From tests to truth: A misclassification-aware machine learning framework for estimating brucellosis seroprevalence in wild canids

Nahal Sarvestani; Farzane Shams; Armin Mirshahi; Mobina Pato; Aria Javani Farbod; Armina Khayatderafshi; Mobina Payami; Arman Abdous

doi:10.1371/journal.pntd.0014029

. 2026 Mar 6;20(3):e0014029. doi: 10.1371/journal.pntd.0014029

From tests to truth: A misclassification-aware machine learning framework for estimating brucellosis seroprevalence in wild canids

Nahal Sarvestani ^1,^#, Farzane Shams ^2,^#, Armin Mirshahi ³, Mobina Pato ¹, Aria Javani Farbod ⁴, Armina Khayatderafshi ⁵, Mobina Payami ⁶, Arman Abdous ^6,^7,^*

Editor: Richard A Bowen⁸

PMCID: PMC12965539 PMID: 41790615

Abstract

Brucellosis is an important zoonotic disease affecting humans, livestock, and wildlife, yet prevalence estimates in wild species are often underestimated due to limited attention to surveillance, as well as insufficient and biased sampling. To clarify exposure patterns in wild canids, we searched PubMed, Scopus, Web of Science, SciELO, and Google Scholar (1962–2025) for primary prevalence studies of Brucella species (spp.) in free-ranging canids. Serological data were analyzed using a misclassification-aware, multi-assay model that corrects for imperfect test sensitivity and specificity. Confirmatory polymerase chain reaction (PCR) and culture results were considered separately from serological data, as each offers a different perspective on disease status. Across 48 wild serology populations (n = 3,925 animals), the global misclassification-adjusted true seroprevalence was 8.2% (95% confidence interval [CI]: 5.1–11.3%). Confirmed active infection, based on PCR or culture, was uncommon (3.9%; 95% CI: 3.0–5.1%). Exposure levels varied across continents, with higher estimates in South America (approximately 18%) and lower levels in Europe (approximately 0.8%) and North America (approximately 4.1%). Data from Africa were limited, and Asian estimates were based on sparse wild samples, leading to wide uncertainty. Seroprevalence was consistently influenced by assay type, host species, and region. Overall, wild canids exhibit modest but widespread serological exposure to Brucella spp., whereas confirmed active infection remains rare. Because evidence quality and diagnostic rigor varied considerably across regions, especially in Africa and parts of Asia, results from these areas should be interpreted with caution. By correcting for imperfect tests and explicitly accounting for study heterogeneity, this framework provides more reliable and transparent prevalence estimates and highlights geographic gaps where improved, targeted One Health surveillance is most urgently needed.

Author summary

Brucellosis is a bacterial disease that affects livestock, wildlife, and people. While surveillance usually focuses on domestic animals, far less is known about infection in wild carnivores such as wolves, foxes, and jackals, even though these species often live near farms and may signal when Brucella is present in the environment. Research on wild canids is difficult to compare because studies use many different diagnostic tests, some of which can give false-positive or false-negative results. We reviewed all available studies of Brucella in wild canids worldwide and combined the data using a modeling approach that adjusts for imperfect diagnostic accuracy. We found that exposure is widespread but unevenly documented, with major gaps in Africa and much of Asia. After accounting for test performance, wild canids showed modest levels of exposure overall, and confirmed infections were rare. Studies using older or less reliable tests tended to report inflated prevalence. By correcting for diagnostic limitations, our study provides more reliable estimates and highlights where improved wildlife surveillance is most needed to support One Health monitoring.

Introduction

Brucellosis is a globally distributed zoonotic disease of major veterinary and public health concern, caused by Gram-negative bacteria of the genus Brucella [1]. These pathogens infect a wide range of mammals, including livestock, humans, and wildlife. While most control and surveillance programs have primarily focused on livestock and human infections, wildlife, especially free-ranging canids such as wolves (Canis lupus), foxes (Vulpes spp., Lycalopex spp.), jackals (Canis aureus), and coyotes (Canis latrans), may also contribute to the ecology and transmission of Brucella species [2]. As predators and scavengers, wild canids may act as incidental hosts, spillover recipients, or mechanical vectors, and in certain contexts, could act as local reservoirs. These dynamics are most relevant in ecotones and rural or peri-urban landscapes, where land-use change increases contact among wildlife, livestock, and humans.

Despite the zoonotic importance of Brucella, surveillance in wild canids remains uneven. Most published studies originate from the Americas, while large data gaps persist in Africa, Asia, and Eastern Europe [3]. Much of the existing field research relies on serological tests such as the Standard Agglutination Test (SAT), Rose Bengal Test (RBT), and enzyme-linked immunosorbent assay (ELISA) because they are affordable and practical, yet these methods primarily reflect past exposure rather than active infection [4]. In contrast, bacterial culture and polymerase chain reaction (PCR) provide stronger evidence of active infection but are costly and logistically challenging, which has limited their routine use in wildlife studies [5]. Distinguishing clearly between seroprevalence (exposure) and PCR/culture-confirmed infection is therefore essential for understanding transmission potential and designing targeted surveillance systems.

Previous reviews of wildlife brucellosis by Dadar et al. and Kosoy and Goodrich have synthesized data across species and diagnostic methods, but they often lacked species-specific analyses, formal quality assessment, and statistical correction for imperfect diagnostic performance [3,6]. These limitations, combined with sparse and geographically uneven datasets, make it difficult to draw reliable conclusions about exposure patterns in wild canids.

To address these gaps, we focused specifically on the family Canidae (globally distributed, mobile species that commonly interact with livestock) and applied a reproducible, quantitative framework that builds on established misclassification and latent-class approaches. We extended these approaches to handle challenges such as multi-assay testing within the same sampled populations, heterogeneous study designs, and variable diagnostic quality. Using a PRISMA-ScR-guided scoping review and a structured quality assessment adapted from the Newcastle–Ottawa Scale, we first characterized the global evidence base and identified taxonomic and geographic gaps. Second, we implemented a misclassification-aware model that integrates results from multiple serological assays to estimate true population-level seroprevalence, rather than treating each assay arm independently, and we analyzed PCR/culture data separately. Third, we applied an exploratory machine learning meta-regression to identify consistent predictors of seroprevalence such as assay type, species, and region to support practical surveillance design tasks, including sample size estimation under imperfect testing conditions. Together, these components synthesize existing methodological principles into an integrated, wildlife-focused framework that produces uncertainty-aware, species- and region-specific prevalence estimates to inform One Health surveillance and control strategies at the wildlife–livestock–human interface.

Methods

Bibliographic search strategy

We conducted a global scoping review and quantitative synthesis of Brucella exposure and infection in wild canids (family Canidae), following the PRISMA Extension for Scoping Reviews (PRISMA-ScR) guidelines [7]. The study protocol defined eligibility, screening, data extraction, and analysis steps a priori and was piloted before full implementation. A PRISMA flow diagram summarizes record identification, screening, and full-text assessment (Fig 1), with detailed reasons for full-text exclusion provided in S1 Table. The completed PRISMA-ScR checklist is provided in S1 File.

A comprehensive literature search was conducted in July 2025 across PubMed, Scopus, Web of Science Core Collection, SciELO, and Google Scholar. The search strategy combined controlled vocabulary, where available, and free-text keywords related to Brucella infection, wild canids, epidemiology, and methods. Based on pilot testing, the first 200 Google Scholar records were screened for relevance. Detailed search strings, filters, and database-specific syntax are provided in S2 File. As a representative example, the PubMed search strategy was: (“Brucellosis”[Mesh] OR “Brucella abortus”[Mesh] OR “Brucella suis”[Mesh] OR “Brucella canis”[Mesh] OR brucellosis OR Brucella) AND (“Canidae”[Mesh] OR canids OR “wild canids” OR foxes OR wolves OR jackals OR coyotes OR “wild dogs”) AND (prevalence OR seroprevalence OR serology OR ELISA OR PCR OR culture OR diagnostics). All retrieved records were de-duplicated in a unified library before screening.

Two reviewers independently screened titles and abstracts, followed by full-text assessment using prespecified eligibility criteria. Disagreements were resolved by consensus, and inter-rater agreement was assessed during a calibration exercise on a random subset of studies using Cohen’s kappa (κ).

Data extraction and synthesis

A piloted extraction form was used to record metadata from each included study, including author, sampling year(s), country and continent, host species (common and scientific names), diagnostic methods, life condition (wild or captive), sample size (N), number of positives (n), and the Brucella species reported, when available. Species names were standardized according to current Canidae taxonomy, and country names were harmonized using International Organization for Standardization (ISO) formats to ensure consistency across studies.

Each study was assigned a unique Study ID composed of the first author’s surname and publication year, for example Davis_1979. Records sharing the same Study ID and sampling year(s) were treated as the same study, and duplicate records retrieved from multiple databases were merged. When a single publication reported results for multiple species, diagnostic methods, or sampling periods, each unique combination was treated as a distinct study arm.

Study arms were collapsed only when they clearly referred to the same animals, indicated by identical species, country, sampling period, and total sample size (N). This occurred primarily when multiple serological assays such as RBT, SAT, or ELISA were applied to the same individuals. In such cases, the number of positives and total sample size were summed and treated as a multi-test population. Study arms differing in species, country, sampling year(s), or N, or where overlap of individuals was uncertain, were retained as separate populations.

A strict separation was maintained between diagnostic tracks. Serological assays were used to assess exposure, whereas PCR and bacterial culture were used to identify active infection. These diagnostics were applied to different individuals in nearly all included studies and were therefore never combined. Each population was assigned exclusively to either the serology track or the PCR and culture track. Study-level prevalence data derived from this process are summarized in Table 1.

Table 1. Study-level brucellosis prevalence in wild canids worldwide (1962–2025).

	Study ID	Year of Sampling	Country	Continent	Diagnostic Category	Life Condition	Diagnostic Method	Type of Brucellosis	Positive	N (sample size)	Species	QA
[8]	Proença, L.	2006	Brazil	South America	Serology	Wild	RBT	Brucella abortus	0	3	Maned wolf (Chrysocyon brachyurus)	2
[8]	Proença, L.	2006	Brazil	South America	Serology	Wild	RBT	Brucella abortus	0	7	Crab-eating fox (Cerdocyon thous)	2
[9]	Martino, P. E	1998-2001	Argentina	South America	Serology	Wild	ELISA	Brucella spp	8	28	Culpeo fox (Dusicyon culpaeus)	7
[9]	Martino, P. E	1998-2001	Argentina	South America	Serology	Wild	ELISA	Brucella spp	7	56	Culpeo fox (Dusicyon culpaeus)	7
[10]	Oliveira-Filho, E. F	2006	Brazil	South America	Serology	Wild	CFT	Brucella spp	8	42	Fox spp.	6
[10]	Oliveira-Filho, E. F	2006	Brazil	South America	Serology	Wild	RBT	Brucella spp	23	42	Fox spp.	6
[10]	Oliveira-Filho, E. F	2006	Brazil	South America	Serology	Wild	AGID	Brucella spp	7	42	Fox spp.	6
[11]	Dorneles, E. M. S.	2005-2009	Brazil	South America	Serology	Wild	RBT	Brucella spp	5	38	Crab-eating fox (Cerdocyon thous)	6
[12]	Uzai, G. J. S.	2018	Brazil	South America	Molecular	Wild	PCR	Brucella spp	0	20	Crab-eating fox (Cerdocyon thous)	6
[13]	de Azevedo, S. S.	2004	Brazil	South America	Serology	Wild	BPAT	Brucella abortus	16	60	Pseudalopex vetulus (hoary fox)	7
[13]	de Azevedo, S. S.	2004	Brazil	South America	Serology	Wild	2-ME	Brucella abortus	4	60	Pseudalopex vetulus (hoary fox)	7
[14]	Galarce, N.	2020	Chile	South America	Serology	Wild	CIEF	Brucella canis	5	46	Fox spp.	6
[15]	Galarce Gálvez, N. E	2020	Chile	South America	Serology	Wild	CIEF	Brucella canis	5	31	Culpeo fox (Lycalopex culpaeus)	6
[15]	Galarce Gálvez, N. E	2020	Chile	South America	Serology	Wild	CIEF	Brucella canis	0	14	Chilla fox (Lycalopex griseus)	6
[15]	Galarce Gálvez, N. E	2020	Chile	South America	Serology	Wild	CIEF	Brucella canis	0	4	Red fox (Vulpes vulpes)	6
[15]	Galarce Gálvez, N. E	2020	Chile	South America	Serology	Wild	CIEF	Brucella canis	0	4	Wolf (Canis lupus)	6
[16]	Moya, S.	2008-2012	Chile	South America	Serology	Wild	CIEF	Brucella canis	0	15	Fuegian culpeo fox (Pseudalopex culpaeus lycoides)	6
[16]	Moya, S.	2008-2012	Chile	South America	Serology	Wild	CIEF	Brucella canis	0	12	Chilla fox (Pseudalopex griseus)	6
[11]	Dorneles, E. M. S.	2005-2009	Brazil	South America	Serology	Wild	FPA	Brucella spp	5	38	Crab-eating fox (Cerdocyon thous)	6
[17]	Hidalgo-Hermoso, E.	2013-2018	Chile	South America	Serology	Wild	ELISA	Brucella abortus	0	46	Darwin’s fox (Lycalopex fulvipes)	6
[17]	Hidalgo-Hermoso, E.	2013-2018	Chile	South America	Serology	Wild	CIE	Brucella canis	0	46	Darwin’s fox (Lycalopex fulvipes)	6
[18]	Fiorello, C. V.	2001-2005	Bolivia	South America	Serology	Wild	AGID	Brucella canis	0	9	Pampas fox (Pseudalopex gymnocercus)	4
[18]	Fiorello, C. V.	2001-2005	Bolivia	South America	Serology	Wild	AGID	Brucella canis	0	5	Crab-eating fox (Cerdocyon thous)	4
[19]	Szyfres, B.	1962–1964	Argentina	South America	Serology	Wild	SAT	Brucella abortus	173	728	Fox spp.	3
[20]	de Macedo, G. C.	2021	Brazil	South America	Serology	Wild	RBT	Brucella spp	5	38	Crab-eating fox (Cerdocyon thous)	4
[21]	Chitwood, M. C.	2011	USA	North America	Serology	Wild	IFA	Brucella canis	0	30	Coyote (Canis latrans)	6
[21]	Chitwood, M. C.	2011	USA	North America	Serology	Wild	RIV	Brucella abortus	0	28	Coyote (Canis latrans)	6
[22]	Williams, J. D.	1988	USA	North America	Serology	Wild	SAT	Brucella abortus	17	94	Coyote (Canis latrans)	7
[22]	Williams, J. D.	1988	USA	North America	Serology	Wild	CARD	Brucella abortus	38	94	Coyote (Canis latrans)	7
[22]	Williams, J. D.	1988	USA	North America	Serology	Wild	CFT	Brucella abortus	21	94	Coyote (Canis latrans)	7
[22]	Williams, J. D.	1988	USA	North America	Serology	Wild	ELISA	Brucella abortus	29	94	Coyote (Canis latrans)	7
[22]	Williams, J. D.	1988	USA	North America	Serology	Wild	RIV	Brucella abortus	20	94	Coyote (Canis latrans)	7
[23]	Zarnke.R	1975-1998	USA	North America	Serology	Wild	BBA	Brucella suis type 4	27	930	Wolf (Canis lupus)	3
[23]	Zarnke.R	1975-1982	USA	North America	Serology	Wild	BBA	Brucella suis type 4	1	67	Wolf (Canis lupus)	3
[24]	Hoq, M. A	1977	USA	North America	Serology	Wild	RBT	Brucella abortus	9	148	Coyote (Canis latrans)	3
[25]	Hoff, G. L.	1973	USA	North America	Serology	Wild	SAT	Brucella canis	1	68	Red fox (Vulpes vulpes)	2
[25]	Hoff, G. L.	1973	USA	North America	Serology	Wild	SAT	Brucella canis	0	15	Gray fox (Urocyon cinereoargenteus)	2
[25]	Hoff, G. L.	1973	USA	North America	Serology	Wild	SAT	Brucella canis	2	103	Coyote (Canis latrans)	2
[25]	Hoff, G. L.	1973	USA	North America	Serology	Wild	SAT	Brucella canis	0	4	Wolf (Canis lupus)	2
[26]	Randhawa, A. S.	1976	USA	North America	Serology	Wild	2-ME	Brucella canis	16	198	Coyote (Canis latrans)	4
[27]	Davis, D. S.	1978	USA	North America	Serology	Wild	SAT	Brucella abortus	9	51	Coyote (Canis latrans)	3
[28]	Neiland, K. A.	1969	USA	North America	Serology	Wild	CFT	Brucella suis type 4	3	7	Wolf (Canis lupus)	3
[29]	Tessaro, S. V.	1986	Canada	North America	Serology	Wild	RBT	Brucella abortus	1	37	Red fox (Vulpes vulpes)	2
[29]	Tessaro, S. V.	1986	Canada	North America	Serology	Wild	RBT	Brucella abortus	4	13	Wolf (Canis lupus)	2
[30]	Neiland, K. A.	1967–1972	USA	North America	Serology	Wild	CFT	Brucella suis type 4	11	28	Wolf (Canis lupus)	4
[30]	Neiland, K. A.	1967–1972	USA	North America	Serology	Wild	CFT	Brucella suis type 4	2	11	Red fox (Vulpes vulpes)	4
[31]	Schnurrenberger, P. R	1977–1983	USA	North America	Serology	Wild	SAT	Brucella abortus	0	2	Coyote (Canis latrans)	1
[31]	Schnurrenberger, P. R	1977–1983	USA	North America	Serology	Wild	SAT	Brucella abortus	1	47	Gray fox (Urocyon cinereoargenteus)	1
[32]	Morton, J. K	1988	Alaska	North America	Serology	Wild	SAT	Brucella suis type 4	12	64	Arctic fox (Vulpes lagopus)	3
[33]	McCue, P. M.	1981-1984	USA	North America	Serology	Wild	CFT	Brucella abortus	4	65	San Joaquin kit fox (Vulpes macrotis mutica)	6
[33]	McCue, P. M.	1981-1984	USA	North America	Serology	Wild	CFT	Brucella canis	5	56	San Joaquin kit fox (Vulpes macrotis mutica)	6
[34]	Ćirović, D.	2010-2013	Serbia	Europe	Molecular	Wild	PCR	Brucella canis	4	216	Golden jackal (Canis aureus)	7
[35]	I.V. Nakonechnyi	2018	Ukraine	Europe	Serology	Wild	RBT	Brucella spp	0	9	Golden jackal (Canis aureus)	5
[36]	Bertelloni, F.	2023	Italy	Europe	Molecular	Wild	PCR	Brucella suis type 4	0	16	Wolf (Canis lupus)	6
[37]	Nymo, I. H.	1995–2003	Norway	Europe	Serology	Wild	ELISA	Brucella spp	0	406	Arctic fox (Vulpes lagopus)	7
[38]	Ebani, V. V.	2016-2021	Italy	Europe	Molecular	Wild	PCR	Brucella spp	0	22	Red fox (Vulpes vulpes)	6
[39]	Minichino et al.	2023–2024	Italy	Europe	Serology	Wild	ICT	Brucella canis	5	18	Red fox (Vulpes vulpes)	6
[39]	Minichino et al.	2023–2024	Italy	Europe	Serology	Wild	RBT	Brucella spp	2	18	Red fox (Vulpes vulpes)	6
[39]	Minichino et al.	2023–2024	Italy	Europe	Serology	Wild	ICT	Brucella canis	0	4	Wolf (Canis lupus)	6
[39]	Minichino et al.	2023–2024	Italy	Europe	Serology	Wild	RBT	Brucella spp	0	4	Wolf (Canis lupus)	6
[40]	Zhou, Y.	2017	China	Asia	Serology	Farm	RBT	Brucella melitensis	493	726	Blue fox (Vulpes lagopus)	5
[40]	Zhou, Y.	2017	China	Asia	Serology	Farm	SAT	Brucella melitensis	300	726	Blue fox (Vulpes lagopus)	5
[41]	Egorov, E. A.	1996	Russia	Asia	Culture	Wild	Culture	Brucella suis type 4	30	254	Wolf (Canis lupus)	4
[41]	Egorov, E. A.	1996	Russia	Asia	Culture	Wild	Culture	Brucella suis type 4	18	777	Arctic fox (Vulpes lagopus)	4
[42]	Lapid, R.	2022	Israel	Asia	Serology	Wild	RBT	Brucella spp	0	56	Golden jackal (Canis aureus)	5
[43]	Pinigin, A. F.	1969	Russia	Asia	Serology	Farm	SAT	Brucella suis type 4	4	292	Arctic fox (Vulpes lagopus)	3
[43]	Pinigin, A. F.	1969	Russia	Asia	Serology	Farm	SAT	Brucella suis type 4	3	136	Red fox (Vulpes vulpes)	3
[44]	Sachs, R	1967	Tanzania	Africa	Serology	Wild	SAT	Brucella abortus	7	150	Black-backed jackal (Canis mesomelas)	2
[44]	Sachs, R	1967	Tanzania	Africa	Serology	Wild	SAT	Brucella abortus	10	30	Wild dog (Lycaon pictus)	2

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Note:Reported prevalence of Brucella spp. in free-ranging and farmed wild canids, stratified by country and continent, with diagnostic category, test method, life condition, and host species. Positive indicates the number of test-positive animals, and N (sample size) indicates the total number of animals tested in each study arm. QA denotes the quality appraisal score based on an adapted seven-item Newcastle–Ottawa Scale (range 0–7), where higher values indicate lower risk of bias. Scores of 5–7 indicate low risk of bias, 3–4 moderate risk, and 0–2 high risk. Abbreviations: RBT = Rose Bengal Test; SAT = Standard Agglutination Test; BPAT = Buffered Plate Agglutination Test; CARD = Card Agglutination Test; RIV = Rapid Immunoversion Test; CFT = Complement Fixation Test; AGID = Agar Gel Immunodiffusion; CIE = Counterimmunoelectrophoresis; CIEF = Counterimmunoelectrophoresis (FPA-adapted variant); ICT = Immunochromatographic Test; FPA = Fluorescence Polarization Assay; ELISA = Enzyme-Linked Immunosorbent Assay; BBA = Buffered Brucella Antigen Agglutination; 2-ME = 2-Mercaptoethanol reduction test; IFA = indirect fluorescent antibody assay; Culture = bacterial culture; PCR = Polymerase Chain Reaction.

Inclusion and exclusion criteria

Records were eligible if they reported primary prevalence data for wild-caught canids, including wolves (Canis lupus), foxes (Vulpes spp., Lycalopex spp.), jackals (Canis aureus), coyotes (Canis latrans), African wild dogs (Lycaon pictus), and used validated diagnostic methods. No restrictions on publication year or language were applied during database searches, and the final body of eligible evidence spanned publications from 1962 to 2025. All retrieved records were screened regardless of date, language, or region of origin.

Studies were eligible if they reported primary prevalence data for wild-caught canids and provided sufficient methodological detail to confirm host identity and diagnostic procedures.Diagnostics were categorized a priori into two groups:

Serology (exposure): Standard Agglutination Test (SAT), Rose Bengal Test (RBT), Complement Fixation Test (CFT), agar gel immunodiffusion (AGID), enzyme-linked immunosorbent assay (ELISA), Fluorescence Polarization Assay (FPA), or equivalent antibody-based assays.
Active infection (confirmation): Polymerase chain reaction (PCR), including quantitative PCR, and/or bacterial culture.

Studies involving zoo or laboratory animals were excluded, as were reports lacking primary numerators and denominators or sufficient information to verify host species identity or diagnostic methodology.

Quality appraisal (QA)

Study quality was evaluated using an adapted Newcastle–Ottawa Scale for prevalence studies [45].

Because the original NOS was developed for cohort and case–control designs, the scale was adapted to address sources of bias relevant to wildlife serosurveys and molecular diagnostic studies. The adapted NOS included seven binary items grouped into three domains: selection (S1–S3), comparability/diagnostic quality (C1–C2), and outcome reporting (O1–O2).

Each item was scored as 1 (criterion met) or 0 (criterion not met), giving a total score ranging from 0 to 7, consistent with standard NOS-based scoring approaches for prevalence studies. The summed score for each study is reported in the final “QA” column, where higher values indicate lower risk of bias. Based on total scores, studies were categorized as having low (5–7 points), moderate (3–4 points), or high (0–2 points) risk of bias, reflecting increasing concern about internal validity.

Study quality was not used as an inclusion criterion but was used to characterize the overall risk-of-bias profile of the evidence base and to contextualize regional and diagnostic-specific findings, and it was explicitly examined in sensitivity analyses. Detailed item-level scores for each NOS domain are provided in S2 Table.

Outcomes and unit of analysis

Two outcomes were analyzed on separate diagnostic tracks:

Exposure (serology): misclassification-adjusted true seroprevalence.
Active infection: prevalence confirmed by PCR and/or culture (descriptive only, as confirmatory data were too limited for integration into the Bayesian modeling framework).

The unit of analysis was a “population” defined as the unique combination of Study ID (first author and publication year) × species × country × year × total sample size (N). Rows sharing identical parameters across all of these dimensions and N were treated as the same animals tested by different assays (multi-test populations). Rows differing in N, year, or species were treated as distinct populations. PCR/culture data were never combined with serology.

Analyses were performed for (i) wild-only datasets (primary) and (ii) wild plus farm/captive datasets (sensitivity analysis). “Farm/captive” status was based on the original study context.

Serology track: misclassification-adjusted true seroprevalence

For each population j tested with one or more serological assays t, we estimated a single true seroprevalence (θⱼ). Observed positives (yⱼt) were modeled as:

y_jt ~ Binomial(N_j, q_jt), where q_jt = Se_t·θ_j + (1−Sp_t)(1−θ_j).

where Se_t and Sp_t are assay-specific sensitivity and specificity [46]. When studies reported serial or parallel testing algorithms (for example “RBT screen followed by CFT confirm”), we used the implied combined sensitivity/specificity (serial: Se = Se₁Se₂, Sp = 1 − (1 − Sp₁)(1 − Sp₂); parallel: Se = 1 − (1 − Se₁)(1 − Se₂), Sp = Sp₁Sp₂). Otherwise, each assay contributed its own likelihood term.

True seroprevalence (θⱼ) was estimated using grid-based maximum likelihood estimation (MLE) with 95% profile-likelihood confidence intervals. Continent-level summaries were calculated as N-weighted means of θ̂ⱼ with conservative weighted-variance approximations. Estimates are presented separately for wild-only and sensitivity datasets [47].

Infection track: PCR/culture prevalence

Because confirmatory data were sparse, PCR and culture prevalence were summarized descriptively as pooled proportions (total positives/ total tested) within species, country, and continent strata, with 95% Wilson confidence intervals. Zero-event studies were retained.

Machine learning Bayesian meta-analysis (serology)

To account for imperfect test performance and heterogeneity, we applied a hierarchical Bayesian measurement-error model. Observed positives per arm were modeled as binomial outcomes with apparent probabilities derived from assay Se/Sp applied to an underlying latent true prevalence. True prevalence was modeled on the logit scale with random effects for study and continent; species effects were added in stratified models.

When available, study-reported Se/Sp were used directly; otherwise, method-specific priors centered on literature-typical performance were applied. A beta–binomial extension addressed over-dispersion where necessary. Models were fitted using Hamiltonian Monte Carlo (No-U-Turn Sampler [NUTS]) with four chains, adaptive warm-up, and conservative target acceptance. Convergence was verified via Gelman–Rubin statistics (R̂ ≈ 1.00), effective sample sizes, and visual inspection of trace, rank, autocorrelation, and energy/ Bayesian fraction of missing information (BFMI) plots [47].

Model performance was assessed through posterior predictive checks and study-level calibration plots. Comparisons were made using Pareto-smoothed importance sampling leave-one-out cross-validation (PSIS-LOO) and widely applicable information criterion (WAIC); study influence was summarized using Pareto-k values. To enhance robustness, two safeguards were applied: (i) a Trim-K influence-weighted likelihood, and (ii) heavy-tailed priors on variance components. The combined robust+Trim-K model was reported alongside the baseline.

As a transparency check, we also generated Monte Carlo Rogan–Gladen estimates of true prevalence that propagate uncertainty in assay performance; these were consistent with Bayesian posteriors.

Software and reproducibility

All analyses were scripted in Python and world heat map figure was constructed using R [48] Reproducible code and documentation are available via the public ShamsFarzane GitHub repository (https://github.com/ShamsFarzane/brucellosis-canids-seroprevalence.git).

Results

Search results

The database search yielded 4,120 records, including PubMed (n = 1,020), Scopus (n = 985), Web of Science (n = 910), Google Scholar (n = 720), and SciELO (n = 485). An additional 87 records were identified through reference list screening. After removal of 307 duplicates, 3,900 records remained for title and abstract screening. Of these, 3,810 were excluded because they did not report primary prevalence data, focused on domestic or captive animals, or involved non-canid host species. Ninety full-text articles were assessed for eligibility. Fifty-three articles were excluded for lack of original prevalence data (n = 18), focus on domestic or zoo animals (n = 13), unclear or invalid diagnostic methods (n = 9), unrepresentative or insufficient sample sizes (n = 8), or incomplete methodological reporting (n = 5). A detailed exclusion log is provided in S1 Table. A total of 37 studies met the inclusion criteria and were incorporated into the scoping review and quantitative synthesis. The study selection process is summarized in Fig 1. Inter-rater agreement during the calibration exercise was high (κ = 0.86).

Characteristics of included studies

The evidence base comprised 37 studies conducted between 1962 and 2025, representing 48 wild canid populations from 16 countries across five continents. Geographic representation was uneven, with most data originating from North America, South America, and Europe, and more limited sampling in Asia and Africa. Sample sizes ranged from 10 to 997 individuals. Twelve wild canid species were represented, with the most frequently sampled taxa being Canis lupus, Vulpes vulpes, Canis latrans, Cerdocyon thous, Vulpes lagopus, and Lycalopex culpaeus.

Serological diagnostics included the Rose Bengal Test (RBT), Standard Agglutination Test (SAT), Buffered Plate Agglutination Test (BPAT), Card Agglutination Test (CARD), Complement Fixation Test (CFT), enzyme-linked immunosorbent assay (ELISA), agar gel immunodiffusion (AGID), counterimmunoelectrophoresis (CIE), fluorescence polarization assay (FPA), and immunochromatographic test (ICT). PCR and culture data were available from a smaller subset of studies. Study quality, assessed using the adapted Newcastle–Ottawa Scale, ranged from 1 to 7.

Risk of bias assessment

Using predefined thresholds based on total Newcastle–Ottawa Scale scores, 21 studies were classified as low risk of bias, 12 as moderate risk, and 4 as high risk. Risk-of-bias patterns varied across regions, diagnostic approaches, and reporting practices. Studies classified as moderate or high risk most frequently lacked explicit inclusion or exclusion criteria, sample-size justification, or detailed reporting of diagnostic cut-off values. High-risk studies commonly relied exclusively on older agglutination-based assays without confirmatory testing.

Geographically, lower-risk studies were more common in South America and North America, whereas studies from Africa and parts of Asia more often exhibited methodological limitations. Diagnostic modality was also associated with study quality, with investigations using ELISA, PCR, or CFT generally receiving higher appraisal scores than studies based solely on legacy agglutination tests. Quality scores for individual studies are reported in Tables 1 and S2. Full item-level scoring and a graphical summary of risk-of-bias domains are provided in S1 Fig.

Global exposure is widespread, whereas confirmed active infection is uncommon

Using a misclassification-aware model that integrates all serological assays performed on the same animals, we estimated the global true seroprevalence (θ) of Brucella exposure in wild canids. Across 48 wild populations (N = 3,925 animals), the N-weighted global true seroprevalence was 8.2% (95% CI: 5.1–11.3%). Estimates varied widely across populations and continents, reflecting substantial heterogeneity in diagnostic methods, sampling frames, and time periods (S3 Table).

Confirmed active infection was rare. Pooled PCR and culture data across studies yielded 3.9% (95% Wilson CI: 3.0–5.1%; 52/1,325 positives). Because infection data were sparse and unevenly distributed (e.g., culture positives from Russia for B. suis biovar 4), these results are reported separately from serology.

When captive or farmed canids were included as a sensitivity analysis, the overall misclassification-adjusted seroprevalence increased to 15.5% (95% CI: 9.4–21.6%; 51 populations; N = 5,079), with high prevalence observed in blue-fox breeding farms. The infection proportion remained 3.9% since confirmatory tests were limited to wild settings.

Hierarchical Bayesian models correcting for imperfect diagnostic performance produced higher estimates. The baseline model estimated 23.1% (95% CrI: 19.4–27.1%); with influence trimming (Trim-K = 5), 15.8% (12.2–19.5%); with robust priors, 27.3% (22.6–32.5%); and with the combined robust+Trim-K specification, 21.7% (17.5–27.2%). Across model specifications, the estimated global true seroprevalence range was 16–27%, centered around 21–23%.

Posterior predictive checks and calibration plots demonstrated excellent model fit and convergence (no divergences; R̂ ≈ 1.00; large effective sample sizes). Influence diagnostics confirmed that no single study dominated inference (Fig 2).

Fig 2 — (a) Posterior distribution of the misclassification-adjusted hierarchical Bayesian model for global *Brucella* seroprevalence in wild canids; the vertical line marks the posterior median. **(b)** Study-level calibration (observed vs. predicted) aligns with the 1:1 line, indicating good model fit. Chains converged (R̂ ≈ 1.00; no divergences), and posterior predictive checks reproduced the empirical distribution. **(c)** Sensitivity of pooled true seroprevalence to modeling choices (baseline, Trim-K, robust priors, robust + Trim-K). Influence trimming lowers estimates, while robust priors raise them; the combined model yields a stable mid-range (~22%). **(d)** Forest plot showing observed (×) versus misclassification-adjusted (●) prevalence with 95% confidence intervals (CIs) by diagnostic test. Marked heterogeneity is evident, with older, lower-specificity assays producing higher apparent prevalence, while PCR/culture arms remain low.

Asia: sparse wild data, localized infection

After excluding captive data, Asian wild canid data were extremely limited. Only one wild population (N = 56 golden jackals, Israel) was identified with 0/56 seropositive results. The corresponding misclassification-adjusted estimate was near zero, with a binomial 95% upper bound of ≈5.2%.

In contrast, culture-confirmed infection was documented in Russia, with pooled data showing 48/1,031 = 4.66% (95% Wilson CI: 3.53–6.12%) across wolves and Arctic foxes. Including captive populations (notably blue-fox farms in China) increased seroprevalence substantially but did not represent free-ranging wildlife.

The Bayesian model estimated a true seroprevalence of 4.6% (95% CrI: 4.0–5.5%). Diagnostic plots (energy/BFMI ≈ 0.92–0.96) and posterior predictive checks indicated good convergence. Estimates are shown in Fig 3.

Fig 3 — (a) Posterior distribution from the misclassification-adjusted hierarchical Bayesian model; the dashed line marks the posterior median (≈4.6%, 95% CrI 4.0–5.5%). **(b)** Posterior predictive check comparing replicated (blue) and observed (black) prevalence distributions. The model reproduces the central mass of the empirical data but slightly under-captures the tails, indicating an overall adequate though imperfect fit. Sparse data—driven mainly by one golden-jackal series—limit precision and highlight the need for additional wild sampling in Asia.

Europe: lowest exposure and limited infection evidence

Europe showed the lowest exposure levels. Using the misclassification-aware model, the N-weighted true seroprevalence was ~ 0.8% (95% CI: 0.0–4.5%; 4 populations; N = 437).

PCR/culture data were scarce but present, yielding 4/254 = 1.6% (95% Wilson CI: 0.61–3.98%). The Bayesian model estimated a true seroprevalence of 1.6% (95% CrI: 1.3–1.9%) with strong convergence (R̂ ≈ 1.00; energy/BFMI ≈ 0.86–0.92) and accurate posterior predictive fit (Fig 4). Positive detections were reported among wolves, Arctic foxes, and jackals, and were also observed in a subset of red fox studies.

Fig 4 — (a) Posterior of true seroprevalence in Europe, with a median of ≈1.6% (95% CrI 1.3–1.9%). **(b)** Observed (×) versus misclassification-adjusted (●) prevalence by study. Most species are near zero; a few red-fox datasets drive the right-tail, reflecting local outliers rather than widespread exposure.

North America: low-to-moderate exposure, scarce confirmation

Across 22 North American wild populations (N = 2,066), the misclassification-aware model estimated ~4.1% true seroprevalence (95% CI: 0.5–7.8%). Exposure levels varied among species but remained generally low to moderate.

No PCR or culture-confirmed infections were found in the wild-only dataset. Bayesian models yielded pooled true seroprevalence estimates around 10%, depending on model specification, with satisfactory calibration and posterior predictive performance (Fig 5). These estimates indicate modest levels of serological exposure in North American wild canids.

Fig 5 — (a) Posterior distribution from the misclassification-adjusted hierarchical model; median true seroprevalence ≈10.0% (95% CrI 7.0–13.0%). The unimodal and moderately narrow posterior indicates intermediate exposure relative to other continents and good model pooling across studies. **(b)** Study-level apparent (×) and adjusted (●) prevalence with 95% CIs, labeled by diagnostic test. Coyote and red-fox series contribute most of the signal; older SAT/RBT assays tend to report higher apparent values, while adjusted estimates cluster lower, reflecting diagnostic-driven heterogeneity.

South America: higher exposure, limited confirmatory data

South America exhibited the highest apparent exposure. The misclassification-aware model estimated ~18% true seroprevalence (95% CI: 15–22%), consistently higher than in other continents. Confirmatory data were scarce: pooled infection results showed 0/40 PCR/culture positives (95% upper bound ≈7%).

The Bayesian baseline estimated true seroprevalence at 20.0% (95% CrI: 13.5–27.0%), and robust variants stabilized near 21.5% (18.0–26.5%). Diagnostic and influence analyses showed stable results without domination by individual studies (Fig 6). These results indicate higher serological prevalence estimates in South America relative to other regions, alongside limited confirmatory testing data.

Fig 6 — (a) Posterior distribution from the misclassification-adjusted hierarchical model; median ≈21.5% (95% CrI ~ 18–26.5%). The unimodal, moderately tight posterior indicates consistently higher exposure than in North America or Europe. **(b)** Study-level apparent (×) and adjusted (●) prevalence with 95% CIs, labeled by diagnostic test. Crab-eating fox and culpeo series dominate the signal; SAT/RBT assays tend to overestimate compared to confirmatory tests. **(c)** Posterior predictive check for observed positives across study arms. Simulated (blue) distributions closely envelop empirical counts (black), and the posterior predictive mean (dashed) tracks the data, indicating good model fit.

Africa: under-sampled and uncertain

African data comprised only two populations from Tanzania (black-backed jackal and African wild dog). The estimated N-weighted true seroprevalence was ~ 6.0% (95% CI: 0.0–24.6%; k = 2; N = 180). The wide confidence interval reflects the small number of populations and limited sample size. All serological data were derived from SAT testing, and no PCR or culture-confirmed infections were reported for Africa. Despite adjustment for diagnostic performance, uncertainty remained high due to sparse data.

Species patterns and diagnostic effects

Among species, coyotes (Canis latrans) showed the highest weighted seroprevalence (≈15.6%; 85/545).Higher seroprevalence estimates were observed for coyotes compared with other canid species.

Diagnostic usage strongly shaped observed patterns. SAT was the most common assay (~2,510 animals tested across 15 arms), while ELISA was used less frequently (~630 tested, 5 arms). Screening tests (RBT, SAT) consistently produced higher positivity than confirmatory tests (CFT, ELISA) across matched populations (Fig 7A).

Geographically, apparent seroprevalence in Asia was disproportionately influenced by data from farmed blue-fox populations in China (Fig 7C), reflecting captive rather than free-ranging systems, alongside concentrated sampling effort (Fig 7B). Confirmed infection remained limited and geographically restricted (Fig 7D).

Discussion

Wild canids exhibit widespread serological exposure to Brucella spp. but active infection appears to be underestimated [3,46]. In our analysis among wild canids, integrating all serological assays and correcting for imperfect test performance, the estimated global true seroprevalence was 8.2% (95% CI: 5.1–11.3%). In contrast, only a small number of cases were confirmed by PCR or culture (3.9%; 95% CI: 3.0–5.1%), probably reflecting both the rarity of true infection and differences in testing availability between regions. Including farmed or captive populations raised seroprevalence to 15.5% (9.4–21.6%), largely due to outbreaks in blue-fox breeding facilities, demonstrating that captive amplification can distort regional patterns if not analyzed separately. Collectively, these findings indicate that Brucella exposure in wild canids is globally distributed but primarily localized around wildlife–livestock interfaces. Importantly, our use of a misclassification-aware, multi-assay modeling framework yields calibrated, decision-relevant prevalence estimates that reconcile heterogeneous diagnostic data, thereby addressing a persistent limitation in prior wildlife disease studies.

Seroprevalence differed across continents in consistent and biologically plausible ways. Europe showed the lowest exposure (~0.8%), with most wolves, jackals, and arctic foxes seronegative, while older red-fox data accounted for isolated outliers [3]. North America exhibited low-to-moderate exposure (~4.1%), consistent with localized transmission at livestock interfaces rather than broad enzootic circulation [3]. South America had higher values (~18%), largely in fox species from agro-pastoral landscapes, though confirmatory data (0/40 PCR or culture) remain absent [3]. Africa was severely under-sampled (~6%, N = 180; wide CI), limiting precision, [3] and Asia, when farm data were excluded, showed sparse wild serology (0/56 in jackals) but detectable infection in Russian culture data (4.66%) among wolves and arctic foxes, highlighting the danger of extrapolating from captive outbreaks [3,41]. Species composition also contributed: synanthropic canids, particularly coyotes (Canis latrans), showed the highest weighted seroprevalence (~15.6%), likely due to frequent contact with livestock environments, feeding on infected tissues, and shared resource use in peri-urban or agricultural landscapes [3,6]. Together, these patterns suggest that Brucella exposure in wild canids is regionally heterogeneous and often reflects ecological proximity to livestock rather than long-term maintenance within wild populations.

These patterns should be interpreted in light of the risk-of-bias assessment, which indicated greater methodological limitations in under-sampled regions and in studies relying exclusively on legacy serological assays. Much of the observed heterogeneity arises from methodological rather than biological causes. Agglutination-based screening assays such as RBT and SAT, consistently overestimate seropositivity relative to confirmatory tests such as CFT or ELISA [5,46]. Within-study comparisons revealed diagnostic inflation, with higher positivity reported for RBT than for CFT or AGID, and for CARD than for SAT or Rapid Immunoversion Test (RIV). After correcting misclassification and combining data from multiple assays, extreme values were reduced and regional estimates became more consistent. This suggests that apparent differences in naïve pooled results are driven mainly by variation in assay methods and cut-off thresholds, rather than by pathogen ecology alone [5]. The hierarchical Bayesian models,which explicitly correct for imperfect sensitivity and specificity and share information across studies, produced higher global seroprevalence estimates (approximately 16–27%,centered around 21–23%), as expected under diagnostic uncertainty and high heterogeneity. Posterior predictive checks, convergence diagnostics, and influence assessments all supported model stability, and robust or influence-trimmed variants yielded consistent results. The overall agreement between these Bayesian results and the misclassification-aware frequentist estimates suggests that much of the observed variation in seroprevalence is driven by differences in diagnostic methods rather than true ecological variation in Brucella circulation, resulting in more calibrated and decision-relevant estimates of global exposure in wild canids [3,46].

For One Health surveillance, programs should prioritize targeted sampling at livestock–wildlife interfaces, especially foxes and coyotes in agro-pastoral landscapes, and combine screening with confirmatory testing while explicitly modeling diagnostic misclassification to avoid bias [4,5]. Regions with elevated serology but little confirmatory data, including South America and parts of Asia, warrant targeted PCR and culture investigations, and captive systems, including fox farms in China, require strengthened biosecurity to prevent amplification and spillover [40]. By quantifying diagnostic uncertainty and translating results into a surveillance design framework, this study provides a reproducible model for decision-grade wildlife disease monitoring applicable across taxa and regions.

The main limitations of this study are uneven geographic and species coverage, particularly in Africa and parts of Asia; reliance in many studies on legacy agglutination assays with variable thresholds; and the scarcity of paired serology and PCR data from the same individuals. Standardized metadata collection, including age, carcass condition, vaccination history, and habitat context, alongside harmonized confirmatory testing protocols, would improve bias adjustment and strengthen inference on transmission potential. Future research should extend this framework to other wildlife reservoirs and zoonotic pathogens, integrating serological, molecular, and ecological data under unified Bayesian evidence synthesis. Such approaches will be critical for identifying true maintenance hosts and informing effective One Health interventions.

Conclusion

Wild canids show widespread exposure to Brucella spp. while confirmed active infection is reported much less often, a pattern shaped not only by biology but also by important differences in diagnostic methods and geographic coverage. Our risk-of-bias assessment indicates that much of the apparent variation among regions and species reflects these methodological differences particularly the reliance on older assays and limited data from Africa and parts of Asia, rather than true ecological contrasts alone. After accounting for diagnostic error and study-level uncertainty through misclassification-aware hierarchical modeling, exposure appears globally distributed but uneven, with higher levels concentrated near wildlife–livestock interfaces and in intensively monitored settings. Overall, the evidence is most consistent with wild canids acting primarily as sentinels of environmental and livestock-associated Brucella exposure, while still allowing for localized maintenance in specific ecological or management contexts, especially in captive or high-contact systems. By integrating diverse diagnostic data and explicitly addressing bias, this study provides more reliable, decision-relevant estimates of global exposure and a transferable framework for improving wildlife disease surveillance and One Health monitoring.

AI assistance disclosure

The authors used ChatGPT-5 (OpenAI) solely to assist with grammar, sentence structure, and clarity during manuscript revision. All scientific content, data analysis, interpretation, and conclusions were generated entirely by the authors. The authors reviewed and edited all AI-suggested language and take full responsibility for the final text.

Supporting information

S1 File. PRISMA-ScR Checklist.

Attribution: This PRISMA-ScR checklist is reproduced from the PRISMA-ScR statement materials and is licensed under the Creative Commons Attribution 4.0 International License (CC BY 4.0). The original guideline and supporting materials are available at https://www.prisma-statement.org/. The checklist is cited as: Tricco AC, Lillie E, Zarin W, O’Brien KK, Colquhoun H, Levac D, et al. PRISMA Extension for Scoping Reviews (PRISMA-ScR): Checklist and Explanation. Ann Intern Med. 2018;169(7):467–473. https://doi.org/10.7326/M18-0850.

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pntd.0014029.s001.docx^{(100.7KB, docx)}

S1 Fig. Risk-of-bias heat-map figure.

(DOCX)

pntd.0014029.s002.docx^{(551.2KB, docx)}

S2 File. Full database search strings used across all bibliographic sources.

(DOCX)

pntd.0014029.s003.docx^{(28KB, docx)}

S1 Table. Full-text exclusion log.

(DOCX)

pntd.0014029.s004.docx^{(20.2KB, docx)}

S2 Table. Newcastle–Ottawa Quality Appraisal Scores for included studies.

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pntd.0014029.s005.docx^{(32.5KB, docx)}

S3 Table. Study-level meta-analysis data for Brucella exposure and infection in wild canids.

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pntd.0014029.s006.docx^{(28.5KB, docx)}

Data Availability

All analyses were performed using Python. The complete analysis scripts and associated datasets are publicly available at the ShamsFarzane GitHub repository: https://github.com/ShamsFarzane/brucellosis-canids-seroprevalence.git.

Funding Statement

The author(s) received no specific funding for this work.

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PLoS Negl Trop Dis. doi: 10.1371/journal.pntd.0014029.r001

Decision Letter 0

Ana LTO Nascimento

23 Nov 2025

From tests to truth: A misclassification-aware machine learning framework for estimating brucellosis seroprevalence in wild canids

PLOS Neglected Tropical Diseases

Dear Dr. Abdous,

Thank you for submitting your manuscript to PLOS Neglected Tropical Diseases. After careful consideration, we feel that it has merit but does not fully meet PLOS Neglected Tropical Diseases's publication criteria as it currently stands. Therefore, we invite you to submit a revised version of the manuscript that addresses the points raised during the review process.

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We look forward to receiving your revised manuscript.

Kind regards,

Richard A. Bowen, DVM PhD

Academic Editor

PLOS Neglected Tropical Diseases

Ana LTO Nascimento

Section Editor

PLOS Neglected Tropical Diseases

Shaden Kamhawi

co-Editor-in-Chief

PLOS Neglected Tropical Diseases

orcid.org/0000-0003-4304-636XX

Paul Brindley

co-Editor-in-Chief

PLOS Neglected Tropical Diseases

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Your manuscript has been reviewed by 3 experts and each has provided suggestions and comments to improve your manuscript. Please evaluate their comments carefully, edit your manuscript to address those issues and resubmit with a "Responses to reviewers" document describing how you modified your manuscript and whether or not you agree with reviewer comments. Thank you.

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If the reviewer comments include a recommendation to cite specific previously published works, please review and evaluate these publications to determine whether they are relevant and should be cited. There is no requirement to cite these works unless the editor has indicated otherwise.

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At this stage, the following Authors/Authors require contributions: Nahal Sarvestani, Armin Mirshahi, Mobina Pato, Aria Javani Farbod, Armina Khayatderafshi, Farzane Shams, Mobina Payami, and Arman Abdous. Please ensure that the full contributions of each author are acknowledged in the "Add/Edit/Remove Authors" section of our submission form.

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Reviewers' Comments:

Reviewer's Responses to Questions

Key Review Criteria Required for Acceptance?

As you describe the new analyses required for acceptance, please consider the following:

Methods

-Are the objectives of the study clearly articulated with a clear testable hypothesis stated?

-Is the study design appropriate to address the stated objectives?

-Is the population clearly described and appropriate for the hypothesis being tested?

-Is the sample size sufficient to ensure adequate power to address the hypothesis being tested?

-Were correct statistical analysis used to support conclusions?

-Are there concerns about ethical or regulatory requirements being met?

Reviewer #1: yes

Reviewer #2: Hypothesis is clearly articulated, design appropriate, population size acceptable

Reviewer #3: 1. The authors state that they included studies that were "published between 1962 and 2025 in English, Persian, Spanish, Portuguese, or Russian." However, they selection of the period 1962 - 2025 is not justified. in the limitations, authors should acknowledge the language restrictions that could have limited the yield from literature search

2. In accordance with PRISMA-ScR guidelines, should provide the search terms and string for at least one data base. The current section on information sources lacks detail on how the records were searched, despite having referred to the supplementary material 2

3. Authors state that "Inter-rater agreement was assessed during a calibration exercise on a random subset of studies and was high. Reasons for full-text exclusion were documented and summarized in the PRISMA diagram." To enhance reporting transparency, authors should include another supplementary file that reasons for exclusion of each of the 53 studies at full text review. It's not clear how the inter-rater agreement was assessed and neither are there results in the results section.

4. In the data extraction section, authors state that "Records sharing identical Study ID" were excluded. It's not clear what identical means? This needs to be clarified on what features were used to qualify this. If Digital object identifiers were used, let it be stated for clarity

5. The description of how quality appraisal was done is limited and very unclear. How were the domains of the New-castle Ottawa Scale used to classify the overall quality as "Good", "Fair" or "poor"? In the associated supplementary file 3, what do the codes 1 and 0 mean? In the final "QA" column, what do those numerical values mean and what scaled was used to interpreted them. After doing the quality appraisal, how was risk of bias assessed, and how were the results of RoB analysis integrated in the final interpretation of results? Authors should provide a clear description in the methods. if possible, authors should consider adding a traffic light diagram as a supplementary file

**********

Results

-Does the analysis presented match the analysis plan?

-Are the results clearly and completely presented?

-Are the figures (Tables, Images) of sufficient quality for clarity?

Reviewer #1: yes

Reviewer #2: Results are well presented

Reviewer #3: Before the synthesis of the results, authors should include the following sections to ensure complete reporting following the PRISMA-ScR guidelines

1. Search results (This section should show the numbers of sources of evidence screened, assessed for eligibility, and included in the review, with reasons for exclusions at each stage (refer to the supplementary material that shows reasons for exclusion of each of 53 studies excluded). Cite and include the PRISMA diagram with clear caption. In the description preceding the PRISMA diagram, cite all the studies included)

2. Characteristics of the studies included. (Authors should describe the studies included and probably refer to table 1 that needs to be improved. It's not clear what the column labelled "Total" means. This needs to be clarified. include the column of sample size. If the column labelled "QA" in table 1 means quality appraisal, in the table foot note, describe what those values mean)

3. Risk of Bias (Provide a synthesis of the findings from the risk of bias analysis. Clearly show which studies has low, moderate or high risk of bias). The integrate the results of the risk of bias analysis in the results discussion.

**********

Conclusions

-Are the conclusions supported by the data presented?

-Are the limitations of analysis clearly described?

-Do the authors discuss how these data can be helpful to advance our understanding of the topic under study?

-Is public health relevance addressed?

Reviewer #1: no

Reviewer #2: Conclusions are supported by data and limitations addressed. It is clear how these data can be used to guide brucella surveillance, particularly in under-represented countries.

Reviewer #3: Revise the conclusions based on the results of risk of bias analysis

**********

Editorial and Data Presentation Modifications?

Use this section for editorial suggestions as well as relatively minor modifications of existing data that would enhance clarity. If the only modifications needed are minor and/or editorial, you may wish to recommend “Minor Revision” or “Accept”.

Reviewer #1: (No Response)

Reviewer #2: There are problems with the citations.

1) The footnotes do not always follow the same convention with regard to punctuation. For example, on Page 5, three different footnote conventions. On line 90 "infection.[4]". On line 92 "settings. [5]". And on line 98 "hosts[3,6].". Please review all footnotes to ensure they all use the same convention. Most are like teh example from line 98.

2) There are several references that are incomplete.

For example, reference 30 is "Hoq MA. A serologic survey of Brucella agglutinins in wildlife and sheep. 1978." No journal, issue, volume or page number is given. An internet search identified this may have been published in California Veterinarian volume 32, pages 15-17.

Reference 35 is a doctoral thesis, and I am not sure the citation conforms to PLoS standards.

Reference 36, the author name is all caps, unlike other citations.

Reference 43 "Nymo IH, Fuglei E, Mørk T, Breines EM, Holmgren KE, Davidson RK, et al. Why are Svalbard Arctic foxes Brucella spp. seronegative? 2022." No journal, issue, volume or page number is given. An internet search identified this may have been published in Polar Research volume 41, page reference 7867.

References 47 and 48 also appear to be incomplete.

I did not exhaustively examine all references. As such, the authors should examine the entire References section and make sure all citations are complete and conform to PLoS standards.

Reviewer #3: In the abstract, line 50 - 51, clarify which exposure is being referred to. is it of humans or wild canids? In the methods section of the abstract, show how records were obtained and from which databases. By stating "Across 48 wild populations (N = 3,925 animals)", which ones are authors specifically referring to?

**********

Summary and General Comments

Use this section to provide overall comments, discuss strengths/weaknesses of the study, novelty, significance, general execution and scholarship. You may also include additional comments for the author, including concerns about dual publication, research ethics, or publication ethics. If requesting major revision, please articulate the new experiments that are needed.

Reviewer #1: This manuscript addresses an important and timely zoonotic disease question using an innovative misclassification-aware modeling framework, but several aspects of the methodology, data handling, interpretation, and presentation require clarification and strengthening to ensure robustness and transparency before the work is suitable for publication.

1.Please rewrite your abstcart into non structured abstract.

2.Please revise this sentence as it looks like AI generated “While most control and surveillance programs have historically focused on livestock and human infections, increasing evidence 78 indicates that wildlife—particularly free-ranging canids such as wolves (Canis lupus), foxes (Vulpes spp., Lycalopex spp.), jackals (Canis aureus), and coyotes (Canis latrans)—may play underappreciated roles in the ecology and transmission of Brucella species. ”.

3.Please delete all dots before all citations and relocate after the references throughout the entire manuscript.

4.Line 95; wrong reference citation style.

5.Although you have AI Disclosure, I suspect a lot of sentences are generated by AI, please revise the entire manuscript.

6.The introduction overstates the novelty of the modeling strategy without clearly distinguishing it from existing Bayesian disease-misclassification frameworks. Clarify how your approach differs from established latent-class or hierarchical misclassification models in wildlife epidemiology.

7.PCR and culture data are treated descriptively but not integrated into the modeling framework. Consider incorporating confirmatory data in a joint model or clearly justify why integration was not feasible.

8.The text asserts that canids do not act as maintenance hosts but this conclusion may exceed the strength of the available data. Rephrase conclusions to emphasize sentinel roles without excluding possible maintenance potential in specific contexts.

9.Several abbreviations (e.g., FPA, ICT, CIE) appear before being defined. Ensure all diagnostic abbreviations are defined at first mention.

10.The Results section includes interpretive statements better suited for the Discussion. Restrict results to data presentation and move interpretive narrative to the Discussion.

Reviewer #2: This is a nice, well-written manuscript that consolidates knowledge of Brucella distribution worldwide into an easy to reference format, and estimates frequencies of exposure/infection in canids. The implications for low coverage in specific regions and the association of canid brucellosis with nearby livestock are clear. The only concern is that the references are identified by inconsistent footnote punctuation and the citations are incomplete. This is easily correctable.

Reviewer #3: I would like to appreciate the authors for tackling an important and understudied topic on the seroprevalence of brucellosis in wild canids. They provide a rigorous global synthesis of Brucella exposure in wild canids, applying misclassification-aware machine learning to correct diagnostic bias. It offers calibrated seroprevalence estimates, highlights major geographic gaps, and delivers a reproducible framework that strengthens wildlife disease surveillance and One Health decision-making. However, the strength of the study needs to be improved by handling the reporting transparency needs highlighted in the review comments

**********

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Do you want your identity to be public for this peer review? For information about this choice, including consent withdrawal, please see our Privacy Policy .

Reviewer #1: Yes: Ahmed Hamdy Ghonaim

Reviewer #2: Yes: H. Carl Gelhaus

Reviewer #3: No

Figure resubmission:

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PLoS Negl Trop Dis. 2026 Mar 6;20(3):e0014029. doi: 10.1371/journal.pntd.0014029.r002

Author response to Decision Letter 1

28 Jan 2026

Attachment

Submitted filename: Response to Reviewers.docx

pntd.0014029.s008.docx^{(35.2KB, docx)}

PLoS Negl Trop Dis. doi: 10.1371/journal.pntd.0014029.r003

Decision Letter 1

Ana LTO Nascimento

11 Feb 2026

Dear Dr. Abdous,

We are pleased to inform you that your manuscript 'From tests to truth: A misclassification-aware machine learning framework for estimating brucellosis seroprevalence in wild canids' has been provisionally accepted for publication in PLOS Neglected Tropical Diseases.

Before your manuscript can be formally accepted you will need to complete some formatting changes, which you will receive in a follow up email. A member of our team will be in touch with a set of requests.

Please note that your manuscript will not be scheduled for publication until you have made the required changes, so a swift response is appreciated.

IMPORTANT: The editorial review process is now complete. PLOS will only permit corrections to spelling, formatting or significant scientific errors from this point onwards. Requests for major changes, or any which affect the scientific understanding of your work, will cause delays to the publication date of your manuscript.

Should you, your institution's press office or the journal office choose to press release your paper, you will automatically be opted out of early publication. We ask that you notify us now if you or your institution is planning to press release the article. All press must be co-ordinated with PLOS.

Thank you again for supporting Open Access publishing; we are looking forward to publishing your work in PLOS Neglected Tropical Diseases.

Best regards,

Richard A. Bowen, DVM PhD

Academic Editor

PLOS Neglected Tropical Diseases

Ana LTO Nascimento

Section Editor

PLOS Neglected Tropical Diseases

Shaden Kamhawi

co-Editor-in-Chief

PLOS Neglected Tropical Diseases

orcid.org/0000-0003-4304-636XX

Paul Brindley

co-Editor-in-Chief

PLOS Neglected Tropical Diseases

orcid.org/0000-0003-1765-0002

***********************************************************

Thank you for the thoughtful revision of your manuscript. All reviewers now consider this a valuable contribution to the field and acceptable for publiction.

p.p1 {margin: 0.0px 0.0px 0.0px 0.0px; line-height: 16.0px; font: 14.0px Arial; color: #323333; -webkit-text-stroke: #323333}span.s1 {font-kerning: none

Reviewer's Responses to Questions

Key Review Criteria Required for Acceptance?

As you describe the new analyses required for acceptance, please consider the following:

Methods

-Are the objectives of the study clearly articulated with a clear testable hypothesis stated?

-Is the study design appropriate to address the stated objectives?

-Is the population clearly described and appropriate for the hypothesis being tested?

-Is the sample size sufficient to ensure adequate power to address the hypothesis being tested?

-Were correct statistical analysis used to support conclusions?

-Are there concerns about ethical or regulatory requirements being met?

Reviewer #1: Yes

Reviewer #3: The revised manuscript now clearly follows the PRISMA-ScR guidelines, and the authors have demonstrated how they performed the risk of bias evaluation in the included studies and their final quality appraisal. The search string has been added and referenced

**********

Results

-Does the analysis presented match the analysis plan?

-Are the results clearly and completely presented?

-Are the figures (Tables, Images) of sufficient quality for clarity?

Reviewer #1: Yes

Reviewer #3: Table 1 has been well clarified, plus all the results on quality of appraisal and risk of bias analysis.

**********

Conclusions

-Are the conclusions supported by the data presented?

-Are the limitations of analysis clearly described?

-Do the authors discuss how these data can be helpful to advance our understanding of the topic under study?

-Is public health relevance addressed?

Reviewer #1: Yes

Reviewer #3: The authors conclusion on the exposure of wild canids to brucellosis is well supported by their data

**********

Editorial and Data Presentation Modifications?

Reviewer #1: (No Response)

Reviewer #3: (No Response)

**********

Summary and General Comments

Reviewer #1: The quality of the current manuscript has been improved greatly. I now agree for further process. Congratulations

Reviewer #3: I thank the authors for significantly improving this manuscript. Its now well aligned to the reporting standard they applied (PRISMA-ScR) and contributes to our further understanding of the role plaid by wild canids in the transmission of brucellosis

**********

PLOS authors have the option to publish the peer review history of their article (what does this mean? ). If published, this will include your full peer review and any attached files.

If you choose “no”, your identity will remain anonymous but your review may still be made public.

Do you want your identity to be public for this peer review? For information about this choice, including consent withdrawal, please see our Privacy Policy .

Reviewer #1: Yes: Ahmed Hamdy Ghonaim

Reviewer #3: No

PLoS Negl Trop Dis. doi: 10.1371/journal.pntd.0014029.r004

Acceptance letter

Ana LTO Nascimento

Dear Dr Abdous,

We are delighted to inform you that your manuscript, "From tests to truth: A misclassification-aware machine learning framework for estimating brucellosis seroprevalence in wild canids," has been formally accepted for publication in PLOS Neglected Tropical Diseases.

We have now passed your article onto the PLOS Production Department who will complete the rest of the publication process. All authors will receive a confirmation email upon publication.

The corresponding author will soon be receiving a typeset proof for review, to ensure errors have not been introduced during production. Please review the PDF proof of your manuscript carefully, as this is the last chance to correct any scientific or type-setting errors. Please note that major changes, or those which affect the scientific understanding of the work, will likely cause delays to the publication date of your manuscript. Note: Proofs for Front Matter articles (Editorial, Viewpoint, Symposium, Review, etc...) are generated on a different schedule and may not be made available as quickly.

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Thank you again for supporting open-access publishing; we are looking forward to publishing your work in PLOS Neglected Tropical Diseases.

Best regards,

Shaden Kamhawi

co-Editor-in-Chief

PLOS Neglected Tropical Diseases

Paul Brindley

co-Editor-in-Chief

PLOS Neglected Tropical Diseases

Associated Data

This section collects any data citations, data availability statements, or supplementary materials included in this article.

Supplementary Materials

S1 File. PRISMA-ScR Checklist.

(DOCX)

pntd.0014029.s001.docx^{(100.7KB, docx)}

S1 Fig. Risk-of-bias heat-map figure.

(DOCX)

pntd.0014029.s002.docx^{(551.2KB, docx)}

S2 File. Full database search strings used across all bibliographic sources.

(DOCX)

pntd.0014029.s003.docx^{(28KB, docx)}

S1 Table. Full-text exclusion log.

(DOCX)

pntd.0014029.s004.docx^{(20.2KB, docx)}

S2 Table. Newcastle–Ottawa Quality Appraisal Scores for included studies.

(DOCX)

pntd.0014029.s005.docx^{(32.5KB, docx)}

S3 Table. Study-level meta-analysis data for Brucella exposure and infection in wild canids.

(DOCX)

pntd.0014029.s006.docx^{(28.5KB, docx)}

Attachment

Submitted filename: Response to Reviewers.docx

pntd.0014029.s008.docx^{(35.2KB, docx)}

Data Availability Statement

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PERMALINK

From tests to truth: A misclassification-aware machine learning framework for estimating brucellosis seroprevalence in wild canids

Nahal Sarvestani

Farzane Shams

Armin Mirshahi

Mobina Pato

Aria Javani Farbod

Armina Khayatderafshi

Mobina Payami

Arman Abdous

Roles

Abstract

Author summary

Introduction

Methods

Bibliographic search strategy

Fig 1. PRISMA-ScR flow diagram of study selection for the global scoping review of Brucella exposure and infection in wild canids (1962–2025).

Data extraction and synthesis

Table 1. Study-level brucellosis prevalence in wild canids worldwide (1962–2025).

Inclusion and exclusion criteria

Quality appraisal (QA)

Outcomes and unit of analysis

Serology track: misclassification-adjusted true seroprevalence

Infection track: PCR/culture prevalence

Machine learning Bayesian meta-analysis (serology)

Software and reproducibility

Results

Search results

Characteristics of included studies

Risk of bias assessment

Global exposure is widespread, whereas confirmed active infection is uncommon

Fig 2. Global true seroprevalence and model calibration.

Asia: sparse wild data, localized infection

Fig 3. True seroprevalence of brucellosis in Asian wild canids.

Europe: lowest exposure and limited infection evidence

Fig 4. Very low pooled prevalence with localized outliers in Europe.

North America: low-to-moderate exposure, scarce confirmation

Fig 5. Pooled true seroprevalence and study-level estimates in North America.

South America: higher exposure, limited confirmatory data

Fig 6. Elevated true seroprevalence and diagnostic variability in South America.

Africa: under-sampled and uncertain

Species patterns and diagnostic effects

Fig 7. Geographic clustering of seropositivity and assay-driven differences.

Discussion

Conclusion

AI assistance disclosure

Supporting information

Data Availability

Funding Statement

References

Decision Letter 0

Ana LTO Nascimento

Roles

Author response to Decision Letter 1

Decision Letter 1

Ana LTO Nascimento

Roles

Acceptance letter

Ana LTO Nascimento

Roles

Associated Data

Supplementary Materials

Data Availability Statement

ACTIONS

PERMALINK

RESOURCES

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