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<journal-meta>
<journal-id journal-id-type="publisher-id">Front. Vet. Sci.</journal-id>
<journal-title>Frontiers in Veterinary Science</journal-title>
<abbrev-journal-title abbrev-type="pubmed">Front. Vet. Sci.</abbrev-journal-title>
<issn pub-type="epub">2297-1769</issn>
<publisher>
<publisher-name>Frontiers Media S.A.</publisher-name>
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<article-meta>
<article-id pub-id-type="doi">10.3389/fvets.2024.1394631</article-id>
<article-categories>
<subj-group subj-group-type="heading">
<subject>Veterinary Science</subject>
<subj-group>
<subject>Systematic Review</subject>
</subj-group>
</subj-group>
</article-categories>
<title-group>
<article-title>Prevalence of canine distemper in minks, foxes and raccoon dogs from 1983 to 2023 in Asia, North America, South America and Europe</article-title>
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<name><surname>Liang</surname> <given-names>Jian</given-names></name>
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<name><surname>Wang</surname> <given-names>Xiaolin</given-names></name>
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<name><surname>Shi</surname> <given-names>Kun</given-names></name>
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<name><surname>Li</surname> <given-names>Jianming</given-names></name>
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<name><surname>Du</surname> <given-names>Rui</given-names></name>
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<aff id="aff1"><sup>1</sup><institution>College of Animal Science and Technology, Jilin Agricultural University</institution>, <addr-line>Changchun, Jilin</addr-line>, <country>China</country></aff>
<aff id="aff2"><sup>2</sup><institution>College of Chinese Medicine Materials, Jilin Agricultural University</institution>, <addr-line>Changchun, Jilin</addr-line>, <country>China</country></aff>
<aff id="aff3"><sup>3</sup><institution>Laboratory of Production and Product Application of Sika Deer of Jilin Province, Jilin Agricultural University</institution>, <addr-line>Changchun, Jilin</addr-line>, <country>China</country></aff>
<author-notes>
<fn fn-type="edited-by" id="fn0001">
<p>Edited by: Jeff Wilson, Novometrix Research Inc., Canada</p>
</fn>
<fn fn-type="edited-by" id="fn0002">
<p>Reviewed by: Raul Alejandro Alegria, University of Chile, Chile</p>
<p>Xuhua Ran, Hainan University, China</p>
</fn>
<corresp id="c001">&#x002A;Correspondence: Xue Leng, <email>xuel@jlau.edu.cn</email></corresp>
<corresp id="c002">Qinglong Gong, <email>gongqinglong1001@163.com</email></corresp>
<corresp id="c003">Rui Du, <email>durui197101@sina.com</email></corresp>
</author-notes>
<pub-date pub-type="epub">
<day>13</day>
<month>08</month>
<year>2024</year>
</pub-date>
<pub-date pub-type="collection">
<year>2024</year>
</pub-date>
<volume>11</volume>
<elocation-id>1394631</elocation-id>
<history>
<date date-type="received">
<day>01</day>
<month>03</month>
<year>2024</year>
</date>
<date date-type="accepted">
<day>15</day>
<month>07</month>
<year>2024</year>
</date>
</history>
<permissions>
<copyright-statement>Copyright &#x00A9; 2024 Liang, Wang, Wang, Wang, Fan, Hu, Leng, Shi, Li, Gong and Du.</copyright-statement>
<copyright-year>2024</copyright-year>
<copyright-holder>Liang, Wang, Wang, Wang, Fan, Hu, Leng, Shi, Li, Gong and Du</copyright-holder>
<license xlink:href="http://creativecommons.org/licenses/by/4.0/">
<p>This is an open-access article distributed under the terms of the Creative Commons Attribution License (CC BY). The use, distribution or reproduction in other forums is permitted, provided the original author(s) and the copyright owner(s) are credited and that the original publication in this journal is cited, in accordance with accepted academic practice. No use, distribution or reproduction is permitted which does not comply with these terms.</p>
</license>
</permissions>
<abstract>
<p>Canine distemper (CD) is a virulent disease caused by the canine distemper virus (CDV) in canines and mustelidaes with high mortality. The incidence of CDV is worldwide distribution and it has caused huge economic losses to multiple industries around the world. There are many studies investigating the prevalence of CD infection, but no comprehensive analysis of CDV infection in minks, foxes and raccoon dogs worldwide has therefore been carried out. The aim of this meta is to provide a comprehensive assessment of the prevalence of CDV infection in minks, foxes and raccoon dogs dogs through a meta-analysis of articles published from around the world. Data from 8,582 small carnivores in 12 countries were used to calculate the combined prevalence of CD. A total of 22.6% (1,937/8,582) of minks, foxes and raccoon dogs tested positive for CD. The prevalence was higher in Asia (13.8, 95% CI: 22.2&#x2013;45.6), especially in South Korea (65.8, 95% CI: 83.3&#x2013;95.8). Our study found that the incidence of CD was also associated with geographic climate, population size, health status, and breeding patterns. CD is more commonly transmitted in minks, foxes and raccoon dogs. However, the concentrated breeding as an economic animal has led to an increase in the prevalence rate. The difference analysis study recommended that countries develop appropriate preventive and control measures based on the prevalence in the minks, foxes, and raccoon dogs industries, and that reducing stocking density is important to reduce the incidence of CDV. In addition, CDV is more common in winter, so vaccination in winter should be strengthened and expanded to reduce the incidence of CD in minks, foxes and raccoon dogs.</p>
</abstract>
<kwd-group>
<kwd>minks</kwd>
<kwd>foxes</kwd>
<kwd>raccoon dogs</kwd>
<kwd>canine distemper</kwd>
<kwd>Meta</kwd>
</kwd-group>
<counts>
<fig-count count="5"/>
<table-count count="4"/>
<equation-count count="0"/>
<ref-count count="39"/>
<page-count count="12"/>
<word-count count="7145"/>
</counts>
<custom-meta-wrap>
<custom-meta>
<meta-name>section-at-acceptance</meta-name>
<meta-value>Veterinary Epidemiology and Economics</meta-value>
</custom-meta>
</custom-meta-wrap>
</article-meta>
</front>
<body>
<sec sec-type="intro" id="sec1">
<label>1</label>
<title>Introduction</title>
<p>Canine distemper virus (CDV) is an enveloped, negative, single-stranded RNA virus belonging to the genus Measles virus, a member of the family Paramyxoviridae (<xref ref-type="bibr" rid="ref1">1</xref>). Other members of the genus, such as rinderpest virus (RPV) and measles virus (MV), are known to cause devastating diseases in animals (<xref ref-type="bibr" rid="ref2">2</xref>). CDV is capable of infecting a wide range of species and poses a serious threat to the conservation of animals. The disease was first reported in Spain (1,761) and is thought to have spread from there to the rest of the world (<xref ref-type="bibr" rid="ref3">3</xref>). Canine distemper has long been a serious and fatal disease of a large number of carnivores, and poses a significant threat to the health of small carnivores such as minks, foxes and raccoon dogs in particular (<xref ref-type="bibr" rid="ref4">4</xref>). CDV is similar to other paramyxoviruses in that the virus contains six structural proteins, nucleocapsid (N), phosphoprotein (P), large (L), matrix (M), hemagglutinin (H), and fusion (F) proteins, as well as two auxiliary Non-structural proteins (C and V), which are present as extra-transcriptional units of the P gene (<xref ref-type="bibr" rid="ref5">5</xref>). Mutations affecting the CDV H protein required for viral attachment to host cell receptors have been associated with virulence and disease emergence in novel host species.</p>
<p>The virus is mainly transmitted directly or indirectly through aerosols and contact with respiratory and ocular secretions (<xref ref-type="bibr" rid="ref6">6</xref>). Other body excretions and secretions (e.g., urine and feces) may contribute to the transmission of the virus during the acute phase of infection above. Generally, CDV exhibits lymphatic, neurological, and epithelial characteristics, resulting in systemic infections of almost all organ systems including respiratory, digestive, urinary, lymphatic, endocrine, cutaneous, skeletal and central nervous system (CNS) (<xref ref-type="bibr" rid="ref7">7</xref>, <xref ref-type="bibr" rid="ref8">8</xref>). The disease course and pathogenesis in CD resemble those of human measles virus infection including, fever, rash, respiratory signs, lymphopenia, and severe immunosuppression with generalized depletion of lymphatic organs during the acute disease phase (<xref ref-type="bibr" rid="ref7">7</xref>). In addition, CDV infection shows a high incidence of neurological complications (<xref ref-type="bibr" rid="ref8">8</xref>). Initially, the natural hosts of CDV were canids but as wildlife habitats have changed and the virus itself has evolved, the natural hosts of CDV have expanded to include Felidae, Viverridae, Mustelidae, Mephitidae, Ursidae, Procyonidae, Ailuride and Huaenidae, among others (<xref ref-type="bibr" rid="ref9">9</xref>). In total, more than 20 carnivorous and non-carnivorous families have been reported to be affected (<xref ref-type="bibr" rid="ref10">10</xref>).</p>
<p>Economically, minks, foxes and raccoon dogs play an indispensable role in the breeding industry of various countries as fur animals. For example, according to the International Fur Association, Northern Europe is the largest mink breeding region in the world, with around 58% of the total, while other major breeding regions include China, North America, Russia, Argentina and Ukraine (<xref ref-type="bibr" rid="ref11">11</xref>). Denmark is currently the world&#x2019;s largest martens producer and furexports are an important pillar of the Danish industry (<xref ref-type="bibr" rid="ref12">12</xref>). The increasing demand for minks, foxes, and raccoon dogs farming is gradually expanding. CDV is expanding host range and high mortality rates seriously threaten the sustainability of the minks, foxes and raccoon dogs farming industry (<xref ref-type="bibr" rid="ref13">13</xref>). Recent studies have shown that lethal infections also occur in non-carnivorous species such as wild boar and non-human primates, suggesting that the pathogen has a remarkable ability to cross species barriers (<xref ref-type="bibr" rid="ref14">14</xref>).</p>
<p>Epidemiological data from around the world indicate that CDV has become a major threat to many protected species, even those outside the order Carnivora (<xref ref-type="bibr" rid="ref15">15</xref>). The importance of infection in multi-host situations is not fully understood due to the lack of epidemiologic information on CDV transmission. Understanding the epidemiology of CDV is important not only for preventive diagnosis of minks, foxes and raccoons, but also for the development of reliable wildlife conservation strategies. However, to the best of our knowledge, there is currently no adequate systematic analysis of the overall prevalence rate of CD infection in minks, foxes, and raccoon dogs in the world, so we conducted a study to estimate the prevalence of CDV infection in mink, fox, and raccoon dog populations globally and to assess potential risk factors associated with the prevalence rate of CD disease. This study will help to understand the epidemiology of CDV in minks, foxes and raccoon dogs and improve timely prevention of CDV-induced diseases in minks, foxes and raccoon dogs.</p>
</sec>
<sec sec-type="materials|methods" id="sec2">
<label>2</label>
<title>Materials and methods</title>
<sec id="sec3">
<label>2.1</label>
<title>Search strategy and selection criteria</title>
<p>PRISMA is used to report the results of our systematic review and meta-analysis. We searched for papers published from 1983 to December 8, 2022 in VIP Chinese Journal Database, CNKI, Wanfang Database, PubMed and ScienceDirect, Web of Science. Our aim was to screen all papers published in English or Chinese on the world epidemic of Canine distemper. We attempted to contact the authors of studies that could not be downloaded from the database for additional information. In the PubMed database, we used the MeSH terms &#x201C;mink,&#x201D; &#x201C;fox,&#x201D; &#x201C;raccoon dog,&#x201D; and &#x201C;Canine distemper&#x201D; to retrieve the medical subject terms and their free words associated with them. The subject terms were linked using the Boolean operator &#x201C;AND&#x201D; and the free terms were linked using the Boolean operator &#x201C;OR&#x201D; to generate the final search formula.</p>
<p>Total: ((((((((((((((Minks) OR (<italic>Mustela vison</italic>)) OR (American Mink)) OR (Mink, American)) OR (<italic>Mustela macrodon</italic>)) OR (Sea Mink)) OR (Mink, Sea)) OR (Minks, Sea)) OR (Sea Minks)) OR (<italic>Mustela lutreola</italic>)) OR (European Mink)) OR (Mink, European)) AND (((Canine Distemper Virus) OR (Canine Distemper Viruses)) OR (Distemper Viruses, Canine))) OR ((((Canine Distemper Virus) OR (Canine Distemper Viruses)) OR (Distemper Viruses, Canine)) AND (((((((((Vulpes) OR (Pseudalopex)) OR (Urocyon)) OR (<italic>Vulpes vulpes</italic>)) OR (Red Fox)) OR (Fox, Red)) OR (Alopex)) OR (Arctic Fox)) OR (Fox, Arctic)))) OR ((((((Dog, Raccoon) OR (Dogs, Raccoon)) OR (Raccoon Dog)) OR (<italic>Nyctereutes procyonoides</italic>)) OR (Nyctereutes)) AND (((Canine Distemper Virus) OR (Canine Distemper Viruses)) OR (Distemper Viruses, Canine))).</p>
<p>In the Web of Science, we use the terms &#x201C;mink,&#x201D; &#x201C;fox,&#x201D; &#x201C;raccoon dog,&#x201D; and &#x201C;Canine distemper &#x201C;. The Boolean operators &#x201C;AND&#x201D; and &#x201C;OR&#x201D; are used to link medical terms in the advanced search.</p>
<p>Search in ScienceDirect using keywords such as &#x201C;mink,&#x201D; &#x201C;fox,&#x201D; &#x201C;raccoon dog,&#x201D; &#x201C;Canine distemper,&#x201D; and &#x201C;research article type.&#x201D; In the advanced search of three Chinese databases (CNKI, Wanfang and VIP databases), the same Chinese search terms are also used, including fuzzy search and synonym expansion.</p>
<p>All retrieved citations are imported into Endnote X9 (9.3.3). Eligible studies were screened according to the following criteria:</p>
<list list-type="order">
<list-item><p>Study subjects must be &#x201C;minks,&#x201D; &#x201C;foxes&#x201D; and &#x201C;raccoon dogs&#x201D;.</p></list-item>
<list-item><p>The aim of the study must be to investigate the positive rate of Canine distemper infection in minks, foxes and raccoon dogs.</p></list-item>
<list-item><p>The data must include the numbers of minks, foxes and raccoon dogs.</p></list-item>
<list-item><p>The research design must be a cross-sectional study.</p></list-item>
<list-item><p>Research must be published in Chinese or English.</p></list-item>
</list>
<p>Studies that did not meet all of the above criteria were excluded. Repeated studies and review studies (non-research papers) were also excluded.</p>
</sec>
<sec id="sec4">
<label>2.2</label>
<title>Selection criteria</title>
<p>The reviewers extracted the following variables from each study separately: year of sampling, first author, year of publication, country, region, assay method, sample type, the season of collection, population size, sex and age of the sampled animals, breeding pattern, type of animals sampled, health status, data of geographical factors (longitude, latitude, mean annual rainfall, altitude, mean annual temperature, mean annual humidity) taken from the National Meteorological Information Center of the China Meteorological Administration. The primary reviewer (QLG) confirms all extracted data. The &#x2018;quality&#x2019; of each included study was assessed by using criteria derived from the GRADE (Grading of Recommendations for Assessment, Development and Evaluation) methodology. The scoring method are used for grading, and each of the criteria mentioned below identified as 1 point: (i) random sampling; (ii) a clear method of detection; (iii) provision of a detailed description of the sampling method; (iv) a clear sampling time; and (v) include four or more risk factors.</p>
</sec>
<sec id="sec5">
<label>2.3</label>
<title>Statistical analysis</title>
<p>We performed meta-analysis using the &#x201C;meta&#x201D; package in R software (&#x201C;R Core Team, Version 4.0.0; &#x201C;R: Language and Environment for Statistical Computing,&#x201D; R Core Team, 2022) (<xref ref-type="bibr" rid="ref16">16</xref>). We use the Freeman-Tukey double anti-sine transform (Named &#x201C;PFT&#x201D; in the tuple) for transform to conform to the normal distribution (<xref ref-type="bibr" rid="ref17">17</xref>). The composite estimates included in the study were described using forest plots. Heterogeneity of prevalence meta-analyses is usually large, so we made judgments in advance and used random effects models to analyze overall prevalence (including subgroups). Differences due to the heterogeneity of the included studies were evaluated using Cochrane Q-statistics and Higgin statistics. In a funnel plot, the symmetry of the graph is judged subjectively. If the points in a funnel plot are symmetrically distributed on either side of the line of symmetry, there is no publication bias, and if they are not symmetrical, there is publication bias in the included studies. At the same time in order to trace potential sources of heterogeneity present in our study, we conducted subgroup analyses and univariate meta-regression. Potential factors include geographical region (Asia, Europe, South America, North America); sampling of years (before 1988 and 2008 or later); Detection methods (serology, molecular biology); feeding pattern (intensive, free-range); age (&#x2264;1&#x2009;year, &#x003E;1&#x2009;year); sex (male, female); season (spring, summer, autumn, winter), rating level (2&#x2013;3 points, 4&#x2013;5 points). Using the data from the National Meteorological Information Center of the China Meteorological Administration, geographical factors were further extracted based on sampling location using subgroup analysis and one-way meta-regression analysis to trace the sources of heterogeneity. The R software codes for this study is shown in Table S2.</p>
</sec>
</sec>
<sec sec-type="results" id="sec6">
<label>3</label>
<title>Results</title>
<sec id="sec7">
<label>3.1</label>
<title>Search results and eligible studies</title>
<p>According to our inclusion criteria, 1,648 articles were collected from six databases, and 33 studies were finally included to establish this meta-analysis (<xref ref-type="fig" rid="fig1">Figure 1</xref>). A total of 9 studies were divided into 4&#x2013;5 points, and 24 studies were divided into 2&#x2013;3 points (Tables S2, S3).</p>
<fig position="float" id="fig1">
<label>Figure 1</label>
<caption>
<p>Flow diagram of eligible studies for searching and selecting.</p>
</caption>
<graphic xlink:href="fvets-11-1394631-g001.tif"/>
</fig>
</sec>
<sec id="sec8">
<label>3.2</label>
<title>Publication bias and sensitivity analysis</title>
<p>As suggested by previous studies, we finally chose PFT to perform rate conversion (<xref ref-type="table" rid="tab1">Table 1</xref>). The forest plots. Results showed a high heterogeneity in the included studies (I2&#x2009;=&#x2009;99.8%, <italic>p</italic>&#x2009;&#x003C;&#x2009;0.01; <xref ref-type="fig" rid="fig2">Figure 2</xref>). In the funnel plot, we observed asymmetry, indicating publication bias in our meta-analysis (<xref ref-type="fig" rid="fig3">Figure 3</xref>). The results of the egger test were the same as for the funnel plot (<italic>t</italic>&#x2009;=&#x2009;&#x2212;5.216, <italic>p</italic>&#x2009;=&#x2009;0.05; <xref ref-type="fig" rid="fig4">Figure 4</xref>; <xref rid="SM1" ref-type="supplementary-material">Supplementary Table S4</xref>). Pruning and padding analyses were shown to indicate publication bias or small sample bias in our included studies (<xref rid="SM1" ref-type="supplementary-material">Supplementary Figure S2</xref>). Sensitivity analyses verified the reliability of the results, and the exclusion of any one study had little effect on the overall quality of the meta-analysis (<xref rid="SM1" ref-type="supplementary-material">Supplementary Figure S1</xref>). We also provide funnel plots for each subgroup to determine whether publication bias or small sample bias was present (<xref rid="SM1" ref-type="supplementary-material">Supplementary Figures S3&#x2013;S10</xref>).</p>
<table-wrap position="float" id="tab1">
<label>Table 1</label>
<caption>
<p>Normal distribution test for the normal rate and the different conversion of the normal rate.</p>
</caption>
<table frame="hsides" rules="groups">
<thead>
<tr>
<th align="left" valign="top">Conversion form</th>
<th align="center" valign="top">W</th>
<th align="center" valign="top">P</th>
</tr>
</thead>
<tbody>
<tr>
<td align="left" valign="top">PRAW</td>
<td align="center" valign="top">0.81465</td>
<td align="center" valign="top">6.218e-05</td>
</tr>
<tr>
<td align="left" valign="top">PLN</td>
<td align="center" valign="top">0.96803</td>
<td align="center" valign="top">0.4278</td>
</tr>
<tr>
<td align="left" valign="top">PLOGIT</td>
<td align="center" valign="top">0.92927</td>
<td align="center" valign="top">0.03336</td>
</tr>
<tr>
<td align="left" valign="top">PAS</td>
<td align="center" valign="top">0.87633</td>
<td align="center" valign="top">0.001369</td>
</tr>
<tr>
<td align="left" valign="top">PFT</td>
<td align="center" valign="top">0.86896</td>
<td align="center" valign="top">0.0009163</td>
</tr>
</tbody>
</table>
<table-wrap-foot>
<p>&#x201C;PRAW&#x201D;: original rate; &#x201C;PLN&#x201D;: logarithmic conversion; &#x201C;PLOGIT&#x201D;: logit transformation; &#x201C;PAS&#x201D;: arcsine transformation; &#x201C;PFT&#x201D;: double-arcsine transformation; &#x201C;NaN&#x201D;: meaningless number; &#x201C;NA&#x201D;: missing data.</p>
</table-wrap-foot>
</table-wrap>
<fig position="float" id="fig2">
<label>Figure 2</label>
<caption>
<p>Forest plot of prevalence of Canine distemper in minks, foxes and raccoon dogs among studies conducted in the World.</p>
</caption>
<graphic xlink:href="fvets-11-1394631-g002.tif"/>
</fig>
<fig position="float" id="fig3">
<label>Figure 3</label>
<caption>
<p>Funnel plot with pseudo 95% confidence interval limits for the examination of publication bias.</p>
</caption>
<graphic xlink:href="fvets-11-1394631-g003.tif"/>
</fig>
<fig position="float" id="fig4">
<label>Figure 4</label>
<caption>
<p>Egger&#x2019;s test for publication bias.</p>
</caption>
<graphic xlink:href="fvets-11-1394631-g004.tif"/>
</fig>
</sec>
<sec id="sec9">
<label>3.3</label>
<title>Meta-analysis</title>
<p>The combined prevalence of CD infection was 22.6% (95% CI: 0.1&#x2013;0.3; 1,937/8,582) of the 33 studies selected (<xref ref-type="table" rid="tab2">Table 2</xref>). Regionally, North America had the highest prevalence 16.1% (95% CI: 2.1&#x2013;100; 46/198), while South America had the lowest prevalence (7.3%; 95% CI: 3.0&#x2013;17.6; 16/251), and all other continents had a prevalence of more than 10% (<xref ref-type="table" rid="tab2">Table 2</xref>). In the sampling year subgroup, we found that the prevalence of CD in minks, foxes and raccoon dogs were on the rise, with the highest infection rate of 25.9% (95% CI: 17.1&#x2013;39.3) after 2010. In terms of species, the prevalence was highest in raccoon dogs (36.1%; 95% CI: 19.4&#x2013;67.2), lowest in foxes (18.9%; 95% CI: 13.1&#x2013;27.3), and 23.2% in minks (95% CI: 17.1&#x2013;31.8); in terms of age, the prevalence was 19.0% (95% CI: 8.2&#x2013;44.2) in animals sampled &#x003E;6&#x2009;months old, which was lower than that of animals &#x003C;6&#x2009;months old 20.1% (95% CI: 11.8&#x2013;34.3); in the subgroup of detection methods, the estimated pooled prevalence of CD using serological detection was 16.9% (95% CI: 11.9&#x2013;24.1) lower than that using molecular biology 27.1% (95% CI: 21.4&#x2013;34.1). In season, CD disease was more prevalent in winter 24.2% (95% CI: 14.1&#x2013;41.7). In the feeding mode subgroup, the prevalence of breeding mode was 30.6% (95% CI: 18.1&#x2013;51.9), which was significantly higher than that of free-range mode 6.9% (95% CI: 4.9&#x2013;9.8) and wild minks, foxes and raccoon dogs 24.1% (95% CI: 12.6&#x2013;46.2). In the population size subgroup, the prevalence of animals &#x003E;500 (9.8%; 95% CI: 6.4&#x2013;14.9) was lower than that of animals &#x003C;500 (17%; 95% CI: 12.9&#x2013;22.3). Prevalence was higher in articles scoring 2&#x2013;3 (23.5%; 95% CI: 17.2&#x2013;32.1). We also conducted subgroup analyses for geographical factors. The highest prevalence was observed at longitudes of 120&#x00B0;E and above 32.0% (95% CI: 21.1&#x2013;48.5), as well as at latitudes of 30&#x00B0;-60&#x00B0;N 25.0% (95% CI: 17.1&#x2013;36.6; <xref ref-type="fig" rid="fig5">Figure 5</xref>).</p>
<table-wrap position="float" id="tab2">
<label>Table 2</label>
<caption>
<p>Studies included in the analysis.</p>
</caption>
<table frame="hsides" rules="groups">
<thead>
<tr>
<th align="left" valign="top">Reference ID</th>
<th align="center" valign="top">Sampling time</th>
<th align="left" valign="top">Country</th>
<th align="left" valign="top">Detection method</th>
<th align="center" valign="top">No. tested</th>
<th align="center" valign="top">No. positive</th>
<th align="center" valign="top">Prevalence</th>
<th align="left" valign="top">Quality level</th>
</tr>
</thead>
<tbody>
<tr>
<td align="left" valign="middle" colspan="8">Asia</td>
</tr>
<tr>
<td align="left" valign="middle">Luo et al. (2008)</td>
<td align="center" valign="middle">2006&#x2013;2007</td>
<td align="left" valign="middle">China</td>
<td align="left" valign="middle">Molecular Biology</td>
<td align="center" valign="middle">522</td>
<td align="center" valign="middle">10</td>
<td align="center" valign="middle">0.019157088</td>
<td align="left" valign="middle">middle</td>
</tr>
<tr>
<td align="left" valign="middle">Liu (2009)</td>
<td align="center" valign="middle">2009&#x2013;2015</td>
<td align="left" valign="middle">China</td>
<td align="left" valign="middle">Molecular Biology</td>
<td align="center" valign="middle">553</td>
<td align="center" valign="middle">236</td>
<td align="center" valign="middle">0.426763110</td>
<td align="left" valign="middle">middle</td>
</tr>
<tr>
<td align="left" valign="middle">Feng et al. (2011)</td>
<td align="center" valign="middle">2009&#x2013;2010</td>
<td align="left" valign="middle">China</td>
<td align="left" valign="middle">Molecular Biology</td>
<td align="center" valign="middle">107</td>
<td align="center" valign="middle">7</td>
<td align="center" valign="middle">0.065420561</td>
<td align="left" valign="middle">middle</td>
</tr>
<tr>
<td align="left" valign="middle">Kameo et al. (2012)</td>
<td align="center" valign="middle">2007&#x2013;2008</td>
<td align="left" valign="middle">Japan</td>
<td align="left" valign="middle">Serology</td>
<td align="center" valign="middle">502</td>
<td align="center" valign="middle">30</td>
<td align="center" valign="middle">0.059760956</td>
<td align="left" valign="middle">middle</td>
</tr>
<tr>
<td align="left" valign="middle">Cha et al. (2012)</td>
<td align="center" valign="middle">2010&#x2013;2011</td>
<td align="left" valign="middle">Korea</td>
<td align="left" valign="middle">Molecular Biology</td>
<td align="center" valign="middle">91</td>
<td align="center" valign="middle">22</td>
<td align="center" valign="middle">0.241758242</td>
<td align="left" valign="middle">middle</td>
</tr>
<tr>
<td align="left" valign="middle">Wang et al. (2013a)</td>
<td align="center" valign="middle">2010&#x2013;2012</td>
<td align="left" valign="middle">China</td>
<td align="left" valign="middle">Molecular Biology</td>
<td align="center" valign="middle">642</td>
<td align="center" valign="middle">149</td>
<td align="center" valign="middle">0.232087227</td>
<td align="left" valign="middle">middle</td>
</tr>
<tr>
<td align="left" valign="middle">Wang et al. (2013b)</td>
<td align="center" valign="middle">2011&#x2013;2012</td>
<td align="left" valign="middle">China</td>
<td align="left" valign="middle">Serology</td>
<td align="center" valign="middle">90</td>
<td align="center" valign="middle">24</td>
<td align="center" valign="middle">0.266666667</td>
<td align="left" valign="middle">middle</td>
</tr>
<tr>
<td align="left" valign="middle">Yang et al. (2013)</td>
<td align="center" valign="middle">2011&#x2013;2012</td>
<td align="left" valign="middle">Korea</td>
<td align="left" valign="middle">Serology</td>
<td align="center" valign="middle">156</td>
<td align="center" valign="middle">30</td>
<td align="center" valign="middle">0.192307692</td>
<td align="left" valign="middle">middle</td>
</tr>
<tr>
<td align="left" valign="middle">Dai et al. (2014)</td>
<td align="center" valign="middle">2013</td>
<td align="left" valign="middle">China</td>
<td align="left" valign="middle">Serology</td>
<td align="center" valign="middle">150</td>
<td align="center" valign="middle">12</td>
<td align="center" valign="middle">0.080000000</td>
<td align="left" valign="middle">high</td>
</tr>
<tr>
<td align="left" valign="middle">Dai (2015)</td>
<td align="center" valign="middle">2013</td>
<td align="left" valign="middle">China</td>
<td align="left" valign="middle">Serology</td>
<td align="center" valign="middle">214</td>
<td align="center" valign="middle">21</td>
<td align="center" valign="middle">0.098130841</td>
<td align="left" valign="middle">high</td>
</tr>
<tr>
<td align="left" valign="middle">Zong (2015)</td>
<td align="center" valign="middle">2014</td>
<td align="left" valign="middle">China</td>
<td align="left" valign="middle">Molecular Biology</td>
<td align="center" valign="middle">321</td>
<td align="center" valign="middle">19</td>
<td align="center" valign="middle">0.059190031</td>
<td align="left" valign="middle">middle</td>
</tr>
<tr>
<td align="left" valign="middle">Suzuki et al. (2015)</td>
<td align="center" valign="middle">2006&#x2013;2012</td>
<td align="left" valign="middle">Japan</td>
<td align="left" valign="middle">Serology</td>
<td align="center" valign="middle">368</td>
<td align="center" valign="middle">62</td>
<td align="center" valign="middle">0.168478261</td>
<td align="left" valign="middle">middle</td>
</tr>
<tr>
<td align="left" valign="middle">Zhang et al. (2015)</td>
<td align="center" valign="middle">UN</td>
<td align="left" valign="middle">China</td>
<td align="left" valign="middle">Serology</td>
<td align="center" valign="middle">300</td>
<td align="center" valign="middle">6</td>
<td align="center" valign="middle">0.020000000</td>
<td align="left" valign="middle">middle</td>
</tr>
<tr>
<td align="left" valign="middle">Zhang et al. (2016)</td>
<td align="center" valign="middle">2011&#x2013;2013</td>
<td align="left" valign="middle">China</td>
<td align="left" valign="middle">Serology</td>
<td align="center" valign="middle">47</td>
<td align="center" valign="middle">4</td>
<td align="center" valign="middle">0.085106383</td>
<td align="left" valign="middle">middle</td>
</tr>
<tr>
<td align="left" valign="middle">Pang (2018)</td>
<td align="center" valign="middle">UN</td>
<td align="left" valign="middle">China</td>
<td align="left" valign="middle">Molecular Biology</td>
<td align="center" valign="middle">1,248</td>
<td align="center" valign="middle">39</td>
<td align="center" valign="middle">0.031250000</td>
<td align="left" valign="middle">middle</td>
</tr>
<tr>
<td align="left" valign="middle">Zhu (2020)</td>
<td align="center" valign="middle">UN</td>
<td align="left" valign="middle">China</td>
<td align="left" valign="middle">Serology</td>
<td align="center" valign="middle">40</td>
<td align="center" valign="middle">6</td>
<td align="center" valign="middle">0.150000000</td>
<td align="left" valign="middle">middle</td>
</tr>
<tr>
<td align="left" valign="middle" colspan="8">Europe</td>
</tr>
<tr>
<td align="left" valign="middle">Truyen et al. (1998)</td>
<td align="center" valign="middle">1991&#x2013;1995</td>
<td align="left" valign="middle">Germany</td>
<td align="left" valign="middle">Serology</td>
<td align="center" valign="middle">383</td>
<td align="center" valign="middle">17</td>
<td align="center" valign="middle">0.044386423</td>
<td align="left" valign="middle">middle</td>
</tr>
<tr>
<td align="left" valign="middle">Fr&#x00F6;lich et al. (2000)</td>
<td align="center" valign="middle">1996&#x2013;1998</td>
<td align="left" valign="middle">Germany</td>
<td align="left" valign="middle">Serology</td>
<td align="center" valign="middle">601</td>
<td align="center" valign="middle">32</td>
<td align="center" valign="middle">0.053244592</td>
<td align="left" valign="middle">middle</td>
</tr>
<tr>
<td align="left" valign="middle">Damien et al. (2002)</td>
<td align="center" valign="middle">1997&#x2013;1997</td>
<td align="left" valign="middle">Luxembourg</td>
<td align="left" valign="middle">Serology</td>
<td align="center" valign="middle">61</td>
<td align="center" valign="middle">8</td>
<td align="center" valign="middle">0.131147541</td>
<td align="left" valign="middle">high</td>
</tr>
<tr>
<td align="left" valign="middle">Philippa et al. (2008)</td>
<td align="center" valign="middle">1996&#x2013;2003</td>
<td align="left" valign="middle">France</td>
<td align="left" valign="middle">Serology</td>
<td align="center" valign="middle">280</td>
<td align="center" valign="middle">24</td>
<td align="center" valign="middle">0.085714286</td>
<td align="left" valign="middle">high</td>
</tr>
<tr>
<td align="left" valign="middle">Sobrino et al. (2008)</td>
<td align="center" valign="middle">1997&#x2013;2007</td>
<td align="left" valign="middle">Spanish</td>
<td align="left" valign="middle">Serology</td>
<td align="center" valign="middle">134</td>
<td align="center" valign="middle">23</td>
<td align="center" valign="middle">0.171641791</td>
<td align="left" valign="middle">middle</td>
</tr>
<tr>
<td align="left" valign="middle">Akerstedt et al. (2010)</td>
<td align="center" valign="middle">1994&#x2013;2005</td>
<td align="left" valign="middle">Norway</td>
<td align="left" valign="middle">Serology</td>
<td align="center" valign="middle">100</td>
<td align="center" valign="middle">12</td>
<td align="center" valign="middle">0.12</td>
<td align="left" valign="middle">middle</td>
</tr>
<tr>
<td align="left" valign="middle">Denzin et al.(2013)</td>
<td align="center" valign="middle">2010&#x2013;2011</td>
<td align="left" valign="middle">Germany</td>
<td align="left" valign="middle">Molecular Biology</td>
<td align="center" valign="middle">761</td>
<td align="center" valign="middle">232</td>
<td align="center" valign="middle">0.304862024</td>
<td align="left" valign="middle">middle</td>
</tr>
<tr>
<td align="left" valign="middle">Lempp et al. (2017)</td>
<td align="center" valign="middle">2013&#x2013;2016</td>
<td align="left" valign="middle">Germany</td>
<td align="left" valign="middle">Serology</td>
<td align="center" valign="middle">30</td>
<td align="center" valign="middle">1</td>
<td align="center" valign="middle">0.033333333</td>
<td align="left" valign="middle">high</td>
</tr>
<tr>
<td align="left" valign="middle">Tryland et al. (2018)</td>
<td align="center" valign="middle">1995&#x2013;2003</td>
<td align="left" valign="middle">Norway</td>
<td align="left" valign="middle">Serology</td>
<td align="center" valign="middle">178</td>
<td align="center" valign="middle">20</td>
<td align="center" valign="middle">0.112359551</td>
<td align="left" valign="middle">middle</td>
</tr>
<tr>
<td align="left" valign="middle">Trogu et al. (2021)</td>
<td align="center" valign="middle">2018&#x2013;2020</td>
<td align="left" valign="middle">Italy</td>
<td align="left" valign="middle">Molecular Biology</td>
<td align="center" valign="middle">133</td>
<td align="center" valign="middle">51</td>
<td align="center" valign="middle">0.383458647</td>
<td align="left" valign="middle">middle</td>
</tr>
<tr>
<td align="left" valign="middle" colspan="8">North America</td>
</tr>
<tr>
<td align="left" valign="middle">Amundson et al. (1981)</td>
<td align="center" valign="middle">1978&#x2013;1979</td>
<td align="left" valign="middle">USA</td>
<td align="left" valign="middle">Serology</td>
<td align="center" valign="middle">57</td>
<td align="center" valign="middle">6</td>
<td align="center" valign="middle">0.105263158</td>
<td align="left" valign="middle">high</td>
</tr>
<tr>
<td align="left" valign="middle">McCue PM and O&#x2019;Farrell TP (1988)</td>
<td align="center" valign="middle">1981&#x2013;1984</td>
<td align="left" valign="middle">USA</td>
<td align="left" valign="middle">Serology</td>
<td align="center" valign="middle">100</td>
<td align="center" valign="middle">4</td>
<td align="center" valign="middle">0.04</td>
<td align="left" valign="middle">high</td>
</tr>
<tr>
<td align="left" valign="middle">Timm et al. (2009)</td>
<td align="center" valign="middle">1999&#x2013;2000</td>
<td align="left" valign="middle">USA</td>
<td align="left" valign="middle">Serology</td>
<td align="center" valign="middle">41</td>
<td align="center" valign="middle">36</td>
<td align="center" valign="middle">0.87804878</td>
<td align="left" valign="middle">middle</td>
</tr>
<tr>
<td align="left" valign="middle" colspan="8">South America</td>
</tr>
<tr>
<td align="left" valign="middle">Martino et al. (2004)</td>
<td align="center" valign="middle">1998&#x2013;2001</td>
<td align="left" valign="middle">Argentina</td>
<td align="left" valign="middle">Serology</td>
<td align="center" valign="middle">84</td>
<td align="center" valign="middle">3</td>
<td align="center" valign="middle">0.035714286</td>
<td align="left" valign="middle">high</td>
</tr>
<tr>
<td align="left" valign="middle">Furtado et al. (2016)</td>
<td align="center" valign="middle">2000&#x2013;2008</td>
<td align="left" valign="middle">Brazil</td>
<td align="left" valign="middle">Serology</td>
<td align="center" valign="top">58</td>
<td align="center" valign="top">7</td>
<td align="center" valign="top">0.120689655</td>
<td align="left" valign="top">high</td>
</tr>
<tr>
<td align="left" valign="top">Martino et al. (2017)</td>
<td align="center" valign="top">2013&#x2013;2015</td>
<td align="left" valign="top">Argentina</td>
<td align="left" valign="top">Serology</td>
<td align="center" valign="top">87</td>
<td align="center" valign="top">2</td>
<td align="center" valign="top">0.022988506</td>
<td align="left" valign="top">middle</td>
</tr>
<tr>
<td align="left" valign="top">Weber et al. (2020)</td>
<td align="center" valign="top">2017&#x2013;2019</td>
<td align="left" valign="top">Brazil</td>
<td align="left" valign="top">Molecular Biology</td>
<td align="center" valign="top">22</td>
<td align="center" valign="top">4</td>
<td align="center" valign="top">0.181818182</td>
<td align="left" valign="top">middle</td>
</tr>
</tbody>
</table>
<table-wrap-foot>
<p>Quality&#x002A;: low (0&#x2013;1), medium (2&#x2013;3), high (4&#x2013;5).</p>
</table-wrap-foot>
</table-wrap>
<fig position="float" id="fig5">
<label>Figure 5</label>
<caption>
<p>Sensitivity analysis.</p>
</caption>
<graphic xlink:href="fvets-11-1394631-g005.tif"/>
</fig>
</sec>
</sec>
<sec sec-type="discussion" id="sec10">
<label>4</label>
<title>Discussion</title>
<p>Infections caused by Canine distemper virus (CDV) are highly lethal to minks, foxes and raccoon dogs and have a significant impact on the fur animal farming industry (<xref ref-type="bibr" rid="ref14">14</xref>). Therefore, we conducted a meta-analysis of Canine distemper virus infections in minks, foxes and raccoon dogs around the world, using stress sampling years and other methods (<xref ref-type="table" rid="tab3">Table 3</xref>).</p>
<table-wrap position="float" id="tab3">
<label>Table 3</label>
<caption>
<p>Pooled prevalence of canine distemper virus of small carnivores in the world.</p>
</caption>
<table frame="hsides" rules="groups">
<thead>
<tr>
<th rowspan="2"/>
<th rowspan="2"/>
<th align="center" valign="top" rowspan="2">No. studies</th>
<th align="center" valign="top" rowspan="2">No. tested</th>
<th align="center" valign="top" rowspan="2">No. positive</th>
<th align="center" valign="top" rowspan="2">% (95% CI&#x002A;)</th>
<th align="center" valign="top" colspan="3">Heterogeneity</th>
<th align="center" valign="top" colspan="2">Univariate meta-regression</th>
</tr>
<tr>
<th align="center" valign="top">&#x03C7;<sup>2</sup></th>
<th align="center" valign="top"><italic>p</italic>-value</th>
<th align="center" valign="top">I<sup>2</sup> (%)</th>
<th align="center" valign="top"><italic>p</italic>-value</th>
<th align="center" valign="top">Coefficient (95% CI)</th>
</tr>
</thead>
<tbody>
<tr>
<td align="left" valign="top" rowspan="5">Region&#x002A;</td>
<td colspan="10"/>
</tr>
<tr>
<td align="left" valign="top">Asia</td>
<td align="center" valign="top">16</td>
<td align="center" valign="top">5,472</td>
<td align="center" valign="top">1,455</td>
<td align="center" valign="top">13.8% (22.2&#x2013;45.6)</td>
<td align="center" valign="top">1384.22</td>
<td align="center" valign="top">0.00</td>
<td align="center" valign="top">99.2%</td>
<td align="center" valign="top">0.0004</td>
<td align="center" valign="top">1.004 (0.4472 to 1.5609)</td>
</tr>
<tr>
<td align="left" valign="top">Europe</td>
<td align="center" valign="top">10</td>
<td align="center" valign="top">2,661</td>
<td align="center" valign="top">420</td>
<td align="center" valign="top">11.2% (7.0&#x2013;19.8)</td>
<td align="center" valign="top">227.47</td>
<td align="center" valign="top">&#x003C; 0.01</td>
<td align="center" valign="top">96.0%</td>
<td/>
<td/>
</tr>
<tr>
<td align="left" valign="top">South America</td>
<td align="center" valign="top">4</td>
<td align="center" valign="top">251</td>
<td align="center" valign="top">16</td>
<td align="center" valign="top">7.3% (3.0&#x2013;17.6)</td>
<td align="center" valign="top">9.577</td>
<td align="center" valign="top">0.02</td>
<td align="center" valign="top">68.7%</td>
<td/>
<td/>
</tr>
<tr>
<td align="left" valign="top">North America</td>
<td align="center" valign="top">3</td>
<td align="center" valign="top">198</td>
<td align="center" valign="top">46</td>
<td align="center" valign="top">16.1% (2.1&#x2013;100)</td>
<td align="center" valign="top">67.53</td>
<td align="center" valign="top">&#x003C; 0.01</td>
<td align="center" valign="top">97.0%</td>
<td/>
<td/>
</tr>
<tr>
<td align="left" valign="top" colspan="11">Sampling yeas</td>
</tr>
<tr>
<td rowspan="3"/>
<td align="left" valign="top">before 2000</td>
<td align="center" valign="top">7</td>
<td align="center" valign="top">1,421</td>
<td align="center" valign="top">123</td>
<td align="center" valign="top">10.3% (2.8&#x2013;38.2)</td>
<td align="center" valign="top">486.25</td>
<td align="center" valign="top">&#x003C; 0.01</td>
<td align="center" valign="top">98.8%</td>
<td align="center" valign="top">0.0189</td>
<td align="center" valign="top">&#x2212;0.8412 (&#x2212;1.5434 to &#x2212;0.1390)</td>
</tr>
<tr>
<td align="left" valign="top">2000 to 2010</td>
<td align="center" valign="top">41</td>
<td align="center" valign="top">1,305</td>
<td align="center" valign="top">424</td>
<td align="center" valign="top">24.7% (16&#x2013;38.0)</td>
<td align="center" valign="top">33.26</td>
<td align="center" valign="top">&#x003C; 0.01</td>
<td align="center" valign="top">91.0%</td>
<td/>
<td/>
</tr>
<tr>
<td align="left" valign="top">2010 or late</td>
<td align="center" valign="top">13</td>
<td align="center" valign="top">4,336</td>
<td align="center" valign="top">1,804</td>
<td align="center" valign="top">25.9% (17.1&#x2013;39.3)</td>
<td align="center" valign="top">919.46</td>
<td align="center" valign="top">&#x003C; 0.01</td>
<td align="center" valign="top">98.7%</td>
<td/>
<td/>
</tr>
<tr>
<td align="left" valign="top" colspan="11">Species</td>
</tr>
<tr>
<td rowspan="3"/>
<td align="left" valign="top">Mink</td>
<td align="center" valign="top">17</td>
<td align="center" valign="top">4,205</td>
<td align="center" valign="top">820</td>
<td align="center" valign="top">23.2% (17.1&#x2013;31.8)</td>
<td align="center" valign="top">336.48</td>
<td align="center" valign="top">&#x003C; 0.01</td>
<td align="center" valign="top">95.2%</td>
<td/>
<td/>
</tr>
<tr>
<td align="left" valign="top">Fox</td>
<td align="center" valign="top">20</td>
<td align="center" valign="top">3,722</td>
<td align="center" valign="top">893</td>
<td align="center" valign="top">18.9% (13.1&#x2013;27.3)</td>
<td align="center" valign="top">1281.28</td>
<td align="center" valign="top">&#x003C; 0.01</td>
<td align="center" valign="top">98.5%</td>
<td/>
<td/>
</tr>
<tr>
<td align="left" valign="top">Racconn Dog</td>
<td align="center" valign="top">7</td>
<td align="center" valign="top">655</td>
<td align="center" valign="top">224</td>
<td align="center" valign="top">36.1% (19.4&#x2013;67.2)</td>
<td align="center" valign="top">231.02</td>
<td align="center" valign="top">&#x003C; 0.01</td>
<td align="center" valign="top">97.4%</td>
<td align="center" valign="top">0.1239</td>
<td align="center" valign="top">0.5549 (&#x2212;0.1520 to 1.2618)</td>
</tr>
<tr>
<td align="left" valign="top" colspan="11">Detection method&#x002A;</td>
</tr>
<tr>
<td rowspan="2"/>
<td align="left" valign="top">Serology</td>
<td align="center" valign="top">23</td>
<td align="center" valign="bottom">4,347</td>
<td align="center" valign="bottom">808</td>
<td align="center" valign="top">16.9% (11.9&#x2013;24.1)</td>
<td align="center" valign="top">1748.73</td>
<td align="center" valign="top">0.00</td>
<td align="center" valign="top">98.7%</td>
<td/>
<td/>
</tr>
<tr>
<td align="left" valign="top">Molecular biology</td>
<td align="center" valign="top">10</td>
<td align="center" valign="bottom">4,235</td>
<td align="center" valign="bottom">1,129</td>
<td align="center" valign="top">27.1% (21.4&#x2013;34.1)</td>
<td align="center" valign="top">169.97</td>
<td align="center" valign="top">&#x003C; 0.01</td>
<td align="center" valign="top">94.7%</td>
<td align="center" valign="top">0.1193</td>
<td align="center" valign="top">0.4259 (&#x2212;0.1100 to 0.9619)</td>
</tr>
<tr>
<td align="left" valign="top" colspan="11">Sample</td>
</tr>
<tr>
<td rowspan="3"/>
<td align="left" valign="top">Seurm</td>
<td align="center" valign="top">20</td>
<td align="center" valign="middle">2,896</td>
<td align="center" valign="middle">555</td>
<td align="center" valign="top">18.5% (13.2&#x2013;25.9)</td>
<td align="center" valign="top">1168.00</td>
<td align="center" valign="top">&#x003C; 0.01</td>
<td align="center" valign="top">98.4%</td>
<td/>
<td/>
</tr>
<tr>
<td align="left" valign="top">Tissue</td>
<td align="center" valign="top">12</td>
<td align="center" valign="middle">4,621</td>
<td align="center" valign="middle">1.217</td>
<td align="center" valign="top">24.2% (19.5&#x2013;30.7)</td>
<td align="center" valign="top">200.15</td>
<td align="center" valign="top">&#x003C; 0.01</td>
<td align="center" valign="top">94.5%</td>
<td align="center" valign="top">0.3947</td>
<td align="center" valign="top">0.2217 (&#x2212;0.289 to 0.7322)</td>
</tr>
<tr>
<td align="left" valign="top">Secretion</td>
<td align="center" valign="top">1</td>
<td align="center" valign="middle">1,065</td>
<td align="center" valign="middle">165</td>
<td align="center" valign="top">19.6% (13.5&#x2013;17.8)</td>
<td align="center" valign="top">0.00</td>
<td align="center" valign="top">un</td>
<td align="center" valign="top">&#x2013;</td>
<td/>
<td/>
</tr>
<tr>
<td align="left" valign="top" colspan="11">Breeding mode</td>
</tr>
<tr>
<td rowspan="3"/>
<td align="left" valign="top">Intensive</td>
<td align="center" valign="top">9</td>
<td align="center" valign="top">4,419</td>
<td align="center" valign="top">1,158</td>
<td align="center" valign="top">30.6%(18.1&#x2013;51.9)</td>
<td align="center" valign="top">1419.50</td>
<td align="center" valign="top">&#x003C; 0.01</td>
<td align="center" valign="top">99.4%</td>
<td align="center" valign="top">0.0229</td>
<td align="center" valign="top">0.8844 (0.1228 to 1.6461)</td>
</tr>
<tr>
<td align="left" valign="top">Free-ranging</td>
<td align="center" valign="top">8</td>
<td align="center" valign="top">1,650</td>
<td align="center" valign="top">103</td>
<td align="center" valign="top">6.9% (4.9&#x2013;9.8)</td>
<td align="center" valign="top">20.01</td>
<td align="center" valign="top">&#x003C; 0.01</td>
<td align="center" valign="top">65.0%</td>
<td/>
<td/>
</tr>
<tr>
<td align="left" valign="top">Wild</td>
<td align="center" valign="top">8</td>
<td align="center" valign="top">898</td>
<td align="center" valign="top">253</td>
<td align="center" valign="top">24.1% (12.6&#x2013;46.2)</td>
<td align="center" valign="top">328.92</td>
<td align="center" valign="top">&#x003C; 0.01</td>
<td align="center" valign="top">97.9%</td>
<td/>
<td/>
</tr>
<tr>
<td align="left" valign="top" colspan="11">Longitude</td>
</tr>
<tr>
<td rowspan="5"/>
<td align="left" valign="top">Less90W</td>
<td align="center" valign="top">4</td>
<td align="center" valign="top">509</td>
<td align="center" valign="top">36</td>
<td align="center" valign="top">6.8% (3.8&#x2013;12.2)</td>
<td align="center" valign="top">6.68</td>
<td align="center" valign="top">0.08</td>
<td align="center" valign="top">55.1%</td>
<td align="center" valign="top">0.0035</td>
<td align="center" valign="top">&#x2212;1.3807 (&#x2212;2.3080 to &#x2212;0.4535)</td>
</tr>
<tr>
<td align="left" valign="top">Less90E</td>
<td align="center" valign="top">3</td>
<td align="center" valign="top">632</td>
<td align="center" valign="top">75</td>
<td align="center" valign="top">14.0% (3.0&#x2013;66.1)</td>
<td align="center" valign="top">113.45</td>
<td align="center" valign="top">&#x003C; 0.01</td>
<td align="center" valign="top">98.2%</td>
<td/>
<td/>
</tr>
<tr>
<td align="left" valign="top">90-120&#x2009;W</td>
<td align="center" valign="top">1</td>
<td align="center" valign="top">100</td>
<td align="center" valign="top">4</td>
<td align="center" valign="top">4.0% (1.5&#x2013;10.5)</td>
<td align="center" valign="top">0.00</td>
<td align="center" valign="top">un</td>
<td align="center" valign="top">&#x2013;</td>
<td/>
<td/>
</tr>
<tr>
<td align="left" valign="top">90-120E</td>
<td align="center" valign="top">4</td>
<td align="center" valign="top">1,469</td>
<td align="center" valign="top">297</td>
<td align="center" valign="top">25.4% (7.4&#x2013;87.0)</td>
<td align="center" valign="top">723.84</td>
<td align="center" valign="top">&#x003C; 0.01</td>
<td align="center" valign="top">99.6%</td>
<td/>
<td/>
</tr>
<tr>
<td align="left" valign="top">more120E</td>
<td align="center" valign="top">10</td>
<td align="center" valign="top">2,709</td>
<td align="center" valign="top">833</td>
<td align="center" valign="top">32.0% (21.1&#x2013;48.5)</td>
<td align="center" valign="top">626.90</td>
<td align="center" valign="top">&#x003C; 0.01</td>
<td align="center" valign="top">98.6%</td>
<td/>
<td/>
</tr>
<tr>
<td align="left" valign="top" colspan="11">Latitude</td>
</tr>
<tr>
<td rowspan="4"/>
<td align="left" valign="top">0-30S</td>
<td align="center" valign="top">1</td>
<td align="center" valign="top">58</td>
<td align="center" valign="top">7</td>
<td align="center" valign="top">12.1% (6.0&#x2013;24.2)</td>
<td align="center" valign="top">0.00</td>
<td align="center" valign="top">un</td>
<td/>
<td/>
<td/>
</tr>
<tr>
<td align="left" valign="top">30-60S</td>
<td align="center" valign="top">2</td>
<td align="center" valign="top">171</td>
<td align="center" valign="top">5</td>
<td align="center" valign="top">3.0% (1.3&#x2013;7.1)</td>
<td align="center" valign="top">0.24</td>
<td align="center" valign="top">0.62</td>
<td align="center" valign="top">0.0%</td>
<td align="center" valign="top">0.0061</td>
<td align="center" valign="top">1.3237 (0.3783 to 2.2692)</td>
</tr>
<tr>
<td align="left" valign="top">30-60&#x2009;N</td>
<td align="center" valign="top">16</td>
<td align="center" valign="top">5,012</td>
<td align="center" valign="top">1,213</td>
<td align="center" valign="top">25.0% (17.1&#x2013;36.6)</td>
<td align="center" valign="top">1775.87</td>
<td align="center" valign="top">0.00</td>
<td align="center" valign="top">99.2%</td>
<td/>
<td/>
</tr>
<tr>
<td align="left" valign="top">60-90&#x2009;N</td>
<td align="center" valign="top">1</td>
<td align="center" valign="top">178</td>
<td align="center" valign="top">20</td>
<td align="center" valign="top">11.2% (7.4&#x2013;17.0)</td>
<td align="center" valign="top">0.00</td>
<td align="center" valign="top">un</td>
<td/>
<td/>
<td/>
</tr>
<tr>
<td align="left" valign="top" colspan="11">Season&#x002A;</td>
</tr>
<tr>
<td rowspan="4"/>
<td align="left" valign="top">Spring</td>
<td align="center" valign="top">4</td>
<td align="center" valign="top">377</td>
<td align="center" valign="top">107</td>
<td align="center" valign="top">17.7% (8.3&#x2013;37.8)</td>
<td align="center" valign="top">20.03</td>
<td align="center" valign="top">&#x003C; 0.01</td>
<td align="center" valign="top">85.0%</td>
<td/>
<td/>
</tr>
<tr>
<td align="left" valign="top">Summer</td>
<td align="center" valign="top">6</td>
<td align="center" valign="top">1,454</td>
<td align="center" valign="top">286</td>
<td align="center" valign="top">16.3% (10.4&#x2013;25.5)</td>
<td align="center" valign="top">68.81</td>
<td align="center" valign="top">&#x003C; 0.01</td>
<td align="center" valign="top">92.7%</td>
<td align="center" valign="top">0.2629</td>
<td align="center" valign="top">&#x2212;0.3520 (&#x2212;0.9682 to 0.2642)</td>
</tr>
<tr>
<td align="left" valign="top">Autumn</td>
<td align="center" valign="top">6</td>
<td align="center" valign="top">1,295</td>
<td align="center" valign="top">301</td>
<td align="center" valign="top">23.7% (16.5&#x2013;34.2)</td>
<td align="center" valign="top">68.82</td>
<td align="center" valign="top">&#x003C; 0.01</td>
<td align="center" valign="top">92.7%</td>
<td/>
<td/>
</tr>
<tr>
<td align="left" valign="top">Winter</td>
<td align="center" valign="top">7</td>
<td align="center" valign="top">649</td>
<td align="center" valign="top">226</td>
<td align="center" valign="top">24.2% (14.1&#x2013;41.7)</td>
<td align="center" valign="top">162.48</td>
<td align="center" valign="top">&#x003C; 0.01</td>
<td align="center" valign="top">96.3%</td>
<td/>
<td/>
</tr>
<tr>
<td align="left" valign="top" colspan="11">Age&#x002A;</td>
</tr>
<tr>
<td rowspan="2"/>
<td align="left" valign="top">Juvenile</td>
<td align="center" valign="top">5</td>
<td align="center" valign="top">2,151</td>
<td align="center" valign="top">569</td>
<td align="center" valign="top">20.1% (11.8&#x2013;34.3)</td>
<td align="center" valign="top">140.10</td>
<td align="center" valign="top">&#x003C; 0.01</td>
<td align="center" valign="top">97.1%</td>
<td align="center" valign="top">0.8135</td>
<td align="center" valign="top">&#x2212;0.1128 (&#x2212;1.0493 to 0.8238)</td>
</tr>
<tr>
<td align="left" valign="top">Adults</td>
<td align="center" valign="top">6</td>
<td align="center" valign="top">638</td>
<td align="center" valign="top">186</td>
<td align="center" valign="top">19.0% (8.2&#x2013;44.2)</td>
<td align="center" valign="top">247.73</td>
<td align="center" valign="top">&#x003C; 0.01</td>
<td align="center" valign="top">98.0%</td>
<td/>
<td/>
</tr>
<tr>
<td align="left" valign="top" colspan="11">Health</td>
</tr>
<tr>
<td rowspan="3"/>
<td align="left" valign="top">Good</td>
<td align="center" valign="top">6</td>
<td align="center" valign="top">3,187</td>
<td align="center" valign="top">807</td>
<td align="center" valign="top">27.2% (18.8&#x2013;39.5)</td>
<td align="center" valign="top">182.83</td>
<td align="center" valign="top">&#x003C; 0.01</td>
<td align="center" valign="top">97.3%</td>
<td align="center" valign="top">&#x003C; 0.01</td>
<td align="center" valign="top">1.8986 (0.9953 to 2.8019)</td>
</tr>
<tr>
<td align="left" valign="top">Dead</td>
<td align="center" valign="top">1</td>
<td align="center" valign="top">601</td>
<td align="center" valign="top">32</td>
<td align="center" valign="top">5.0% (3.5&#x2013;6.9)</td>
<td align="center" valign="top">0.00</td>
<td align="center" valign="top">un</td>
<td align="center" valign="top">&#x2013;</td>
<td/>
<td/>
</tr>
<tr>
<td align="left" valign="top">Sick</td>
<td align="center" valign="top">1</td>
<td align="center" valign="top">87</td>
<td align="center" valign="top">2</td>
<td align="center" valign="top">2.3% (0.6&#x2013;9.1)</td>
<td align="center" valign="top">0.00</td>
<td align="center" valign="top">un</td>
<td align="center" valign="top">&#x2013;</td>
<td/>
<td/>
</tr>
<tr>
<td align="left" valign="top" colspan="11">Population</td>
</tr>
<tr>
<td rowspan="2"/>
<td align="left" valign="top">&#x003C;500</td>
<td align="center" valign="top">3</td>
<td align="center" valign="top">832</td>
<td align="center" valign="top">148</td>
<td align="center" valign="top">17% (12.9&#x2013;22.3)</td>
<td align="center" valign="top">5.41</td>
<td align="center" valign="top">0.07</td>
<td align="center" valign="top">63.0</td>
<td align="center" valign="top">0.0296</td>
<td align="center" valign="top">0.5480 (0.0541 to 1.0419)</td>
</tr>
<tr>
<td align="left" valign="top">&#x003E;500</td>
<td align="center" valign="top">3</td>
<td align="center" valign="top">394</td>
<td align="center" valign="top">37</td>
<td align="center" valign="top">9.8% (6.4&#x2013;14.9)</td>
<td align="center" valign="top">2.75</td>
<td align="center" valign="top">0.25</td>
<td align="center" valign="top">27.3%</td>
<td/>
<td/>
</tr>
<tr>
<td align="left" valign="top" colspan="11">Quality level</td>
</tr>
<tr>
<td/>
<td align="left" valign="top">Middle</td>
<td align="center" valign="top">24</td>
<td align="center" valign="top">7,712</td>
<td align="center" valign="top">1,815</td>
<td align="center" valign="top">23.5% (17.-32.1)</td>
<td align="center" valign="top">2462.80</td>
<td align="center" valign="top">0.00</td>
<td align="center" valign="top">99.1%</td>
<td align="center" valign="top">0.0279</td>
<td align="center" valign="top">0.7162 (0.0778 to 1.3545)</td>
</tr>
<tr>
<td/>
<td align="left" valign="top">High</td>
<td align="center" valign="top">9</td>
<td align="center" valign="top">870</td>
<td align="center" valign="top">122</td>
<td align="center" valign="top">11.4% (6.6&#x2013;20.1)</td>
<td align="center" valign="top">79.49</td>
<td align="center" valign="top">&#x003C; 0.01</td>
<td align="center" valign="top">89.9%%</td>
<td/>
<td/>
</tr>
<tr>
<td align="left" valign="top">Total</td>
<td/>
<td align="center" valign="top">33</td>
<td align="center" valign="top">8,582</td>
<td align="center" valign="top">1,937</td>
<td align="center" valign="top">22.6% (15.0&#x2013;26.0)</td>
<td align="center" valign="top">2679.02</td>
<td align="center" valign="top">0.000</td>
<td align="center" valign="top">98.8%</td>
<td/>
<td/>
</tr>
</tbody>
</table>
<table-wrap-foot>
<p>CI&#x002A;: Confidence interval; Region&#x002A;: Asia (China, Japan, Korea), Europe (Norway, Luxembourg. Germany, France, Spanish, Italy), North America (the United State), South America (Argentina, Brazil). Method&#x002A;: Serology: Colloidal Gold; ELISA (Enzyme Linked Immunosorbent Assay); VNT (Virus Neutralization Test); SNT (Serum Neutralization Test); IFA (Indirect Immunoinfluscent Assay); NPLA (neutralizing peroxidase-linked antibody Assay), Molecular Biology: Quick Detection of Test Paper; PCR (Polymerase Chain Reaction); RT-PCR (Reverse Transcription PCR). Season&#x002A;: Spring: Mar. to May; Summer: Jun. to Aug.; Autumn: Sep. to Nov.; Winter: Dec. to Feb. Age&#x002A;: adult (&#x003E; 6&#x2009;month) and juvenile (&#x2264; 6&#x2009;month). Incorporate article in <xref rid="SM1" ref-type="supplementary-material">Supplementary Figures</xref>.</p>
</table-wrap-foot>
</table-wrap>
<p>The prevalence of CDV in minks, foxes and raccoon dogs was 10.3% before 2000. The reason for the low prevalence may be related to the policies of some countries toward minks, foxes and raccoon dogs. For example, the UK amended the &#x201C;European Convention for the Protection of Farm Animals &#x201C;in 1992 (<xref ref-type="bibr" rid="ref18">18</xref>). The United States Federal Government enacted the Animal Welfare Act in 1966, followed by the Improving Laboratory Animal Standards Act in 1985, and the EU introduced the animal welfare related bill &#x201C;Council Directive 98 /58 /EC &#x201C;in 1998 (<xref ref-type="bibr" rid="ref19">19</xref>). However, there was no significant difference between the prevalence rates from 2000 to 2010 and that from 2010 to the present. There are also many countries that have introduced relevant welfare bills, but the popularity of CD is still spreading in various countries. Our analysis showed that as the improvement of economic level, people&#x2019;s demand for fur animals is gradually increasing. In European social cognition, people attach great importance to fur, believing that it can not only keep warm and cover the body, but also show their status and wealth (<xref ref-type="bibr" rid="ref20">20</xref>). People should not only focus on the economic value of fur-bearing animals, but also regulate breeding and prevent CD (<xref ref-type="table" rid="tab4">Table 4</xref>).</p>
<table-wrap position="float" id="tab4">
<label>Table 4</label>
<caption>
<p>Estimation of prevalence rate of canine distemper small carnivores in various countries.</p>
</caption>
<table frame="hsides" rules="groups">
<thead>
<tr>
<th align="left" valign="top">Countries</th>
<th align="center" valign="top">No. studies</th>
<th align="left" valign="top">Region</th>
<th align="center" valign="top">No. tested</th>
<th align="center" valign="top">No. positive</th>
<th align="center" valign="top">% Prevalence</th>
<th align="center" valign="top">% (95% CI)</th>
</tr>
</thead>
<tbody>
<tr>
<td align="left" valign="middle">China</td>
<td align="center" valign="middle">12</td>
<td align="left" valign="middle">Asia</td>
<td align="center" valign="middle">4,937</td>
<td align="center" valign="middle">1,278</td>
<td align="center" valign="middle">25.9%</td>
<td align="center" valign="top">19.8&#x2013;48.3</td>
</tr>
<tr>
<td align="left" valign="middle">Japan</td>
<td align="center" valign="middle">2</td>
<td align="left" valign="middle">Asia</td>
<td align="center" valign="middle">339</td>
<td align="center" valign="middle">48</td>
<td align="center" valign="middle">14.2%</td>
<td align="center" valign="top">9.4&#x2013;29.2</td>
</tr>
<tr>
<td align="left" valign="middle">Korea</td>
<td align="center" valign="middle">2</td>
<td align="left" valign="middle">Asia</td>
<td align="center" valign="middle">196</td>
<td align="center" valign="middle">129</td>
<td align="center" valign="middle">65.8%</td>
<td align="center" valign="top">83.3&#x2013;95.8</td>
</tr>
<tr>
<td align="left" valign="middle">France</td>
<td align="center" valign="middle">1</td>
<td align="left" valign="middle">Europe</td>
<td align="center" valign="middle">280</td>
<td align="center" valign="middle">24</td>
<td align="center" valign="middle">8.6%</td>
<td align="center" valign="top">5.9&#x2013;12.6</td>
</tr>
<tr>
<td align="left" valign="middle">Germany</td>
<td align="center" valign="middle">4</td>
<td align="left" valign="middle">Europe</td>
<td align="center" valign="middle">1775</td>
<td align="center" valign="middle">282</td>
<td align="center" valign="middle">15.9%</td>
<td align="center" valign="top">2.0&#x2013;28.1</td>
</tr>
<tr>
<td align="left" valign="middle">Italy</td>
<td align="center" valign="middle">1</td>
<td align="left" valign="middle">Europe</td>
<td align="center" valign="middle">133</td>
<td align="center" valign="middle">51</td>
<td align="center" valign="middle">38.3%</td>
<td align="center" valign="top">30.9&#x2013;47.6</td>
</tr>
<tr>
<td align="left" valign="middle">Luxembourg</td>
<td align="center" valign="middle">1</td>
<td align="left" valign="middle">Europe</td>
<td align="center" valign="middle">61</td>
<td align="center" valign="middle">8</td>
<td align="center" valign="middle">13.1%</td>
<td align="center" valign="top">6.9&#x2013;25.0</td>
</tr>
<tr>
<td align="left" valign="middle">Norway</td>
<td align="center" valign="middle">2</td>
<td align="left" valign="middle">Europe</td>
<td align="center" valign="middle">278</td>
<td align="center" valign="middle">32</td>
<td align="center" valign="middle">11.5%</td>
<td align="center" valign="top">8.3&#x2013;16.0</td>
</tr>
<tr>
<td align="left" valign="middle">Spanish</td>
<td align="center" valign="middle">1</td>
<td align="left" valign="middle">Europe</td>
<td align="center" valign="middle">134</td>
<td align="center" valign="middle">23</td>
<td align="center" valign="middle">17.2%</td>
<td align="center" valign="top">11.8&#x2013;24.9</td>
</tr>
<tr>
<td align="left" valign="middle">USA</td>
<td align="center" valign="middle">3</td>
<td align="left" valign="middle">North America</td>
<td align="center" valign="middle">198</td>
<td align="center" valign="middle">46</td>
<td align="center" valign="middle">23.2%</td>
<td align="center" valign="top">2.1&#x2013;100</td>
</tr>
<tr>
<td align="left" valign="middle">Argentina</td>
<td align="center" valign="middle">2</td>
<td align="left" valign="middle">South America</td>
<td align="center" valign="middle">171</td>
<td align="center" valign="middle">5</td>
<td align="center" valign="middle">2.9%</td>
<td align="center" valign="top">1.3&#x2013;7.1</td>
</tr>
<tr>
<td align="left" valign="middle">Brazil</td>
<td align="center" valign="middle">2</td>
<td align="left" valign="middle">South America</td>
<td align="center" valign="middle">80</td>
<td align="center" valign="middle">11</td>
<td align="center" valign="middle">13.8%</td>
<td align="center" valign="top">8.2&#x2013;24.4</td>
</tr>
<tr>
<td align="left" valign="top">Total</td>
<td align="center" valign="top">33</td>
<td/>
<td align="center" valign="middle">8,582</td>
<td align="center" valign="middle">1937</td>
<td align="center" valign="middle">22.6%</td>
<td align="center" valign="top">11.4&#x2013;24.8</td>
</tr>
</tbody>
</table>
</table-wrap>
<p>This paper found that CDV is widely prevalent in minks, foxes and raccoon dogs, but the prevalence rate is the highest in raccoon dogs. This finding was similar to the prevalence previously found in German mink (<xref ref-type="bibr" rid="ref21">21</xref>). The high prevalence of CDV may be related to the natural habitat of these species. Their proximity to human life makes them more likely to have direct or indirect contact with dogs infected with CDV (<xref ref-type="bibr" rid="ref22">22</xref>). Studies have shown that in Germany, the CDV strains in dogs and free-range carnivores are the same, indicating that these dogs can be used as an external virus source for free-range populations (<xref ref-type="bibr" rid="ref23">23</xref>). This was also the main reason for the spread of CDV in wild minks, foxes and raccoon dogs.</p>
<p>In the national group, South Korea had the highest positive rate. We think this may be related to the &#x201C;breeding fever &#x201C;of fur animals in South Korea in the 1920s (<xref ref-type="bibr" rid="ref24">24</xref>). With regard to CDV in Italy, there have been several outbreaks of foxes (<italic>Vulpes Vulpes</italic>), badgers (<italic>Meles Meles</italic>) and minks in the alpine regions of northeastern Italy over the past decade, particularly in the Trentino-Al Adige, Veneto and Friuli-Venetian-Giulia region (<xref ref-type="bibr" rid="ref25">25</xref>). The ease of transmission of the virus and its rapid arrival in the former alpine and urbanized areas of Italy, South Bavaria and Switzerland, which also leads to a high positive rate in Italy (<xref ref-type="bibr" rid="ref26">26</xref>, <xref ref-type="bibr" rid="ref27">27</xref>). The United States, China as a big country of fur animal breeding with the increase of fur animal prices, breeding volume and breeding density are increasing (<xref ref-type="bibr" rid="ref28">28</xref>). With the frequent introduction at home and abroad, this has caused great pressure on the prevention and control of CD (<xref ref-type="bibr" rid="ref29">29</xref>). Although the CD vaccine has been widely used in China&#x2019;s fur animal breeding industry, the mortality of fur animals such as minks, foxes and raccoon dogs caused by CDV remains high (<xref ref-type="bibr" rid="ref29">29</xref>). According to the analysis of geographical grouping, we found that the prevalence of CDV was higher in the range of longitude 120E-180 and latitude 30-60&#x2009;N. Through our study, we found that Asia and North America are just in this latitude and longitude range. At the same time, we believe that the two continents in the temperate monsoon climate have similar climatic characteristics. The annual temperature difference is large and the winter is cold. This is consistent with our findings - higher incidence in winter. But this is not consistent with the results of Dorji (<xref ref-type="bibr" rid="ref30">30</xref>). The prevalence of CD was higher in winter. We combined the latitude and longitude, season and other factors to infer that this may be related to the breeding mode of minks, foxes and raccoon dogs. For farmers who raise fur animals for a living, the high incidence of CD can cause significant economic losses.</p>
<p>In the breeding mode subgroup, the positive rate of farms was the highest. CDV can be transmitted mainly through direct contact with diseased animals or through air or food (<xref ref-type="bibr" rid="ref31">31</xref>). Mink, fox and raccoon dog currently account for a large proportion of economic animal farms, and large-scale cage breeding has become the mainstream breeding method of farms. We analyzed that the high breeding density of minks, foxes and raccoon dogs in the farm and the untimely ventilation led to the high prevalence of CD. In addition, some disinfectants have a killing effect on CDV (<xref ref-type="bibr" rid="ref32">32</xref>). We suggest that farms should be kept clean, use disinfectant to clean the breeding room, reduce breeding density and timely ventilation play an important role in controlling CD.</p>
<p>A subpopulation analysis revealed that minks, foxes, and raccoon dogs with a population size of less than 500 were more susceptible to CDV. The wild minks, foxes and raccoon dogs that hunt small animals in small groups may break into human territory in order to hunt and contact with domestic dogs, which will cause the spread of CDV. Rural areas are often habitats for wild carnivores. These pathogens are often transmitted from domestic dogs to wild carnivores through occasional contact. These animals may be susceptible to CDV. In addition, wild carnivores are usually small in number and low in density (<xref ref-type="bibr" rid="ref33">33</xref>). Therefore, they are often not suitable for maintaining the infection of highly pathogenic pluripotent viruses such as CDV (<xref ref-type="bibr" rid="ref34">34</xref>). This finding is consistent with previous research, suggesting a potential contributing factor to the current epidemic of canine distemper. It is advisable for dog owners to maintain their pets&#x2019; vaccination schedules and minimize their contact with wildlife.</p>
<p>In age group, there was no significant difference in the prevalence minks, foxes and raccoon dogs between young (less than 6&#x2009;months) and adult (more than 6&#x2009;months). We speculate that due to the large number of minks, foxes and raccoon dogs; adult female and male animals are vaccinated, while young animals obtain natural antibodies through breast milk. Since the promotion and application of CD vaccine and mink parvovirus enteritis vaccine in the 1880s, CD and mink parvovirus enteritis in minks, foxes and raccoon dogs in the immune area have been better controlled (<xref ref-type="bibr" rid="ref35">35</xref>). Therefore, we suggest that more attention should be paid to young animals, and timely vaccination should be given. It is of great significance to reduce the prevalence of CD by universal vaccination.</p>
<p>In the species subgroup, the prevalence of raccoon dogs was much higher than that of foxes and minks. Raccoon dogs were introduced from Russia to South Korea in the late 1920s for the production of fur. With the prosperity of silver fox and goat fur farms in Asian countries, the raccoon dog industry in South Korea has rapidly shrunk (<xref ref-type="bibr" rid="ref32">32</xref>). Raccoon dogs living in fur farms escaped from Korea and became wild animals (<xref ref-type="bibr" rid="ref36">36</xref>). Due to the absence of natural enemies, the population density of raccoon dogs has increased. If exposed to infected carnivores, it is possible to spread disease between domestic carnivores and wild raccoon dogs (<xref ref-type="bibr" rid="ref37">37</xref>). This is one reason why the prevalence of CD in Korea is as high as 65.8% (95% CI, 83.3&#x2013;95.8). We should control the scale of breeding and check the facilities regularly.</p>
<p>Most of the included studies used serological detection and molecular biological detection. The sensitivity and specificity of molecular biology are significantly higher than those of serology (<xref ref-type="bibr" rid="ref38">38</xref>). Therefore, we analyze that the reason for the low positive rate of serological detection may be related to the frequent occurrence of false positive reactions during the detection process and the reduction of detection sensitivity (<xref ref-type="bibr" rid="ref39">39</xref>). These methods are the mainstream CD diagnosis methods. We suggest that the detection method should be reasonably selected to reduce the occurrence of error and false positive reaction. In the collection of samples, we found that there was no significant difference in plasma, tissue and secretion, which may be related to the detection method. Our regression analysis shows that these methods have significant differences in reported prevalence and may be an important source of heterogeneity in this analysis.</p>
<p>We evaluated the global prevalence of CDV infection in minks, foxes and raccoon dogs through a meta-analysis of 33 systems. CD is very harmful to the fur industry. Sampling year, regional distribution, geographical factors, feeding patterns, detection methods and other factors affect the prevalence of CDV infection. It is suggested to reduce the contact of domestic dogs, carry out technical training and improve the technical level according to the feeding methods, geographical factors and climatic environment in different regions. In addition, comprehensive control strategies are adopted, such as disease prevention in various places. In addition, comprehensive control strategies such as disease prevention, immunization, quarantine and disinfection should also be standardized. We believe that due to widespread vaccination, the prevalence of the disease on farms is very low, so timely vaccination has a good control effect on the spread of CDV. In addition, in order to further explore the factors of mink, fox and raccoon dog infected with CD, it is necessary to carry out detailed epidemiological investigation in more areas.</p>
<p>There were three limitations to this study. First, when determining the search methodology, we attempted to create multiple databases to obtain more comprehensive articles, but there may have been research omissions due to database and language limitations. Second, the small number of studies from North and South America may affect the analysis of results in these regions. Third, the lack of some information (such as whether minks, foxes and raccoon dogs have fever and diarrhea) will affect the analysis results. However, we believe that this analysis can reflect the real epidemic situation of CD infection in minks, foxes and raccoon dogs on all continents.</p>
</sec>
<sec sec-type="data-availability" id="sec11">
<title>Data availability statement</title>
<p>The datasets presented in this study can be found in online repositories. The names of the repository/repositories and accession number(s) can be found in the article/<xref rid="SM1" ref-type="supplementary-material">Supplementary material</xref>.</p>
</sec>
<sec sec-type="author-contributions" id="sec12">
<title>Author contributions</title>
<p>JL: Writing &#x2013; review &#x0026; editing, Writing &#x2013; original draft, Visualization, Validation, Supervision, Software, Resources, Project administration, Methodology, Investigation, Funding acquisition, Formal analysis, Data curation, Conceptualization. TW: Writing &#x2013; original draft, Validation, Supervision, Software, Project administration, Methodology, Investigation, Formal analysis, Data curation, Conceptualization. QW: Writing &#x2013; review &#x0026; editing, Writing &#x2013; original draft, Visualization, Supervision, Software, Resources, Methodology, Investigation, Formal analysis, Data curation, Conceptualization. XW: Writing &#x2013; review &#x0026; editing, Writing &#x2013; original draft, Supervision, Software, Methodology, Investigation, Formal analysis, Data curation, Conceptualization. XF: Writing &#x2013; review &#x0026; editing, Writing &#x2013; original draft, Supervision, Software, Methodology, Investigation, Data curation, Conceptualization. TH: Writing &#x2013; review &#x0026; editing, Writing &#x2013; original draft, Validation, Supervision, Software, Methodology, Investigation, Data curation, Conceptualization. XL: Writing &#x2013; review &#x0026; editing, Writing &#x2013; original draft, Visualization, Validation, Supervision, Software, Resources, Project administration, Methodology, Investigation, Funding acquisition, Formal analysis, Data curation, Conceptualization. SK: Writing &#x2013; review &#x0026; editing, Writing &#x2013; original draft, Supervision, Software, Methodology, Investigation, Data curation, Conceptualization. JL: Writing &#x2013; review &#x0026; editing, Writing &#x2013; original draft, Supervision, Software, Methodology, Investigation, Data curation, Conceptualization. QG: Writing &#x2013; review &#x0026; editing, Writing &#x2013; original draft, Visualization, Validation, Supervision, Software, Resources, Project administration, Methodology, Investigation, Data curation, Conceptualization. RD: Writing &#x2013; review &#x0026; editing, Writing &#x2013; original draft, Visualization, Validation, Supervision, Software, Resources, Project administration, Methodology, Investigation, Funding acquisition, Formal analysis, Data curation, Conceptualization.</p>
</sec>
</body>
<back>
<sec sec-type="funding-information" id="sec13">
<title>Funding</title>
<p>The author(s) declare that financial support was received for the research, authorship, and/or publication of this article. Science and Technology Development Project of Jilin Province (grant numbers 20220101332JC and YDZJ202301ZYTS334), Jilin Provincial Department of Education Science and Technology Research Project (JJKH20230410KJ).</p>
</sec>
<sec sec-type="COI-statement" id="sec14">
<title>Conflict of interest</title>
<p>The authors declare that the research was conducted in the absence of any commercial or financial relationships that could be construed as a potential conflict of interest.</p>
</sec>
<sec sec-type="disclaimer" id="sec15">
<title>Publisher&#x2019;s note</title>
<p>All claims expressed in this article are solely those of the authors and do not necessarily represent those of their affiliated organizations, or those of the publisher, the editors and the reviewers. Any product that may be evaluated in this article, or claim that may be made by its manufacturer, is not guaranteed or endorsed by the publisher.</p>
</sec>
<sec sec-type="supplementary-material" id="sec2001">
<title>Supplementary material</title>
<p>The Supplementary material for this article can be found online at: <ext-link xlink:href="https://www.frontiersin.org/articles/10.3389/fvets.2024.1394631/full#supplementary-material" ext-link-type="uri">https://www.frontiersin.org/articles/10.3389/fvets.2024.1394631/full#supplementary-material</ext-link></p>
<supplementary-material xlink:href="Data_Sheet_1.pdf" id="SM1" mimetype="application/pdf" xmlns:xlink="http://www.w3.org/1999/xlink"/>
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