Skip to main content
Download PDF
  • Systematic Review
  • Open access
  • Published:

Epidemiology of arboviruses in humans and livestock in Ethiopia: a systematic review and meta-analysis

BMC Infectious Diseases volume 25, Article number: 458 (2025) Cite this article

Abstract

Background

Arbovirus infections are a global public health threat, accounting for approximately 73% of the total emerging and re-emerging human infections, where the burden is worsened in sub-Saharan Africa, including Ethiopia. However, the surveillance system has been still challenged, and their burden and magnitude are not well estimated due to underestimates of true arbovirus burdens by passive case detections. To support targeted evidence-based public health decision-making, comprehensive evidence of arbovirus prevalence is crucial. Thus, the aim of this study was to assess the prevalence of arboviruses in humans and livestock in Ethiopia.

Method

Articles were extensively searched in bibliographic databases and gray literatures using entry terms or phrases. PRISMA 2020 flow diagram was used and data among studies meeting eligibility criteria extracted in MS Excel sheet and exported into STATA-17 software for analysis. A random-effects model was used to compute the pooled magnitude of arboviruses in humans and livestock. The heterogeneity was quantified using the I2 value. Publication bias was assessed using a funnel plot and Egger’s test. Sensitivity analysis, subgroup analysis and meta-regression were performed to explore heterogeneity.

Result

Of the 1957 studies identified, 39 human and 6 livestock studies were eligible for meta-analysis. The overall pooled sero-epidemiology of arboviruses in humans using anti-IgG and anti-IgM was 15.43% (95% CI: 12.11-18.76) and 10.04% (95% CI: 6.46-13.62), respectively. The molecular prevalence of arboviruses in humans was 38.42% (95% CI: 21.77-55.08). The pooled prevalence of arboviruses in livestock was 15.77% (95% CI: 0.45, 31.08). Dengue virus, Yellow fever virus, Zika virus, Rift valley fever, West Nile virus, and chikungunya virus in humans and Rift valley fever, West Nile virus, and Schmallenberg virus in livestock were reported.

Conclusion

The magnitude of arboviruses in humans and livestock in Ethiopia alarms the need for immediate multi-sectoral interventions such as strengthening laboratory diagnostic capacities, undertaking an integrated regular national surveillance, and implementation of one-health initiatives and a planetary health approach.

Peer Review reports

Background

Globally, arbovirus infect millions of individuals and pose a significant emerging and re-emerging public health threats. These viruses account for approximately 73% of all newly identified human infections [1]. Arboviral diseases affect over half of the world's population, resulting hundreds of millions of cases annually. For instance, the dengue virus alone is responsible for more than 390 million infections each year [2], while yellow fever caused 84,000–170,000 severe cases and 29,000–60,000 deaths in Africa in 2013 [1]. The global economic burden is estimated at $8–9 billion per year, encompassing costs related to illness and vector control. This burden is expected to increase as climate change affects transmission dynamics [3]. The cumulative reported cost of arboviral diseases was estimated to be $87.3 billion from 1975 to 2020 [4] and the costs of vector control programs was ranged from 5.62 to 73.5 million USD [5].

The predominant arboviruses co-circulating in the African region includes Yellow fever virus (YFV), dengue virus (DENV), Chikungunya virus (CHIKV), Rift Valley fever (RVF), West Nile virus (WNV), and Zika virus (ZIKV) [6]. In sub-Saharan Africa (SSA), these arboviral diseases impose enormous health and economic burdens, leading to increased mortality, morbidity, and disability in the region [7, 8]. Thus, there is an urgent need to strengthen epidemiological characterization and enhance control capabilities [7, 8].

Global trends such as climate change, population growth, and human activities like increased travel and trade connectivity, are facilitating the geographic expansion and evolution of arboviruses, altering vector life cycles and viral transmission dynamics, which heightens the risk of disease outbreaks [1, 9, 10]. On the contrary, surveillance systems continue to face significant challenges due to different factors. Passive case detection often underestimates the true arbovirus burdens due to the non-specific clinical presentations of DENV, CHIKV, and ZIKV [2]. Additionally, limited diagnostic capacities in resource limited countries hinder effective epidemiological analyses and response monitoring [11]. The lack of viability for confirmatory testing of surveillance samples [12] as well as integrated vector surveillance, is rarely sustained, yet critical for outbreak preparedness.

Moreover, these infections are associated with loss of productivity and increased healthcare costs as a result of prolonged illness and disability from infections. Emerging outbreaks might occur due to the invasion of naïve populations by Chikungunya's, which leads to escalating demand for services [13]. The problem is also certainly higher in rural communities where farming and livestock are carried out. In forested areas, the risk of yellow fever is higher, particularly in immunologically naive travelers and workers. However, knowledge of circulating virus strains involved in transmission remains limited in high-risk areas of the African region.

Despite the substantial burden of arboviral diseases in SSA, critical gaps remain in generating comprehensive evidence necessary for targeted public health decision-making. Although some initiatives have improved certain research aspects, the lack of integrated, multi-disciplinary investigations hampers our understanding of the dynamic relationships driving local transmission [14]. Furthermore, up-dated and comprehensive evidence is needed for outbreak preparedness; however, comprehensive data of cocirculating strains of arboviruses in Ethiopia is lacking. This review aims to address this gap by enhancing our understanding of arbovirus epidemiology in Ethiopia, providing a foundation for evidence-based decision-making in integrated disease prevention and control from human and animal health dimensions. Therefore, the aim of this systematic review and meta-analysis (SRMA) is to determine the overall pooled magnitude of arboviruses among humans and livestock in Ethiopia.

Methods

Protocol registration and guidelines

This SRMA was developed and reported according to the Preferred Reporting Items for Systematic Reviews and Meta-Analyses Guidelines (PRISMA) [15] as indicated in Supplementary File 1. The protocol for this review was originally registered in the International Prospective Register of Systematic Reviews (PROSPERO) database with registration identification number CRD42024561271, available from: https://www.crd.york.ac.uk/prospero/display_record.php?ID=CRD42024561271.

Data source, search strategy and selection of studies

A systematic and comprehensive search of the existing literature was carried out using multiple electronic databases, including PubMed/Medline, Epistimonikos, and Embase. Additionally, grey literature such as Wiley Online Library, Science Direct, Research Gate, African Journal Online (AJOL), Google Scholar, and Web of Sciences were used to retrieve potentially eligible studies reporting the prevalence of the common arboviruses (Dengue virus, Yellow fever virus, West Nile virus, Zika virus, Chikungunya virus, Rift valley fever, and Schmallenberg virus) among humans and livestock in Ethiopia. In order to include additional relevant studies omitted during electronic database searches, a snowball search was conducted using the bibliographies of the identified studies. Generally, the search was conducted from April 20, 2024, to May 20, 2024.

An extensive comprehensive search strategy was employed using the condition, context, population, and outcome of interest (CoCoPop) framework to formulate research questions [16]. The research question was formulated as “What is the prevalence of arboviral infections in humans and livestock’s’ of Ethiopia?”. Moreover, the CoCoPop framework was condition (arboviral infections), context (Ethiopia), and population (humans and livestock’s). To access all eligible studies, Medical Subject Headings (MeSH) terms and combination key words including “prevalence”, “outbreak”, “sero-prevalence”, “Sero-positivity”, “detection”, “epidemiology”, “burden”, “magnitude”, “Arbovirus”, “Dengue virus”, “Zika virus”, “West Nile Virus”, Yellow fever virus”, “Chikungunya virus”, “Chikungunya fever”, “Schmallenberg virus”, “YFV”, “DENV”, “RVF”, “ZIKV”, “CHIKV”, “SBV”, and “Ethiopia” were employed. Boolean operators such as “OR” and “AND” were used as necessary in the advanced search databases. Furthermore, the bibliographies of included studies were reviewed for additional articles, and authors were contacted to obtain any missing papers. Duplicates were removed after the search results were organized using Endnote 20 software (Clarivate Analytics USA). Three independent reviewers (AG, MAB, and EA) identified the articles from databases and other sources and removed duplicates. Subsequently, two independent reviewers (HD, LW) screened the titles and abstracts of all retrieved studies, cross-checked by a third reviewer (MT), followed by assessment of the full texts of potentially eligible studies against inclusion criteria by two reviewers (AS and MT), verified by a third reviewer (HE), and added to the extraction collection. Disagreements during each screening stage were resolved through consensus between the two reviewers and/or intervention of a third reviewer (MT).

Eligibility criteria

All articles published in peer-reviewed journals or grey literature in English language and reporting the prevalence of arbovirus infection and/or its genetic variants were included in this SRMA. Moreover, all laboratory-based observational studies, including cross-sectional studies, case-control, and cohort studies using laboratory detection methods such as polymerase chain reaction (PCR) and enzyme-linked immunosorbent assays (ELISA) for detecting arboviruses such as RNA, IgG, IgM, Non-structural protein-1 and plaque reduction neutralization tests (PRNT) from clinical specimens and livestock in Ethiopia until April 30, 2024, were included. However, reviews, qualitative studies, author replies, letters to the editor, case reports and commentaries were excluded from this SRMA.

Risk of bias assessment

The quality of studies included in this SRMA was evaluated by three authors (ZM, MAB and BK) using the Joanna Briggs Institute (JBI) quality appraisal tool designed for prevalence studies [17]. The critical appraisal checklist was used to evaluate the methodological quality of studies included in the SRMA. Finally, studies with an average quality score of 50% or more were considered to be high quality (low risk of bias) and consequently included in the analysis. The evaluation involved screening studies at the title, abstract, and full text levels. A supplementary file provides further details on the critical appraisal process and scoring (Supplementary File 2).

Outcome measurement

The primary outcome variable of this SRMA was the pooled prevalence of arboviral infections in humans and livestock in Ethiopia.

Data extraction, synthesis and statistical analysis

The data extraction of eligible studies was performed by four reviewers (SS, BE, MMT, and NKM) using a Microsoft Excel worksheet. The extracted data includes authors names, publication year, region, study area, setting, study design, approach, population type, laboratory diagnosis method, diagnostic marker, sample size, number and type of arboviruses. The three reviewers meticulously reviewed and verified their extraction results, resolving any discrepancies through discussions and cross-verification of data (Supplementary File 3).

The meta-analysis was performed using STATA version 17.0 software (Stata Corp., College Station, TX). The point estimate and 95% confidence interval of the pooled prevalence of arbovirus was computed. A random-effects model of analysis was used to estimate the pooled prevalence of arboviruses [18]. Cochrane’s Q test and I2 statistics were used to assess heterogeneity between studies [19], in which an I2 of more than 75% was considered substantial heterogeneity. A p value of >0.05 was used to declare that the effect was homogeneous. Therefore, to adjust the observed variability, a random effect model was used. The presence of heterogeneity was further analyzed for its source using subgroup analysis, which was computed based on region, study design, approach, type of arbovirus, setting, population type, laboratory diagnostic method, and diagnostic markers. Likewise, meta-regression was also conducted to identify the possible source of heterogeneity. Finally, a sensitivity analysis was performed to evaluate the impact of individual studies on the overall pooled estimate using a leave-one-out analysis to examine the influence of a single study on the overall estimate of arboviruses. Furthermore, publication bias was assessed using visual inspection of the symmetry of the funnel plot and Egger’s test statistics [20,21,22].

Result

Literature search and eligible studies

During the initial electronic search and manual search of reference lists, a total of 1957 articles were retrieved. Approximately 541 articles were excluded because of duplication, 1346 studies were excluded by reviewing their title, abstract and body, and 22 articles were excluded because they were not related to the objective or outcome of this study. Finally, after methodological quality assessment, 52 human studies and 6 livestock studies were included in this systematic review, however, seven studies were excluded in the quantitative synthesis or meta-analysis due to their sample size being lower than 30. Thus, 41 and 6 studies among humans and livestock were included in the final analysis, respectively (Figure 1).

Fig. 1
figure 1

Flow diagram for the selection process of eligible studies

Full size image

Study characteristics

The studies were conducted from 2014 to 2023 in nine national regional states and the two city administrations of Ethiopia. Considering the districts where the studies were conducted in SNNRP (Arba Minch Zuria, South Omo Zone, Mareka district) and the Somalia region (Kabradihar, Dollo, Dollo ado, Adadle, Godey) (Figure 2, Table 1). Only six human studies were case-control, while all animal studies were cross-sectional, and all are published in English. PCR, ELISA, IFT, and PRNT were employed for the detection of arboviruses targeting the RNA of the virus, its antigen, and antibodies (IgG and IgM), however, ELISA was found to be the commonly used method. The maximum sample size from the included studies was 41,162. In all the studies included in the systematic review, a total of 56,057 humans and 3639 livestock were involved. Of the included participants, 2,584 humans were positive by either of PCR, ELISA, IFT, NS1, PRNT, anti-IgG, and anti-IgM, and 980 livestock were positive by anti-IgG antibodies of arboviruses. In human studies, six arboviruses (DENV, YFV, ZIKV, RVF, WNV, and CHIKV) from the three families (Flaviviridae, Bunyaviridae, and Togaviridae) and three genera (Flavivirus, Phlebovirus, and Alphavirus) were reported, while among livestock studies, three arboviruses (RVF, WNV, and SBV) from two families (Bunyaviridae and Flaviviridae) and three genera (Flavivirus, Phlebovirus, and Orthobunyavirus) were reported. The overall risk of bias assessment score of included studies was 82.44% (Table 1).

Fig. 2
figure 2

Map showing Ethiopian regions for which data were included in the systematic review and meta-analysis created using https://www.mapchart.net/world-subdivisions.html. The depth of different color shading indicates the number of studies performed in each region of SNNPR the country.

Full size image
Table 1 Characteristics of included studies for assessing the epidemiology of Arboviruses among humans and livestock in Ethiopia
Full size table

Epidemiology of Arboviruses in Ethiopia

After the qualitative synthesis, studies with fewer than 30 participants (eight studies) were excluded from quantitative analysis (meta-analysis). Regardless of the diagnostic markers, the overall pooled prevalence of arboviruses was 11.96% (95% CI: 10.82%, 13.09%), and 15.77% (95% CI: 0.45, 31.08%), respectively in human (Figure 3) and livestock (Figs 4, 5). The existence of heterogeneity between studies was assessed by visual (subjective) techniques using the Galbraith plot to check whether all the points lie within the 95% confidence bounds and the Forest plot to check whether the confidence intervals of studies (the summary effects) overlap with each other. To avoid the subjective nature of visual techniques, the I2 test was used. Consequently, there was substantial heterogeneity between the included studies in human and livestock studies with an I2 value of 98.65% and 99.59%, respectively. Therefore, we have used certain strategies to address heterogeneity, such as carrying-out the meta-analysis using the random effect model, followed by exploring the sources of heterogeneity using sensitivity analysis, subgroup analysis, and meta regression. Thus, Galbraith plot revealed the presence of studies that were outside of the 95% confidence interval in human studies and livestock studies. However, in the leave one-out meta-analysis (sensitivity) analysis, none of the omitted analyses were lies outside the confidence interval for combined analysis. As a result, the result was analyzed considering stratification variables through setting, diagnostic methods and markers, approaches used and the type of viruses reported as reported in the following sections.

Fig. 3
figure 3

Forest plot showing pooled prevalence of arboviruses among humans in Ethiopia

Full size image
Fig. 4
figure 4

Forest plot showing pooled prevalence of arboviruses IgM seroprevalence among livestock in Ethiopia

Full size image
Fig. 5
figure 5

Forest plot showing pooled prevalence of arboviruses IgM seroprevalence among livestock in Ethiopia

Full size image

Arboviruses epidemiology based on diagnostic markers and methodology among humans

A total of 46 prevalence studies for different arboviruses using different diagnostic methods were identified in humans, of which 10 estimated seroprevalence prospectively from 2018 to 2022. The studies covered 6 of the regional states of Ethiopia with an estimated seroprevalence of 2.7% to 22.53%. Furthermore, the overall sero-prevalence of IgM in humans was 10.02% (95% CI: 6.44%, 13.61%), with a significant level of heterogeneity (I2 = 95.72%). Among eighteen [18] studies that were reported arboviruses in humans using IgG detection, 11 were conducted at the community level and all of the 18 studies were prospective studies published from 2018 to 2021 with an estimated seroprevalence of 3.47% to 49.52%. The overall sero-prevalence of IgG in humans was 15.43% (95% CI: 12.11%, 18.75%), with a significant level of heterogeneity (I2 = 98.02%). Additionally, seven studies with 232 cases were reported arboviruses in two regional states (Afar and Somalia) and one city administration (Dire Dawa) of Ethiopia. The estimated prevalence was reported from 12.3% in 2020 to 67.74% in 2019. The overall pooled prevalence of arboviral RNA was 36.82% (95% CI: 14.82%,58.81%) with a similar substantial heterogeneity of serological markers (I2 = 97.73%). Furthermore, two studies estimated arboviruses using NS1 detection in 2022 and 2023 with an overall pooled prevalence of arboviruses in humans using NS1 detection was 21.45% (95% CI: −16.75%,59.63%). One the other hand, a single study was performed using PRNT and reported four different arboviruses from the genus Flaviviruses (i.e., DENV, ZIKV, YFV and WNV (Table 2, Figure 6).

Table 2 Pooled prevalence of arboviruses among humans in Ethiopia using different diagnostic markers
Full size table

Considering the diagnostic methods used for the detection of arboviruses, the predominancy was observed in molecular methods (36.82%) followed by ELISA (17.09%) and IFT (10.44%). Almost all human studies were cross-sectional studies employed a prospective approach however, their pooled effect was not statistically varied. Furthermore, there was a significant difference (P <0.001) in the epidemiology of arbovirus between studies conducted at the clinical settings (17.91%) and the community level (8.49%) (Figure 6). However, all of livestock studies were a prospective and cross-sectional studies conducted at the community level (Table 1).

Geographic distribution and trends of arboviruses among humans and livestock in Ethiopia

Arboviruses among humans were reported in multiple regions of Ethiopia particularly in hottest areas and boundaries of neighboring countries such as Metema and Humera, Dire Dawa, Somalia and Gambella (Figure 7). According to the cumulative meta-analysis, there was a variability of prevalence of arboviruses ranged from 0.04% to 57.9%. It showed that the arbovirus prevalence can vary considerably across different populations, geographical regions, and study settings (Figure 8). The former studies (i.e., studies of 2014 and 2016 were single) but showing the peak level of arbovirus. Likewise, the peak level of arbovirus in humans pooled from multiple studies was reported in 2022(27.68%), 2019 (23.72%), and 2023 (21.71%) (Figure 9).

Fig. 6
figure 6

Forest plot showing subgroup analysis of arboviruses by study design, approach, diagnostic method and markers among humans in Ethiopia

Full size image
Fig. 7
figure 7

Forest plot showing distribution of arboviruses among humans in Ethiopia by region and districts

Full size image
Fig. 8
figure 8

Forest plot showing cumulative meta-analysis of arboviruses among humans in Ethiopia

Full size image

Among livestock, arboviruses were reported from camels, goats, sheep, and cattle (Table 1). The subgroup analysis for revealed that majority of studies were conducted at SNNRP and Gambella regional states with a pooled prevalence of 4.91% and 6.4%, respectively. Two families and three genera were reported in livestock studies (Table 3).

Table 3 Subgroup analysis of arboviruses among livestock in Ethiopia
Full size table

Types of arboviruses among humans and livestock in Ethiopia

Regardless of the type of diagnostic methods and diagnostic markers used, the overall pooled prevalence of CHIKV, DENV, RVF, WNV, YFV, and ZIKV was 16.69% (7.0%, 26.39%), 23.41% (19.02%, 27.79%), 13.2% (8.39%, 18.01%), 4.43% (1.78%, 7.07%), 10.48% (6.58%, 14.37%), and 7.18% (2.72%, 11.65%), respectively. Three genus and families of arboviruses were reported in humans, of which, the predominant genus/families were Flaviviruses/Flaviviridae, which was found to be reported in 34 studies with an overall pooled prevalence of 12.62% (11.08%, 14.15%; P<0.001) (Figure 10). However, in livestock studies the most frequently reported arboviruses were the Bunyaviridae family, with a pooled prevalence of 21.1% (−2.09%, 44.3%). Half of the studies were RFV, with a pooled prevalence of 9.26%. Moreover, the three reported viruses among livestock were detected using IgG (Table 3). Furthermore, DENV was predominantly reported among 3 studies from Dire Dawa and Tigray region with a pooled prevalence of 48.67% (95% CI: 21.71%−75.63%) and 26.49% (13.11%−39.87%), respectively, while it was reported with high frequency from a single study from both Somalia and Afar region with a prevalence of 57.89% and 41.56%, respectively. Yellow fever was most common in SNNRP reported from four studies with a pooled prevalence of 18.71%, and CHIKV virus was most frequently reported from SNNRP, Gambella and Somalia respectively. Additionally, WNV was predominant in SNNRP and Oromia with a pooled prevalence of 5.86% and 4.92%, respectively.

Fig. 9
figure 9

Prevalence of arboviruses in humans over time in Ethiopia. The graph is created by chart.js library.

Full size image

Meta-regression analyses

To investigate the impacts of active potential factors in the heterogeneity of prevalence of arboviruses in Ethiopia, a random-effects meta-regression test has been done using the DerSimonian–Laird method. Furthermore, meta-regression analysis was computed with each potential factors and covariates individually with the effect size. Diagnostic markers (R2 = 1.90%), quality of studies (R2 = 64.72%), publication year (R2 = 59.70%), sample size (R2 = 62.11%), approaches used (R2 = 63.48%), setting (R2 = 72.92%), and type of viruses (R2 = 42.66%) were significantly explained (P<0.005) heterogeneity between studies among humans. Furthermore, in livestock studies, heterogeneity was statistically explained by the two covariates (publication year and sample size) and one factor (quality of studies) with an R2 of greater than 89% (Table 4).

Fig. 10
figure 10

Forest plot showing distribution of arboviruses among humans in Ethiopia by type of pathogen and quality of studies

Full size image
Table 4 Meta-regression analysis of potential factors and covariates of Arboviruses among humans and livestock in Ethiopia
Full size table

Publication bias

To check the publication bias, funnel plot analysis was performed. The asymmetry of the funnel plot in the figure below indicates the presence of publication bias (Supplementary file-4). To avoid the subjective nature of visual inspection of the asymmetric distribution of the studies, Egger’s test statistics was performed, and the presence of publication bias was confirmed for both humans and livestock studies with p-value of less than 0.001 and 0.024, respectively. Thus, the non-parametric trim and fill analysis was performed. Four additional articles were imputed in humans yielding the total number of articles to be 45 with a pooled prevalence of 12.72% (95% CI: 10.67%, 14.01%). Similarly, in livestock studies, two articles were imputed and the total number of studies reached 8 with a pooled prevalence of 21.2% (7.72%, 34.68%). Fortunately, the pooled prevalence of the final model (observed and imputed) wasn’t significantly differed from the observed value (Table 5).

Table 5 Nonparametric trim- and -fill analysis of publication bias for assessing arboviruses in humans and livestock in Ethiopia
Full size table

Discussion

Arbovirus infections have continued to be a global burden of climate-sensitive vector borne infectious diseases, particularly in resource-limited countries, including Ethiopia, where sufficient diagnostic facilities aren’t available. The increased prevalence rate of arbovirus infections alarms the need for immediate intervention strategies from the interface of human and animal health. Thus, this systematic review and meta-analysis provides a comprehensive overview of the epidemiology of arboviruses in humans and livestock in Ethiopia. This study provides critical insights into the dynamics of these zoonotic diseases and further highlights the significant burden and widespread distribution of arboviruses in both humans and animals across the different regions of the country. A cross-sectional study design coupled with a prospective methodological approach was the most commonly employed design in the included studies. The geographical distribution spanned multiple regions, indicating that arboviruses pose a nationwide public health threat in Ethiopia, however, the most frequent studies were reported from the SNNRP and Somalia regions. Six different arboviruses (DENV, YFV, ZIKV, RVF, WNV, CHIKV) were reported in humans and three in livestock (RVF, WNV, SBV), representing the diverse viral families and genera.

In this SRMA, the pooled prevalence of arboviral RNA, IgG, and IgM sero-prevalence among humans was 38.42%, 15.43%, and 10.04%, respectively. This indicates that for every six, ten and three suspected individuals, at least one individual can have an arboviral infections based on IgG, IgM and viral RNA detection. Moreover, this finding implies that there is a need for robust and nationwide surveillance and early warning systems; expansion of diagnostic capacities or facilities for early detection of arboviruses across the region; integrated vector control programs tailored to the local ecological contexts; integrated multi-sectoral collaborations working in the same vein as the One Health approach; and climate change consideration. Therefore, the national-level public health policies should focus on evidence-based integrated arbovirus surveillance and control, explore the potential of cross-border transmission dynamics of arboviruses, and international collaboration for integrated control. Moreover, a planetary health approach could be an important approach to control anthropogenic activities such as urbanization, land use change, and deforestation, all of which aggravate the extent of climate change and the circulation of arboviruses [1, 7]. On the other hand, the wide spread distribution of arboviruses suggests a significant economic burden on the healthcare system and the affected individuals. Thus, for an integrated arbovirus prevention and control program, funding is crucial, which knocks the door of national and international organizations.

ELISA and IFT were the most frequently used diagnostic methods for arbovirus detection, while PCR and PRNT weren’t commonly used in Ethiopia. This might be due to the fact that serological tests are easy to use, sensitive, cheap, readily available, and important for understanding population exposures in the past, however, their epidemiological application is limited by the potential for cross-reactivity with antibodies of different arboviruses because of the molecular mimicry of nucleotide homology they share and can be affected by history of vaccination [56,57,58]. Furthermore, nucleic acid-based tests aren’t easily applicable and require sophisticated resources and well-trained expertise unlikely to serological tests. Hence, real-time (quantitative) polymerase chain reaction (qPCR) and plaque reduction neutralization test (PRNT) are required to validate the findings of these serologic assays [59], and confirm outbreaks, but not for routine screening techniques in resource-limited countries like Africa [60].

Subgroup analyses regarding the epidemiological profile of arboviruses were conducted in this study. The most commonly reported arboviruses in humans were DENV, CHIKV, and YFV. The overall pooled IgG seroprevalence of DENV (18.07%) and CHIKV (21.45%) was consistent with the global seroprevalence of Dengue virus (24%) and Chikungunya virus (21.6%) in blood donors [60], however, the present findings of DENV was lower than the pooled seroprevalences of DENV (38%) [61]. This difference might be due to the variations in the time period and number of articles included in the meta-analysis, where it includes 133 articles from 44 countries published from 2000, while the present study articles published from 2014 were included.

The epidemics of YFV in Ethiopia were reported between 1960 and 1962, with an estimate of 100,000 cases and 30,000 deaths [62]. This meta-analysis also documented that the burden of YFV was 14.77% and 6.76%, respectively in IgG and IgM seroprevalence in humans, which was consistent with previously reported 9.4% pooled prevalence of YFV in SSA [63]. This finding revealed that the burden consistently existed across the African region, suggesting ongoing challenges in managing and preventing the disease in the region. Furthermore, considering the cross-border risk of the disease, the finding highlights the urgent need for regional collaboration in the control and prevention of arboviruses in general and YFV in particular.

The overall pooled IgG seroprevalence of ZIKV among humans was 15.37%. The present finding was consistent with the global seroprevalence of ZIKV, 21% in 2024 and 18% in 2021 [61, 64], which further indicates that ZIKV is an existing global burden found in Ethiopia. Moreover, the overall pooled IgG seroprevalence of ZIKV in our study was slightly higher than the seroprevalence of ZIKV, 5.1% [60] and 1.02% [65] among blood donors, which could be varied by their degree of risk exposure differences. Additionally, studies included in this meta-analysis were carried-out among suspected cases, which might contribute to the presence of a slight increment of ZIKV compared to blood donors.

In our meta-analysis, the overall pooled IgG and IgM seroprevalence of WNV in humans was 7.35% and 3.53%, respectively; these pooled values were lower than those met-analysis study reported 76.5% and 7.1% anti-IgG and anti-IgM seroprevalences of WNV in Nigeria [56]. Moreover, the pooled IgG seroprevalence of WNV among livestock in this study (5.08%) was lower than the pooled seroprevalence of WNV (8%) among equids from 16 European countries [66]. This variation might be due to the variations of livestock, where the livestock included in this meta-analysis were only cattle, sheep, goats and camels. This also further suggests the effective surveillance and control measures across the human and animal domains in order to reduce the transmission dynamics of YFV.

RVF (9.26%) was predominantly reported in multiple studies of livestock, but a single report was found in humans. The burden of RVF in livestock’s’ of the present meta-analysis was consistent with the 9.3% report of RVF in animals reported from Africa [67]. The existing burden of RVF might have a socio-economic impact both at national and international levels [68] with direct costs of morbidity and death in both humans and animals, which further leads to an economic crisis. Additionally, animal losses can have knock-on effects altering herd dynamics [69].

Meta-regression analysis was used to explore the sources of heterogeneity. Hence, sample size was found to explain the largest source of heterogeneity in IgG seroprevalence studies both in humans and livestock. Likewise, in IgM and viral RNA prevalence, type of viruses was found the influential factors for explaining the sources of heterogeneity between studies. Furthermore, Publication bias was assessed by using the visual funnel plots and statistical Egger’s test. Both methods confirmed the presence of publication bias. Thus, non-parametric trim-and -fill analysis was performed but there was no statistical difference between the observed value and final trim- and -fill analysis (observed and imputed) of pooled prevalence of arboviruses in humans and livestock.

Future studies shall focus on circulating arboviruses coupled with vector distribution and association and vaccination status and effectiveness. This study has certain limitations. Thus, caution must be applied when generalizing the pooled estimates to the target population. Although I2 is not an absolute measure of heterogeneity, high heterogeneity was observed. This variation might not be solely attributed to publication bias; rather, it might be due to variations in the sample size, diagnostic methodology and markers, and variations in types of arboviruses. Therefore, the random effect model was used to limit the influence of the study heterogeneity, and subgroup analyses, sensitivity analyses, and meta-regression were performed to rule out study variability. The meta-regression finding revealed types of diagnostic method, sample size, family/genus, type of viruses and district explained the heterogeneity between studies at different levels.

Conclusion and recommendation

The overall national pooled prevalence of arbovirus infections in humans and livestock in Ethiopia was notable, which alarms the need for immediate multi-sectoral interventions. DENV, CHIKV, and YFV in humans and RVF and WNV in livestock were the most commonly reported arboviruses. The most commonly employed diagnostic methods for arbovirus infections were serological tests (i.e., ELISA and IFT). Therefore, targeted evidence-based public health interventions are essential to mitigate the risk of arboviral outbreaks. This requires implementing national surveillance systems to monitor the long-term impact of these interventions, and fostering a coordinated, multi-sectoral One Health initiative that integrates human, animal, and environmental health to optimize vector control strategies in light of climate change, alongside the national immunization programs. Anthropogenic activities are blamed to be associated with an increased circulation of arboviruses. Therefore, planetary health, which is a holistic approach encompassing multisystem and multilateral strategies, could be crucial. This approach promotes collaboration among personnel and organizations at various level globally, helping to maintain ecological balance and strengthen arboviral prevention and control programs.

Data availability

All the datasets used and/or analyzed during the current study are available in the manuscript.

Abbreviations

CC:

Case Control

CS:

Cross-sectional

CHIKV:

Chikungunya Virus

DENV:

Dengue Fever Virus

ELISA:

Enzyme-Linked Immunosorbent Assay

IFT:

Immune Fluorescent Test

IgG:

Immunoglobulin G

IgM:

Immunoglobulin M

NS1:

Non-structural protein-1

PCR:

Polymerase Chain Reaction

PRISMA:

Preferred Reporting Items for Systematic Review and Meta-analysis

PRNT:

Plaque Reduction Neutralization Test

RVF:

Rift Valley Fever

SBV:

Schmallenberg Virus

SNNPR:

Southern Nations, Nationalities, and People Region

SSA:

Sub-Saharan Africa

WNV:

West Nile Virus

YFV:

Yellow Fever Virus

ZIKV:

Zika Virus

References

  1. Tajudeen YA, Oladipo HJ, Oladunjoye IO, Yusuf RO, Sodiq H, Omotosho AO, et al. Emerging arboviruses of public health concern in Africa: Priorities for future research and control strategies. Challenges. 2022;13(60):1–11.

  2. Bhatt S, Gething PW, Brady OJ, Messina JP, Farlow AW, Moyes CL, et al. The global distribution and burden of dengue. Nature. 2013;496:504–7.

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  3. Shepard DS, Undurraga EA, Halasa YA, Stanaway JD. The global economic burden of dengue: a systematic analysis. Lancet Infect Dis. 2016;16(8):935–41.

    Article  PubMed  Google Scholar 

  4. Roiz D, Pontifes P, Jourdain F, Diagne C, Leroy B, Vaissière A-C, et al. The rising global economic costs of Aedes and Aedes-borne diseases. 2023.

  5. Thompson R, Martin Del Campo J, Constenla D. A review of the economic evidence of Aedes-borne arboviruses and Aedes-borne arboviral disease prevention and control strategies. Expert review of vaccines. 2020;19(2):143–62.

    Article  CAS  PubMed  Google Scholar 

  6. Venter M. Assessing the zoonotic potential of arboviruses of African origin. Curr Opin Virol. 2018;28:74–84.

    Article  PubMed  Google Scholar 

  7. Massengo NRB, Tinto B, Simonin Y. One health approach to arbovirus control in Africa: Interests, challenges, and difficulties. Microorganisms. 2023;11(6):1496.

  8. Marchi S, Trombetta CM, Montomoli E. Emerging and re-emerging arboviral diseases as a global health problem. Public health - emerging and re-emerging issues. InTech; 2018. Available from: http://dx.doi.org/10.5772/intechopen.77382.

  9. Caminade C, Medlock JM, Ducheyne E, McIntyre KM, Leach S, Baylis M, et al. Suitability of European climate for the Asian tiger mosquito Aedes albopictus: recent trends and future scenarios. J R Soc Interface. 2012;9(75):2708–17.

    Article  PubMed  PubMed Central  Google Scholar 

  10. Gibney KB, Jones A, Armien B, Seixas G, Soul B, Bres V, et al. Dengue virus evolution and introduction dynamics in Mozambique. Journal of virology. 2021;95(15):e0042421.

    Google Scholar 

  11. Simmons CP, Farrar JJ, Nguyen vV, B. W. Dengue. . New England Journal of Medicine. 2012;366(9):1423-32.

  12. Brault AC, Powers AM, Ortiz D, Estrada-Franco JG, Navarro-Lopez R, SC. W. Venezuelan encephalitis emergence: enhanced vector infection from a single amino acid substitution in the envelope glycoprotein. . Proc Natl Acad Sci 2007;104(50):4649-54.

  13. Costa LB, Barreto FKdA, Barreto MCA, Santos THPd, Andrade MdMOd, Farias LABG, et al. Epidemiology and economic burden of chikungunya: a systematic literature review. Tropical Medicine and Infectious Disease. 2023;8(6):301.

  14. WHO. Global vector control response 2017–2030. Geneva: World Health Organization. 2017.

  15. 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. Syst Rev. 2021;10(1):89.

    Article  PubMed  PubMed Central  Google Scholar 

  16. Munn Z, Stern C, Aromataris E, Lockwood C, Jordan Z. What kind of systematic review should I conduct? A proposed typology and guidance for systematic reviewers in the medical and health sciences. BMC medical research methodology. 2018;18:1–9.

    Article  Google Scholar 

  17. Munn Z, Moola S, Riitano D, Lisy K. The development of a critical appraisal tool for use in systematic reviews addressing questions of prevalence. Int J Health Policy Manag. 2014;3(3):123–8.

    Article  PubMed  PubMed Central  Google Scholar 

  18. Barendregt JJ, Doi SA, Lee YY, Norman RE, Vos T. Meta-analysis of prevalence. J Epidemiol Community Health. 2013;67(11):974-8.

  19. Higgins JP, Thompson SG. Quantifying heterogeneity in a meta-analysis. Stat Med. 2002;21(11):1539–58.

    Article  PubMed  Google Scholar 

  20. Shi L, Lin L. The trim-and-fill method for publication bias: practical guidelines and recommendations based on a large database of meta-analyses. Medicine (Baltimore). 2019;98(23):e15987.

    Article  PubMed  PubMed Central  Google Scholar 

  21. Duval S, R. T. Trim and fill: a simple funnel‐plot–based method of testing and adjusting for publication bias in meta‐analysis. . Biometrics. 2000;56(2):455-63.

  22. Egger M. Smith GD, AN. P Meta-analysis: principles and procedures BMJ. 1997;315(7121):1533–7.

    CAS  Google Scholar 

  23. Woyessa AB, Mengesha M, Kassa W, Kifle E, Wondabeku M, Girmay A, et al. The first acute febrile illness investigation associated with dengue fever in Ethiopia, 2013: A descriptive analysis. Ethiop J Health Dev. 2014;28(3):155–61.

    Google Scholar 

  24. Ahmed Y, Salah A. Epidemiology of Dengue Fever in Ethiopian Somali Region: Retrospective Health Facility Based Study. Central African Journal of Public Health. 2016;2(2):51-6.

  25. Ferede G, Tiruneh M, Abate E, Wondimeneh Y, Damtie D, Gadisa E, et al. A serologic study of dengue in northwest Ethiopia: Suggesting preventive and control measures. PLoS Negl Trop Dis. 2018;12(5):e0006430.

    Article  PubMed  PubMed Central  Google Scholar 

  26. Geleta EN. Serological evidence of dengue fever and its associated factors in health facilities in the Borena Zone. South Ethiopia Res Rep Trop Med. 2019;10:129–36.

    PubMed  Google Scholar 

  27. Eshetu D, Shimelis T, Nigussie E, Shumie G, Chali W, Yeshitela B, et al. Seropositivity to dengue and associated risk factors among non-malarias acute febrile patients in Arba Minch districts, southern Ethiopia. BMC Infectious Diseases. 2020;20(639):1–6.

    Google Scholar 

  28. Gutu MA, Bekele A, Seid Y, Mohammed Y, Gemechu F, Woyessa AB, et al. Another dengue fever outbreak in Eastern Ethiopia-An emerging public health threat. PLoS Negl Trop Dis. 2021;15(1): e0008992.

    Article  PubMed  PubMed Central  Google Scholar 

  29. Akelew Y, Pareyn M, Lemma, M, Negash M, Bewket G, et al. Aetiologies of acute undifferentiated febrile illness at the emergency ward of the University of Gondar Hospital, Ethiopia. Trop Med Int Health. 2022;27:271-9.

  30. Sisay C, Waldetensai A, Seyoum M, Asrat Y, Tayachew A, Wossen M, et al. Detection of serotype 1-Dengue fever outbreak in Dire Dawa city, Eastern Ethiopia. Ethiop j public health nutr. 2022;5(1):49–54.

    Google Scholar 

  31. Mekuriaw W, Kinde S, Kindu B, Mulualem Y, Hailu G, Gebresilassie A, et al. Epidemiological, entomological, and climatological investigation of the 2019 dengue fever outbreak in Gewane district, Afar Region, North-East Ethiopia. Insects. 2022;13(1066):1–13.

  32. Mesfin Z, Ali A, Abagero A, Asefa Z. Dengue Fever Outbreak Investigation in Werder Town, Dollo Zone, Somali Region. Ethiopia Infect Drug Resist. 2022;15:7207–17.

    Article  PubMed  Google Scholar 

  33. Biru M, Geleta D, Tesfaye N, Aleyu M, A. T. Dengue Fever Outbreak Investigation and Response in Dire Dawa City Administration, Ethiopia,. Journal of Medicine. Physiology and Biophysics. 2017;2020(63):23–7.

    Google Scholar 

  34. Shimelis T, Mulu A, Mengesha M, Alemu A, Mihret A, Tadesse BT, et al. Detection of dengue virus infection in children presenting with fever in Hawassa, southern Ethiopia. Sci Rep. 2023;13(1):7997.

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  35. Mengesha Tsegaye M, Beyene B, Ayele W, Abebe A, Tareke I, Sall A, et al. Sero-prevalence of yellow fever and related Flavi viruses in Ethiopia: a public health perspective. BMC Public Health. 2018;18(1):1011.

    Article  PubMed  PubMed Central  Google Scholar 

  36. Sisay C, Tadesse A, Workie F, Yizengaw A, Ali A, Yusuf J, et al. Description of serotype 3 dengue fever virus: clinical, surveillance and geographical expansion in the Northeastern Ethiopia, 2023. Sci J Public Health. 2023;11(4):123–31.

  37. Degife LH, Worku Y, Belay D, Bekele A, Hailemariam Z. Factors associated with dengue fever outbreak in Dire Dawa administration city, October 2015, Ethiopia - case control study. BMC Public Health. 2019;19(1):650.

    Article  PubMed  PubMed Central  Google Scholar 

  38. Ferede G, Tiruneh M, Abate E, Wondimeneh Y, Tessema B, UG. L. Distribution of Dengue Virus Serotype based on neutralization assay in Northwest Ethiopia Ethiop J Health Biomed Sci. 2023;13(1):13-21.

  39. Nigussie E, Shimelis T, Eshetu D, Shumie G, Chali W, Aseffa A, et al. SEROPOSITIVITY OF YELLOW FEVER VIRUS AMONG ACUTE FEBRILE PATIENTS ATTENDING SELECTED HEALTH FACILITIES IN BORENA DISTRICT, SOUTHERN ETHIOPIA. Ethiop Med J. 2020;58(1):58-62.

  40. Endale A, Michlmayr D, Abegaz WE, Asebe G, Larrick JW, Medhin G, et al. Community-based sero-prevalence of chikungunya and yellow fever in the South Omo Valley of Southern Ethiopia. PLoS Negl Trop Dis. 2020;14(9):e0008549.

    Article  PubMed  PubMed Central  Google Scholar 

  41. Lilay A, Asamene N, Bekele A, Mengesha M, Wendabeku M, Tareke I, et al. Reemergence of yellow fever in Ethiopia after 50 years, 2013: epidemiological and entomological investigations. BMC Infect Dis. 2017;17(1):343.

    Article  PubMed  PubMed Central  Google Scholar 

  42. Eshetu D, T A, TB W. Prevalence of Yellow fever virus infection and associated factors among acute febrile patients in Arbaminch districts, Southern Ethiopia. Preprint. Available from: https://doiorg.publicaciones.saludcastillayleon.es/10.21203/rs.2.23497/v1.

  43. Asebe G, Michlmayr D, Mamo G, Abegaz WE, Endale A, Medhin G, et al. Seroprevalence of Yellow fever, Chikungunya, and Zika virus at a community level in the Gambella Region, South West Ethiopia. PLoS One. 2021;16(7):e0253953.

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  44. Mulchandani R, Massebo F, Bocho F, Jeffries CL, Walker T, Messenger LA. A community-level investigation following a yellow fever virus outbreak in South Omo Zone. South-West Ethiopia PeerJ. 2019;7:e6466.

    PubMed  Google Scholar 

  45. Eshetu D, Kifle T, Agaje BG, Hirigo AT. Seropositivity of West Nile Virus Among Acute Febrile Patients in Southern Ethiopia. Infect Drug Resist. 2020;13:1491–7.

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  46. Nigussie E. Seroprevalence of West Nile Virus and Associated Factors in Borena District, Southern Ethiopia. Archives of Infect Diseases & Therapy. 2021;5(2):55-9.

  47. Ibrahim M, Schelling E, Zinsstag J, Hattendorf J, Andargie E, Tschopp R. Sero-prevalence of brucellosis, Q-fever and Rift Valley fever in humans and livestock in Somali Region, Ethiopia. PLoS Negl Trop Dis. 2021;15(1):e0008100.

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  48. Geleta D, Tesfaye N, Ayigegn H, Waldetensai A, Gemechu F, H. A. Epidemiological Description of Chikungunya Virus Outbreak in Dire Dawa Administrative City, Eastern Ethiopia, 2019. . International Journal of Clinical and Experimental Medical Sciences. 2020;6(3):41-5.

  49. Ferede G, Tiruneh M, Abate E, Wondimeneh Y, Gadisa E, Howe R, et al. Evidence of chikungunya virus infection among febrile patients in northwest Ethiopia. Int J Infect Dis. 2020;104:183–8.

    Article  PubMed  Google Scholar 

  50. Takele DB, Diriba S, Shikur M, Yoseph W, Abyot B, Y. A, et al. Factors Associated with Chikungunya Fever Outbreak in Ethiopia. Epidemiol Sci 2020;10(381):1-3.

  51. Assefa TY, Berhan E, Gemechu F, N. T. Risk Factors of Chikungunya Outbreak in Mareka District, Southern Ethiopia, 2019: Unmatched Case Control Study. . International Journal of Infectious Diseases and Therapy. 2020;5(3):64-9.

  52. Alayu M, Teshome T, Amare H, Kinde S, Belay D, Z A. Risk factors for Chikungunya outbreak in Kebridhar City, Somali Ethiopia, 2019. Unmatched Case-Control Study; 2020. Preprint. Available from: https://doiorg.publicaciones.saludcastillayleon.es/10.1101/2020.01.21.913673.

  53. Asebe G, Mamo G, Michlmayr D, Abegaz WE, Endale A, Medhin G, et al. Seroprevalence of Rift Valley Fever and West Nile Fever in Cattle in Gambella Region, South West Ethiopia. Vet Med (Auckl). 2020;11:119–30.

    PubMed  PubMed Central  Google Scholar 

  54. Endale A, Michlmayr D, Abegaz WE, Geda B, Asebe G, Medhin G, et al. Sero-prevalence of West Nile virus and Rift Valley fever virus infections among cattle under extensive production system in South Omo area, southern Ethiopia. Trop Anim Health Prod. 2021;53(1):92.

    Article  PubMed  Google Scholar 

  55. Sibhat B, Ayelet G, Gebremedhin EZ, Skjerve E, Asmare K. Seroprevalence of Schmallenberg virus in dairy cattle in Ethiopia. Acta Trop. 2018;178:61–7.

    Article  PubMed  Google Scholar 

  56. Abdullahi IN, Emeribe AU, Ghamba PE, Omosigho PO, Bello ZM, Oderinde BS, et al. Distribution pattern and prevalence of West Nile virus infection in Nigeria from 1950 to 2020: a systematic review. Epidemiol Health. 2020;42:e2020071.

    Article  PubMed  PubMed Central  Google Scholar 

  57. Lustig Y, Sofer D, Bucris ED, Mendelson E. Surveillance and Diagnosis of West Nile Virus in the Face of Flavivirus Cross-Reactivity. Front Microbiol. 2018;9:2421.

    Article  PubMed  PubMed Central  Google Scholar 

  58. Okechukwu C, Nasir I, Muhammad K, CN. C. Implications of Flaviviruses cross-reactivity and vaccination programs on their serodiagnosis. N Y Sci J. 2017;10(2):19-22.

  59. Nigussie E, Atlaw D, Negash G, Gezahegn H, Baressa G, Tasew A, et al. A dengue virus infection in Ethiopia: a systematic review and meta-analysis. BMC Infect Dis. 2024;24(1):297.

    Article  PubMed  PubMed Central  Google Scholar 

  60. Gimenez-Richarte A, de Salazar MO, Arbona C, Gimenez-Richarte MP, Collado M, Fernandez PL, et al. Prevalence of Chikungunya, Dengue and Zika viruses in blood donors: a systematic literature review and meta-analysis. Blood Transfus. 2022;20(4):267–80.

    PubMed  PubMed Central  Google Scholar 

  61. Li Z, Wang J, Cheng X, Hu H, Guo C, Huang J, et al. The worldwide seroprevalence of DENV, CHIKV and ZIKV infection: A systematic review and meta-analysis. PLoS Negl Trop Dis. 2021;15(4):e0009337.

    Article  PubMed  PubMed Central  Google Scholar 

  62. Serie C, Andral L, Poirier A, Lindrec A, Neri P. Studies on yellow fever in Ethiopia. 6. Epidemiologic study. . Bull World Health Organ 1968;38:879–84.

  63. Oyono MG, Kenmoe S, Abanda NN, Takuissu GR, Ebogo-Belobo JT, Kenfack-Momo R, et al. Epidemiology of yellow fever virus in humans, arthropods, and non-human primates in sub-Saharan Africa: A systematic review and meta-analysis. PLoS Negl Trop Dis. 2022;16(7):e0010610.

    Article  PubMed  PubMed Central  Google Scholar 

  64. Saba Villarroel PM, Hamel R, Gumpangseth N, Yainoy S, Koomhin P, Misse D, et al. Global seroprevalence of Zika virus in asymptomatic individuals: A systematic review. PLoS Negl Trop Dis. 2024;18(4):e0011842.

    Article  PubMed  PubMed Central  Google Scholar 

  65. Liu R, Wang X, Ma Y, Wu J, Mao C, Yuan L, et al. Prevalence of Zika virus in blood donations: a systematic review and meta-analysis. BMC Infect Dis. 2019;19(1):590.

    Article  PubMed  PubMed Central  Google Scholar 

  66. Metz MBC, Olufemi OT, Daly JM, Barba M. Systematic review and meta-analysis of seroprevalence studies of West Nile virus in equids in Europe between 2001 and 2018. Transbound Emerg Dis. 2021;68(4):1814–23.

    Article  PubMed  Google Scholar 

  67. Ebogo-Belobo JT, Kenmoe S, Abanda NN, Bowo-Ngandji A, Mbaga DS, Magoudjou-Pekam JN, et al. Contemporary epidemiological data of Rift Valley fever virus in humans, mosquitoes and other animal species in Africa: A systematic review and meta-analysis. Vet Med Sci. 2023;9(5):2309–28.

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  68. Peyre M, Chevalier V, Abdo-Salem S, Velthuis A, Antoine-Moussiaux N, Thiry E, et al. A Systematic Scoping Study of the Socio-Economic Impact of Rift Valley Fever: Research Gaps and Needs. Zoonoses Public Health. 2015;62(5):309–25.

    Article  CAS  PubMed  Google Scholar 

  69. Wright D, Kortekaas J, Bowden TA, Warimwe GM. Rift Valley fever: biology and epidemiology. J Gen Virol. 2019;100(8):1187–99.

    Article  CAS  PubMed  Google Scholar 

Download references

Acknowledgements

We acknowledged the authors of the published paper.

Clinical trial number

Not applicable.

Funding

Not applicable.

Author information

Authors and Affiliations

  1. Department of Medical Laboratory Sciences, College of Medicine and Health Sciences, Wollo University, Dessie, Ethiopia

    Alemu Gedefie, Habtu Debash, Yeshi Metaferia, Melaku Ashagrie Belete, Sisay Desale, Saleamlak Sebsibe, Mihret Tilahun, Bruktawit Eshetu, Agumas Shibabaw, Yeshimebet Kassa, Hussen Ebrahim, Zewudu Mulatie, Ermiyas Alemayehu & Melkam Tesfaye

  2. Department of Biomedical Sciences, College of Medicine and Health Sciences, Wollo University, Dessie, Ethiopia

    Altaseb Beyene Kassaw & Gossa Mankelkl

  3. Department of Environmental Health Sciences, College of Medicine and Health Sciences, Wollo University, Dessie, Ethiopia

    Lebasie Woretaw

  4. Department of Biomedical Sciences, College of Medicine and Health Sciences, Samara University, Samara, Ethiopia

    Berhanu Kebede

  5. Amhara Public Health Institute Dessie Branch, Dessie, Ethiopia

    Minwuyelet Maru Temesgen

  6. Dessie Town Health Office, Dessie, Ethiopia

    Natan Kassaye Msganew

Authors
  1. Alemu Gedefie
    View author publications

    You can also search for this author inPubMed Google Scholar

  2. Habtu Debash
    View author publications

    You can also search for this author inPubMed Google Scholar

  3. Altaseb Beyene Kassaw
    View author publications

    You can also search for this author inPubMed Google Scholar

  4. Gossa Mankelkl
    View author publications

    You can also search for this author inPubMed Google Scholar

  5. Yeshi Metaferia
    View author publications

    You can also search for this author inPubMed Google Scholar

  6. Melaku Ashagrie Belete
    View author publications

    You can also search for this author inPubMed Google Scholar

  7. Sisay Desale
    View author publications

    You can also search for this author inPubMed Google Scholar

  8. Saleamlak Sebsibe
    View author publications

    You can also search for this author inPubMed Google Scholar

  9. Mihret Tilahun
    View author publications

    You can also search for this author inPubMed Google Scholar

  10. Bruktawit Eshetu
    View author publications

    You can also search for this author inPubMed Google Scholar

  11. Agumas Shibabaw
    View author publications

    You can also search for this author inPubMed Google Scholar

  12. Yeshimebet Kassa
    View author publications

    You can also search for this author inPubMed Google Scholar

  13. Hussen Ebrahim
    View author publications

    You can also search for this author inPubMed Google Scholar

  14. Zewudu Mulatie
    View author publications

    You can also search for this author inPubMed Google Scholar

  15. Ermiyas Alemayehu
    View author publications

    You can also search for this author inPubMed Google Scholar

  16. Lebasie Woretaw
    View author publications

    You can also search for this author inPubMed Google Scholar

  17. Berhanu Kebede
    View author publications

    You can also search for this author inPubMed Google Scholar

  18. Minwuyelet Maru Temesgen
    View author publications

    You can also search for this author inPubMed Google Scholar

  19. Natan Kassaye Msganew
    View author publications

    You can also search for this author inPubMed Google Scholar

  20. Melkam Tesfaye
    View author publications

    You can also search for this author inPubMed Google Scholar

Contributions

Gedefie A, Debash H, Kassaw AB, Belete MA, Desale S, Sebsibe S, Tilahun M, Mulatie Z, Shibabaw A, Tesfaye M. involved in the conception and design, data collection, analysis, and interpretation of the data. Gedefie A, Mankelki G, Metaferia Y, Eshetu B, Kassa Y, Ebrahim H, Alemayehu E, Woretaw L, Kebede L, Temesgen MM, and Msganew NK took part in drafting the article or revising it critically for important intellectual content. Finally, all authors read and approve the final manuscript before submission.

Corresponding author

Correspondence to Alemu Gedefie.

Ethics declarations

Ethics approval and consent to participate

Not applicable.

Consent for publication

Not applicable.

Competing interests

The authors declare no competing interests.

Additional information

Publisher’s Note

Springer Nature remains neutral with regard to jurisdictional claims in published maps and institutional affiliations.

Supplementary Information

Rights and permissions

Open Access This article is licensed under a Creative Commons Attribution-NonCommercial-NoDerivatives 4.0 International License, which permits any non-commercial use, sharing, distribution and reproduction in any medium or format, as long as you give appropriate credit to the original author(s) and the source, provide a link to the Creative Commons licence, and indicate if you modified the licensed material. You do not have permission under this licence to share adapted material derived from this article or parts of it. The images or other third party material in this article are included in the article’s Creative Commons licence, unless indicated otherwise in a credit line to the material. If material is not included in the article’s Creative Commons licence and your intended use is not permitted by statutory regulation or exceeds the permitted use, you will need to obtain permission directly from the copyright holder. To view a copy of this licence, visit http://creativecommons.org/licenses/by-nc-nd/4.0/.

Reprints and permissions

About this article

Check for updates. Verify currency and authenticity via CrossMark

Cite this article

Gedefie, A., Debash, H., Kassaw, A.B. et al. Epidemiology of arboviruses in humans and livestock in Ethiopia: a systematic review and meta-analysis. BMC Infect Dis 25, 458 (2025). https://doiorg.publicaciones.saludcastillayleon.es/10.1186/s12879-025-10824-7

Download citation

  • Received:

  • Accepted:

  • Published:

  • DOI: https://doiorg.publicaciones.saludcastillayleon.es/10.1186/s12879-025-10824-7

Keywords

BMC Infectious Diseases

ISSN: 1471-2334

Contact us