou can also search for this author in

PubMed Google ScholarEmily McCallenView作者出版物您也可以在

PubMed Google ScholarChristine CaseyView author publicationsYou can also search for this author in

PubMed谷歌学术评论CaseyView作者出版物您也可以在

PubMed Google ScholarLindsey M. O’BrienView author publicationsYou can also search for this author in

PubMed Google ScholarLindsey M.O'BrienView作者出版物您也可以在

PubMed Google ScholarJonathan K. TrudeauView author publicationsYou can also search for this author in

PubMed谷歌学者Jonathan K.TrudeauView作者出版物您也可以在

PubMed Google ScholarChad StewartView author publicationsYou can also search for this author in

PubMed Google ScholarChad StewartView作者出版物您也可以在

PubMed Google ScholarMichelle CarstensenView author publicationsYou can also search for this author in

PubMed谷歌ScholarMichelle CarstensenView作者出版物您也可以在

PubMed Google ScholarWilliam T. McKinleyView author publicationsYou can also search for this author in

PubMed Google ScholarWilliam T.McKinleyView作者出版物您也可以在

PubMed Google ScholarKevin P. HynesView author publicationsYou can also search for this author in

PubMed Google ScholarKevin P.HynesView作者出版物您也可以在

PubMed Google ScholarAshley E. StevensView author publicationsYou can also search for this author in

PubMed Google ScholarAshley E.StevensView作者出版物您也可以在

PubMed Google ScholarLandon A. MillerView author publicationsYou can also search for this author in

PubMed Google ScholarLandon A.MillerView作者出版物您也可以在

PubMed Google ScholarMerril CookView author publicationsYou can also search for this author in

PubMed Google Scholarmarril CookView作者出版物您也可以在

PubMed Google ScholarRyan T. MyersView author publicationsYou can also search for this author in

PubMed Google ScholarRyan T.MyersView作者出版物您也可以在

PubMed Google ScholarJonathan ShawView author publicationsYou can also search for this author in

PubMed Google ScholarJonathanShawview作者出版物您也可以在

PubMed Google ScholarMichael J. TonkovichView author publicationsYou can also search for this author in

PubMed Google Scholarmamichael J.TonkovichView作者出版物您也可以在

PubMed Google ScholarJames D. KellyView author publicationsYou can also search for this author in

PubMed谷歌学者James D.KellyView作者出版物您也可以在

PubMed Google ScholarDaniel M. GroveView author publicationsYou can also search for this author in

PubMed Google ScholardanielM.GroveView作者出版物您也可以在

PubMed Google ScholarDaniel J. StormView author publicationsYou can also search for this author in

PubMed Google ScholarDaniel J.StormView作者出版物您也可以在

PubMed Google ScholarKrysten L. SchulerView author publicationsYou can also search for this author in

PubMed Google ScholarKrysten L.SchulerView作者出版物您也可以在

PubMed Google ScholarContributionsConceptualization: MSA; BJH; KLS. Funding: JGB; JG; BJH; CSJ; KLS. Literature review: BJH; MSA; RCA; KLS. Wrote software: MSA; BJH. Conducted the analysis: MSA; BJH. Provided data and applied agency expertise CRM; JRB; BC; CHK; TMH; JNC; KMBW; EM; CC; LMO; JKT; MC; WTM; CS; KPH; AES; LAM; MC; RTM; JS; MJT; JDK; DMG; DJS.

PubMed谷歌学术贡献概念：MSA；BJH公司；吉隆坡。资金来源：日本国债；JG；BJH公司；CSJ；吉隆坡。文献综述：BJH；MSA；RCA；吉隆坡。编写软件：MSA；BJH公司。进行了分析：MSA；BJH公司。提供数据和应用机构专业知识CRM；JRB；不列颠哥伦比亚省；CHK公司；TMH；JNC；KMBW；EM；抄送；LMO；JKT；MC；WTM；CS；KPH；不良事件；林；MC；RTM；JS；美赞臣；JDK公司；DMG；DJ。

Wrote the draft: BJH; MSA. All authors provided critical edits and gave approval for submission.Corresponding authorCorrespondence to.

写了草稿：BJH；MSA。所有作者都提供了重要的编辑并批准了提交。对应作者对应。

Md Sohel Ahmed.Ethics declarations

医学博士Sohel Ahmed。道德宣言

Competing interests

相互竞争的利益

MSA, BJH, RCA, JGB, JG, NAH, CGC, CRM, JRB, BC, CHK, TMH, JNC, KMBW, EM, CC, LMO, JKT, MC, WTM, KPH, AES, LAM, CS, MC, RTM, JS, MJT, JDK, DMG, DJS, and KLS declare no competing interests. A portion of this work was completed when CIM was a consultant at Desert Centered Ecology, LLC; CSJ was a consultant at Christopher S.

MSA、BJH、RCA、JGB、JG、NAH、CGC、CRM、JRB、BC、CHK、TMH、JNC、KMBW、EM、CC、LMO、JKT、MC、WTM、KPH、AES、LAM、CS、MC、RTM、JS、MJT、JDK、DMG、DJS和KLS声明没有利益冲突。这项工作的一部分是在CIM担任沙漠中心生态有限责任公司顾问时完成的；CSJ是克里斯托弗S的顾问。

Jennelle; and FHH was a consultant at Florian H. Hodel..

詹内尔；FHH是Florian H.Hodel的顾问。。

Additional informationPublisher's noteSpringer Nature remains neutral with regard to jurisdictional claims in published maps and institutional affiliations.Supplementary InformationSupplementary Information.Rights and permissions

Additional informationPublisher的noteSpringer Nature在已发布地图和机构隶属关系中的管辖权主张方面保持中立。补充信息补充信息。权限和权限

Open Access This article is licensed under a Creative Commons Attribution 4.0 International License, which permits use, sharing, adaptation, distribution and reproduction in any medium or format, as long as you give appropriate credit to the original author(s) and the source, provide a link to the Creative Commons licence, and indicate if changes were made.

开放获取本文是根据知识共享署名4.0国际许可证授权的，该许可证允许以任何媒体或格式使用，共享，改编，分发和复制，只要您对原始作者和来源给予适当的信任，提供知识共享许可证的链接，并指出是否进行了更改。

The images or other third party material in this article are included in the article's Creative Commons licence, unless indicated otherwise in a credit line to the material. If material is not included in the article's Creative Commons licence and your intended use is not permitted by statutory regulation or exceeds the permitted use, you will need to obtain permission directly from the copyright holder.

本文中的图像或其他第三方材料包含在文章的知识共享许可中，除非在材料的信用额度中另有说明。。

To view a copy of this licence, visit http://creativecommons.org/licenses/by/4.0/..

要查看此许可证的副本，请访问http://creativecommons.org/licenses/by/4.0/..

Reprints and permissionsAbout this articleCite this articleAhmed, M.S., Hanley, B.J., Mitchell, C.I. et al. Predicting chronic wasting disease in white-tailed deer at the county scale using machine learning.

转载和许可本文引用本文Ahmed，M.S.，Hanley，B.J.，Mitchell，C.I.等人使用机器学习预测县范围内白尾鹿的慢性消耗性疾病。

Sci Rep 14, 14373 (2024). https://doi.org/10.1038/s41598-024-65002-7Download citationReceived: 31 October 2023Accepted: 15 June 2024Published: 22 June 2024DOI: https://doi.org/10.1038/s41598-024-65002-7Share this articleAnyone you share the following link with will be able to read this content:Get shareable linkSorry, a shareable link is not currently available for this article.Copy to clipboard.

Sci Rep 1414373（2024）。https://doi.org/10.1038/s41598-024-65002-7Download引文接收日期：2023年10月31日接收日期：2024年6月15日发布日期：2024年6月22日OI：https://doi.org/10.1038/s41598-024-65002-7Share本文与您共享以下链接的任何人都可以阅读此内容：获取可共享链接对不起，本文目前没有可共享的链接。复制到剪贴板。

Provided by the Springer Nature SharedIt content-sharing initiative

由Springer Nature SharedIt内容共享计划提供

Subjects

主题

Ecological epidemiologySoftwareStatistics

生态流行病学软件统计

CommentsBy submitting a comment you agree to abide by our Terms and Community Guidelines. If you find something abusive or that does not comply with our terms or guidelines please flag it as inappropriate.

评论通过提交评论，您同意遵守我们的条款和社区指南。如果您发现有虐待行为或不符合我们的条款或准则，请将其标记为不合适。