ABSTRACT
Background and Aim: Hepatitis E virus (HEV) is an important cause of acute viral hepatitis worldwide and remains a neglected public health concern in sub-Saharan Africa. Although several African studies have reported HEV circulation, no comprehensive synthesis has integrated human, animal, and environmental evidence from West Africa. This study aimed to determine the prevalence, geographic distribution, risk-group characteristics, and genotype diversity of HEV in West Africa using a One Health approach.
Materials and Methods: A systematic review and meta-analysis was conducted according to the Preferred Reporting Items for Systematic Reviews and Meta-Analyses guidelines and registered in PROSPERO (CRD420251178069). PubMed, ScienceDirect, Google Scholar, and reference lists were searched for studies published between 1997 and August 2025. Studies involving humans, animals, and environmental samples from West African countries were included. Data extraction and quality assessment were performed independently by two reviewers. A random-effects meta-analysis using Comprehensive Meta-Analysis software version 3.0 was conducted to estimate the pooled prevalence and investigate heterogeneity. Human populations were stratified into low- and high-risk groups according to exposure characteristics.
Results: Ninety-six eligible studies conducted in 12 countries and involving 36,014 individuals were included. A total of 9,465 HEV-positive cases were identified. The pooled overall seroprevalence was 9.7% (95% confidence interval [CI]: 8.2%–11.5%), with substantial heterogeneity (I² = 97.53%). The pooled prevalence of anti-HEV immunoglobulin (Ig)G was 15.7%, ranging from 2.7% in Guinea to 29.3% in Burkina Faso. High-risk populations exhibited higher prevalences of IgG (25.9%), IgM (4.6%), and total antibodies (16.9%) than low-risk groups. Animal reservoirs, particularly pigs, showed high infection frequencies, whereas environmental contamination reached 24% in vegetables. Human genotypes 1e, 2b, 3e, 3f, and 4b, and animal- and environment-associated genotypes 3a and 3c were identified across the region.
Conclusion: HEV infection is widely distributed in West Africa, with marked differences among countries and population groups. The coexistence of human, animal, and environmental reservoirs highlights the importance of integrated One Health surveillance and targeted prevention strategies to reduce the regional burden of HEV infection.
Keywords: environmental contamination, genotype distribution, hepatitis E virus, meta-analysis, One Health, prevalence, systematic review, West Africa.
INTRODUCTION
Hepatitis E virus (HEV) is a major public health concern worldwide, causing an estimated 20 million infections annually [1]. It is recognized as the leading cause of acute viral hepatitis globally [2, 3]. To date, eight HEV genotypes capable of infecting mammals have been identified. HEV-1 and HEV-2 are restricted to humans, whereas HEV-3 and HEV-4 infect both humans and a variety of animal species, particularly pigs and deer [4]. In addition, HEV-5 and HEV-6 have been detected mainly in wild boars. More recently, HEV-7 and HEV-8 have been identified in camels and are considered potential zoonotic genotypes. However, the epidemiological distribution of HEV-7 remains poorly characterized. HEV-8 was first identified in Chinese Bactrian camels in 2016 [5].
In Africa, knowledge of HEV has expanded considerably over the past decade, particularly regarding circulating genotypes, potential animal reservoirs, seroprevalence in the general population and specific subgroups, and associated risk factors. Besides pigs [6], several animal species, including wild boars, deer, sheep, rabbits, and camels, have been reported as hosts of HEV [7]. Direct evidence of zoonotic transmission has been demonstrated for pigs, wild boars, and deer [8–10].
The countries of West Africa share close geographical boundaries and exhibit similar socioeconomic and environmental characteristics that may facilitate the circulation and persistence of HEV. Seroprevalence levels reported in several countries are comparable to those observed in HEV-endemic regions worldwide. Pigs, which are considered major reservoirs of the virus, are believed to play an important role in environmental dissemination and zoonotic transmission. In addition, inadequate sanitation, close human-animal interactions, population mobility, and occupational exposure may contribute to sustained viral circulation. Despite these factors, the overall burden and epidemiological dynamics of HEV infection in the region remain insufficiently characterized, thereby limiting the implementation of effective prevention and control strategies.
Although previous systematic reviews have examined HEV epidemiology at the continental level [11], among pregnant women in Africa [12], or in animal populations across the continent [13], important knowledge gaps remain. Existing reviews have largely focused on a single host category or a specific population and have not provided a comprehensive synthesis dedicated to the ecologically and socioeconomically interconnected countries of West Africa. Furthermore, none of these studies have simultaneously integrated human, animal, and environmental data within a One Health framework while accounting for differences between low- and high-risk populations. Recent developments, including newly reported outbreaks, the identification of uncommon genotypes such as genotype 4b in humans, and changes in surveillance practices following the coronavirus disease 2019 (COVID-19) pandemic, underscore the need for an updated regional assessment. Consequently, a comprehensive evaluation of HEV circulation across multiple compartments and population groups in West Africa remains lacking.
Therefore, this study aimed to perform a systematic review and meta-analysis of HEV infection in West Africa using a One Health approach. Specifically, the study sought to estimate pooled prevalence according to country, serological marker, and population risk category; evaluate the occurrence of HEV among animal reservoirs and environmental matrixes; characterize the distribution of circulating genotypes across the region; and provide updated evidence regarding the epidemiological characteristics of HEV infection in West Africa. By integrating human, animal, and environmental data, this study represents the first comprehensive multi-compartment synthesis of HEV epidemiology in West Africa and provides evidence to support targeted surveillance and prevention strategies in this high-burden region.
MATERIALS AND METHODS
Ethical approval
Ethical approval was not required for this study because it was based exclusively on previously published data and did not involve direct interaction with humans or animals. The study was conducted according to internationally accepted standards for systematic reviews and meta-analyses and followed the Preferred Reporting Items for Systematic Reviews and Meta-Analyses (PRISMA) guidelines [14]. Data extraction and synthesis were performed using publicly available information, and no identifiable personal information was collected.
Study period and location
Data were collected and extracted between January and August 2025, and statistical analyses were performed between August and October 2025. The analysis focused on countries in West Africa. In addition, Cameroon was included despite its Central African location because previous studies have frequently considered it alongside neighboring West African countries due to their eco-epidemiological similarities. Furthermore, previous meta-analyses of hepatitis A and HEV infections in Africa have identified Cameroon and Nigeria as major contributors to the available data [15]. Therefore, the inclusion of Cameroon was intended to maximize consistency with existing datasets and provide a more comprehensive regional assessment.
Study design
This systematic review and meta-analysis was registered in the International Prospective Register of Systematic Reviews (PROSPERO) under registration number CRD420251178069 (available at https://www.crd. york.ac.uk/PROSPERO/view/CRD420251178069). The study was conducted in accordance with the PRISMA guidelines [14]. The review was designed using a One Health framework to integrate evidence from human, animal, and environmental sources and to provide a comprehensive understanding of HEV epidemiology in West Africa.
>Search strategy
This review included studies published between January 1997 and August 2025. Potentially relevant studies were identified through electronic searches of PubMed, ScienceDirect, and Google Scholar databases, supplemented by manual searches of the reference lists of eligible studies and review articles. No language restrictions were applied. To provide a genuine One Health perspective, studies involving humans, animals, and environmental matrixes from the same subregion were included, an approach that has rarely been adopted in previous HEV meta-analyses.
A comprehensive literature search was also performed in the MEDLINE database using combinations of the terms "hepatitis E virus," "hepatitis E," and "HEV" together with the names of West African countries, including Burkina Faso, Cameroon, Niger, Nigeria, Ghana, Liberia, Senegal, Togo, Sierra Leone, Cape Verde, Mali, Benin, Ivory Coast, Gambia, Guinea, Guinea-Bissau, and Mauritania. Separate filters were applied for human, animal, and environmental studies (S1 File). Additional studies were identified from the bibliographies of selected articles and relevant reviews.
Two independent investigators screened titles and abstracts to identify potentially eligible studies. Full-text articles were subsequently assessed for eligibility. Any discrepancies regarding study identification and selection were resolved through discussion until consensus was reached.
Selection criteria
The analysis included studies reporting the prevalence of HEV infection in humans, animals, or environmental samples in West Africa. Eligible studies met the following criteria: (i) sample size ≥50; (ii) reporting of HEV RNA prevalence and/or genotype information; and (iii) use of standardized and commercially available assays for the detection of anti-HEV antibodies. The inclusion of multiple compartments enabled direct comparison of prevalence across the transmission continuum and provided a broader perspective than previous single-compartment reviews.
Case reports, case series, review articles, commentaries, studies involving individuals residing outside West Africa, and duplicate publications were excluded. When multiple publications reported overlapping data, the study containing the most complete dataset was retained.
HEV infection was defined as detection of HEV RNA by polymerase chain reaction (PCR) or of immunoglobulin (Ig) G, IgM, or total anti-HEV antibodies using commercially available enzyme-linked immunosorbent assay (ELISA) kits.
Data extraction
The following information was extracted from each eligible study: first author, year of publication, country of origin, study population, sample size, genotype information, number of HEV-positive individuals or animals, and diagnostic methods employed. Data extraction was performed independently by two investigators, and disagreements were resolved by consensus.
For the meta-analysis of sporadic HEV infection, human populations were categorized by country and infection risk into low- and high-risk groups. This novel stratification was adopted to reflect regional zoonotic exposure and the increased susceptibility of immunocompromised populations.
The low-risk group included apparently healthy individuals, students, specific ethnic groups, individuals categorized as the general population in the original studies, blood donors, pregnant women, and hospitalized patients. The high-risk group comprised individuals with occupational exposure to animals, patients with chronic liver disease, human immunodeficiency virus (HIV)-positive individuals, and solid organ transplant recipients.
Data from low-risk and high-risk human cohorts and animal species were extracted irrespective of whether they originated from a single study. Each human cohort or animal species within a study was considered an independent unit of analysis.
In addition, temporal trends were evaluated by comparing studies conducted before and after 2020 to investigate changes in HEV epidemiology that had not been explored in previous reviews.
Quality assessment
The methodological quality of the included studies was evaluated independently by two reviewers using the Joanna Briggs Institute Critical Appraisal Checklist for Studies Reporting Prevalence Data [16]. The checklist consists of nine items, each scored as positive or negative. Studies scoring 0–3, 4–6, and 7–9 points were classified as having high, moderate, and low risk of bias, respectively (S2 File). Any disagreements between reviewers were resolved through discussion to ensure consistency.
Statistical analysis
Data analysis was performed using Comprehensive Meta-Analysis software version 3.0 (Biostat, Englewood, NJ, USA) [17]. A random-effects meta-regression model was used to account for variability among studies. HEV prevalence was calculated by dividing the number of positive cases by the sample size, then multiplying by 100.
Pooled prevalence estimates and their corresponding 95% confidence intervals (CI) were presented using forest plots, whereas publication bias was evaluated using funnel plots. Subgroup analyses and meta-regression analyses were conducted to explore potential sources of heterogeneity. Subgroup analyses were performed according to risk category, laboratory method, number of diagnostic tests used, and animal species.
Heterogeneity among studies was assessed using the Cochran Q test and the I² statistic [18]. Statistical significance was defined as p < 0.05 [19]. I² values of 25%–50%, 51%–75%, and >75% were interpreted as indicating low, moderate, and high heterogeneity, respectively [20]. Interstudy heterogeneity was considered significant when the p-value derived from the Cochran Q test was <0.05.
Funnel plots were constructed by plotting the logit event rate against the standard error to assess asymmetry and potential publication bias. In addition, Orwin's Fail-Safe N test, Begg's adjusted rank correlation test, and Egger's regression asymmetry test were used to evaluate publication bias, with p < 0.05 considered indicative of significant bias [21]. To account for the influence of small studies, funnel plot symmetry was further assessed using the trim-and-fill method proposed by Duval and Tweedie [22].
RESULTS
Study selection and characteristics
The initial search yielded 200 records covering 11 of the 16 West African countries and Cameroon, a Central African country. Unlike previous Africa-wide reviews dominated by East and North African data, the present synthesis primarily reflects evidence from West African countries, particularly Nigeria, Burkina Faso, Ghana, and Cameroon. Furthermore, the inclusion of animal populations (nine species) and environmental matrixes expanded the scope beyond previous reviews that focused exclusively on humans or animals.
No information on HEV infection was available for Cape Verde, Guinea-Bissau, Liberia, Mali, and Mauritania. After removing 72 duplicate records, 128 articles remained for screening. Following title and abstract screening, 14 records were excluded. The full texts of the remaining 114 studies were assessed for eligibility, and 18 additional studies were excluded. Ultimately, 96 studies published between 1997 and August 2025 were included (Figure 1).
Among the included studies, 41 were conducted in Nigeria, 15 in Ghana, 14 in Burkina Faso, 10 in Cameroon, 4 in Senegal, 2 each in Guinea, Côte d'Ivoire, Niger, Togo, and Sierra Leone, and 1 each in Benin and Gambia (Figure 2). The investigated populations included the general population, pregnant women, blood donors, butchers, farmers, symptomatic individuals, individuals with HIV, adolescents, and children. Studies involving animals, vegetables, and wastewater were also included.
Most studies were classified as having a low risk of bias. Five studies were rated as having a moderate risk of bias because of inadequate response rates, incomplete descriptions of the study population or setting, and limited representativeness of specific populations. Detailed characteristics of the included studies are presented in Table 1 [9, 23–116].
Seroprevalence of IgG, IgM, and total anti-HEV antibodies in West African countries
Among 36,014 individuals tested across West African countries, pooled prevalences of IgG, IgM, and total anti-HEV antibodies were 15.7%, 4.6%, and 14.1%, respectively (Figure 2). Burkina Faso exhibited the highest pooled IgG seroprevalence (29.3%), whereas Guinea showed the lowest prevalence (2.7%). Niger recorded the highest prevalence of IgM and total anti-HEV antibodies, at 38.4% and 32.1%, respectively. Conversely, the lowest prevalences were observed in Benin for IgM (1.4%) and in Togo for total antibodies (5.6%). Country-specific and population-specific prevalences are summarized in Table 2 and S3 File.
High-risk populations: The high-risk group, comprising individuals in contact with animals and patients with chronic diseases, exhibited pooled prevalences of 25.9%, 4.6%, and 16.9% for IgG, IgM, and total anti-HEV antibodies, respectively (Table 2 and S3 File).
Patients with chronic diseases: Individuals with chronic liver disease or HIV infection had pooled prevalences of 18.0%, 2.5%, and 11.4% for IgG, IgM, and total anti-HEV antibodies, respectively (Table 2 and S3 File).
Individuals in contact with animals: Individuals exposed to animal reservoirs, including pig farmers, ruminant farmers, butchers, meat handlers, food handlers, and animal workers, showed the highest IgG seroprevalence among all groups, reaching 33.1%. The corresponding prevalences of IgM and total anti-HEV antibodies were 7.6% and 20.4%, respectively (Table 2 and S3 File). These findings indicate substantial occupational exposure to HEV.
Low-risk populations: Low-risk populations, including the general population, blood donors, pregnant women, and hospitalized patients, showed pooled prevalences of 13.1%, 4.8%, and 13.6% for IgG, IgM, and total anti-HEV antibodies, respectively (Table 2 and S3 File).
General population: Among approximately 4,900 individuals, IgG antibodies were detected in 15.0%. IgM and total anti-HEV antibodies were present in 8.3% and 17.0% of participants, respectively (Table 2 and S3 File).
Blood donors: Because blood donors differ from the general population regarding health status, age distribution, and selection criteria, they were analyzed separately. The pooled prevalences of IgG, IgM, and total anti-HEV antibodies among blood donors were 10.3%, 4.5%, and 5.6%, respectively (Table 2 and S3 File).
Pregnant women: Pregnant women are considered a vulnerable population because of the severe consequences associated with HEV infection. Among 5,498 pregnant women, pooled prevalences of IgG, IgM, and total anti-HEV antibodies were 11.3%, 2.8%, and 8.0%, respectively (Table 2 and S3 File).
Hospitalized patients: Among 12,546 hospitalized individuals, including patients with viral hemorrhagic fever, acute infections, jaundice, elevated body temperature, and abnormal liver enzyme concentrations, pooled prevalences of IgG, IgM, and total anti-HEV antibodies were 20.2%, 10.2%, and 17.5%, respectively (Table 2 and S3 File).
Figure 1. Preferred Reporting Items for Systematic Reviews and Meta-Analyses flow diagram illustrating the study selection process and the distribution of studies included in the quantitative synthesis of hepatitis E virus infection in humans, animals, and environmental samples in West Africa.
Figure 2. Forest plots showing pooled prevalences of immunoglobulin G, immunoglobulin M, and total antibody levels against hepatitis E virus across different West African countries and population groups. Horizontal lines represent 95% confidence intervals, and diamond symbols indicate pooled prevalence estimates.
| Reference | Study period | Country | Population/source | Marker/test | Diagnostic method | Sample size/type | Positive cases | Genotype | Quality score |
|---|---|---|---|---|---|---|---|---|---|
| [23] | 2021 | Nigeria | General population | IgG; IgM | ELISA | 454 blood; 454 blood | 190; 0 | – | 9 |
| [24] | – | Nigeria | Pregnant women | IgG; IgM | RDT | 300 blood; 300 blood | 5; 3 | – | 8 |
| [25] | 2007 | Nigeria | Pregnant women; general population | Total antibody | ELISA | 60 blood; 126 blood | 5; 20 | – | 9 |
| [26] | 2016–2018 | Nigeria | General population | IgM | ELISA | 359 blood | 131 | – | 9 |
| [27] | 2008 | Ghana | Contact with animals | IgG; IgM | ELISA | 123 blood; 123 blood | 68; 55 | – | 9 |
| [28] | 2009 | Ghana | Contact with animals | IgM; IgG | ELISA | 105 blood; 105 blood | 40; 0 | – | 9 |
| [29] | 2008 | Ghana | Pregnant women | IgG; IgM | ELISA | 157 blood; 157 blood | 16; 29 | – | 9 |
| [30] | 2019–2020 | Nigeria | Pregnant women; chronic patients | IgG | ELISA | 156 blood; 44 blood | 49; 14 | – | 9 |
| [31] | 2016 | Nigeria | Pregnant women | IgM | ELISA | 180 blood | 24 | – | 9 |
| [32] | 2020 | Nigeria | Pregnant women | HEV antigen | ELISA | 100 blood | 8 | – | 9 |
| [33] | – | Nigeria | Pigs | Total antibody | ELISA | 168 blood | 41 | – | 8 |
| [34] | 2017 | Nigeria | Goats | Total antibody | ELISA | 176 blood | 49 | – | 9 |
| [35] | 2017 | Nigeria | Cattle; goats; pigs | Total antibody | ELISA | 30 blood; 26 blood; 120 blood | 0; 0; 69 | – | 9 |
| [36] | 2019 | Ghana | General population; contact with animals; pigs | IgG; IgM; HEV antigen; total antibody | ELISA | 1365 blood; 105 blood; 296 blood; 474 blood | 162; 10; 16; 0; 9; 3; 26; 296 | – | 9 |
| [37] | 2021–2022 | Ghana | Pregnant women | IgG; IgM; HEV antigen; RNA | ELISA; real-time RT-PCR | 1000 blood; 1000 blood; 1000 blood; 1000 serum | 83; 0; 10; 198 | – | 9 |
| [38] | 2020 | Nigeria | Blood donors | HEV antigen | ELISA | 102 blood | 15 | – | 9 |
| [39] | 2016 | Nigeria | Pregnant women | Total antibody | ELISA | 182 blood | 18 | – | 9 |
| [40] | 2011 | Benin | Pregnant women | IgG; IgM | ELISA | 278 blood; 278 blood | 45; 4 | – | 9 |
| [41] | 2012 | Cameroon | Pigs | RNA | Nested RT-PCR | 345 liver | 3 | – | 9 |
| [42] | 2013–2016 | Burkina Faso | Hospital patients | IgG; IgM; RNA | ELISA; nested RT-PCR | 900 blood; 900 blood; 900 serum | 164; 19; 19 | 2b | 9 |
| [43] | 2021 | Senegal | Pregnant women | IgM; IgG | ELISA | 1227 blood; 1230 blood | 6; 91 | – | 9 |
| [9] | 2017–2019 | Guinea | Pigs | RNA; total antibody | Nested RT-PCR; ELISA | 105 feces; 886 blood | 2; 193 | 3c | 9 |
| [44] | 2012 | Nigeria | General population | IgG | ELISA | 402 blood | 31 | – | 9 |
| [45] | 2011 | Ghana | Cattle; goats; pigs; sheep | IgG; RNA | ELISA; RT-PCR | 105 blood; 124 blood; 89 blood/serum; 102 blood | 25; 7; 69; 9; 13 | 3 | 9 |
| [46] | 2020–2021 | Nigeria | Blood donors | IgG; IgM | ELISA | 370 blood; 370 blood | 4; 7 | – | 9 |
| [47] | 2008–2010 | Ghana; Cameroon | Chronic patients | IgG; IgM; RNA | EIA; nested RT-PCR | 402 blood; 389 blood; 1029 serum; 515 serum | 182; 3; 43; 0; 0; 0 | – | 9 |
| [48] | 2018 | Nigeria | Contact with animals | IgM | ELISA | 177 blood | 16 | – | 9 |
| [49] | – | Sierra Leone | General population | Total antibody | ELISA | 66 blood | 5 | – | 7 |
| [50] | 2012–2013 | Nigeria | General population | IgM | ELISA | 750 blood | 1 | – | 9 |
| [51] | 2023 | Nigeria | Pregnant women | IgM; IgG | ELISA | 230 blood; 230 blood | 10; 18 | – | 9 |
| [52] | – | Nigeria | Pregnant women | IgM; IgG | ELISA | 210 blood; 210 blood | 4; 6 | – | – |
| [53] | – | Nigeria | Chronic patients | IgM | ELISA | 155 blood | 3 | – | 8 |
| [54] | 2012 | Nigeria | Cattle; goats; pigs; sheep | IgG; IgM | ELISA | 37, 43, 67, and 19 blood samples | 0; 0; 14; 2; 11; 11; 2; 0 | – | 9 |
| [55] | – | Nigeria | General population; pregnant women; chronic patients; contact with animals | IgG; IgM | ELISA | 190; 108; 80; 48 blood samples | 91; 0; 45; 1; 24; 1; 30; 4 | – | 8 |
| [56] | 2014 | Burkina Faso | Pregnant women | IgG | ELISA | 179 blood | 19 | – | 9 |
| [57] | 2014 | Nigeria | Chronic patients | IgM | ELISA | 275 blood | 15 | – | 9 |
| [58] | 2017 | Niger | Hospital patients | IgM; RNA | ELISA; real-time RT-PCR | 1917 blood; 1917 serum | 736; 736 | – | 9 |
| [59] | 2017–2023 | Niger | Hospital patients | Total antibody; RNA | ELISA; one-step RT-PCR | 2820 blood; 906 serum | 906; 21 | 1; 2b | 9 |
| [60] | 2017 | Ghana | Contact with animals; general population; pigs | Total antibody | ELISA | 220 blood; 102 blood; 245 blood | 89; 31; 208 | – | 9 |
| [61] | 2017 | Nigeria | General population | IgG | ELISA | 414 blood | 9 | – | 9 |
| [62] | 2023 | Cameroon | Blood donors; chronic patients; pregnant women | IgG; IgM; RNA | ELISA; nested RT-PCR | 289; 233; 190 blood/fecal samples | 25; 4; 6; 17; 6; 0; 7; 2; 2 | 3a; 3e | 9 |
| [63] | 2012 | Ghana | Blood donors | IgG; IgM; RNA | ELISA; one-step RT-qPCR | 239 blood; 239 serum | 11; 14; 0 | – | 9 |
| [64] | 2012 | Nigeria | Contact with animals; pigs | IgG; IgM | ELISA | 73 blood; 221 blood | 13; 1; 214; 3 | – | 9 |
| [65] | 2020 | Cameroon | Hospital patients | RNA | Nested RT-PCR | 24 serum | 20 | 1e; 3f | 6 |
| [66] | 2009–2015 | Cameroon | General population; pregnant women; chronic patients | IgG; IgM | ELISA | 450; 183; 270 blood samples | 46; 179; 9; 27; 29; 25 | – | 9 |
| [67] | 2021–2023 | Cameroon | Hospital patients | IgG; IgM; RNA | ELISA; one-step RT-PCR | 543 blood; 79 serum | 77; 89; 12 | 3f; 4b | 9 |
| [68] | 2012 | Cameroon | Pigs | IgG; IgM | ELISA | 162 blood; 162 blood | 10; 64 | – | 9 |
| [69] | 1997–2006 | Cameroon | Monkeys | IgG; IgM | ELISA | 172 blood; 172 blood | 9; 4 | – | 9 |
| [70] | 2017–2018 | Cameroon | Pigs | IgG; IgM; RNA | ELISA; nested RT-PCR | 453 blood; 136 fecal | 121; 136; 8 | 3 | 9 |
| [71] | 2018–2019 | Cameroon | Environment | RNA | Nested RT-PCR | 157 sewage samples | 3 | 3a | 6 |
| [72] | 2020–2021 | Burkina Faso | Environment | RNA | Nested RT-PCR | 86 vegetable samples | 21 | – | 6 |
| [73] | 2017 | Ghana | Pregnant women | IgM; IgG | ELISA | 398 blood; 398 blood | 1; 48 | – | 9 |
| [74] | – | Nigeria | Chronic patients | IgG; IgM | EIA; ELISA | 180 blood; 180 blood | 20; 2 | – | 8 |
| [75] | 2010–2011 | Ghana | Hospital patients | Total antibody | ELISA | 103 blood | 6 | – | 9 |
| [76] | – | Nigeria | Pregnant women | IgG; IgM | ELISA | 200 blood; 200 blood | 44; 30 | – | 8 |
| [77] | 2008 | Nigeria | General population | Total antibody | ELISA | 132 blood | 79 | – | 9 |
| [78] | 2016 | Nigeria | Blood donors | IgG; IgM | ELISA | 151 blood; 151 blood | 8; 2 | – | 9 |
| [79] | 2014–2015 | Nigeria | General population | Total antibody | ELISA | 186 blood | 5 | – | 9 |
| [80] | 2019 | Nigeria | Pregnant women | IgM | ELISA | 904 blood | 66 | – | 9 |
| [81] | 2020 | Nigeria | Chronic patients; contact with animals | IgM | ELISA | 121 blood; 156 blood | 13; 5 | – | 9 |
| [82] | 2020–2021 | Nigeria | General population; contact with animals | IgG; IgM; RNA | ELISA; one-step RT-PCR | 307 blood; 445 blood; 122 serum | 27; 0; 85; 10; 0 | – | 9 |
| [83] | 2018 | Nigeria | Blood donors | IgG | ELISA | 90 blood | 21 | – | – |
| [84] | – | Nigeria | Pregnant women | IgM | ELISA | 199 blood | 7 | – | 8 |
| [85] | 2018–2019 | Nigeria | Poultry | RNA | Nested RT-PCR | 88 serum; 110 fecal samples | 11; 10 | 2a | 9 |
| [86] | – | Guinea | General population | IgG; IgM; RNA | ELISA; real-time RT-PCR | 74 blood; 74 serum | 2; 1; 1 | – | 8 |
| [87] | 2019 | Nigeria | General population | Total antibody; IgM; RNA | Indirect ELISA; one-step RT-semi-nested PCR | 653 blood; 123 serum | 98; 25; 0 | – | 9 |
| [88] | 2014–2017 | Nigeria | Chronic patients; contact with animals; pregnant women; general population | Total antibody; IgM; RNA | ELISA; one-step RT-semi-nested PCR | 411; 89; 160 blood samples; 76 serum | 47; 7; 10; 9; 2; 1; 0 | – | 9 |
| [89] | 2015–2016 | Burkina Faso | Poultry | RNA | One-step RT-PCR | 173 liver samples | 29 | – | 9 |
| [90] | 2015–2017 | Burkina Faso | Cattle; goats; hares; rabbits; sheep | Total antibody | ELISA | 72; 81; 19; 100; 75 blood samples | 19; 23; 10; 60; 9 | – | 9 |
| [91] | 2015 | Burkina Faso; Mali; Niger | Camels | Total antibody | ELISA | 133 blood | 11 | – | 9 |
| [92] | 2011–2012 | Nigeria | Pigs | IgG; RNA | ELISA; one-step RT-PCR | 286 blood; 90 fecal samples | 159; 69 | 3 | 9 |
| [93] | 2015–2016 | Ghana | Hospital patients | IgM; IgG; RNA | ELISA; one-step RT-PCR | 155 blood; 155 serum | 3; 51; 0 | – | 9 |
| [94] | – | Côte d'Ivoire | Hospital patients | Total antibody | ELISA | 111 blood | 24 | – | 8 |
| [95] | 2012–2014 | Senegal | General population; rats; environment | RNA; IgM | One-step RT-PCR; ELISA | 1617 serum; 906 blood; 75 serum; 65 water | 771; 334; 0; 0 | 2b | 9 |
| [96] | 2012–2014 | Senegal | General population | IgG; IgM | ELISA | 760 blood; 760 blood | 251; 331 | – | 9 |
| [97] | 2017–2019 | Nigeria | Poultry; pigs | RNA | Nested RT-PCR | 172 fecal; 238 fecal samples | 5; 10 | – | 9 |
| [98] | 2020 | Burkina Faso | General population | IgM | RDT | 160 blood | 134 | – | 9 |
| [99] | 2022 | Nigeria | Hospital patients | IgM | ELISA | 350 blood | 71 | – | 9 |
| [100] | 2020 | Togo | Pregnant women; blood donors | IgM; total antibody | ELISA; RDT | 94 blood; 195 blood; 178 blood | 5; 29; 10 | – | 9 |
| [101] | 2021–2022 | Togo | Contact with animals; pigs | IgM; IgG; total antibody; RNA | ELISA; nested RT-PCR | 89 blood; 176 blood; 250 fecal samples | 18; 5; 141; 6 | – | 9 |
| [102] | 2017–2018 | Burkina Faso | Environment | RNA | Nested RT-PCR | 318 water samples | 12 | – | 6 |
| [103] | 2016–2018 | Côte d'Ivoire | Hospital patients; pregnant women | IgG | ELISA | 92 blood; 200 blood | 17; 3 | – | 9 |
| [104] | 2013 | Gambia | General population | IgG; RNA | EIA; nested RT-PCR | 204 blood; 204 serum | 28; 0 | – | 9 |
| [105] | 2017 | Burkina Faso | Pregnant women | IgM; IgG | ELISA | 90 blood; 90 blood | 0; 50 | – | 9 |
| [106] | 2016–2017 | Sierra Leone | Pigs | RNA; total antibody | Nested RT-PCR; ELISA | 217 serum; 1086 blood | 2; 44 | 3 | 9 |
| [107] | 2022 | Senegal | Pigs | RNA | One-step RT-PCR | 74 meat/liver samples | 4 | 3 | 9 |
| [108] | 2008 | Ghana | Blood donors | IgG; IgM | ELISA | 471 blood; 471 blood | 122; 197 | – | 9 |
| [109] | 2017 | Burkina Faso | Cattle; pigs | Total antibody | ELISA | 475 blood; 192 blood | 24; 155 | – | 9 |
| [110] | 2010–2012 | Burkina Faso | Blood donors; pregnant women | IgG | ELISA | 178 blood; 189 blood | 34; 22 | – | 9 |
| [111] | 2012–2013 | Burkina Faso | Contact with animals; pigs | IgM; IgG; RNA; total antibody | ELISA; nested RT-PCR | 100 blood; 157 fecal; 100 blood | 1; 76; 1; 80 | 3 | 9 |
| [112] | 2014 | Burkina Faso | Blood donors | IgM; IgG | ELISA | 1497 blood; 1497 blood | 28; 584 | – | 9 |
| [113] | 2022 | Burkina Faso | Environment | RNA | Nested RT-PCR | 80 water samples | 14 | – | 9 |
| [114] | 2019 | Nigeria | Blood donors | IgG; IgM | ELISA | 104 blood; 104 blood | 3; 2 | – | 9 |
| [115] | 2021–2022 | Nigeria | Contact with animals | IgG; IgM | ELISA | 100 blood; 100 blood | 68; 17 | – | 9 |
| [116] | 2015–2016 | Ghana | Contact with animals; general population; pigs | IgG; RNA | ELISA; one-step RT-PCR | 264 blood; 280 blood; 210 serum; 720 serum | 114; 96; 0; 23 | 3 | 9 |
Table 1. Descriptive characteristics of included studies.
| Reference | Study period | Country | Population/source | Marker/test | Diagnostic method | Sample size/type | Positive cases | Genotype | Quality score |
|---|---|---|---|---|---|---|---|---|---|
| [23] | 2021 | Nigeria | General population | IgG; IgM | ELISA | 454 blood; 454 blood | 190; 0 | – | 9 |
| [24] | – | Nigeria | Pregnant women | IgG; IgM | RDT | 300 blood; 300 blood | 5; 3 | – | 8 |
| [25] | 2007 | Nigeria | Pregnant women; general population | Total antibody | ELISA | 60 blood; 126 blood | 5; 20 | – | 9 |
| [26] | 2016–2018 | Nigeria | General population | IgM | ELISA | 359 blood | 131 | – | 9 |
| [27] | 2008 | Ghana | Contact with animals | IgG; IgM | ELISA | 123 blood; 123 blood | 68; 55 | – | 9 |
| [28] | 2009 | Ghana | Contact with animals | IgM; IgG | ELISA | 105 blood; 105 blood | 40; 0 | – | 9 |
| [29] | 2008 | Ghana | Pregnant women | IgG; IgM | ELISA | 157 blood; 157 blood | 16; 29 | – | 9 |
| [30] | 2019–2020 | Nigeria | Pregnant women; chronic patients | IgG | ELISA | 156 blood; 44 blood | 49; 14 | – | 9 |
| [31] | 2016 | Nigeria | Pregnant women | IgM | ELISA | 180 blood | 24 | – | 9 |
| [32] | 2020 | Nigeria | Pregnant women | HEV antigen | ELISA | 100 blood | 8 | – | 9 |
| [33] | – | Nigeria | Pigs | Total antibody | ELISA | 168 blood | 41 | – | 8 |
| [34] | 2017 | Nigeria | Goats | Total antibody | ELISA | 176 blood | 49 | – | 9 |
| [35] | 2017 | Nigeria | Cattle; goats; pigs | Total antibody | ELISA | 30 blood; 26 blood; 120 blood | 0; 0; 69 | – | 9 |
| [36] | 2019 | Ghana | General population; contact with animals; pigs | IgG; IgM; HEV antigen; total antibody | ELISA | 1365 blood; 105 blood; 296 blood; 474 blood | 162; 10; 16; 0; 9; 3; 26; 296 | – | 9 |
| [37] | 2021–2022 | Ghana | Pregnant women | IgG; IgM; HEV antigen; RNA | ELISA; real-time RT-PCR | 1000 blood; 1000 blood; 1000 blood; 1000 serum | 83; 0; 10; 198 | – | 9 |
| [38] | 2020 | Nigeria | Blood donors | HEV antigen | ELISA | 102 blood | 15 | – | 9 |
| [39] | 2016 | Nigeria | Pregnant women | Total antibody | ELISA | 182 blood | 18 | – | 9 |
| [40] | 2011 | Benin | Pregnant women | IgG; IgM | ELISA | 278 blood; 278 blood | 45; 4 | – | 9 |
| [41] | 2012 | Cameroon | Pigs | RNA | Nested RT-PCR | 345 liver | 3 | – | 9 |
| [42] | 2013–2016 | Burkina Faso | Hospital patients | IgG; IgM; RNA | ELISA; nested RT-PCR | 900 blood; 900 blood; 900 serum | 164; 19; 19 | 2b | 9 |
| [43] | 2021 | Senegal | Pregnant women | IgM; IgG | ELISA | 1227 blood; 1230 blood | 6; 91 | – | 9 |
| [9] | 2017–2019 | Guinea | Pigs | RNA; total antibody | Nested RT-PCR; ELISA | 105 feces; 886 blood | 2; 193 | 3c | 9 |
| [44] | 2012 | Nigeria | General population | IgG | ELISA | 402 blood | 31 | – | 9 |
| [45] | 2011 | Ghana | Cattle; goats; pigs; sheep | IgG; RNA | ELISA; RT-PCR | 105 blood; 124 blood; 89 blood/serum; 102 blood | 25; 7; 69; 9; 13 | 3 | 9 |
| [46] | 2020–2021 | Nigeria | Blood donors | IgG; IgM | ELISA | 370 blood; 370 blood | 4; 7 | – | 9 |
| [47] | 2008–2010 | Ghana; Cameroon | Chronic patients | IgG; IgM; RNA | EIA; nested RT-PCR | 402 blood; 389 blood; 1029 serum; 515 serum | 182; 3; 43; 0; 0; 0 | – | 9 |
| [48] | 2018 | Nigeria | Contact with animals | IgM | ELISA | 177 blood | 16 | – | 9 |
| [49] | – | Sierra Leone | General population | Total antibody | ELISA | 66 blood | 5 | – | 7 |
| [50] | 2012–2013 | Nigeria | General population | IgM | ELISA | 750 blood | 1 | – | 9 |
| [51] | 2023 | Nigeria | Pregnant women | IgM; IgG | ELISA | 230 blood; 230 blood | 10; 18 | – | 9 |
| [52] | – | Nigeria | Pregnant women | IgM; IgG | ELISA | 210 blood; 210 blood | 4; 6 | – | – |
| [53] | – | Nigeria | Chronic patients | IgM | ELISA | 155 blood | 3 | – | 8 |
| [54] | 2012 | Nigeria | Cattle; goats; pigs; sheep | IgG; IgM | ELISA | 37, 43, 67, and 19 blood samples | 0; 0; 14; 2; 11; 11; 2; 0 | – | 9 |
| [55] | – | Nigeria | General population; pregnant women; chronic patients; contact with animals | IgG; IgM | ELISA | 190; 108; 80; 48 blood samples | 91; 0; 45; 1; 24; 1; 30; 4 | – | 8 |
| [56] | 2014 | Burkina Faso | Pregnant women | IgG | ELISA | 179 blood | 19 | – | 9 |
| [57] | 2014 | Nigeria | Chronic patients | IgM | ELISA | 275 blood | 15 | – | 9 |
| [58] | 2017 | Niger | Hospital patients | IgM; RNA | ELISA; real-time RT-PCR | 1917 blood; 1917 serum | 736; 736 | – | 9 |
| [59] | 2017–2023 | Niger | Hospital patients | Total antibody; RNA | ELISA; one-step RT-PCR | 2820 blood; 906 serum | 906; 21 | 1; 2b | 9 |
| [60] | 2017 | Ghana | Contact with animals; general population; pigs | Total antibody | ELISA | 220 blood; 102 blood; 245 blood | 89; 31; 208 | – | 9 |
| [61] | 2017 | Nigeria | General population | IgG | ELISA | 414 blood | 9 | – | 9 |
| [62] | 2023 | Cameroon | Blood donors; chronic patients; pregnant women | IgG; IgM; RNA | ELISA; nested RT-PCR | 289; 233; 190 blood/fecal samples | 25; 4; 6; 17; 6; 0; 7; 2; 2 | 3a; 3e | 9 |
| [63] | 2012 | Ghana | Blood donors | IgG; IgM; RNA | ELISA; one-step RT-qPCR | 239 blood; 239 serum | 11; 14; 0 | – | 9 |
| [64] | 2012 | Nigeria | Contact with animals; pigs | IgG; IgM | ELISA | 73 blood; 221 blood | 13; 1; 214; 3 | – | 9 |
| [65] | 2020 | Cameroon | Hospital patients | RNA | Nested RT-PCR | 24 serum | 20 | 1e; 3f | 6 |
| [66] | 2009–2015 | Cameroon | General population; pregnant women; chronic patients | IgG; IgM | ELISA | 450; 183; 270 blood samples | 46; 179; 9; 27; 29; 25 | – | 9 |
| [67] | 2021–2023 | Cameroon | Hospital patients | IgG; IgM; RNA | ELISA; one-step RT-PCR | 543 blood; 79 serum | 77; 89; 12 | 3f; 4b | 9 |
| [68] | 2012 | Cameroon | Pigs | IgG; IgM | ELISA | 162 blood; 162 blood | 10; 64 | – | 9 |
| [69] | 1997–2006 | Cameroon | Monkeys | IgG; IgM | ELISA | 172 blood; 172 blood | 9; 4 | – | 9 |
| [70] | 2017–2018 | Cameroon | Pigs | IgG; IgM; RNA | ELISA; nested RT-PCR | 453 blood; 136 fecal | 121; 136; 8 | 3 | 9 |
| [71] | 2018–2019 | Cameroon | Environment | RNA | Nested RT-PCR | 157 sewage samples | 3 | 3a | 6 |
| [72] | 2020–2021 | Burkina Faso | Environment | RNA | Nested RT-PCR | 86 vegetable samples | 21 | – | 6 |
| [73] | 2017 | Ghana | Pregnant women | IgM; IgG | ELISA | 398 blood; 398 blood | 1; 48 | – | 9 |
| [74] | – | Nigeria | Chronic patients | IgG; IgM | EIA; ELISA | 180 blood; 180 blood | 20; 2 | – | 8 |
| [75] | 2010–2011 | Ghana | Hospital patients | Total antibody | ELISA | 103 blood | 6 | – | 9 |
| [76] | – | Nigeria | Pregnant women | IgG; IgM | ELISA | 200 blood; 200 blood | 44; 30 | – | 8 |
| [77] | 2008 | Nigeria | General population | Total antibody | ELISA | 132 blood | 79 | – | 9 |
| [78] | 2016 | Nigeria | Blood donors | IgG; IgM | ELISA | 151 blood; 151 blood | 8; 2 | – | 9 |
| [79] | 2014–2015 | Nigeria | General population | Total antibody | ELISA | 186 blood | 5 | – | 9 |
| [80] | 2019 | Nigeria | Pregnant women | IgM | ELISA | 904 blood | 66 | – | 9 |
| [81] | 2020 | Nigeria | Chronic patients; contact with animals | IgM | ELISA | 121 blood; 156 blood | 13; 5 | – | 9 |
| [82] | 2020–2021 | Nigeria | General population; contact with animals | IgG; IgM; RNA | ELISA; one-step RT-PCR | 307 blood; 445 blood; 122 serum | 27; 0; 85; 10; 0 | – | 9 |
| [83] | 2018 | Nigeria | Blood donors | IgG | ELISA | 90 blood | 21 | – | – |
| [84] | – | Nigeria | Pregnant women | IgM | ELISA | 199 blood | 7 | – | 8 |
| [85] | 2018–2019 | Nigeria | Poultry | RNA | Nested RT-PCR | 88 serum; 110 fecal samples | 11; 10 | 2a | 9 |
| [86] | – | Guinea | General population | IgG; IgM; RNA | ELISA; real-time RT-PCR | 74 blood; 74 serum | 2; 1; 1 | – | 8 |
| [87] | 2019 | Nigeria | General population | Total antibody; IgM; RNA | Indirect ELISA; one-step RT-semi-nested PCR | 653 blood; 123 serum | 98; 25; 0 | – | 9 |
| [88] | 2014–2017 | Nigeria | Chronic patients; contact with animals; pregnant women; general population | Total antibody; IgM; RNA | ELISA; one-step RT-semi-nested PCR | 411; 89; 160 blood samples; 76 serum | 47; 7; 10; 9; 2; 1; 0 | – | 9 |
| [89] | 2015–2016 | Burkina Faso | Poultry | RNA | One-step RT-PCR | 173 liver samples | 29 | – | 9 |
| [90] | 2015–2017 | Burkina Faso | Cattle; goats; hares; rabbits; sheep | Total antibody | ELISA | 72; 81; 19; 100; 75 blood samples | 19; 23; 10; 60; 9 | – | 9 |
| [91] | 2015 | Burkina Faso; Mali; Niger | Camels | Total antibody | ELISA | 133 blood | 11 | – | 9 |
| [92] | 2011–2012 | Nigeria | Pigs | IgG; RNA | ELISA; one-step RT-PCR | 286 blood; 90 fecal samples | 159; 69 | 3 | 9 |
| [93] | 2015–2016 | Ghana | Hospital patients | IgM; IgG; RNA | ELISA; one-step RT-PCR | 155 blood; 155 serum | 3; 51; 0 | – | 9 |
| [94] | – | Côte d'Ivoire | Hospital patients | Total antibody | ELISA | 111 blood | 24 | – | 8 |
| [95] | 2012–2014 | Senegal | General population; rats; environment | RNA; IgM | One-step RT-PCR; ELISA | 1617 serum; 906 blood; 75 serum; 65 water | 771; 334; 0; 0 | 2b | 9 |
| [96] | 2012–2014 | Senegal | General population | IgG; IgM | ELISA | 760 blood; 760 blood | 251; 331 | – | 9 |
| [97] | 2017–2019 | Nigeria | Poultry; pigs | RNA | Nested RT-PCR | 172 fecal; 238 fecal samples | 5; 10 | – | 9 |
| [98] | 2020 | Burkina Faso | General population | IgM | RDT | 160 blood | 134 | – | 9 |
| [99] | 2022 | Nigeria | Hospital patients | IgM | ELISA | 350 blood | 71 | – | 9 |
| [100] | 2020 | Togo | Pregnant women; blood donors | IgM; total antibody | ELISA; RDT | 94 blood; 195 blood; 178 blood | 5; 29; 10 | – | 9 |
| [101] | 2021–2022 | Togo | Contact with animals; pigs | IgM; IgG; total antibody; RNA | ELISA; nested RT-PCR | 89 blood; 176 blood; 250 fecal samples | 18; 5; 141; 6 | – | 9 |
| [102] | 2017–2018 | Burkina Faso | Environment | RNA | Nested RT-PCR | 318 water samples | 12 | – | 6 |
| [103] | 2016–2018 | Côte d'Ivoire | Hospital patients; pregnant women | IgG | ELISA | 92 blood; 200 blood | 17; 3 | – | 9 |
| [104] | 2013 | Gambia | General population | IgG; RNA | EIA; nested RT-PCR | 204 blood; 204 serum | 28; 0 | – | 9 |
| [105] | 2017 | Burkina Faso | Pregnant women | IgM; IgG | ELISA | 90 blood; 90 blood | 0; 50 | – | 9 |
| [106] | 2016–2017 | Sierra Leone | Pigs | RNA; total antibody | Nested RT-PCR; ELISA | 217 serum; 1086 blood | 2; 44 | 3 | 9 |
| [107] | 2022 | Senegal | Pigs | RNA | One-step RT-PCR | 74 meat/liver samples | 4 | 3 | 9 |
| [108] | 2008 | Ghana | Blood donors | IgG; IgM | ELISA | 471 blood; 471 blood | 122; 197 | – | 9 |
| [109] | 2017 | Burkina Faso | Cattle; pigs | Total antibody | ELISA | 475 blood; 192 blood | 24; 155 | – | 9 |
| [110] | 2010–2012 | Burkina Faso | Blood donors; pregnant women | IgG | ELISA | 178 blood; 189 blood | 34; 22 | – | 9 |
| [111] | 2012–2013 | Burkina Faso | Contact with animals; pigs | IgM; IgG; RNA; total antibody | ELISA; nested RT-PCR | 100 blood; 157 fecal; 100 blood | 1; 76; 1; 80 | 3 | 9 |
| [112] | 2014 | Burkina Faso | Blood donors | IgM; IgG | ELISA | 1497 blood; 1497 blood | 28; 584 | – | 9 |
| [113] | 2022 | Burkina Faso | Environment | RNA | Nested RT-PCR | 80 water samples | 14 | – | 9 |
| [114] | 2019 | Nigeria | Blood donors | IgG; IgM | ELISA | 104 blood; 104 blood | 3; 2 | – | 9 |
| [115] | 2021–2022 | Nigeria | Contact with animals | IgG; IgM | ELISA | 100 blood; 100 blood | 68; 17 | – | 9 |
| [116] | 2015–2016 | Ghana | Contact with animals; general population; pigs | IgG; RNA | ELISA; one-step RT-PCR | 264 blood; 280 blood; 210 serum; 720 serum | 114; 96; 0; 23 | 3 | 9 |
| Category | Sub group | IgG studies | IgG prevalence % (95% CI) | IgG I² (%) | IgG p-value | IgM studies | IgM prevalence % (95% CI) | IgM I² (%) | IgM p-value | Total antibody studies | Total antibody prevalence % (95% CI) | Total antibody I² (%) | Total antibody p-value |
|---|---|---|---|---|---|---|---|---|---|---|---|---|---|
| Country | Benin | 1 | 16.2 (2.8–56.3) | 0.0 | 1.00 | 1 | 1.4 (0–19.6) | 0.0 | 1.00 | – | – | – | – |
| Country | Burkina Faso | 7 | 29.3 (16.7–46.0) | 97.8 | 0.001 | 5 | 5.6 (1.6–18.0) | 99.1 | 0.001 | – | – | – | – |
| Country | Cameroon | 8 | 8.4 (4.4–15.3) | 72.7 | 0.001 | 8 | 5.6 (2.1–13.8) | 96.8 | 0.001 | – | – | – | – |
| Country | Gambia | 1 | 13.7 (2.3–51.9) | 0.0 | 1.00 | – | – | – | – | – | – | – | – |
| Country | Ghana | 13 | 19.4 (12.3–29.3) | 97.8 | 0.001 | 11 | 4.8 (2.1–10.8) | 97.5 | 0.001 | 3 | 22.0 (8.1–47.4) | 93.3 | 0.001 |
| Country | Guinea | 1 | 2.7 (0–22.4) | 0.0 | 1.00 | 1 | 1.4 (0–27.0) | 0.0 | 1.00 | – | – | – | – |
| Country | Côte d'Ivoire | 2 | 6.5 (1.6–22.8) | 94.4 | 0.001 | – | – | – | – | 1 | 21.6 (3.6–66.9) | 0.0 | 1.00 |
| Country | Niger | – | – | – | – | 1 | 38.4 (4.3–89.7) | 0.0 | 1.00 | 1 | 32.1 (6.4–76.7) | 0.0 | 1.00 |
| Country | Nigeria | 23 | 16.1 (11.3–22.3) | 96.5 | 0.001 | 32 | 3.2 (2.0–5.3) | 94.3 | 0.001 | 9 | 11.9 (6.4–21.0) | 95.6 | 0.001 |
| Country | Senegal | 2 | 16.6 (5.0–42.9) | 99.5 | 0.001 | 3 | 12.5 (3.0–40.1) | 98.7 | 0.001 | – | – | – | – |
| Country | Sierra Leone | – | – | – | – | – | – | – | – | 1 | 7.6 (1.0–41.1) | 0.0 | 1.00 |
| Country | Togo | 1 | 5.6 (0–32.2) | 0.0 | 1.00 | 3 | 12.2 (2.8–39.9) | 75.3 | 0.018 | 1 | 5.6 (0–31.4) | 0.0 | 1.00 |
| Population group | Blood donors | 9 | 10.3 (5.7–18.0) | 97.1 | 0.001 | 8 | 4.5 (1.9–10.2) | 98.4 | 0.001 | 1 | 5.6 (0–32.4) | 0.0 | 1.00 |
| Population group | Chronic patients | 7 | 18.0 (9.7–31.0) | 97.0 | 0.001 | 10 | 2.5 (1.1–5.5) | 80.0 | 0.001 | 1 | 11.4 (1.7–49.0) | 0.0 | 1.00 |
| Population group | Contact with animals | 10 | 33.1 (20.9–48.1) | 96.3 | 0.001 | 12 | 7.6 (3.7–14.9) | 94.2 | 0.001 | 2 | 20.4 (5.6–52.5) | 96.0 | 0.001 |
| Population group | General population | 11 | 15.0 (9.0–23.9) | 98.0 | 0.001 | 12 | 8.3 (4.0–16.3) | 98.3 | 0.001 | 6 | 17.0 (8.1–32.3) | 96.6 | 0.001 |
| Population group | Hospital patients | 4 | 20.2 (9.0–39.2) | 88.9 | 0.001 | 5 | 10.2 (3.7–25.2) | 98.7 | 0.001 | 3 | 17.5 (6.1–41.0) | 93.0 | 0.001 |
| Population group | Pregnant women | 18 | 11.3 (7.5–16.7) | 96.0 | 0.001 | 18 | 2.8 (1.5–5.1) | 91.3 | 0.001 | 3 | 8.0 (2.5–22.7) | 0.0 | 0.40 |
| HEV acquisition risk | High-risk groups | 17 | 25.9 (18.0–35.7) | 96.7 | 0.002 | 22 | 4.6 (2.7–7.8) | 94.0 | 0.020 | 3 | 16.9 (6.5–37.4) | 97.3 | 0.001 |
| HEV acquisition risk | Low-risk groups | 42 | 13.1 (10.1–16.8) | 97.2 | 0.002 | 43 | 4.8 (3.3–7.1) | 98.1 | 0.020 | 13 | 13.6 (8.5–21.0) | 96.1 | 0.001 |
| Study period | 1997–2019 | 41 | 21.2 (16.9–26.3) | 96.7 | 0.001 | 41 | 4.2 (2.7–6.3) | 97.8 | 0.001 | 15 | 15.2 (10.3–21.7) | 97.3 | 0.001 |
| Study period | 2020–2025 | 11 | 7.4 (4.4–12.2) | 97.4 | 0.001 | 16 | 5.1 (2.7–9.7) | 97.0 | 0.001 | 1 | 5.6 (1.1–24.9) | 0.0 | 1.00 |
Table 2. Prevalence of IgG, IgM, and total anti-hepatitis E virus antibodies according to country, population group, risk category, and study period.
| Category | Sub group | IgG studies | IgG prevalence % (95% CI) | IgG I² (%) | IgG p-value | IgM studies | IgM prevalence % (95% CI) | IgM I² (%) | IgM p-value | Total antibody studies | Total antibody prevalence % (95% CI) | Total antibody I² (%) | Total antibody p-value |
|---|---|---|---|---|---|---|---|---|---|---|---|---|---|
| Country | Benin | 1 | 16.2 (2.8–56.3) | 0.0 | 1.00 | 1 | 1.4 (0–19.6) | 0.0 | 1.00 | – | – | – | – |
| Country | Burkina Faso | 7 | 29.3 (16.7–46.0) | 97.8 | 0.001 | 5 | 5.6 (1.6–18.0) | 99.1 | 0.001 | – | – | – | – |
| Country | Cameroon | 8 | 8.4 (4.4–15.3) | 72.7 | 0.001 | 8 | 5.6 (2.1–13.8) | 96.8 | 0.001 | – | – | – | – |
| Country | Gambia | 1 | 13.7 (2.3–51.9) | 0.0 | 1.00 | – | – | – | – | – | – | – | – |
| Country | Ghana | 13 | 19.4 (12.3–29.3) | 97.8 | 0.001 | 11 | 4.8 (2.1–10.8) | 97.5 | 0.001 | 3 | 22.0 (8.1–47.4) | 93.3 | 0.001 |
| Country | Guinea | 1 | 2.7 (0–22.4) | 0.0 | 1.00 | 1 | 1.4 (0–27.0) | 0.0 | 1.00 | – | – | – | – |
| Country | Côte d'Ivoire | 2 | 6.5 (1.6–22.8) | 94.4 | 0.001 | – | – | – | – | 1 | 21.6 (3.6–66.9) | 0.0 | 1.00 |
| Country | Niger | – | – | – | – | 1 | 38.4 (4.3–89.7) | 0.0 | 1.00 | 1 | 32.1 (6.4–76.7) | 0.0 | 1.00 |
| Country | Nigeria | 23 | 16.1 (11.3–22.3) | 96.5 | 0.001 | 32 | 3.2 (2.0–5.3) | 94.3 | 0.001 | 9 | 11.9 (6.4–21.0) | 95.6 | 0.001 |
| Country | Senegal | 2 | 16.6 (5.0–42.9) | 99.5 | 0.001 | 3 | 12.5 (3.0–40.1) | 98.7 | 0.001 | – | – | – | – |
| Country | Sierra Leone | – | – | – | – | – | – | – | – | 1 | 7.6 (1.0–41.1) | 0.0 | 1.00 |
| Country | Togo | 1 | 5.6 (0–32.2) | 0.0 | 1.00 | 3 | 12.2 (2.8–39.9) | 75.3 | 0.018 | 1 | 5.6 (0–31.4) | 0.0 | 1.00 |
| Population group | Blood donors | 9 | 10.3 (5.7–18.0) | 97.1 | 0.001 | 8 | 4.5 (1.9–10.2) | 98.4 | 0.001 | 1 | 5.6 (0–32.4) | 0.0 | 1.00 |
| Population group | Chronic patients | 7 | 18.0 (9.7–31.0) | 97.0 | 0.001 | 10 | 2.5 (1.1–5.5) | 80.0 | 0.001 | 1 | 11.4 (1.7–49.0) | 0.0 | 1.00 |
| Population group | Contact with animals | 10 | 33.1 (20.9–48.1) | 96.3 | 0.001 | 12 | 7.6 (3.7–14.9) | 94.2 | 0.001 | 2 | 20.4 (5.6–52.5) | 96.0 | 0.001 |
| Population group | General population | 11 | 15.0 (9.0–23.9) | 98.0 | 0.001 | 12 | 8.3 (4.0–16.3) | 98.3 | 0.001 | 6 | 17.0 (8.1–32.3) | 96.6 | 0.001 |
| Population group | Hospital patients | 4 | 20.2 (9.0–39.2) | 88.9 | 0.001 | 5 | 10.2 (3.7–25.2) | 98.7 | 0.001 | 3 | 17.5 (6.1–41.0) | 93.0 | 0.001 |
| Population group | Pregnant women | 18 | 11.3 (7.5–16.7) | 96.0 | 0.001 | 18 | 2.8 (1.5–5.1) | 91.3 | 0.001 | 3 | 8.0 (2.5–22.7) | 0.0 | 0.40 |
| HEV acquisition risk | High-risk groups | 17 | 25.9 (18.0–35.7) | 96.7 | 0.002 | 22 | 4.6 (2.7–7.8) | 94.0 | 0.020 | 3 | 16.9 (6.5–37.4) | 97.3 | 0.001 |
| HEV acquisition risk | Low-risk groups | 42 | 13.1 (10.1–16.8) | 97.2 | 0.002 | 43 | 4.8 (3.3–7.1) | 98.1 | 0.020 | 13 | 13.6 (8.5–21.0) | 96.1 | 0.001 |
| Study period | 1997–2019 | 41 | 21.2 (16.9–26.3) | 96.7 | 0.001 | 41 | 4.2 (2.7–6.3) | 97.8 | 0.001 | 15 | 15.2 (10.3–21.7) | 97.3 | 0.001 |
| Study period | 2020–2025 | 11 | 7.4 (4.4–12.2) | 97.4 | 0.001 | 16 | 5.1 (2.7–9.7) | 97.0 | 0.001 | 1 | 5.6 (1.1–24.9) | 0.0 | 1.00 |
Note Seroprevalence according to population groups
HEV RNA prevalence
Nineteen studies reported HEV RNA positivity. HEV RNA prevalence ranged from 0% to 47.7%, with all but one study reporting values below 11%.
The highest prevalence (47.7%) was reported among suspected HEV cases in Senegal, where IgM seroprevalence reached 36.9%. Additional nonzero HEV RNA prevalences were observed among suspected HEV cases in Niger (38.4% and 2.3%), healthcare workers in Guinea (1.4%), pregnant women in Ghana (19.8%), hospitalized patients, pregnant women, and blood donors in Cameroon (15.2%, 1.1%, and 2.1%, respectively), and patients with viral hemorrhagic fever in Burkina Faso (2.1%) (Figure 3).
Temporal trends of HEV seroprevalence
Temporal analysis revealed a decline in IgG seroprevalence, from 21.2% during 1997–2019 to 7.4% during 2020–2025. Similarly, total anti-HEV antibody prevalence decreased from 15.2% to 5.6%. In contrast, IgM prevalence increased slightly, from 4.2% to 5.1% (Table 2 and S3 File).
HEV prevalence in animals
A total of 9,779 animals representing nine species were included, comprising birds (n = 543), camels (n = 133), cattle (n = 1,169), hares (n = 19), monkeys (n = 172), pigs (n = 7,353), rabbits (n = 100), rats (n = 75), and sheep (n = 215). Pooled prevalences according to biological markers were:
1. IgG: 24.5% (95% CI: 12.8%–41.7%)
2. IgM: 9.7% (95% CI: 5.2%–17.4%)
3. HEV antigen: 8.8% (95% CI: 6.0%–12.6%)
4. Total antibodies: 33.0% (95% CI: 19.5%–49.8%)
5. HEV RNA: 4.8% (95% CI: 2.0%–10.8%)
Substantial heterogeneity was observed among studies (Table 1). Pigs consistently exhibited the highest prevalence, emphasizing their importance as reservoirs and their zoonotic potential. HEV RNA prevalence varied significantly among species, ranging from 0% in rats to 8.9% in birds (p < 0.001) (Figure 4).
IgG seroprevalence ranged from 5.2% in monkeys to 47.7% in pigs (p < 0.001) (Figure 5). IgM prevalence ranged from 2.3% in sheep and monkeys to 17.3% in pigs (p < 0.005) (Figure 6), whereas total anti-HEV antibody prevalence ranged from 8.0% in cattle and camels to 60.0% in rabbits (p < 0.001) (Figure 7).
Figure 3. Forest plot showing the pooled prevalence of hepatitis E virus ribonucleic acid in different human populations in West Africa. Horizontal lines represent 95% confidence intervals, and diamond symbols indicate pooled prevalence estimates.
Figure 4. Forest plot showing hepatitis E virus ribonucleic acid prevalence according to animal species in West Africa. Horizontal lines represent 95% confidence intervals, and diamond symbols indicate pooled prevalence estimates.
HEV prevalence in environmental samples
Meta-analysis of five studies yielded an overall HEV RNA prevalence of 6.6% (95% CI: 2.1%–18.4%) (Figure 8). Subgroup analysis according to environmental matrixes revealed prevalences ranging from 0% in water samples to 24.4% in vegetables, with significant differences among matrixes (p < 0.001).
These findings provide one of the first quantitative assessments of environmental HEV contamination in West Africa and highlight the potential importance of foodborne and waterborne transmission associated with regional agricultural practices.
Figure 5. Forest plot showing immunoglobulin G seroprevalence according to animal species in West Africa. Horizontal lines represent 95% confidence intervals, and diamond symbols indicate pooled prevalence estimates.
Figure 6. Forest plot showing immunoglobulin M seroprevalence according to animal species in West Africa. Horizontal lines represent 95% confidence intervals, and diamond symbols indicate pooled prevalence estimates.
Figure 7. Forest plot showing total antibody prevalence against hepatitis E virus according to animal species in West Africa. Horizontal lines represent 95% confidence intervals, and diamond symbols indicate pooled prevalence estimates.
Figure 8. Forest plot showing the prevalence of hepatitis E virus ribonucleic acid in environmental samples collected in West Africa. Horizontal lines represent 95% confidence intervals, and diamond symbols indicate pooled prevalence estimates.
HEV genotypes in West Africa
Six human studies reported HEV genotypes (Table 3). Four major genotypes (HEV-1, HEV-2, HEV-3, and HEV-4) were identified in humans. HEV-1 was detected in Niger, whereas subtype 2b was identified in Niger, Senegal, and Burkina Faso. In Cameroon, subtypes 1e, 3e, 3f, and the rare subtype 4b were detected. Nine animal studies identified avian HEV genotype 2 and HEV-3. HEV-3 was detected in pigs from Cameroon, Sierra Leone, Senegal, Ghana, Nigeria, Guinea (subtype 3c), and Burkina Faso. Avian HEV genotype 2 was identified in poultry in Nigeria. Environmental studies demonstrated the presence of HEV-3 subtype 3a in sewage samples collected in Cameroon.
The distribution of HEV genotypes in West Africa is summarized in Table 3 [9, 42, 45, 59, 62, 65–67, 70, 71, 85, 92, 95, 106, 107, 111, 116]. The detection of subtype 4b and several HEV-3 subtypes contributes important information to the limited regional genotype data.
| Genotype | Studies | Source | Country |
|---|---|---|---|
| HEV-1 | [59] | Suspected HEV cases | Niger |
| HEV-1e | [65] | Suspected HEV cases | Cameroon |
| HEV-2b | [42, 59, 95] | Suspected HEV cases; patients with viral hemorrhagic fever | Niger, Senegal, Burkina Faso |
| Avian HEV-2 | [85] | Poultry | Nigeria |
| HEV-3 | [45, 70, 92, 106, 107, 111, 116] | Pigs | Nigeria, Burkina Faso, Ghana, Cameroon, Sierra Leone, Senegal |
| HEV-3a | [71] | Sewage | Cameroon |
| HEV-3c | [9] | Pigs | Guinea |
| HEV-3e | [62] | Pregnant women | Cameroon |
| HEV-3f | [66, 67] | Patients with acute febrile jaundice; suspected HEV cases | Cameroon |
| HEV-4b | [67] | Patients with acute febrile jaundice | Cameroon |
Table 3. Distribution of hepatitis E virus genotypes reported in West Africa.
| Genotype | Studies | Source | Country |
|---|---|---|---|
| HEV-1 | [59] | Suspected HEV cases | Niger |
| HEV-1e | [65] | Suspected HEV cases | Cameroon |
| HEV-2b | [42, 59, 95] | Suspected HEV cases; patients with viral hemorrhagic fever | Niger, Senegal, Burkina Faso |
| Avian HEV-2 | [85] | Poultry | Nigeria |
| HEV-3 | [45, 70, 92, 106, 107, 111, 116] | Pigs | Nigeria, Burkina Faso, Ghana, Cameroon, Sierra Leone, Senegal |
| HEV-3a | [71] | Sewage | Cameroon |
| HEV-3c | [9] | Pigs | Guinea |
| HEV-3e | [62] | Pregnant women | Cameroon |
| HEV-3f | [66, 67] | Patients with acute febrile jaundice; suspected HEV cases | Cameroon |
| HEV-4b | [67] | Patients with acute febrile jaundice | Cameroon |
Phylogenetic analysis
Reference sequences representing different HEV genotypes were retrieved from GenBank (S4 File). Sequence alignment was performed using the MUSCLE algorithm implemented in MEGA version 12 (https://www.megasoftware.net). Phylogenetic trees were constructed using the neighbor-joining method with the p-distance model and 1,000 bootstrap replicates. Time-scaled phylogenetic analysis was performed using the RelTime method (Figure 9).
Assessment of publication bias
Visual inspection of the funnel plot suggested asymmetry (Figure 10). This finding was statistically supported by Egger's regression test (p < 0.001) and Begg's rank correlation test (p = 0.002), indicating publication bias.
However, the trim-and-fill method did not suggest that studies had been omitted to correct funnel plot asymmetry (S5 File). Considerable heterogeneity was observed among studies (I² = 97.5%). This heterogeneity may be partly explained by differences in sample size, population characteristics, and diagnostic assays, which may vary in sensitivity, specificity, and predictive value.
Figure 9. Time-scaled phylogenetic tree of hepatitis E virus sequences detected in humans, animals, and environmental samples in West Africa. Reference sequences retrieved from GenBank were aligned using the MUSCLE algorithm implemented in Molecular Evolutionary Genetics Analysis version 12. The phylogenetic tree was constructed using the neighbor-joining method with the p-distance model and 1,000 bootstrap replicates. Branch lengths represent relative evolutionary times.
Figure 10. Funnel plot of the standard error against the logit event rate used to assess publication bias among studies included in the meta-analysis. Each circle represents an individual study, whereas the central vertical line and the diagonal boundaries represent the pooled estimate and its pseudo-95 % confidence limits, respectively.
DISCUSSION
Overall burden of HEV infection in West Africa
This study represents the first dedicated systematic review and meta-analysis of HEV in West Africa, integrating data from more than 36,000 human samples, along with animal and environmental evidence, up to 2025. By adopting a One Health approach, this work addresses an important gap left by previous continental reviews, which either underrepresented West African countries or focused on a single compartment. The study provides a comprehensive assessment of IgG, IgM, total anti-HEV antibodies, and HEV RNA across a wide range of populations in 11 of the 16 West African countries and one in Cameroon, a Central African country, for which data were available.
Although Cameroon is administratively part of Central Africa, its inclusion is justified by ecological and epidemiological similarities with neighboring West African countries. Moreover, sensitivity analyses excluding Cameroon did not substantially alter pooled prevalence estimates (data not shown), supporting the robustness of the findings.
The number of available studies varied considerably among countries. Nigeria contributed the largest number of studies (n = 43), whereas Benin had limited information. No eligible studies were identified from Cape Verde, Guinea-Bissau, Liberia, Mali, or Mauritania, highlighting important regional knowledge gaps.
The pooled prevalences of IgG, IgM, and total anti-HEV antibodies were 15.7%, 4.6%, and 14.1%, respectively, indicating widespread exposure to HEV in the region. Although the pooled IgG prevalence was lower than the previously reported overall African estimate of 21.76% [117], marked heterogeneity was observed across countries and population groups. In particular, individuals exposed to animals exhibited a substantially higher seroprevalence rate (33.1%) than that previously reported in broader reviews.
HEV infection in the general population
Rapid population growth in West Africa presents both opportunities and major public health challenges. Infectious diseases, including malaria, HIV, and viral hepatitis, continue to impose a significant burden on the region. In the present study, IgG and total anti-HEV antibody prevalences of 15.0% and 17.0%, respectively, among the general population suggest that HEV infection is endemic throughout the subregion.
Population displacement driven by armed conflict, insecurity, and socioeconomic instability may contribute to the dissemination of viruses. During the 2017 outbreak around Lake Chad, interconnected regions of Niger, Nigeria, Chad, and Cameroon experienced extensive HEV transmission facilitated by trade routes, population movements, livestock theft, and conflict-related humanitarian crises. Inadequate responses were partly attributed to limited research on HEV in emergency settings [118].
The burden of disease, together with the relative neglect of HEV by public health and research communities and the limited therapeutic options available, fulfills the criteria proposed by the World Health Organization Strategic and Technical Advisory Group for recognition as a neglected tropical disease [68, 97]. Therefore, greater investment in surveillance, research, and awareness programs is warranted. In addition, evaluating the feasibility of deploying the HEV 239 vaccine, which has recently been used during outbreaks in Sudan, may be an important preventive strategy in West Africa.
High-risk populations
Recent studies from Cameroon and Nigeria indicate that individuals with chronic liver diseases, HIV infection, or frequent occupational exposure to animals constitute high-risk populations for HEV infection [62, 115]. In the present study, these groups exhibited IgG and IgM prevalences of 25.9% and 4.6%, respectively.
Immunocompromised individuals are particularly vulnerable to chronic and severe forms of HEV infection [119–121]. HIV infection may increase susceptibility to zoonotic HEV infection and facilitate viral persistence [122].
Livestock production and pork processing have become increasingly important economic activities throughout West Africa. Among individuals with regular contact with animals, the prevalences of IgG and total anti-HEV antibodies were 33.1% and 20.4%, respectively. Notably, the observed IgG prevalence exceeded the global estimate of 24.04% reported among veterinarians, slaughterhouse workers, and pig industry personnel [117].
The IgM prevalence of 7.6% further indicates ongoing transmission among these occupational groups. Poor hygiene conditions, insufficient biosafety measures, and prolonged exposure to infected animals likely contribute to these high rates [123]. By contrast, studies conducted in Europe have generally reported lower prevalence [124], probably reflecting more industrialized slaughter practices and stricter biosecurity measures.
Significant variations were observed both between countries and within countries. Such differences cannot be explained solely by cultural or dietary habits and suggest that additional environmental and ecological determinants may influence HEV transmission
Low-risk populations
Low-risk populations, including the general population, blood donors, pregnant women, and hospitalized patients, exhibited lower seroprevalence rates, suggesting lower levels of exposure.
Among blood donors, the prevalences of IgG and total anti-HEV antibodies were 10.3% and 5.6%, respectively. These values were lower than those reported in North America and Europe, where seroprevalence rates ranged from 13% to 19% [125]. Geographic differences may reflect variations in socioeconomic conditions, climate, and cultural practices [126, 127].
The prevalence of IgM among blood donors was 4.5%, indicating a potential risk of transfusion-transmitted HEV infection. Similar values have been reported in western India [128]. Since blood donor screening programs in West Africa generally focus on HIV and hepatitis B and C viruses, asymptomatic HEV infections may remain undetected. Furthermore, the detection of HEV RNA in blood products in Cameroon [62] underscores the importance of implementing nucleic acid testing strategies, such as minipool testing, to reduce transfusion-associated transmission [118].
Pregnant women constitute another vulnerable population because physiological immunosuppression during pregnancy increases susceptibility to severe disease. In the present study, IgG and total anti-HEV antibody prevalences among pregnant women were 11.3% and 8.0%, respectively, whereas IgM prevalence was 2.8%, indicating active infection and the possibility of vertical transmission.
The observed prevalence differed considerably from those reported in Ethiopia (31.6%) [129], Sudan (61.29%) [130], and India (5.22% IgM prevalence) [131]. Such discrepancies may reflect differences in endemicity, hygiene conditions, and exposure to risk factors. HEV infection during pregnancy has been associated with maternal mortality, fetal loss, stillbirth, and preterm delivery [62]. Therefore, integrating HEV testing into antenatal care programs in endemic settings may facilitate early diagnosis and improve maternal and neonatal outcomes.
Acute and chronic hepatitis E
The role of HEV in chronic hepatitis is well established [132]. In the present study, HEV RNA prevalence generally remained below 11%, suggesting the presence of a largely undetected epidemic. Because relatively few studies investigated HEV RNA, the true burden is likely underestimated.
High IgM prevalence and RNA positivity indicate active viral circulation. Inadequate sanitation and limited access to safe drinking water continue to favor fecal-oral transmission throughout West Africa. Integration of HEV surveillance into existing measles and poliomyelitis surveillance systems could provide an effective and cost-efficient approach to outbreak detection and response [11].
Environmental conditions, inappropriate agricultural practices, and poor waste management further contribute to viral dissemination.
Animal reservoirs of HEV
Numerous studies worldwide have demonstrated the presence of HEV markers in diverse animal species [9, 10, 133–136]. Consistent with previous investigations [36, 137], pigs were the principal reservoir in the present study.
Other species, including poultry, camels, cattle, hares, monkeys, rabbits, rats, and sheep, may also contribute to viral maintenance and transmission. Similar observations have been reported in domestic animals such as dogs, cats, rabbits, and horses [134, 138, 139]. Recently documented rat-to-human transmission in Hong Kong further reinforces concerns regarding emerging reservoirs [140].
The serological evidence observed in cattle raises concerns about potential milk contamination, especially in populations that consume unpasteurized products [141]. Because these animals are essential sources of food and income in West Africa, their inclusion in surveillance programs is crucial [1, 142].
Environmental contamination
The detection of HEV on vegetables, with a prevalence of 24.4%, highlights an underrecognized food safety concern. The use of untreated manure and wastewater for irrigation likely contributes to contamination.
HEV RNA was also detected in wastewater, with an overall prevalence of 6.6%. Although lower than values reported in Europe [143, 144], these findings should not be underestimated. A limited number of studies, small sample sizes, and methodological differences may partly explain the lower prevalence estimates.
Given the widespread discharge of slaughterhouse effluents, livestock waste, and untreated sewage into the environment, strengthening wastewater treatment and environmental monitoring programs is essential for reducing transmission.
Genotype diversity and molecular epidemiology
HEV exhibits remarkable genetic diversity. In the present study, genotypes HEV-1, HEV-2, HEV-3, and HEV-4 were identified in humans. Their coexistence suggests multiple transmission routes and outbreak clusters associated with distinct risk factors [145–147].
The detection of HEV-3 in pigs and avian HEV genotype 2 in poultry suggests that domestic animals contribute to viral maintenance and environmental dissemination. These observations are consistent with reports from Europe, Asia, and the Americas [145, 148, 149].
Importantly, the detection of the rare HEV-4b subtype and multiple HEV-3 subtypes in humans, animals, and environmental samples expands previous knowledge and provides evidence of ongoing zoonotic spillover in West Africa. Although molecular data remain limited, these findings underscore the need for expanded genotype surveillance and phylogenetic investigations.
The diversity observed in West Africa parallels findings from East Asia and the Middle East, where HEV-3, HEV-4, HEV-5, HEV-6, HEV-7, and HEV-8 have been identified in wild boar and camel populations [150–153]. Additional molecular studies are required to clarify transmission dynamics and assess the burden of animal-associated infections.
Limitations of the study
Publication bias was identified by both Begg's rank correlation test (tau = −0.129; p = 0.004) and Egger's regression test (intercept = −5.26; p < 0.001). The apparent discrepancy between Egger's method and the trim-and-fill procedure is likely attributable to the extremely high heterogeneity observed among studies.
Although subgroup analyses slightly reduced heterogeneity, the principal determinants remain unclear. Consequently, pooled estimates should be interpreted cautiously. Furthermore, limited information regarding risk factors prevented detailed analyses.
Finally, despite providing the most comprehensive synthesis currently available for West Africa, the absence of data from several countries emphasizes persistent knowledge gaps that require future investigation.
Public health implications
By adopting a One Health framework rarely used in previous HEV reviews, this study highlights the importance of integrated surveillance encompassing human populations, animal reservoirs, and environmental matrixes. Strengthening biosecurity in pig farming, implementing wastewater monitoring programs, expanding blood donor screening, and evaluating the feasibility of HEV 239 vaccination in high-risk settings could substantially reduce the burden of HEV infection in West Africa.
The findings provide updated evidence for researchers, clinicians, and policymakers and support the development of coordinated strategies to control zoonotic HEV transmission throughout the region.
CONCLUSION
This systematic review and meta-analysis provide the first comprehensive One Health synthesis of HEV epidemiology in West Africa, integrating evidence from human populations, animal reservoirs, and environmental matrixes. Analysis of more than 36,000 human samples from 11 West African countries and Cameroon revealed pooled prevalences of 15.7%, 4.6%, and 14.1% for IgG, IgM, and total anti-HEV antibodies, respectively, indicating substantial exposure to HEV in the region. Individuals with occupational contact with animals exhibited the highest IgG seroprevalence (33.1%), emphasizing the importance of zoonotic transmission. Pigs were identified as the principal animal reservoir, whereas the detection of HEV RNA in vegetables and wastewater confirmed environmental contamination and highlighted additional transmission pathways. Moreover, the identification of HEV-1, HEV-2, HEV-3, and HEV-4, including the rare HEV-4b subtype, demonstrated considerable genotype diversity and provided evidence of ongoing interspecies transmission.
These findings have important public health implications. Strengthening integrated surveillance systems that involve humans, livestock, and environmental sources; improving sanitation and wastewater management; reinforcing biosecurity practices in animal production systems; and considering HEV screening among blood donors and other high-risk populations could substantially reduce the burden of infection. Furthermore, evaluation of the feasibility of implementing the HEV 239 vaccine in endemic settings may represent an additional preventive strategy.
A major strength of this study is its comprehensive One Health approach, which integrates human, animal, and environmental data and incorporates the most recent evidence available through 2025.
Future studies should prioritize underrepresented countries, investigate environmental and behavioral determinants of infection, expand molecular surveillance and phylogenetic analyses, and evaluate the effectiveness of integrated control strategies. Longitudinal studies involving humans, animals, and environmental matrixes are also needed to better understand transmission dynamics.
Overall, HEV remains an underrecognized but important zoonotic and foodborne pathogen in West Africa. Adoption of a coordinated One Health strategy will be essential to improving surveillance, strengthening prevention measures, and reducing the burden of HEV infection in the region.
DATA AVAILABILITY
The supplementary data can be made available from the corresponding author upon request.
GENERATIVE AI DECLARATION
The authors declare that no generative artificial intelligence (AI) or AI-assisted technologies were used in the writing, analysis, or preparation of this manuscript.
AUTHORS’ CONTRIBUTIONS
KVS, KAT, and NB: Conceptualized the study. KVS and KAT: Study design and data acquisition. KVS, KAT, BD, JBO, ET, BLO, PR, and NB: Analyzed and interpreted the data. KVS, KAT, and BD: Drafted the manuscript. JBO, ET, BLO, PR, and NB: Intellectual content of the manuscript. All authors read and approved the final manuscript.
COMPETING INTERESTS
The authors declare that they have no competing interests.
PUBLISHER’S NOTE
Veterinary World (Publisher of International Journal of One Health) remains neutral with regard to jurisdictional claims in the published institutional affiliations.
ACKNOWLEDGMENTS
We would like to thank the Togolese government's scholarship and internship department for funding this study. We would also like to thank Bakary Doukouré for producing the map of West Africa.
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