Advertisement

  • Loading metrics

Open Access

Peer-reviewed

Research Article

Systematic review of health risk assessment in Africa’s bushmeat trade: Are there any risks assessed?

  • Claude Vianney Amougou,

    Roles Data curation, Formal analysis, Investigation, Methodology, Visualization, Writing – original draft

    Affiliation Zoology Unit, Laboratory of Biology and Physiology of Animal Organisms, Faculty of Sciences, University of Douala, Douala, Cameroon

    ⨯
  • Alain Didier Missoup ,

    Roles Conceptualization, Funding acquisition, Methodology, Project administration, Resources, Supervision, Validation, Writing – review & editing

    admissoup@ymail.com (ADM), philippe.gaubert@ird.fr (PG)

    Affiliation Zoology Unit, Laboratory of Biology and Physiology of Animal Organisms, Faculty of Sciences, University of Douala, Douala, Cameroon

    ⨯
  • Maurice Tindo,

    Roles Resources, Supervision, Writing – review & editing

    Affiliation Zoology Unit, Laboratory of Biology and Physiology of Animal Organisms, Faculty of Sciences, University of Douala, Douala, Cameroon

    ⨯
  • Philippe Gaubert

    Roles Conceptualization, Funding acquisition, Methodology, Project administration, Resources, Supervision, Validation, Writing – original draft

    admissoup@ymail.com (ADM), philippe.gaubert@ird.fr (PG)

    Affiliations Centre de Recherche sur la Biodiversité et l’Environnement, Université de Toulouse, CNRS, IRD, Toulouse INP, Toulouse, France, CIIMAR/CIMAR, Interdisciplinary Centre of Marine and Environmental Research, University of Porto, Matosinhos, Portugal, Laboratoire d’Ecologie Appliquée, Faculté des Sciences Agronomiques, Université d’Abomey-Calavi, Calavi, Benin

    ⨯

Abstract

Background

The bushmeat trade in tropical Africa represents a major route for zoonotic disease emergence. Yet, the extent to which health risks have been quantitatively assessed remains unclear. Therefore, our study aimed at systematically reviewing health risk assessments conducted in the African bushmeat trade, and identifying methodological patterns and research gaps.

Methodology/principal findings

Following PRISMA guidelines, we searched Web of Science and Google Scholar (to November 2024) using multilingual Boolean queries. Eligible studies included any research assessing health, zoonotic, or food-borne risks in bushmeat supply chains across Africa. Two co-authors independently cross-validated c. 23% of extracted data. Descriptive statistics and generalized linear models were used to explore publication patterns and predictors of research output. From 449 records finally identified, 129 met inclusion criteria. Ethnobiological and public health surveillance approaches dominated (41.1% each), while epidemiological studies were scarce. Most publications appeared after 2017, mainly from Cameroon, Nigeria, and the Democratic Republic of Congo, with epidemic occurrence significantly predicting national research output. Interviews were the most frequent method (44.8%), while pathogen detection occurred in 40.3% of studies, identifying 66 confirmed human pathogens (23 viruses, 19 bacteria, 24 parasites). More than 88% of studies did not report survey effort, and none implemented a formal quantitative health risk assessment.

Conclusions/significance

Quantitative health risk assessment in the African bushmeat trade remains unattainable due to scarce data on pathogen prevalence, exposure, and host–pathogen interactions. Only formal recognition and state-regulated management of the trade—incorporating molecular surveillance, host-pathogen ecological data, and supply-chain mapping within a One Health framework—will enable reliable risk quantification.

Author summary

The bushmeat trade in tropical Africa is a recognized pathway for zoonotic disease emergence, yet the health risks associated with this trade remain poorly quantified. We conducted a systematic review of studies assessing health risks in bushmeat value chains across Africa, following PRISMA guidelines. Of 449 records identified, 129 studies met inclusion criteria. Most research employed ethnobiological or public health surveillance approaches, while epidemiological studies were rare. Research output was concentrated in Cameroon, Nigeria, and the Democratic Republic of Congo, with epidemic events driving survey effort. Interviews were the predominant method, and direct pathogen detection occurred in less than half of studies, identifying 66 confirmed human pathogens, including viruses, bacteria, and parasites. Notably, over 88% of studies did not report survey effort, and none performed a formal quantitative health risk assessment. Our findings highlight that robust risk quantification is currently unfeasible due to limited data on pathogen prevalence, human exposure, and host–pathogen interactions. To enable reliable assessment, the bushmeat trade must be formally recognized and managed by national authorities, integrating molecular surveillance, ecological data, and supply-chain mapping within a One Health framework. This approach is essential for anticipating zoonotic spillovers and guiding evidence-based public health interventions.

Citation: Amougou CV, Missoup AD, Tindo M, Gaubert P (2026) Systematic review of health risk assessment in Africa’s bushmeat trade: Are there any risks assessed? PLoS Negl Trop Dis 20(5): e0014308. https://doi.org/10.1371/journal.pntd.0014308

Editor: Richard A. Bowen, Colorado State University, UNITED STATES OF AMERICA

Received: November 14, 2025; Accepted: April 27, 2026; Published: May 18, 2026

Copyright: © 2026 Amougou et al. This is an open access article distributed under the terms of the Creative Commons Attribution License, which permits unrestricted use, distribution, and reproduction in any medium, provided the original author and source are credited.

Data Availability: All data and related metadata on which the systematic review is based are provided as appendix.

Funding: This research was supported by ANRS-MIE (AFRICoV – ANRS COV6) (ADM and PG) and FCT (BUSHRISK – IC&DT 02/SAICT/2017; n◦ 032130) (PG). CVA received support from Institut de Recherche pour le Développement through a grant “Mobilités Sud–Nord” (2023). The funders had no role in study design, data collection and analysis, decision to publish, or preparation of the manuscript.

Competing interests: The authors have declared that no competing interests exist.

Introduction

While a portion of the wildlife trade operates legally under international frameworks such as the Convention on International Trade in Endangered Species of Wild Fauna and Flora (CITES), a substantial share remains illicit. The illegal wildlife trade (IWT)—involving the trafficking of live or dead wild species and their derivatives—constitutes a highly profitable shadow economy that sustains trafficking networks across multiple geographic scales [1,2]. The IWT has long been recognized by both scientific and inter-governmental sectors as a significant driver of zoonotic spillover risks [3,4], facilitating an estimated one billion human–wildlife interactions each year [5]. These risks are further exacerbated by economic globalization [6], as starkly demonstrated by the COVID-19 pandemic [7], which heightened global awareness of the public health threats linked to wildlife trafficking. In response, there have been increasing calls to adopt an integrated “One Health” approach to mitigate the risk of future zoonotic outbreaks (e.g., [8]).

The bushmeat trade (BT) is an informal, largely unregulated, and predominantly illegal activity occurring across tropical regions, primarily targeting terrestrial vertebrates [9]. In Africa, the BT represents a long-standing cultural tradition and serves as a critical source of both animal protein and income for many rural communities [10]. In the Congo Basin alone, around 4.5 million tons of bushmeat are harvested annually, placing unsustainable pressure on wildlife populations [11,12]. Beyond its ecological impacts, the BT poses serious public health risks, functioning as a vector for zoonotic pathogens such as Ebola, Mpox, and HIV [13–15]. The growing encroachment of human activities, including deforestation and habitat fragmentation, further elevates the risk of zoonotic spillovers, underscoring the urgent need to address the health hazards associated with bushmeat consumption in Africa [16,17].

Although more pathogens linked to bushmeat are now being studied, the specific practices that increase the risk of transmission are still not well understood or systematically mapped [18–20]. Several studies have identified risky behaviors that can lead to cross-species transmission, including contact with contaminated materials and blood during hunting, transportation and sale of bushmeat [21–23]. Long-distance bushmeat transport increases the chances of zoonotic spillovers, especially for pathogens that can survive in changing environments [24,25]. Direct spillover can also happen through bites, scratches, or cuts during hunting or butchering [26,27].

Practices at risk are often driven by deep-rooted economic, cultural, social, and dietary factors (e.g., [19,28,29]), and are usually more common where people face poverty or food insecurity [30,31]. Moreover, people involved in the bushmeat supply chain—such as hunters, butchers, sellers, and consumers—do not all have the same understanding of health risks. While some, especially urban sellers, recognize the link between bushmeat and zoonotic pandemics (e.g., [26,32], others have limited or unclear perceptions. This gap is often due to limited access to health services and weak disease reporting systems in many regions [19]. Public health messaging may also be perceived as exaggerated, and ultimately ignored, while the use of mitigation measures such as wearing gloves, thorough cooking, or face masks remains rare, particularly in informal markets [33,34].

In this study, we focus on the notion of health risk assessment within the context of Africa’s BT. Although numerous studies have addressed zoonotic risks associated with the BT in Africa (see above), it remains unclear how such risks have been assessed, and to what extent existing assessments can inform national and regional zoonotic spillover prevention strategies. Health risk refers to the probability that exposure to specific agents or conditions will negatively affect human health (https://newsinhealth.nih.gov/2016/10/understanding-health-risks). In the BT, health risk can thus be defined as the probability of zoonotic spillovers or food-borne infections arising from human contact with animal fluids, carcasses, or meat consumption [35]. Such risk is conditioned by multiple factors, including the diversity and volume of species traded, the structure of supply networks, pathogen prevalence, and individual susceptibility to infection [36,37].

Here, we present a systematic review of health risk assessments conducted in the context of the BT in tropical Africa. Specifically, we identify global trends in health surveys on bushmeat consumption and trade, and characterize the experimental designs used to quantify associated health risks. On this basis, we discuss the feasibility of quantifying health risks in the African bushmeat context and propose pathways to strengthen health risk assessment frameworks along BT networks in tropical Africa.

Methods

Database on health risk assessment of the bushmeat trade in African rainforests

This study was conducted in accordance with the Preferred Reporting Items for Systematic Reviews and Meta-Analyses (PRISMA) guidelines and procedures [38] (Fig 1, S1 Table). We focused on articles published in scientific journals, to assess global trends in health surveys related to the BT and investigate how scientists assess and quantify the health risks associated with the consumption, handling, and trade of bushmeat. We used full-text peer-reviewed articles—available through open access—that refer to ‘wild meat’ or ‘bushmeat’ in relation to health risk assessment. Review articles were included in the database if only they contained secondary data, new data or if we were unable to access the primary data. Only studies conducted within African tropical rainforests were considered, here including the Congolian region (as defined in [39]), together with remnant forest patches within the Sudanian region (e.g., Senegal and Guinea-Bissau; see [40]). This geographic restriction was applied to focus on areas where the BT is particularly pronounced and has been identified as a significant threat to human health [41,42]. This also ensures that climate and vegetation contexts are similar across the study area; thereby reducing the possibility of bias due to data heterogeneity, since wildlife handling practices, bushmeat species and therefore the associated health risks are likely to be similar.

thumbnail
Download:
Fig 1. PRISMA flow diagram illustrating the article search and screening process of the systematic review on health risk assessments related to the bushmeat in African tropical rainforests.

https://doi.org/10.1371/journal.pntd.0014308.g001

The literature search was conducted between October 2023 and November 2024, with no restriction on publication years. Boolean search terms were used to identify literature from two databases; namely the Web of Science and Google Scholar. The reason being that the Web of Science generally contains high-quality studies, which are selected through rigorous indexing and precise filtering tools. On the other hand, Google Scholar provides access to a wider range of literature, particularly local and grey literature, which are often absent from traditional databases. Combining the two databases enables us to access the vast majority of available information on the subject. The search was designed such as not to exclude articles written in French, English, Spanish or Portuguese. In the Web of Science (WoS), the Boolean search string ((health risks OR health hazards OR health threats OR risks OR health OR zoonos* OR pathogen*) AND (bushmeat markets OR venison OR wild meat markets OR bushmeat OR wild meat) AND (Africa OR sub-Saharan Africa)) was used. The search returned 154 articles, of which only those containing “health risks” (or “health hazards”, “health threats” or “pathogens”), “market” and “bushmeat” (or wild meat/ wildmeat) in their title and/or abstract were included in the database (N = 69). The same search string was used in Google Scholar, but given the great number of returned articles (N = 5050), we narrowed down the search with the following string: ((health OR sanitary risk*) AND zoonoses* AND (bushmeat OR “wild meat”) AND market* AND Africa), to obtain 2877 articles. After merging the two databases, duplicate articles were manually removed (N = 181), references not being articles published in scientific journals (e.g., books, reports; N = 665) and articles not containing “bushmeat” (or wild meat or venison) and “health” in the title or the abstract were discarded (N = 1736) to form an initial reading list of 449 records. Studies conducted outside bushmeat markets (e.g., in a protected area, a rural community or a hospital) were excluded if not referring to wild animals as meat or as being intended for human consumption. Specifically, rural community studies were included when they directly addressed bushmeat-related activities. Studies focusing on domestic animals were also excluded. On this basis and from the criteria previously described, we excluded reviews and articles for which we did not have access to primary data, studies that did not provide sufficient data or did not address bushmeat risk assessment, and those conducted outside the targeted study area (see S2 Table for full details). The final list of publications consisted of 129 articles (compiled on 22/11/2024; S3 Table and S1 Appendix). Two co-authors (ADM and PG) independently reviewed 15 randomly chosen articles (23.3% of the total) of the list for cross-check validation.

In accordance with PRISMA guidelines [38], risk of bias was considered at both the review and study levels. At the review level, potential publication bias may result from the reliance on Web of Science and Google Scholar, which, despite broad coverage, may incompletely capture grey or non-indexed literature. Although multilingual search terms were used, language and indexing biases cannot be entirely excluded. In addition, the screening strategy based on titles and abstracts may have led to the exclusion of relevant studies using inconsistent terminology.

At the study level, no standardized risk-of-bias assessment tool was applied due to the substantial heterogeneity in study designs, objectives, and outcome measures. However, several sources of bias were identified. Selection bias may arise from the geographic restriction to tropical African forest bioregions and from study-specific sampling strategies that were often poorly described. Reporting bias was substantial, as most studies did not provide key methodological information (e.g., survey effort, sampling duration), limiting the assessment of internal validity. Measurement bias is also likely, given the predominance of self-reported data (e.g., interviews) and the variability in laboratory and observational methods used to identify pathogens or risk practices.

Missing data were not imputed; instead, analyses were conducted using available data only. When key methodological or outcome information was absent, this was recorded during data extraction and considered in the interpretation of results. Data extraction bias was mitigated through independent cross-validation of a subset of studies (23.3%) by two co-authors. Nevertheless, the high degree of methodological heterogeneity precluded quantitative synthesis and formal bias comparison across studies, and may affect the robustness and generalizability of the reported trends.

Previous reviews have discussed health risks associated with bushmeat in tropical Africa, either by highlighting the ecological and epidemiological factors of these risks [35,42,43], analyzing the practices at risks of stakeholders in the bushmeat supply chain [44], or examining the potential for exposure to and/or contamination by zoonotic pathogens [45–47]. To our knowledge, our review is the sole to critically scrutinize the assessment of health risks related to the bushmeat trade in African tropical rainforests.

Data extraction

Articles were categorised into three main research areas, including public health surveillance (collection of data on pathogens and/or biomonitoring in relation to health), ethnobiology (study of human behaviour, practices at risk and perceptions in relation to bushmeat handling) and epidemiology (means of transmission and/or causal links between certain factors / practices and zoonotic diseases).

We collected and analyzed data to identify global trends in health surveys on bushmeat consumption and trade, and to characterize the experimental designs used in these studies. We focused on temporal indicators such as year of publication, study period, time lag between (i) survey and publication years and (ii) that of epidemic outbreak (when applicable) and publication. We also collected geographic information on the country, location of study site, type of study site, and study scale.

More information of the context and methodology of the studies were gathered, including survey effort, survey targets, main objective(s), and risk assessment approach. Based on the various components of health risk assessment (identify and assess the hazard, estimate/evaluate exposure; [48]), we scored whether the studies had conducted an actual estimate of the health risks, through the establishment of a probability, a score or any other conclusion in risk weighting. We also collected information on (i) whether each study was general or focused on specific pathogens, and (ii) the pathogens targeted. We classified the pathogenicity of the microorganisms based on databases from public health sources, including the Centers for Disease Control and Prevention [49,50], the World Health Organization [51], and NCBI (https://www.ncbi.nlm.nih.gov/pathogens/organisms/), together with reference peer-reviewed literature [52]. Specific disease associations were verified for each taxon.

Data analysis

Descriptive statistics and graphical outputs were conducted in Microsoft Excel (Microsoft 365) using dynamic cross-tabulations, and RStudio version 4.3.1 [53] with ggplot2, reshape2, tidyverse and rnaturalearth packages. We used AER package to run a General Linear Model (GLM) based on the quasi-poisson regression model to quantify the relationship between the number of scientific publications (dependent variable) and a series of country-specific predictor variables. These included GDP (see [54,55]), urbanization rate (considered a catalyst for the spread of zoonoses; [56]), together with forest cover and the occurrence of major epidemics (i.e., large scale, rapidly spreading outbreaks with significant health, social, or economic impact), both of which may increase research prioritization in impacted countries [57,58]). GDP and urbanization rate data were obtained from World Bank (https://www.worldbank.org; year 2022), while forest cover percentages were obtained from UN Food and Agriculture Organization (FAO, https://ourworldindata.org/forest-area; year 2020). The presence or absence of zoonotic outbreaks (Ebola, Monkeypox, Lassa fever, and Marburg) was determined through searches in the Google database, supplemented with information from Pasteur Institute (https://www.pasteur.fr/fr/centre-medical/fiches-maladies) and World Health Organization (https://www.who.int/fr/emergencies/disease-outbreak-news).

Results

Among the three research fields investigating zoonotic risks associated with bushmeat in African tropical rainforests, ethnobiology and public health surveillance were the main contributors, accounting for 41.1% of the scientific output, respectively, while epidemiology represented 17.8%. Four of the 94 scientific journals involved accounted for approximately 20.2% of the articles: EcoHealth (6.9%), Emerging Infectious Diseases (4.7%), PLoS ONE (4.7%), and PLoS Neglected Tropical Diseases (3.9%).

Most scientific articles on the topic (63.5%) were published from 2017. The majority of articles (70.7%) was conducted within one year of survey, with a maximum duration of 15 years. The data collection period was not reported in 17.8% of the cases. The time lag between the end of the survey period and the publication ranged from 0 to 14 years, with the majority of articles (50.9%) being published within two years (Fig 2). Only 24 studies (18.6%) were reported to have been conducted in response to a zoonotic outbreak. The time lag between the occurrence of the epidemic in question and the publication of the study ranged from a few months to 14 years (mean = 3.8 yrs; SD = 3.4).

thumbnail
Download:
Fig 2. Time lag between survey and publication year of risk assessment studies on the bushmeat trade in African tropical rainforests.

Time lag is expressed in years.

https://doi.org/10.1371/journal.pntd.0014308.g002

Cameroon (n = 40), Nigeria (30), and the Democratic Republic of Congo (26) were the most studied countries (Fig 3). Most of the studies (88.4%) were deployed at the national level, while only 5.4% investigated an international scale. Study sites varied but were predominantly represented by rural communities (~36.5%) and urban markets (~19.9%). Approximately 76.2% of the articles focused on a single type of study site (Fig 4).

thumbnail
Download:
Fig 3. Per-country number of scientific publications on health risk assessment in African tropical rainforests together with assessment methods.

Pie charts show the proportion of assessment methods used in each country (e.g., pie charts in Togo and Cameroon represent one and 40 articles, respectively). Base map layer obtained from Natural Earth (public domain data), via the rnaturalearth R package. Country boundaries sourced from: https://www.naturalearthdata.com/downloads/50m-cultural-vectors/50m-admin-0-countries/. License information available at: https://www.naturalearthdata.com/about/terms-of-use/.

https://doi.org/10.1371/journal.pntd.0014308.g003

thumbnail
Download:
Fig 4. Type of study sites targeted by health risk assessment surveys related to the bushmeat trade in African tropical rainforests.

A: Forest and protected areas; B: Hospitals; C: Households and schools; D: Rural communities; E: Rural markets; F: Urban communities; G: Urban markets.

https://doi.org/10.1371/journal.pntd.0014308.g004

Approximately 83.7% of the studies did not report the survey effort (number of days over which data collection occurred). When the information was given, data collection was deployed from 3 to 2670 days (mean = 178.9 days; SD = 574.5). Health risk assessment was conducted on humans (56.1%) and on the bushmeat species (39.4%), while only 4.5% considered both humans and bushmeat. Most studies relied on a single assessment method (80.6%). Interviews were the predominant approach (44.8%), with a marked increase from 2017 (Fig 5). The use of other approaches was moderate, including biological tests (17.5%), article reviews (7.2%), observation of risk practices (7.8%) and DNA-typing (22.7%).

thumbnail
Download:
Fig 5. Temporal distribution of survey methods used in health risk assessment related to the bushmeat trade in African tropical rainforests.

https://doi.org/10.1371/journal.pntd.0014308.g005

All the scientific studies focused on identifying potential risks or hazards linked to bushmeat without addressing the likelihood of their occurrence. This was done either through observational studies on human behavior or laboratory-based epidemiological research. Eventually, 53.5% of the studies did not propose any recommendations.

GLM analysis suggests that the presence of major epidemics and, to a lesser extent (marginally significant effect), GDP were the variables significantly explaining the number of health risk surveys conducted among countries (Table 1).

thumbnail
Download:
Table 1. Generalized linear model assessing the influence of GDP, forest cover, urbanization rate, and major epidemics on the number of bushmeat health risk surveys conducted in African tropical rainforests.

https://doi.org/10.1371/journal.pntd.0014308.t001

The microbes and other organisms identified in the bushmeat included 36 viruses, 38 bacteria and 38 parasites (Table 2). Our study revealed that 66 of the identified microbes and parasites contain taxa known to be pathogens in humans (23 viruses, 19 bacteria and 24 parasites). Another 11 viruses and 10 bacteria are opportunistic or uncertain human pathogens. The other taxa reported are host pathogens not known to cause human disease or commensal microbes.

thumbnail
Download:
Table 2. List of the microbes and parasites identified from bushmeat surveys in African tropical rainforests.

https://doi.org/10.1371/journal.pntd.0014308.t002

Among the hundred vertebrate species reported as hosts (S4 Table), mammals were the most frequent, while 12.7% of animals could not be specifically identified. Pathogens were screened in only 55 articles (40.3%). A majority of authors (92.5%) focused exclusively on the identification of a single group of pathogens, with 56.9% of these studies concentrating on viral diseases.

Discussion

Global trends in health surveys on bushmeat consumption and trade

The first objective of our review was to examine global trends in health risk assessments conducted by scientists on bushmeat consumption in African tropical rainforests over the past 25 years. Our study revealed a co-dominance of investigations based on ethnobiological and public health surveillance approaches. Because understanding the practices and behaviors of local populations is key to evaluating the associated health risks [59], especially given that the risk of zoonotic spillovers is strongly influenced by cultural practices and close contact with wildlife [58], ethnobiology stands as a major research domain in health risk assessments. Additionally, the frequent use of public health surveillance aligns with the global recognition of bushmeat as a significant source of zoonotic risks [35,42,43]. By accumulating long-term data, public health surveillance can provide a valuable tool for monitoring zoonotic spillover risks in human populations [60]. In contrast, relatively few studies have adopted an epidemiological framework, despite its effectiveness in quantifying health risks [42]. Overall, this global trend in health risk assessments underscores the prevailing lack of an interdisciplinary framework to comprehensively address the health risks associated with the BT in African tropical rainforests, despite repeated calls for more integrative approaches, such as One Health [36,61,62].

Approximately 20% of the scientific surveys were published in four journals—two specializing in emerging diseases, one in global health sciences, and one with a broad, inclusive scope. Alongside the other 90 journals, this publication scheme shall contribute to the widespread dissemination of multidisciplinary findings to a diverse audience. Most research on bushmeat health surveys has been published since 2017, possibly reflecting a growing interest in the topic—likely influenced by recent zoonotic outbreaks [58]. This interest may be particularly relevant in the context of emerging epidemics and pandemics such as Ebola and COVID-19 [63,64]. However, the increase in publications does not necessarily indicate a reactive investment in understanding and mitigating health risks related to bushmeat consumption. Instead, it should be considered alongside the overall growth rate of scientific publications in the Life Sciences, which averages 5.07% per year, with a doubling time of 14.0 years [65].

Cameroon, Nigeria, and DR Congo accounted for approximately three-quarters of the bushmeat health surveys, aligning with Groom et al. [54]’s findings on the global distribution of bushmeat survey efforts across African tropical rainforests. These countries have experienced significant epidemic outbreaks [66,67] and rank among the highest GDPs in the study region—both factors that correlate positively with the global frequency of bushmeat surveys. Additionally, they serve as key hubs for national and international bushmeat trade [68–70], including the trafficking of pangolins [54,71], likely promoting the prioritisation of health risk surveys.

Most studies have been conducted at the national level, with only 5.4% examining the international scale. National-scale surveys were typically limited to a single site, likely failing to capture the complex, interconnected network of the BT. The scarcity of studies investigating international trade reflects a broader issue of inadequate scale design in bushmeat research [54]. This shortfall is partially attributed to a global lack of funding (e.g., [72]) but also to the insufficient prioritization of bushmeat-related health risks in the northern hemisphere [73]. Notably, the large volumes of bushmeat entering Europe and North America each year (e.g., [70,74,75]) pose a significant yet under-assessed threat to public health in these regions [35,76].

The predominant targets of the health surveys were rural communities, and to a lesser extent, urban bushmeat markets. Due to their proximity to and involvement in hunting and bushmeat trade-related activities, rural populations face a heightened risk of direct contact with bushmeat species, particularly during fresh carcass processing—a critical high-risk phase in the bushmeat trade chain (e.g., [77]). The observed similarities between human and wildlife viromes further underscore the likelihood of such interactions, especially with non-human primates [27,78]. Urban bushmeat markets, on the other hand, increase the frequency and diversity of human-animal interactions, potentially creating multiple opportunities for zoonotic disease emergence and spread [79]. However, the health risks across the entire bushmeat trade chain—from hunting to final consumption—remain insufficiently assessed and properly scaled (see [30, 80]). Notably, as the trade chain progresses, the time elapsed since the animal’s death increases, which may reduce the risk of zoonotic transmission, particularly for RNA viruses, despite their persistent detectability [81,82], while increasing the risk of food-borne disease spillover [30].

Our study identified a codominance of pathogenic taxa to humans among viruses, bacteria and parasites (23, 19 and 24 genera, respectively). These findings align with a recent review by Moloney et al. [83] on the pathogenic spectrum detected in wild meat. More than half of the pathogens reported in bushmeat samples are involved in zoonotic spillovers –or likely cause diseases– in humans. Such substantial pathogen diversity highlights the significant potential of bushmeat as a source of emerging infectious diseases [23,43]. Most surveys focused on viral diseases, as viruses are highly transmissible, evolve rapidly, and often have limited therapeutic options, making them significant epidemic and pandemic threats [84]. Health risk assessments of the bushmeat trade have primarily targeted major viral agents, including the Ebola virus [85–87], various strains of simian immunodeficiency viruses [88], and the Mpox virus [89]. However, we observed a striking absence of broad-spectrum pathogen surveillance in bushmeat, which could hinder the proactive identification of emerging threats, including the prospective search for Disease X [83].

Among the approximately 100 vertebrate species identified as pathogen hosts in this review, the majority were mammals, aligning with previous findings on bushmeat diversity and research focus (e.g., [90]). Mammals are the primary target of health risk surveillance in the bushmeat trade because (i) they constitute the dominant taxonomic group in markets [54] and (ii) their phylogenetic proximity to humans increases the likelihood of zoonotic transmission [91]. In contrast, other vertebrate taxa present in the bushmeat trade, such as birds, reptiles and amphibians, remain largely under-investigated despite their known potential for zoonotic transmission [92–94]. A key limitation of bushmeat surveys is the frequent uncertainty in taxonomic identification of host species [54]. Our review revealed that health risk assessments of the bushmeat trade were also affected by inaccuracies in taxonomic identification, potentially obscuring host-pathogen relationships and hindering our understanding of zoonotic spillovers. To address these challenges, bushmeat health surveys could benefit from routinely integrating DNA-based methods for more precise host identification (e.g., [95,96]). This recommendation also extends to the “untargeted” screening of pathogen communities (see [83,97]), as our review found that only about one-quarter of health risk surveys incorporated molecular tools.

Experimental design in bushmeat health surveys and a blatant lack of zoonotic risk quantification

Our systematic review revealed significant flaws in the experimental design of health surveys targeting the bushmeat trade in African tropical rainforests. Nearly one-fourth of the studies failed to report the data collection period, while approximately 83.7% did not disclose survey effort (site.days). The lack of transparency in temporal and survey efforts undermines the reproducibility of study designs and may hamper the ability of health authorities to assess the applicability of study findings [98]. Bushmeat surveys were generally short, with a mean data collection period of about two months, and three-quarters focused on a single type of study site. Similar design limitations have been noted in biodiversity-oriented bushmeat surveys [54], where experimental protocols fail to capture the complexity and dynamics of the bushmeat trade chain. In health surveys, appropriate temporal and spatial sampling scales can participate in an accurate assessment of zoonotic risk dynamics along the chain of complex interactions characterizing the bushmeat trade network [16,99]. Most bushmeat health risk surveys were conducted at the national level, with only 5.4% addressing the international scale, while recent outbreaks such as Mpox highlight the global public health relevance of the BT in a globally interconnected world [76,100]. Health risk assessments primarily targeted human communities, often through interviews on risk perceptions and behaviors (e.g., [101–103]). However, only one-third of these surveys examined bushmeat species, and less than 5% covered human-wildlife interactions, despite the critical role of host biology and human-animal interfaces in characterizing and anticipating zoonotic spillovers [104,105]. Moreover, direct investigations of zoonotic pathogens were relatively scarce, with interviews being the dominant survey method. This limited focus on pathogen detection may constitute an additional barrier to comprehensive zoonotic risk assessment in African tropical rainforests, given that it is a fundamental component of health risk quantification [106].

The publication delays in scientific research, as seen in health risk surveys related to the bushmeat (median = 2 years post-survey), along with the time lag between zoonotic spillovers and their publication (mean = 3.8 years), and the low proportion of studies conducted in direct response to outbreaks, highlight a key limitation: peer-reviewed research is not an immediate tool for epidemic or pandemic response. Instead, it might be considered as serving to analyze the processes and impacts of zoonotic outbreaks and potentially anticipate future risks (e.g., [107,108]). This is especially relevant in recent years, as researchers have increasingly relied on historical data to assess long-term trends or address emerging questions—such as the impact of zoonotic diseases in a post-pandemic context (e.g., [107,109,110]).

Despite a noticeable rise in scientific output since 2017 and –more globally– a bias of the bushmeat health risk surveys towards the African continent [62], we could not identify any study providing a probabilistic assessment of zoonotic spillover. The overwhelming majority of health surveys have concentrated on identifying potential hazards associated with bushmeat consumption and trade, rather than evaluating the likelihood of these risks occurring. Moreover, nearly half of the studies reviewed did not provide specific recommendations for mitigating health risks, despite widespread recognition of the human health threats posed by bushmeat practices (e.g., [42,111,112]). In contrast, the transmission of zoonotic diseases from domestic animals and livestock (such as cattle, pigs, poultry, dogs, cats, goats, etc.) to humans is a relatively well-studied topic (e.g., [113–116]), particularly in the context of livestock intensification and human-animal interactions [117,118]. Several studies have quantified these risks, using probabilistic methods (e.g., [119–122]).

Bushmeat species can host a wide array of pathogens—including viruses, bacteria, and parasites—whose presence is influenced by a multitude of factors and evolves over time and space, necessitating long-term monitoring for a comprehensive understanding [123]. Although the advent of metagenomics has enabled untargeted screening of entire pathogen communities, health-directed insights remain limited, as many studies lack essential data such as pathogen prevalence and host range—both of which are central to accurate zoonotic risk assessment [104]. Compounding this issue is a broader, global gap in knowledge regarding the actual consequences of exposure to many of the identified pathogens [42], and how the destructive process of unregulated hunting on ecosystem equilibrium may increase the risk of zoonotic spillovers [77,124]. As a result, trait- and network-based approaches—despite their widespread use in zoonotic risk quantification—are particularly difficult to apply in the African bushmeat context due to these persistent data deficiencies [see 104].

Bushmeat surveys are further constrained by limited access to key points along the supply chain—particularly the hunting stage, which often takes place in remote forested areas, and the opaque networks of middlemen that mediate trade [54]. These limitations hinder the ability to conduct comprehensive, system-wide exposure assessments along the bushmeat chain. The predominantly illegal and/or informal nature of the BT [79] fosters mistrust among stakeholders, complicating the implementation of surveys and regulatory measures [125]. Moreover, efforts to investigate and document bushmeat-related health risks may be perceived as threats to livelihoods and cultural traditions [28,126]. The lack of risk quantification is also rooted in divergent priorities among researchers, policymakers, donors, and local communities in many low-income countries [127,128]. In tropical Africa, public health funding is often directed toward well-established diseases (see [129]), leaving limited resources for research on bushmeat and other endemic zoonotic threats [18,130].

The failure to quantify health risks associated with bushmeat consumption and handling poses a significant public health challenge for African tropical rainforests —a region recognized as a hotspot for both BT and the emergence of zoonotic diseases (see [99]). In the absence of robust risk assessments, healthcare systems may remain ill-equipped to anticipate and respond to outbreaks, with populations risking repeated exposure to high-priority zoonotic diseases [13,131]. Although health risks can also be mitigated through precautionary, qualitative, and evidence-informed approaches that focus on exposure reduction and adaptive management [132–134], inaccurate anticipation of zoonotic risks can have serious repercussions for the food security and economic stability of rural communities and bushmeat stakeholders, who may lose a critical livelihood resource while also facing stigma during outbreaks and trade bans (e.g., [32, 135]).

Avenues for reinforcing health risk assessment in the African bushmeat trade

Although progress has been made in predicting zoonotic spillovers and the epidemic potential of pathogens through advances in zoonotic risk assessment protocols [136], quantitative evaluations of such risks remain virtually absent from the bushmeat literature. This may be due to the fact that available approaches, such as Quantitative Risk Assessment Model (QRAM), require knowledge on parameters such as pathogen prevalence, dose-response relationships and frequency of exposure [137], that are difficult to measure in the African bushmeat context.

Because bushmeat research has largely focused on specific pathogens in post-epidemic, reactive investigations (see [83]), our understanding of pathogen diversity, distribution, and prevalence in bushmeat species remains limited [43]. However, advances in high-throughput sequencing now enable the broad, “universal” screening of microbiomes from virtually any RNA or DNA matrix [138,139]. This approach makes it possible to identify key indicators of spillover risk, such as the frequency of pathogen occurrence across multiple host species and pathogen load in specific tissues. With the increasing portability and affordability of these technologies, their application under tropical field conditions has become feasible [140,141]. The systematic integration of such approaches into bushmeat surveys would provide critical insights into health risks.

Modeling dose–response relationships in bushmeat species remains challenging due to the scarcity of experimental exposure data [142]. Although some efforts have been made, they have largely focused on a few major zoonotic agents (e.g., Ebola virus; [143]). The complexity in modeling such relationships also arises from the fact that response likely results from interplay between latent infections, host resistance, and pathogen passage histories (i.e., multiple infections), all of which relate to the ecology of the host species [144,145]. Improving risk characterization therefore depends on addressing critical knowledge gaps concerning the natural history of bushmeat species and their interactions in the wild.

Given the cryptic and unregulated nature of the bushmeat trade, estimating the frequency of exposure to health risks remains challenging. A necessary first step toward this goal would be to map the complexity of the bushmeat supply chain and adapt a Hazard Analysis and Critical Control Points framework to this informal network, thereby identifying Critical Control Points (CCPs) at which exposure frequency can be quantified (e.g., [146]). These CCPs should also account for food-borne disease risks occurring at the consumer end of the chain, which remain poorly characterized in the bushmeat context [30]. Moreover, understanding the dynamics of the bushmeat trade is essential, as accurate risk quantification depends on knowledge of the most frequently traded species (which maximize contacts with humans; [147]), the seasonality of hunting volumes (which may interact with pathogen transmission cycles; [148]), and the configuration of market stalls and traders’ practices (which can facilitate interspecies spillover, particularly when live animals are displayed; [149]).

Recently, Fourchault et al. [150] developed a categorical risk-scoring framework to assess the likelihood of zoonotic pathogen spillover in traditional medicine markets in Africa. Although this semi-quantitative approach is effective for identifying critical control points, guiding local intervention strategies, and operating under data-scarce conditions (e.g., [151]), its scoring weights rely heavily on literature-based data and strong a priori assumptions. As a result, it may oversimplify complex host–pathogen–environment interactions and does not provide a direct quantification of health risk.

Based on our review, we conclude that the capacity for health risk assessment in the context of the African BT remains far from being achieved. Substantial efforts are needed to unravel the structure and seasonal dynamics of bushmeat supply chains and to identify biodiversity and zoonotic pathogen trade hotspots. Importantly, special attention should be given to bridging the “species barrier” that still separates human and non-human health studies (see [152]). We argue that adopting a One Health framework for health surveillance of the BT—integrating simultaneous monitoring of wildlife hosts, vectors, and humans at the animal–human interface—is essential for advancing quantitative risk assessment. Given that wildlife trade in general, and the BT in particular, represent likely candidates for the next “Disease X” [153], we urge scientists, practitioners, and intergovernmental agencies to adopt this interdisciplinary, cross-sectoral approach in the near future. Ultimately, only the formal recognition of the bushmeat supply chain by national authorities, followed by its structured regulation and oversight, will make comprehensive and durable health risk quantification possible.

Supporting information

S1 Table. PRISMA 2020 checklist for reporting systematic reviews.

This table summarizes compliance with the PRISMA Statement guidelines. Each item corresponds to recommended reporting standards, with locations where the criteria are addressed in the manuscript.

https://doi.org/10.1371/journal.pntd.0014308.s001

(DOCX)

S2 Table. Database of all records identified in the literature search, including excluded records and reasons for exclusion.

https://doi.org/10.1371/journal.pntd.0014308.s002

(XLSB)

S3 Table. Summary of the 129 peer-reviewed articles included in the study.

Latitude and longitude are expressed in decimal degrees.

https://doi.org/10.1371/journal.pntd.0014308.s003

(XLSX)

S4 Table. List of the 139 bushmeat taxa recorded as hosts of microbes and other organisms, mostly pathogens.

https://doi.org/10.1371/journal.pntd.0014308.s004

(DOCX)

S1 Appendix. Definition of selected variables and descriptors extracted from the systematic review (see S3 Table).

https://doi.org/10.1371/journal.pntd.0014308.s005

(DOCX)

Acknowledgments

We thank the research teams of the Laboratory of Biology and Physiology of Animal Organisms and the Laboratory of Biology and Physiology of Plant Organisms (University of Douala), as well as the Centre de Recherche sur la Biodiversité et l’Environnement (University of Toulouse), for their stimulating discussions and technical support. Marylène Balastre (Cotonou, Benin) contributed to refining the main figures of the manuscript. We thank three reviewers for their valuable comments, which helped improve the earlier version of the manuscript.

References

  1. 1. Lescuyer G, Nasi R. Financial and economic values of bushmeat in rural and urban livelihoods in Cameroon: inputs to the development of public policy. Int For Rev. 2016;18:93–107.
  2. 2. Wittemyer G, Northrup JM, Blanc J, Douglas-Hamilton I, Omondi P, Burnham KP. Illegal killing for ivory drives global decline in African elephants. Proc Natl Acad Sci U S A. 2014;111(36):13117–21. pmid:25136107
  3. 3. Bezerra-Santos MA, Mendoza-Roldan JA, Thompson RCA, Dantas-Torres F, Otranto D. Illegal wildlife trade: a gateway to zoonotic infectious diseases. Trends Parasitol. 2021;37(3):181–4. pmid:33454218
  4. 4. United Nations Office on Drugs and Crime. World Wildlife Crime Report 2024: Trafficking in Protected Species. Vienna, Austria: United Nations Publications; 2024.
    • 5. Karesh WB, Cook RA, Bennett EL, Newcomb J. Wildlife trade and global disease emergence. Emerg Infect Dis. 2005;11(7):1000–2. pmid:16022772
    • 6. Gallo-Cajiao E, Lieberman S, Dolšak N, Prakash A, Labonté R, Biggs D, et al. Global governance for pandemic prevention and the wildlife trade. Lancet Planet Health. 2023;7(4):e336–45. pmid:37019574
    • 7. Roche B, Morand S. Biodiversity loss, first step for viral emergences. Med Sci (Paris). 2022;38(12):1039–42. pmid:36692263
    • 8. Karesh WB, Grillo T, Machalaba C, Roberts H, Diaz F, Muset S, et al. Guidelines for addressing disease risks in wildlife trade. One Health. 2025;20:100998. pmid:40123917
    • 9. Fa JE, Peres CA, Meeuwig J. Bushmeat exploitation in tropical forests: an intercontinental comparison. Conserv Biol. 2002;16(1):232–7. pmid:35701970
    • 10. Wilkie DS, Wieland M, Boulet H, Le Bel S, van Vliet N, Cornelis D. Eating and conserving bushmeat in Africa. Afr J Ecol. 2016;54:402–14.
    • 11. Robinson JG, Bennett EL. Will alleviating poverty solve the bushmeat crisis?. Oryx. 2002;36(4):332–332.
    • 12. Nasi R, Taber A, Van Vliet N. Empty forests, empty stomachs? Bushmeat and livelihoods in the Congo and Amazon basins. Int For Rev. 2011;13:355–68.
    • 13. Akoi Boré J, Timothy JWS, Tipton T, Kekoura I, Hall Y, Hood G, et al. Serological evidence of zoonotic filovirus exposure among bushmeat hunters in Guinea. Nat Commun. 2024;15(1):4171. pmid:38755147
    • 14. Beiras CG, Malembi E, Escrig-Sarreta R, Ahuka S, Mbala P, Mavoko HM, et al. Concurrent outbreaks of mpox in Africa-an update. Lancet. 2025;405(10472):86–96. pmid:39674184
    • 15. Wolfe ND, Switzer WM, Carr JK, Bhullar VB, Shanmugam V, Tamoufe U, et al. Naturally acquired simian retrovirus infections in central African hunters. Lancet. 2004;363(9413):932–7. pmid:15043960
    • 16. Milbank C, Vira B. Wildmeat consumption and zoonotic spillover: contextualising disease emergence and policy responses. Lancet Planet Health. 2022;6:439–48.
    • 17. Rulli MC, Santini M, Hayman DTS, D’Odorico P. The nexus between forest fragmentation in Africa and Ebola virus disease outbreaks. Sci Rep. 2017;7:41613. pmid:28195145
    • 18. Ateudjieu J, Siewe Fodjo JN, Ambomatei C, Tchio-Nighie KH, Zoung Kanyi Bissek AC. Zoonotic Diseases in Sub-Saharan Africa: a systematic review and meta-analysis. Zoonotic Dis. 2023;3:251–65.
    • 19. Dell BM, Souza MJ, Willcox AS. Attitudes, practices, and zoonoses awareness of community members involved in the bushmeat trade near Murchison Falls National Park, northern Uganda. PLoS One. 2020;15(9):e0239599. pmid:32986741
    • 20. Kamins AO, Restif O, Ntiamoa-Baidu Y, Suu-Ire R, Hayman DTS, Cunningham AA, et al. Uncovering the fruit bat bushmeat commodity chain and the true extent of fruit bat hunting in Ghana, West Africa. Biol Conserv. 2011;144(12):3000–8. pmid:22514356
    • 21. Jay-Russell MT. What is the risk from wild animals in food-borne pathogen contamination of plants?. CABI Rev. 2014;8:1–16.
    • 22. Sileshi GW, Gebeyehu S. Emerging infectious diseases threatening food security and economies in Africa. Global Food Security. 2021;28:100479.
    • 23. Hilderink MH, de Winter II. No need to beat around the bushmeat-The role of wildlife trade and conservation initiatives in the emergence of zoonotic diseases. Heliyon. 2021;7(7):e07692. pmid:34386637
    • 24. Brown C. Emerging zoonoses and pathogens of public health significance--an overview. Rev Sci Tech. 2004;23(2):435–42. pmid:15702711
    • 25. Huong NQ, Nga NTT, Long NV, Luu BD, Latinne A, Pruvot M. Coronavirus testing indicates transmission risk increases along wildlife supply chains for human consumption in Viet Nam, 2013–2014. PLoS One. 2020;15:1–20.
    • 26. LeBreton M, Prosser AT, Tamoufe U, Sateren W, Mpoudi-Ngole E, Diffo JLD. Patterns of bushmeat hunting and perceptions of disease risk among central African communities. Animal Conservation. 2006;9:357–63.
    • 27. Paige SB, Frost SDW, Gibson MA, Jones JH, Shankar A, Switzer WM, et al. Beyond bushmeat: animal contact, injury, and zoonotic disease risk in Western Uganda. Ecohealth. 2014;11(4):534–43. pmid:24845574
    • 28. Nguiffo S, Talla M. La législation relative à la faune sauvage au Cameroun: entre usages locaux et perception légale. Unasylva. 2010;61:14–8.
    • 29. Fa JE, Albrechtsen L, Johnson PJ, Macdonald DW. Linkages between household wealth, bushmeat and other animal protein consumption are not invariant: evidence from Rio Muni, Equatorial Guinea. Animal Conservation. 2009;12:599–610.
    • 30. van Vliet N, Muhindo J, Nyumu J, Enns C, Massé F, Bersaglio B, et al. Understanding factors that shape exposure to zoonotic and food-borne diseases across wild meat trade chains. Hum Ecol Interdiscip J. 2022;50(6):983–95. pmid:36408298
    • 31. Roesel K, Grace D. Sécurité sanitaire des aliments et marchés informels: les produits d’origine animale en Afrique subsaharienne. Rome, Italy: FAO; 2016.
      • 32. Gaubert P, Djagoun CAMS, Missoup AD, Ales N, Amougou CV, Dipita AD, et al. Vendors’ perceptions on the bushmeat trade dynamics across West and central Africa during the COVID-19 pandemic: lessons learned on sanitary measures and awareness campaigns. Environmental Science & Policy. 2024;152:103649.
      • 33. Jenkins J, Lawundeh W, Hanson T, Brown H. Human-animal entanglements in bushmeat trading in Sierra Leone: an ethnographic assessment of a potential zoonotic interface. PLoS One. 2024;19(3):e0298929. pmid:38547141
      • 34. Lucas A, Kumakamba C, Saylors K, Obel E, Kamenga R, Makuwa M, et al. Risk perceptions and behaviors of actors in the wild animal value chain in Kinshasa, Democratic Republic of Congo. PLoS One. 2022;17(2):e0261601. pmid:35171910
      • 35. Karesh WB, Noble E. The bushmeat trade: increased opportunities for transmission of zoonotic disease. Mt Sinai J Med. 2009;76(5):429–34. pmid:19787649
      • 36. Alhaji NB, Odetokun IA, Lawan MK, Adeiza AM, Nafarnda WD, Salihu MJ. Risk assessment and preventive health behaviours toward COVID-19 amongst bushmeat handlers in Nigerian wildlife markets: drivers and One Health challenge. Acta Trop. 2022;235:106621. pmid:35908578
      • 37. Pruvot M, Khammavong K, Milavong P, Philavong C, Reinharz D, Mayxay M, et al. Toward a quantification of risks at the nexus of conservation and health: the case of bushmeat markets in Lao PDR. Sci Total Environ. 2019;676:732–45. pmid:31054417
      • 38. Page MJ, McKenzie JE, Bossuyt PM, Boutron I, Hoffmann TC, Mulrow CD, et al. The PRISMA 2020 statement: an updated guideline for reporting systematic reviews. BMJ. 2021;372:n71. pmid:33782057
      • 39. Linder HP, de Klerk HM, Born J, Burgess ND, Fjeldså J, Rahbek C. The partitioning of Africa: statistically defined biogeographical regions in sub-Saharan Africa. J Biogeogr. 2012;39:1189–205.
      • 40. Sayer JA, Harcourt CS, Collins NM. The conservation atlas of tropical forests – Africa. Cambridge, UK: Palgrave Macmillan London; 1992.
        • 41. Leahy E, Mutua F, Grace D, Lambertini E, Thomas LF. Foodborne zoonoses control in low- and middle-income countries: identifying aspects of interventions relevant to traditional markets which act as hurdles when mitigating disease transmission. Front Sustain Food Syst. 2022;6.
        • 42. Van Vliet N, Moreno J, Gómez J, Zhou W, Fa JE. Bushmeat and human health: assessing evidence in tropical and sub-tropical forests. Ethnobiol Conserv. 2017;3:1–45.
        • 43. Kurpiers LA, Schulte-Herbrüggen B, Ejotre I, Reeder DAM. Bushmeat and emerging infectious diseases: lessons from Africa. In: Angelici FM, editor. Problematic wildlife: a cross-disciplinary approach. Cham: Springer International Publishing; 2015. p. 507–51.
          • 44. Tumelty L, Fa JE, Coad L, Friant S, Mbane J, Kamogne CT, et al. A systematic mapping review of links between handling wild meat and zoonotic diseases. One Health. 2023;17:100637. pmid:38024256
          • 45. Chaix E, Boni M, Guillier L, Bertagnoli S, Mailles A, Collignon C, et al. Risk of Monkeypox virus (MPXV) transmission through the handling and consumption of food. Microb Risk Anal. 2022;22:100237. pmid:36320929
          • 46. Changula K, Kajihara M, Mweene AS, Takada A. Ebola and Marburg virus diseases in Africa: increased risk of outbreaks in previously unaffected areas?. Microbiol Immunol. 2014;58(9):483–91. pmid:25040642
          • 47. Locatelli S, Peeters M. Cross-species transmission of simian retroviruses: how and why they could lead to the emergence of new diseases in the human population. AIDS. 2012;26(6):659–73. pmid:22441170
          • 48. Glorennec P, Bonvallot N, Noisel N, Rousselle C, Jailler M. Evaluation des risques sanitaires. In: Goupil-Sormany I, Debia M, Glorennec P, Gonzalez J-P, Noisel N, editors. Environnement et santé publique: fondements et pratiques. Rennes, France: Presses de l’EHESP; 2023. p. 361–99.
            • 49. Centers for Disease Control and Prevention CDC. Guidelines for the prevention and treatment of opportunistic infections among HIV-exposed and HIV-infected children. Atlanta (GA): CDC; 2009.
              • 50. Centers for Disease Control and Prevention (CDC), National Institutes of Health (NIH). Biosafety in microbiological and biomedical laboratories. 6th ed. Atlanta (GA): CDC; 2020.
                • 51. World Health Organization WHO. Prioritization of pathogens to guide discovery, research and development of new antibiotics for drug-resistant bacterial infections, including tuberculosis. Geneva, Switzerland: WHO; 2017.
                  • 52. Sati H, Carrara E, Savoldi A, Hansen P, Garlasco J, Campagnaro E, et al. The WHO Bacterial Priority Pathogens List 2024: a prioritisation study to guide research, development, and public health strategies against antimicrobial resistance. Lancet Infect Dis. 2025;25(9):1033–43. pmid:40245910
                  • 53. Posit Team. RStudio: Integrated Development Environment for R. Boston, MA: Posit Software, PBC; 2025.
                    • 54. Heighton SP, Gaubert P. A timely systematic review on pangolin research, commercialization, and popularization to identify knowledge gaps and produce conservation guidelines. Biol Conserv. 2021;256:1–13.
                    • 55. Groom B, Tedesco PA, Gaubert P. Systematic review of bushmeat surveys in the tropical African rainforest and recommendations for best scientific practices: a matter of protocol, scale and reporting. Biol Conserv. 2023;283:1–10.
                    • 56. Murray KA, Allen T, Loh E, Machalaba C, Daszak P. Emerging viral zoonoses from wildlife associated with animal-based food systems: risks and opportunities. In: Morand S, Dujardin JP, editors. Food safety risks from wildlife. Cham: Springer; 2016. p. 31–57.
                      • 57. Bourgarel M, Wauquier N. Menaces virales émergentes au Gabon: capacités sanitaires et réponse aux risques de zoonoses émergentes en Afrique centrale. Emerg Health Threats J. 2010;3:7099.
                      • 58. Rush ER, Dale E, Aguirre AA. Illegal wildlife trade and emerging infectious diseases: pervasive impacts to species, ecosystems and human health. Animals (Basel). 2021;11(6):1821. pmid:34207364
                      • 59. Harpet C. De l’anthropologie des décharges à l’évaluation interdisciplinaire des risques sanitaires. Natures Sciences Sociétés. 2003;11(4):361–70.
                      • 60. Che D. Quels éléments président à l’émergence d’une épidémie virale? Peut-on la prévoir? Presse Med. 2019;48:1528–35.
                      • 61. Kuhn C, Hayibor KM, Acheampong AT, Pires LSA, Costa-Ribeiro MCV, Burrone MS, et al. How studies on zoonotic risks in wildlife implement the one health approach - a systematic review. One Health. 2024;19:100929. pmid:39585343
                      • 62. Peros CS, Dasgupta R, Kumar P, Johnson BA. Bushmeat, wet markets, and the risks of pandemics: exploring the nexus through systematic review of scientific disclosures. Environ Sci Policy. 2021;124:1–11. pmid:36536884
                      • 63. Ioannidis JPA, Salholz-Hillel M, Boyack KW, Baas J. The rapid, massive growth of COVID-19 authors in the scientific literature. R Soc Open Sci. 2021;8(9):210389. pmid:34527271
                      • 64. Kawuki J, Yu X, Musa TH. Bibliometric Analysis of Ebola Research Indexed in Web of Science and Scopus (2010-2020). Biomed Res Int. 2020;2020:5476567. pmid:32964036
                      • 65. Bornmann L, Haunschild R, Mutz R. Growth rates of modern science: a latent piecewise growth curve approach to model publication numbers from established and new literature databases. Humanit Soc Sci Commun. 2021;8:1–15.
                      • 66. Pigott DM, Golding N, Mylne A, Huang Z, Henry AJ, Weiss DJ, et al. Mapping the zoonotic niche of Ebola virus disease in Africa. Elife. 2014;3:e04395. pmid:25201877
                      • 67. Kmiec D, Kirchhoff F. Monkeypox: a new threat?. Int J Mol Sci. 2022;23:1–4.
                      • 68. Taylor G, Scharlemann JPW, Rowcliffe M, Kümpel N, Harfoot MBJ, Fa JE, et al. Synthesising bushmeat research effort in West and Central Africa: a new regional database. Biol Conserv. 2015;181:199–205.
                      • 69. Momballa MC, Nguemwo SB. Species in bushmeat trade in Cameroon and the Republic of the Congo. Yaounde, Cameroon: TRAFFIC; 2021.
                        • 70. Chaber A-L, Moloney GK, Renault V, Morrison-Lanjouw S, Garigliany M, Flandroy L, et al. Examining the international bushmeat traffic in Belgium: a threat to conservation and public health. One Health. 2023;17:100605. pmid:37577053
                        • 71. Ingram DJ, Cronin DT, Challender DWS, Venditti DM, Gonder MK. Characterising trafficking and trade of pangolins in the Gulf of Guinea. Glob Ecol Conserv. 2019;17:1–11.
                        • 72. Munsamy JI, Parrish A, Steel G. Conducting research in a resource-constrained environment: avoiding the pitfalls. Healthc Low Resour Settings. 2014;2(1).
                        • 73. Halbwax M. Addressing the illegal wildlife trade in the European Union as a public health issue to draw decision makers attention. Biol Conserv. 2020;251:108798. pmid:33071292
                        • 74. Chaber A, Allebone‐Webb S, Lignereux Y, Cunningham AA, Marcus Rowcliffe J. The scale of illegal meat importation from Africa to Europe via Paris. Conservation Letters. 2010;3(5):317–21.
                        • 75. Bair-Brake H, Bell T, Higgins A, Bailey N, Duda M, Shapiro S, et al. Is that a rodent in your luggage? A mixed method approach to describe bushmeat importation into the United States. Zoonoses Public Health. 2014;61(2):97–104. pmid:23678947
                        • 76. Smith KM, Anthony SJ, Switzer WM, Epstein JH, Seimon T, Jia H, et al. Zoonotic viruses associated with illegally imported wildlife products. PLoS One. 2012;7(1):e29505. pmid:22253731
                        • 77. Wolfe ND, Daszak P, Kilpatrick AM, Burke DS. Bushmeat hunting, deforestation, and prediction of zoonoses emergence. Emerg Infect Dis. 2005;11(12):1822–7. pmid:16485465
                        • 78. Narat V, Salmona M, Kampo M, Heyer T, Rachik AS, Mercier-Delarue S, et al. Higher convergence of human-great ape enteric eukaryotic viromes in central African forest than in a European zoo: a One Health analysis. Nat Commun. 2023;14(1):3674. pmid:37339968
                        • 79. Saylors KE, Mouiche MM, Lucas A, McIver DJ, Matsida A, Clary C, et al. Market characteristics and zoonotic disease risk perception in Cameroon bushmeat markets. Soc Sci Med. 2021;268:113358. pmid:32992090
                        • 80. Bachand N, Ravel A, Onanga R, Arsenault J, Gonzalez J-P. Public health significance of zoonotic bacterial pathogens from bushmeat sold in urban markets of Gabon, Central Africa. J Wildl Dis. 2012;48(3):785–9. pmid:22740547
                        • 81. Temmam S, Davoust B, Chaber A-L, Lignereux Y, Michelle C, Monteil-Bouchard S, et al. Screening for viral pathogens in African Simian Bushmeat Seized at A French Airport. Transbound Emerg Dis. 2017;64(4):1159–67. pmid:26876732
                        • 82. Geraerts M, Gombeer S, Nebesse C, Akaibe D, Akaibe D, Baelo P, et al. A wide diversity of viruses detected in African mammals involved in the wild meat supply chain. PLoS Pathog. 2025;21(12):e1013643. pmid:41460777
                        • 83. Moloney GK, Gaubert P, Gryseels S, Verheyen E, Chaber A-L. Investigating Infectious Organisms of Public Health Concern Associated with Wild Meat. Transbound Emerg Dis. 2023;2023:5901974. pmid:40303741
                        • 84. Watkins K. Emerging infectious diseases: a review. Curr Emerg Hosp Med Rep. 2018;6(3):86–93. pmid:32226656
                        • 85. Leroy EM, Epelboin A, Mondonge V, Pourrut X, Gonzalez JP, Muyembe-Tamfum JJ. Human Ebola outbreak resulting from direct exposure to fruit bats in Luebo, Democratic Republic of Congo, 2007. Vector Borne Zoonotic Dis. 2009;9:723–8.
                        • 86. Marí Saéz A, Weiss S, Nowak K, Lapeyre V, Zimmermann F, Düx A, et al. Investigating the zoonotic origin of the West African Ebola epidemic. EMBO Mol Med. 2015;7(1):17–23. pmid:25550396
                        • 87. Lee-Cruz L, Lenormand M, Cappelle J, Caron A, De Nys H, Peeters M, et al. Mapping of Ebola virus spillover: Suitability and seasonal variability at the landscape scale. PLoS Negl Trop Dis. 2021;15(8):e0009683. pmid:34424896
                        • 88. Peeters M, D’Arc M, Delaporte E. Origin and diversity of human retroviruses. AIDS Rev. 2014;16(1):23–34. pmid:24584106
                        • 89. Falendysz EA, Lopera JG, Rocke TE, Osorio JE. Monkeypox virus in animals: current knowledge of viral transmission and pathogenesis in wild animal reservoirs and captive animal models. Viruses. 2023;15(4):905. pmid:37112885
                        • 90. Petrozzi F, Amori G, Franco D, Gaubert P, Pacini N, Eniang EA. Ecology of the bushmeat trade in West and Central Africa. Trop Ecol. 2016;57:547–59.
                        • 91. Davies TJ, Pedersen AB. Phylogeny and geography predict pathogen community similarity in wild primates and humans. Proc Biol Sci. 2008;275(1643):1695–701. pmid:18445561
                        • 92. Malik YS, Milton AAP, Ghatak S, Ghosh S. Role of birds in transmitting zoonotic pathogens. Singapore: Springer; 2021.
                          • 93. Mendoza-Roldan JA, Modry D, Otranto D. Zoonotic parasites of reptiles: a crawling threat. Trends Parasitol. 2020;36(8):677–87. pmid:32448703
                          • 94. Mendoza-Roldan JA, Mendoza-Roldan MA, Otranto D. Reptile vector-borne diseases of zoonotic concern. Int J Parasitol Parasites Wildl. 2021;15:132–42. pmid:34026483
                          • 95. Din Dipita A, Missoup AD, Tindo M, Gaubert P. DNA-typing improves illegal wildlife trade surveys: tracing the Cameroonian bushmeat trade. Biol Conserv. 2022;269:1–10.
                          • 96. Gossé KJ, Gonedelé-Bi S, Justy F, Chaber AL, Kramoko B, Gaubert P. DNA-typing surveillance of bushmeat in Côte d’Ivoire: a multi-faceted tool for wildlife trade management in West Africa. Conserv Genet. 2022;23:1073–88.
                          • 97. Anthony SJ, Epstein JH, Murray KA, Navarrete-Macias I, Zambrana-Torrelio CM, Solovyov A, et al. A strategy to estimate unknown viral diversity in mammals. mBio. 2013;4(5):e00598-13. pmid:24003179
                          • 98. Munafò MR, Nosek BA, Bishop DVM, Button KS, Chambers CD, du Sert NP, et al. A manifesto for reproducible science. Nat Hum Behav. 2017;1:0021. pmid:33954258
                          • 99. Jagadesh S, Zhao C, Mulchandani R, Van Boeckel TP. Mapping global bushmeat activities to improve zoonotic spillover surveillance by using geospatial modeling. Emerg Infect Dis. 2023;29(4):742–50. pmid:36957996
                          • 100. Shanmugaraj B. Mpox global health crisis: Implications and actions. Asian Pacific Journal of Tropical Medicine. 2025;18(1):1–2.
                          • 101. Wolfe ND, Prosser TA, Carr JK, Tamoufe U, Mpoudi-Ngole E, Torimiro JN, et al. Exposure to nonhuman primates in rural Cameroon. Emerg Infect Dis. 2004;10(12):2094–9. pmid:15663844
                          • 102. Kuukyi FS, Amfo-Otu R, Wiafe E. Consumer views of bushmeat consumption in two Ghanaian markets. Appl Res J. 2014;2014:1–10.
                          • 103. Odetokun IA, Oniyanda O, Raza A, Akpabio U, Musawa AI, Hambali IU, et al. Assessment of the public knowledge, attitudes, and perceptions of Nigerians regarding preparedness for future pandemics. Discov Public Health. 2024;21(1).
                          • 104. Wille M, Geoghegan JL, Holmes EC. How accurately can we assess zoonotic risk?. PLoS Biol. 2021;19(4):e3001135. pmid:33878111
                          • 105. Authored by the members of the One Health High-Level Expert Panel (OHHLEP), Markotter W, Mettenleiter TC, Adisasmito WB, Almuhairi S, Barton Behravesh C, et al. Prevention of zoonotic spillover: From relying on response to reducing the risk at source. PLoS Pathog. 2023;19(10):e1011504. pmid:37796834
                          • 106. Birgen BJ, Njue LG, Kaindi DM, Ogutu FO. Qualitative risk assessment of campylobacter jejuni in street vended poultry in informal settlements of Nairobi County. EJNFS. 2019;:28–37.
                          • 107. Meurens F, Dunoyer C, Fourichon C, Gerdts V, Haddad N, Kortekaas J. Risks of zoonotic disease emergence at the interface of wildlife and livestock systems. Animal. 2021;15:1–17.
                          • 108. Mukaratirwa S, La Grange L, Pfukenyi DM. Trichinella infections in animals and humans in sub-Saharan Africa: a review. Acta Trop. 2013;125(1):82–9. pmid:23041114
                          • 109. Miambo RD, Afonso SMS, Noormahomed EV, Pondja A, Mukaratirwa S. Echinococcosis in humans and animals in Southern Africa Development Community countries: a systematic review. Food Waterborne Parasitol. 2020;20:e00087. pmid:32995581
                          • 110. Oppenheim B, Lidow N, Ayscue P, Saylors K, Mbala P, Kumakamba C, et al. Knowledge and beliefs about Ebola virus in a conflict-affected area: early evidence from the North Kivu outbreak. J Glob Health. 2019;9(2):020311. pmid:31656600
                          • 111. Chaber A-L, Cunningham A. Public health risks from illegally imported African Bushmeat and Smoked Fish. EcoHealth. 2015;13(1):135–8.
                          • 112. Peeters M, Courgnaud V, Abela B, Auzel P, Pourrut X, Bibollet-Ruche F, et al. Risk to human health from a plethora of simian immunodeficiency viruses in primate bushmeat. Emerg Infect Dis. 2002;8(5):451–7. pmid:11996677
                          • 113. Woldemariam T, Mohammed T, Zewude A, Chanyalew M, Khalifa HO, Mamo G, et al. Zoonotic transmission of the Mycobacterium tuberculosis complex between cattle and humans in Central Ethiopia. Front Vet Sci. 2025;12:1527279. pmid:40129575
                          • 114. Zangue CT, Kouamo J, Ngoula F, Tawali LPM, Ndebé MMF, Somnjom DE, et al. Knowledge, attitudes, practices and zoonotic risk perception of Bovine Q fever (Coxiella burnetii) among cattle farmers and veterinary personnel in Northern Regions of Cameroon. Epidemiologia (Basel). 2022;3(4):482–92. pmid:36416792
                          • 115. Meseko C, Olaleye D, Capua I, Cattoli G. Swine influenza in sub-saharan Africa--current knowledge and emerging insights. Zoonoses Public Health. 2014;61(4):229–37. pmid:23826898
                          • 116. Ayele WY, Neill SD, Zinsstag J, Weiss MG, Pavlik I. Bovine tuberculosis: an old disease but a new threat to Africa. Int J Tuberc Lung Dis. 2004;8(8):924–37. pmid:15305473
                          • 117. An C, Shen L, Sun M, Sun Y, Fan S, Zhao C, et al. Exploring risk transfer of human brucellosis in the context of livestock agriculture transition: a case study in Shaanxi, China. Front Public Health. 2023;10:1009854. pmid:36777766
                          • 118. Mwebe R, Nakavuma J, Moriyón I. Brucellosis seroprevalence in livestock in Uganda from 1998 to 2008: a retrospective study. Trop Anim Health Prod. 2011;43(3):603–8. pmid:21082245
                          • 119. Ssemanda JN, den Besten HMW, van Wagenberg CPA, Zwietering MH. Quantitative assessment of food safety interventions for Campylobacter spp. and Salmonella spp. along the chicken meat supply chain in Burkina Faso and Ethiopia. Int J Food Microbiol. 2024;415:1–20.
                          • 120. Ndaki EM, Muma JB, M’kandawire E, Musawa G, Mukuma M, Karimuribo E, et al. A quantitative risk assessment of human exposure to brucellosis through the consumption of contaminated raw Cow Milk in Arusha, Tanzania. JABS. 2023;6(2).
                          • 121. Akil L, Ahmad HA. Quantitative risk assessment model of human salmonellosis resulting from consumption of broiler chicken. Diseases. 2019;7(1):19. pmid:30736421
                          • 122. Roy S, McElwain TF, Wan Y. A network control theory approach to modeling and optimal control of zoonoses: case study of brucellosis transmission in sub-Saharan Africa. PLoS Negl Trop Dis. 2011;5(10):e1259. pmid:22022621
                          • 123. Lachish S, Murray KA. The certainty of uncertainty: potential sources of bias and imprecision in disease ecology studies. Front Vet Sci. 2018;5:90. pmid:29872662
                          • 124. Kilonzo C, Stopka TJ, Chomel B. illegal animal and (bush) meat trade associated risk of spread of viral infections. Viral Infections and Global Change. Wiley; 2013. p. 179–94. https://doi.org/10.1002/9781118297469.ch10
                            • 125. Lockhart W, Backman A. Health care management competencies: identifying the GAPs. Healthc Manage Forum. 2009;22(2):30–7. pmid:19736878
                            • 126. Taloussock F. La « grande chasse » chez les Pygmées Baka du sud-est Cameroun: contribution à l’anthropologie de l’environnement. Yaounde, Cameroon: Université de Yaoundé I; 2011.
                              • 127. Bardosh KL, Scoones JC, Grace D, Kalema-Zikusoka G, Jones KE, de Balogh K. Engaging research with policy and action: challenges of responding to zoonotic disease in Africa. Philos Trans R Soc Lond B Biol Sci. 2017;372:1–10.
                              • 128. Cleaveland S, Sharp J, Abela-Ridder B, Allan KJ, Buza J, Crump JA, et al. One Health contributions towards more effective and equitable approaches to health in low- and middle-income countries. Philos Trans R Soc Lond B Biol Sci. 2017;372(1725):20160168. pmid:28584176
                              • 129. Adegnika OS, Honkpehedji YJ, Mougeni Lotola F, Agnandji ST, Adegnika AA, Lell B, et al. Funding patterns for biomedical research and infectious diseases burden in Gabon. BMC Public Health. 2021;21(1):2155. pmid:34819025
                              • 130. Alimi Y, Wabacha J. Strengthening coordination and collaboration of one health approach for zoonotic diseases in Africa. One Health Outlook. 2023;5(1):10. pmid:37533113
                              • 131. Katani R, Schilling MA, Lyimo B, Eblate E, Martin A, Tonui T. Identification of Bacillus anthracis, Brucella spp., and Coxiella burnetii DNA signatures from bushmeat. Sci Rep. 2021;11:1–11.
                              • 132. Movsisyan A, Arnold L, Copeland L, Evans R, Littlecott H, Moore G, et al. Adapting evidence-informed population health interventions for new contexts: a scoping review of current practice. Health Res Policy Syst. 2021;19(1):13. pmid:33546707
                              • 133. Chibueze Izah S, Ogwu MC. Risk assessment and health impact studies: strategic tools for managing environmental health. Environmental Science and Engineering. Springer Nature Switzerland; 2025. p. 313–46. https://doi.org/10.1007/978-3-031-81966-7_12
                                • 134. Alqahtani MMJ, Arnout BA, Fadhel FH, Sufyan NSSl. Risk perceptions of COVID-19 and its impact on precautionary behavior: a qualitative study. Patient Educ Couns. 2021;104(8):1860–7. pmid:33612345
                                • 135. Bonwitt J, Dawson M, Kandeh M, Ansumana R, Sahr F, Brown H, et al. Unintended consequences of the “bushmeat ban” in West Africa during the 2013-2016 Ebola virus disease epidemic. Soc Sci Med. 2018;200:166–73. pmid:29421463
                                • 136. Carlson CJ, Farrell MJ, Grange Z, Han BA, Mollentze N, Phelan AL, et al. The future of zoonotic risk prediction. Philos Trans R Soc Lond B Biol Sci. 2021;376(1837):20200358. pmid:34538140
                                • 137. Smid JH, Verloo D, Barker GC, Havelaar AH. Strengths and weaknesses of Monte Carlo simulation models and Bayesian belief networks in microbial risk assessment. Int J Food Microbiol. 2010;139 Suppl 1:S57-63. pmid:20071046
                                • 138. Ko KKK, Chng KR, Nagarajan N. Metagenomics-enabled microbial surveillance. Nat Microbiol. 2022;7(4):486–96. pmid:35365786
                                • 139. Lefterova MI, Suarez CJ, Banaei N, Pinsky BA. Next-generation sequencing for infectious disease diagnosis and management. J Mol Diagn. 2015;17:623–34.
                                • 140. Quick J, Loman NJ, Duraffour S, Simpson JT, Severi E, Cowley L, et al. Real-time, portable genome sequencing for Ebola surveillance. Nature. 2016;530(7589):228–32. pmid:26840485
                                • 141. Hoenen T, Groseth A, Rosenke K, Fischer RJ, Hoenen A, Judson SD, et al. Nanopore sequencing as a rapidly deployable ebola outbreak tool. Emerg Infect Dis. 2016;22(2):331–4. pmid:26812583
                                • 142. Lunn TJ, Restif O, Peel AJ, Munster VJ, de Wit E, Sokolow S, et al. Dose-response and transmission: the nexus between reservoir hosts, environment and recipient hosts. Philos Trans R Soc Lond B Biol Sci. 2019;374(1782):20190016. pmid:31401955
                                • 143. Mitchell J, Dean K, Haas C. Ebola virus dose response model for aerosolized exposures: insights from primate data. Risk Anal. 2020;40(11):2390–8. pmid:32638435
                                • 144. Galante D, Gainer RS, Hugh-Jones ME. Environmental relationships and anthrax epidemiology: field experiences of host resistance as opposed to dose-dependent experiments. Acta Trop. 2024;252:107128. pmid:38309609
                                • 145. Gale P, Simons RRL, Horigan V, Snary EL, Fooks AR, Drew TW. The challenge of using experimental infectivity data in risk assessment for Ebola virus: why ecology may be important. J Appl Microbiol. 2016;120(1):17–28. pmid:26480954
                                • 146. Edmunds KL, Hunter PR, Few R, Bell DJ. Hazard analysis of critical control points assessment as a tool to respond to emerging infectious disease outbreaks. PLoS One. 2013;8(8):e72279. pmid:23967294
                                • 147. Narat V, Kampo M, Heyer T, Rupp S, Ambata P, Njouom R, et al. Using physical contact heterogeneity and frequency to characterize dynamics of human exposure to nonhuman primate bodily fluids in central Africa. PLoS Negl Trop Dis. 2018;12(12):e0006976. pmid:30589843
                                • 148. Van Vliet N, Muhindo J, Nyumu JK, Nasi R. From the forest to the dish: a comprehensive study of the wildmeat value chain in Yangambi, Democratic Republic of Congo. Front Ecol Evol. 2019;7:1–20.
                                • 149. Webster RG. Wet markets--a continuing source of severe acute respiratory syndrome and influenza?. Lancet. 2004;363(9404):234–6. pmid:14738798
                                • 150. Fourchault L, Lamane A, Dimitri Romaric NM, Saliu GT, Gryseels S, Verheyen E, et al. Public health risks of traditional zootherapeutic practices in Africa. One Health. 2025;21:101178. pmid:41492289
                                • 151. McEachran MC, Sampedro F, Travis DA, Phelps NBD. An expert-based risk ranking framework for assessing potential pathogens in the live baitfish trade. Transbound Emerg Dis. 2021;68(6):3463–73. pmid:33295097
                                • 152. Friant S, Simons D, Harden C, Imirzian N, Bjornstad O, Gibb R, et al. Scaling Lassa Virus Dynamics within Anthropogenic Ecosystems (SCAPES) Study Protocol: a mixed-methods observational cohort study of humans, rodents, and landscapes in Nigeria. Wellcome Open Res. 2026;11:125.
                                • 153. Mitu RA, Islam MR. The current pathogenicity and potential risk evaluation of marburg virus to cause mysterious “Disease X”-an update on recent evidences. Environ Health Insights. 2024;18:11786302241235809. pmid:38440221