Economic Impact of Lumpy Skin Disease in Africa: A Systematic Review

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This systematic review evaluates the economic impact of LSD, associated financial costs, and management strategies across affected African regions. Methods Following PRISMA guidelines, a comprehensive search of PubMed and Google Scholar was conducted, identifying studies published in English from inception through 18-08-2024 to 02-10-2024. Eligible studies focused on the economic implications of LSD, including direct and indirect costs. The Newcastle-Ottawa Scale was used to assess risk of bias. Results Of the 160 records identified, 31 studies met the inclusion criteria. Economic losses from LSD outbreaks ranged from $ 1.2 million to $ 2.5 million per outbreak, significantly impacting milk and meat production, hide quality, and trade. Key management strategies identified include vaccination and enhanced biosecurity measures, though vaccine access remains challenging. Conclusion This review highlights the substantial economic burden of LSD on Africa’s livestock industry and emphasizes the need for improved vaccine access and robust disease surveillance. Enhanced control measures are essential to mitigate LSD’s economic impact and safeguard livestock productivity. Lumpy Skin Disease (LSD) Economic impact Livestock industry Disease management Outbreak costs Trade restrictions Figures Figure 1 1.0 Introduction Lumpy skin disease (LSD) is an economically significant viral disease primarily affecting cattle and buffalo. It is caused by the Lumpy Skin Disease Virus (LSDV), a member of the Capripoxvirus genus within the Poxviridae family. Characterized by skin nodules, fever, and lymphadenopathy, LSDV infections result in considerable morbidity and can lead to severe economic losses in livestock populations. Mechanical transmission through arthropod vectors, such as biting flies ( Stomoxys calcitrans ) and ticks ( Rhipicephalus appendiculatus ), compound the challenge of controlling LSD, particularly in endemic regions where environmental factors support the proliferation of these vectors (Tuppurainen et al., 2017 ). Historically confined to sub-Saharan Africa, LSD has spread to the Middle East, Europe, and Asia, and is now recognized as a global threat (Megha et al., 2022 ). The transboundary nature of LSD disrupts international trade, posing significant challenges to livestock production and food security in previously unaffected regions (Tuppurainen et al., 2013 ). In Africa, where livestock contributes substantially to food, income, and draft power, LSD’s high morbidity rate results in substantial economic impacts, including reduced milk production, decreased beef quality, infertility, abortion, and permanent damage to hides, all of which significantly lower livestock market value (Megha et al., 2022 ; Yadav et al., 2024 ). Although LSD’s mortality rate is generally low, the disease imposes considerable financial burdens, particularly on smallholder farmers who rely on livestock for their livelihoods (Chacha, 2024 ). The economic impact of LSD extends beyond direct production losses; outbreaks necessitate increased expenditures on veterinary services, including costs for treatment, vaccination, and implementation of biosecurity measures, which further compound the financial burden on affected regions (Abdi & Alage, 2018 ). Furthermore, the epidemiology of LSD is complex, influenced by environmental factors such as warm, humid climates, as well as herd management practices, including communal grazing and movement of livestock across regions. These factors facilitate the spread of LSD and underscore the need for data-driven control strategies (Birhanu et al., 2015 ; Manyenya et al., 2024 ). Despite the significant threat LSD poses to livestock production and rural livelihoods in Africa, there remains a notable gap in epidemiological data on its prevalence, incidence, and economic consequences. This lack of comprehensive data hinders the development of effective control strategies and limits the ability to fully quantify the economic toll of the disease on the livestock sector (Nuvey et al., 2022 ). Given the widespread prevalence of LSD and its far-reaching effects on livestock-dependent economies, a systematic review of the disease’s economic impact and the efficacy of management strategies is essential. Understanding these economic implications and identifying effective interventions are critical steps in informing policies that can mitigate LSD’s impact on livestock health, food security, and the economy. This systematic review aims to address these gaps by evaluating the economic impact of LSD in Africa. Specifically, this review will quantify the financial costs associated with LSD outbreaks, including direct production losses and indirect control-related expenses. Furthermore, it will identify effective disease management and control measures to reduce the burden of LSD on the African livestock sector. 2.0 Method 2.1 Study Protocols This systematic review was conducted in line with the Preferred Reporting Items for Systematic Reviews and Meta-Analyses (PRISMA) guidelines, providing a structured approach to evaluate the economic impact of Lumpy Skin Disease (LSD) across Africa. The study protocol focused on assessing the financial burden of LSD in terms of production losses, costs associated with disease control, and identifying effective management strategies. The Newcastle-Ottawa Scale (NOS) was used to evaluate the quality of included studies, ensuring that each piece of evidence could reliably contribute to a holistic understanding of LSD’s economic impact. 2.2 Eligibility Criteria Studies were selected based on specific eligibility criteria. Eligible studies focused on LSD’s impact on cattle and buffalo populations within Africa, with attention to economic factors, including direct costs (e.g., production losses such as milk and meat yield) and indirect costs (e.g., treatment, vaccination, and biosecurity measures). Quantitative outcomes on economic impact, epidemiological measures (e.g., prevalence and incidence), and management strategies were also required. Retrospective, prospective, and cross-sectional studies, as well as systematic reviews or economic analyses, were considered. Only English-language studies published in peer-reviewed journals or full reports with relevant data were included, while opinion pieces, editorials, and studies lacking detailed economic data were excluded. 2.3 Information Sources and Search Strategy The search for relevant studies was conducted in two major databases: PubMed and Google Scholar. Search terms included combinations of “Lumpy Skin Disease,” “LSD,” “economic impact,” “financial cost,” and “Africa,” using Boolean operators "AND" and "OR" to optimize the search breadth and specificity. Sample search strings included “Lumpy Skin Disease” AND “economic impact” AND “Africa.” Additionally, reference lists of selected studies were reviewed to identify further studies not captured in the initial search. The search concluded on 6th October 2024, capturing the most recent and relevant data available. 2.4 Selection of Studies The study selection process followed PRISMA recommendations. After removing duplicate records, two independent reviewers screened the titles and abstracts to assess relevance, followed by a full-text review of studies meeting the initial eligibility criteria. Any disagreements between reviewers were resolved by discussion. A PRISMA flow diagram (Fig. 1 ) illustrates the detailed selection process, showing records identified, screened, excluded, and included in the final analysis. 2.5 Data Extraction Data extraction was carried out by two independent reviewers using a standardized data extraction form. Information collected included study characteristics (authors, publication year, country, study design), population details, economic outcomes, and management strategies employed. This structured extraction ensured consistency across studies and allowed for comprehensive comparisons. All extracted data were cross-checked by a third reviewer to ensure accuracy and mitigate potential discrepancies. 2.6 Quality Assessment The quality of each study was evaluated using the Newcastle-Ottawa Scale (NOS) for observational studies. This scale assesses three key domains: selection (including representativeness of the sample), comparability (adjustment for confounding factors), and outcome (reliability of outcome measurements). Studies were scored on these criteria, with scores between 7 and 9 indicating low risk of bias, 4 to 6 as moderate, and 0 to 3 as high risk. Studies deemed high risk were included in the qualitative synthesis but excluded from quantitative analysis where applicable. 3.0 Results 3.1 Study Selection A total of 160 records were initially retrieved from the academic databases PubMed and Google Scholar. After removing duplicate records (n = 8) and performing an initial abstract screening for relevance, 146 articles proceeded to the eligibility assessment stage. During this eligibility screening, 141 full-text articles were evaluated, and 110 studies were subsequently excluded. Reasons for exclusion included lack of economic focus (n = 34), study locations outside of Africa (n = 31), insufficient data on economic outcomes (n = 36), and irrelevance to the search terms for Lumpy Skin Disease (n = 9). Following these criteria, 31 studies met the eligibility requirements and were included in this review. The PRISMA flow diagram (Figure 1) provides a visual summary of the study selection process. 3.2 Characteristics of Included Studies The included studies represented a range of geographical locations and economic analyses relevant to the impact of LSD in Africa. Key characteristics of the studies are summarized in Table 1. Most studies were conducted in East Africa (notably Ethiopia, Kenya, and Tanzania) and Southern Africa (primarily South Africa), with a few from West Africa, including Nigeria. The designs of these studies included cross-sectional observational studies (n = 12), retrospective analyses (n = 9), economic evaluations (n = 5), and simulation-based models (n = 5), which examined both direct and indirect costs associated with LSD outbreaks. The most commonly reported economic impact metrics included production losses (milk and meat yield, hide quality), mortality and morbidity rates, and direct financial losses to farmers and the broader livestock industry. Several studies also included cost-benefit analyses of disease management strategies, primarily vaccination and biosecurity measures. 3.3 Risk of Bias in Included Studies The Newcastle-Ottawa Scale (NOS) was employed to assess the risk of bias across studies. The scoring criteria included study selection, comparability, and outcome measurement, with total scores determining the classification as low (7 - 9 points), moderate (4 - 6 points), or high risk of bias (0 - 3 points). Out of the 31 studies reviewed, 12 demonstrated low risk of bias, indicating robust methodologies with clear outcomes, while 15 were rated as moderate risk due to limitations in sampling techniques or lack of control for confounding factors. Four studies were classified as high risk, primarily due to inadequate sampling methodologies and lack of comparative analysis. 3.4 Results of Individual Studies The reviewed studies on LSD employed a wide range of methodologies, including cross-sectional surveys, retrospective analyses, outbreak investigations, experimental infections, qualitative interviews, simulation-based economic models, and systematic reviews, conducted across several African countries Ethiopia, Tanzania, Nigeria, South Africa, Uganda, Kenya, Cameroon, Zimbabwe, Ghana, Senegal, and Egypt. Their objectives covered epidemiological patterns, seroprevalence, morbidity, mortality, vaccine efficacy, economic and financial impacts, disease transmission dynamics, molecular and genomic characterization of LSDV strains, and community-level perceptions of disease burden. Populations studied included cattle of different ages, breeds, and production systems, with some studies extending to other hosts (e.g., guinea pigs) and vectors (e.g., ticks). Reported outcomes ranged from clinical signs, production and reproductive losses, and vaccine effectiveness to economic modeling and qualitative insights into livestock keepers’ priorities. The overall risk of bias varied, with most studies assessed as moderate, several as low, and a few as high, reflecting differences in design robustness, data quality, and scope, but collectively these studies provide valuable insights into LSD epidemiology, control, and impact across diverse settings. 3.4.1 Direct Costs of Lumpy Skin Disease LSD’s direct economic impacts were substantial, with production losses consistently reported across studies. Decreased milk and meat yield, reduced hide quality, and compromised reproductive performance were prominent outcomes. In Ethiopia, Birhanu et al. reported a 20 % reduction in milk yield and an estimated financial loss of $6.43 per affected zebu and $58 per affected Holstein Friesian cattle. Other studies highlighted high morbidity rates, with some LSD outbreaks reporting rates of 85 %, which translated to extensive financial losses from reduced productivity and increased treatment expenses. Mortality rates were generally low (1-3 %) but still resulted in significant economic strain, especially for smallholder farmers reliant on livestock for their primary income. 3.4.2 Indirect Costs Related to LSD Management Indirect costs associated with LSD management were also substantial. Treatment costs included veterinary fees, anti-inflammatory and antibiotic medications, and wound care, representing a significant financial burden for affected farmers. Additionally, biosecurity expenses for quarantine, decontamination, and vector control added to the indirect costs of LSD control. For example, smallholder farmers in Tanzania and South Africa reported prohibitive expenses in implementing biosecurity measures, compounding the financial impact of the disease. Vaccination emerged as a key preventive measure in several studies, with reported efficacy rates ranging from 80 – 90 %. However, the accessibility and affordability of vaccines varied widely, limiting their uptake. Smallholder farmers faced challenges in affording the vaccination costs, which further highlighted socioeconomic disparities in LSD management across regions. 3.4.3 Economic Impact of Trade Restrictions LSD’s impact extended beyond production losses to influence local and international trade significantly. Regional trade restrictions and international bans on livestock products from LSD-affected areas resulted in considerable financial losses. In South Africa, dairy and beef sectors faced substantial economic downturns during LSD outbreaks due to export restrictions and carcass condemnations. Similarly, Kenya and Nigeria experienced financial setbacks as they faced export limitations imposed to maintain international trade standards. Such restrictions affected not only large-scale commercial farmers but also had far-reaching implications for local markets reliant on livestock trade for income and food security. 3.4.4 Socioeconomic Implications for Rural Communities The socioeconomic burden of LSD was particularly profound in rural communities, where livestock forms a critical part of household economies. Direct impacts, such as reduced productivity and increased treatment costs, contributed to food insecurity and income loss among smallholder farmers. For instance, a study conducted in Nigeria reported that economic losses per outbreak ranged from $9.6 to $6,340, depending on the size of the herd and production system. Additionally, the high costs associated with LSD management disproportionately affected smallholder farmers, exacerbating poverty levels and hindering sustainable livestock production in economically vulnerable areas. These findings highlight the considerable economic burden of LSD on Africa's livestock sector, highlighting the urgent need for affordable management solutions and greater vaccine accessibility. 4.0 Discussion This systematic review consolidates the economic impact of Lumpy Skin Disease (LSD) across Africa, emphasizing significant financial burdens in the livestock sector due to production losses, disease management expenses, and wider socioeconomic effects. The direct costs associated with LSD were largely driven by decreased milk and meat yields and reduced hide quality, impacting both smallholder and commercial farmers. In Ethiopia, LSD-related milk production losses reached up to 20%, translating into significant income reductions for affected households (Birhanu et al., 2015 )​. Furthermore, the indirect costs of LSD management, including veterinary care and biosecurity measures, impose additional economic challenges, especially for resource-constrained smallholder farmers​. The findings align with global trends, where LSD has similarly impacted livestock productivity and economic stability in regions beyond Africa, including the Middle East and parts of Europe (Moudgil et al., 2023 ). However, in Africa, the economic toll of LSD appears disproportionately high due to the critical role of livestock in rural economies and the limited infrastructure for disease management (Tuppurainen et al., 2013 ; Nuvey et al., 2022 ). This mirrors trends observed in other transboundary animal diseases, such as Rift Valley fever and foot-and-mouth disease, which also contribute to severe economic strain among African smallholders who often rely on livestock for their livelihoods (Gari et al., 2011 ; Mukolwe et al., 2021 ). While both large-scale and smallholder farms suffer from LSD, the financial impact is more pronounced for subsistence farmers, who are especially vulnerable to income disruptions caused by reduced milk and meat yields (Gari et al., 2010 ). These findings underscore the necessity for greater vaccine access and infrastructure to support African farmers in managing LSD outbreaks more effectively. Unlike regions with developed veterinary systems where vaccination programs are accessible and supported by infrastructure, the lack of affordable and readily available vaccines in Africa exacerbates the economic impact of LSD and poses challenges to sustainable disease control (Mdlulwa et al., 2018 ). 4.1 Economic Impact on Trade and Regional Market Stability The review highlights LSD’s far-reaching impact on both local and international trade, as outbreaks often lead to restrictions on livestock and livestock products, impairing economic stability. In South Africa, Kenya, and Nigeria, where livestock exports represent a considerable part of the agricultural economy, trade limitations due to LSD outbreaks have reduced export revenues, disrupted local markets, and heightened food insecurity (Ntombimbini and Klein, 2015 ; Clemmons et al., 2021 ). These findings suggest that implementing proactive and regionally coordinated LSD management practices could mitigate economic losses and stabilize trade. Moreover, the disease’s impact extends to secondary industries, such as the leather industry, which depends on high-quality hides for production. Reduced hide quality due to LSD lesions undermines the leather export market, further compounding the economic strain on affected regions (Farah & Ahmed, 2018 ). This aligns with previous findings on the broader economic effects of LSD, where outbreaks affect both immediate livestock productivity and the long-term viability of industries reliant on livestock byproducts (Al-Salihi, 2014 ; Kappes et al., 2023 ). 4.2 Management and Control Strategies: Efficacy and Barriers Vaccination is widely recognized as a primary strategy for LSD prevention and control, with reported efficacy rates ranging between 80% and 90% in reducing disease incidence (Birhanu et al., 2015 ; Mdlulwa et al., 2018 ). However, vaccine access remains inconsistent, particularly in rural regions with limited veterinary infrastructure. The cost of vaccines further restricts access for smallholder farmers, who are less equipped to bear the financial burden of preventive measures. This discrepancy highlights a critical inequality in LSD management: commercial farms, with greater resources, are better positioned to implement vaccination and biosecurity measures, whereas smallholders are more susceptible to repeated outbreaks (Tuppurainen et al., 2017 ; Clemmons et al., 2021 ). Effective biosecurity measures, including animal quarantine and vector control, also play a significant role in controlling LSD transmission. However, logistical challenges in rural, resource-limited settings hinder the consistent application of these measures. The implementation of multivalent vaccines, such as combined LSD-RVF vaccines, could provide a more cost-effective solution for farmers in high-risk regions, reducing the need for multiple vaccination rounds and potentially lessening the disease’s overall impact (Mdlulwa et al., 2018 ; Ntombimbini and Klein, 2015 ). 4.3 Socioeconomic Implications for Rural Communities The socioeconomic impact of LSD on rural African communities is profound, as livestock represents a primary source of income and food security for many smallholder farmers. The cumulative costs of managing outbreaks, reduced productivity, and loss of trade income exacerbate food insecurity and poverty among already vulnerable populations. For example, a study conducted in Nigeria estimated that economic losses per LSD outbreak could range from $ 9.6 to $ 6,340, depending on the herd size and production system, with poorer households disproportionately affected (Limon et al., 2020 ). Additionally, the high costs associated with disease control measures deepen poverty and limit the capacity of smallholder farmers to recover from recurring outbreaks (Chacha, 2024 ; Mukolwe et al., 2021 ). 4.4 Future Directions Longitudinal economic evaluations are crucial to understand the cumulative impact of repeated LSD outbreaks on African livestock-dependent communities. While immediate economic losses due to outbreaks are significant, the prolonged financial toll on rural farmers likely extends beyond single-event losses. Studies have shown that frequent disease outbreaks lead to compounded productivity losses, increased treatment costs, and deeper economic hardship over time (Tadesse et al., 2020 ). Long-term studies that track the economic impacts of LSD over multiple outbreak cycles would provide a more comprehensive understanding of these effects and help to identify the most sustainable management practices. Additionally, cost-benefit analyses across different farming systems would inform policymakers on which interventions(such as vaccination, biosecurity, or vector control)offer the greatest economic returns for smallholder farmers (Gari et al., 2011 ; Kiplagat et al., 2020). The development of multivalent vaccines targeting LSD alongside other transboundary livestock diseases, such as Rift Valley Fever (RVF), represents a promising avenue for research. Multivalent vaccines can reduce the logistical challenges and financial burden associated with administering separate vaccines for each disease (Mdlulwa et al., 2018 ). Research focusing on the efficacy, cost, and accessibility of these combined vaccines is needed, as current vaccines are often financially out of reach for smallholder farmers. Studies in South Africa have demonstrated the potential of combined LSD-RVF vaccines to improve disease control in regions with high prevalence, reducing the need for frequent vaccinations and offering dual protection against multiple diseases (Ntombimbini & Klein, 2015 ). Future research should examine delivery mechanisms, such as mobile veterinary services or community-based vaccination programs, to improve vaccine access in remote areas where veterinary infrastructure is sparse (Akshay et al., 2023 ). Expanding biosecurity practices tailored to resource-limited settings is another priority. Current biosecurity recommendations, including vector control, animal movement restrictions, and early detection, are effective in controlling LSD but present implementation challenges for rural farmers with limited resources (Subasinghe et al., 2023 ). Research should focus on developing cost-effective, scalable biosecurity solutions that smallholder farmers can adopt. For example, innovations in low-cost vector control measures, such as insecticide-treated livestock housing, could significantly reduce transmission rates in endemic areas (Farah & Ahmed, 2018 ). Community-led surveillance systems, in which local farmers are trained to recognize and report LSD symptoms early, could enhance outbreak response and containment, particularly in high-risk regions (Moje et al., 2023 ). Such systems have proven effective in controlling other livestock diseases and may offer a practical solution for managing LSD in African countries (Clemmons et al., 2021 ). Finally, enhancing regional cooperation on LSD management and trade policies is essential to support Africa’s livestock export market. Outbreaks of LSD often lead to trade restrictions, negatively impacting the economies of countries where livestock exports are critical (Kiplagat et al., 2020). Regional collaboration across African countries, with standardized LSD control protocols and harmonized trade policies, could stabilize livestock markets and protect farmers from abrupt financial losses due to trade bans (Ntombimbini & Klein, 2015 ). Shared resources for disease surveillance and data exchange could facilitate timely responses and minimize the spread of LSD across borders. Collaborative efforts with international bodies such as the World Organisation for Animal Health (OIE) could strengthen the veterinary infrastructure and disease management capabilities in affected regions (Tuppurainen et al., 2017 ). Enhanced regional and international cooperation will be critical to address the economic impact of LSD comprehensively, supporting resilience in Africa’s livestock sector. 4.5 Conclusion This systematic review reveals the significant economic impact of Lumpy Skin Disease (LSD) on Africa’s livestock sector, manifesting through production losses, increased management costs, and trade restrictions. The disease reduces milk and meat yields, lowers hide quality, and disrupts reproductive performance, directly affecting commercial and smallholder farmers. The financial strain is exacerbated by the high costs of veterinary treatments, biosecurity measures, and limited vaccine accessibility, which especially burdens smallholder farmers. Vaccination, although effective, faces challenges in affordability and availability, emphasizing the need for enhanced biosecurity practices and regional cooperation. To mitigate LSD’s economic impact and support sustainable development, this review calls for affordable vaccine access, improved biosecurity, and coordinated policy interventions across the affected regions. Declarations Author Contributions : NL: Conceptualization, supervision, project administration, writing—original draft preparation, writing—review and editing, data curation, resources. MTA: writing—original draft preparation, writing—review and editing, data curation. FAA: writing—original draft preparation, writing—review and editing, data curation. CCO: writing—original draft preparation, writing—review and editing, data curation. AOU: supervision, writing—original draft preparation, writing—review and editing, data curation. USO: Conceptualization, supervision, writing—original draft preparation, writing—review and editing, data curation, resources. MAA: writing—original draft preparation, writing—review and editing, data curation. Ethical approval Not Applicable. Research involving human and/or animal participants Not Applicable. Informed consent Not Applicable. Funding This study received no external funding. Data Availability Statement Data is available upon request from the corresponding author. Acknowledgments: The authors gratefully acknowledge all those who contributed to this study. Conflicts of Interest The authors declare no conflict of interest. References Abdi, F. F., & Alage, A. C. B. (2018). A Case Report on Clinical Management of Lumpy Skin Disease in Bull. Journal of Veterinary Science & Technology, 9 (3). doi:10.4172/2157-7579.1000538 Abebaw, B. (2024). Prevalence of Lumpy Skin Disease in Africa: A Systematic Review and Meta-Analysis from 2007 to 2023. Vet Med Int, 2024 , 9991106. doi:10.1155/2024/9991106 Abera, Z. (2019). Survey on Distribution, Associated factors of Lumpy Skin Disease Occurrence and Its Vaccine Efficacy in selected Districts of East Wollega Zone, Western Oromiya. 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Epidemiological Status and Economic Impact of Lumpy Skin Disease-Review. Peer-Reviewed, Refereed, Open Access Journal , 2322 – 0392. doi:10.18782/2322-0392.1284 Tesfaye, S., Regassa, F., Beyene, G., Leta, S., & Paeshuyse, J. (2024). Spatiotemporal analysis and forecasting of lumpy skin disease outbreaks in Ethiopia based on retrospective outbreak reports. Front Vet Sci, 11 , 1277007. doi:10.3389/fvets.2024.1277007 Tuppurainen, E. S., Lubinga, J. C., Stoltsz, W. H., Troskie, M., Carpenter, S. T., Coetzer, J. A., . . . Oura, C. A. (2013). Mechanical transmission of lumpy skin disease virus by Rhipicephalus appendiculatus male ticks. Epidemiol Infect, 141 (2), 425-430. doi:10.1017/S0950268812000805 Tuppurainen, E. S., Venter, E. H., Coetzer, J. A., & Bell-Sakyi, L. (2015). Lumpy skin disease: attempted propagation in tick cell lines and presence of viral DNA in field ticks collected from naturally-infected cattle. Ticks Tick Borne Dis, 6 (2), 134-140. doi:10.1016/j.ttbdis.2014.11.002 Tuppurainen, E. S. M., Venter, E. H., Shisler, J. L., Gari, G., Mekonnen, G. A., Juleff, N., . . . Babiuk, L. A. (2017). Review: Capripoxvirus Diseases: Current Status and Opportunities for Control. Transbound Emerg Dis, 64 (3), 729-745. doi:10.1111/tbed.12444 Vudriko, P., Ekiri, A. B., Endacott, I., Williams, S., Gityamwi, N., Byaruhanga, J., . . . Varga, G. J. F. i. V. S. (2021). A survey of priority livestock diseases and laboratory diagnostic needs of animal health professionals and farmers in Uganda. 8 , 721800. Wagh, H., & Patel, M. (2023). Role of Phytochemicals in Lumpy Virus Skin Disease. Wolff, J., Tuppurainen, E., Adedeji, A., Meseko, C., Asala, O., Adole, J., . . . Hoffmann, B. (2021). Characterization of a Nigerian Lumpy Skin Disease Virus Isolate after Experimental Infection of Cattle. Pathogens, 11 (1). doi:10.3390/pathogens11010016 Yadav, D., Rao, G., Paliwal, D., Singh, A., Alam, A., Kumar Sharma, P., . . . Kumar, Y. (2024). Cracking the Code of Lumpy Skin Disease: Identifying Causes, Symptoms and Treatment Options for Livestock Farmers. Infectious Disorders-Drug Targets, 24 (5), 57-71. Yadav, M. P., Singh, R. K., & Malik, Y. S. (2020). Emerging and transboundary animal viral diseases: Perspectives and preparedness. Emerging transboundary animal viruses , 1-25. Zelalem, A., Hailu, D., & Getachew, G. (2015). Assessment of Distribution and Associated Risk Factors of Lumpy Skin Disease in Selected Districts of West Wollega Zone, Western Ethiopia. Academic Journal of Animal Diseases, 4 (3). doi:10.5829/idosi.ajad.2015.4.3.95251 Tables Table 1: Description of the included studies. Study Methodology Study Design Outcome Measures Intervention Region Population Characteristics Risk of Bias Abera (2019) Cross-sectional study using random sampling of 2 districts and 8 associations, structured questionnaire for data collection. Cross-sectional 1) Factors contributing to LSD occurrence 2) Efficacy of the LSD vaccine None specified Tanga and Pwani, Tanzania Cattle of various ages (calves, young, adults) and sizes (small, medium, large); unvaccinated cattle. Low Tesfaye et al. (2024) Collection of LSD outbreak data, time-series analysis, forecasting, and retrospective space-time cluster analysis. Retrospective observational 1) Number of LSD outbreaks, 2) LSD-positive cases, 3) LSD-related deaths, 4) LSD-related slaughters None specified Ethiopia Not specified. Moderate Birhanu et al. (2015) Cross-sectional survey with purposive sampling, primary and secondary data collection, and financial loss estimation model. Cross-sectional 1) Epidemiological variables (incidence, mortality), 2) Production losses, 3) Financial impacts of LSD Annual vaccination against LSD, free for farmers Afar and Tigray, Ethiopia Total cattle population of 299,959; varying vaccination histories; predominantly adult females and males; seasonal mobility. Low Ntombimbini and Klein (2015) Use of primary and secondary data through surveys focused on LSD and RVF, stratified random sampling. Survey and observational 1) Economic costs of LSD and RVF, 2) Socioeconomic impacts, 3) Relationship between farmer characteristics and impacts LSD vaccine administered at an average cost, RVF vaccination mentioned South Africa Majority over 60 years, 43% female, most have less than high school education; varied livestock ownership. Moderate Kebede (2019) Two-week acceptability phase for guinea pig diets, factorial experiment with dietary treatments. Experimental Palatability and intake of supplements, reproductive performance, abortion rates, birth weights Various dietary treatments Nigeria, Cameroon Adult female guinea pigs, local and crossbred; owned by volunteers. Moderate Makoga et al. (2024) Multistage sampling method, blood sample collection, serological testing for LSDV antibodies. Cross-sectional Seroprevalence of LSDV antibodies in unvaccinated cattle None specified Tanga and Pwani, Tanzania Cattle of different ages and sizes, all unvaccinated. Moderate Haga et al. (2024) Virus isolation and purification, DNA extraction, Illumina sequencing of LSDV genomes. Retrospective observational No specific outcome measured; genomic analysis of LSDV strains None specified Various (Sri Lanka, Mongolia, Nigeria, Ethiopia, Senegal, Ghana, Cameroon) Not specified. High Wolff et al. (2021) Experimental infection of cattle with monitoring of clinical signs and sample collection for analysis. Experimental infection 1) Clinical signs, 2) Viremia, 3) Viral shedding, 4) Seroconversion Experimental infection with Nigerian LSDV isolate Nigeria 8 female Holstein Friesian cattle aged 4-6 months, clinically healthy. Moderate Leliso et al. (2021) Outbreak investigation with sample collection from affected animals, processing for virus isolation and molecular characterization. Observational field investigation No clearly defined outcome measured None specified Bale Administrative Zone, Ethiopia Cattle of all ages, higher morbidity in males; varied production systems. High Tuppurainen, E. S. et al. (2013) Experimental infection of cattle and tick transfer to study virus transmission. Experimental model Transmission of LSDV via ticks Infection of donor animals and tick transfer South Africa 3 young female Bonsmara cattle, no vaccination against LSD practiced in the herd. High Ayelet et al. (2014) Experimental infection with monitoring and molecular analysis of samples collected from infected cattle. Observational and retrospective analysis 1) Morbidity and mortality rates, 2) Virus isolation and detection, 3) Vaccine efficacy assessment Experimental infection with Nigerian LSDV isolate Central region, Ethiopia Cattle from feedlot and smallholder farms, predominantly males; higher morbidity in young cattle. Moderate Girma et al. (2019) - Purposive selection of study sites based on LSD outbreak reports. Observational study; purposive selection and epidemiological data collection through questionnaires. 1) Isolation and molecular characterization of LSDV 1) Annual vaccination in large farms Oromia, Ethiopia - Crossbred dairy cattle, mostly cows and heifers Moderate Risk - Inclusion of 191 dairy cattle from smallholder farms and 1551 from large farms. 2) Assessment of vaccine effectiveness 2) Vaccination during outbreaks in smallholder farms - Smallholder (2-30 cattle) and big farms (>100 cattle) - Virus isolation and PCR testing. 3) Investigation of outbreaks - Urban and intensively managed smallholder farms Mdlulwa et al. (2018) - Conceptual model for economic analysis. Ex-ante, non-controlled simulation-based cost-benefit analysis. Economic impact of LSD RVF 2-in-1 vaccine on dairy production; measures like net present value and benefit-cost ratio. New multivalent vaccine for LSD and RVF. South Africa - Dairy farms with herd sizes from < 50 to 1000 cows Moderate Risk - Cost-benefit analysis of LSD RVF 2-in-1 vaccine. - Breeds include Jersey and mixed breeds - Simulation modeling for different scenarios. Atai et al. (2021) - Qualitative survey in response to LSD outbreaks. Non-controlled observational qualitative survey and epidemiological investigation. Epidemiological features and economic impact of LSD outbreaks; morbidity, mortality rates, and impact on markets. Not mentioned. Bokkos LGA, Nigeria - Residents engaged in livestock and crop production Moderate Risk - Collection of skin scab samples analyzed with PCR. - Herd sizes ranged from 35 to 150 cattle, with a total of 1,164 owned by 15 farmers - Focus group discussions with farmers. Moje et al. (2023) - Sampling cattle from different management systems. Longitudinal observational study; purposive sampling at different time points. Humoral immune response; serum neutralization antibody titers before and after vaccination. Administration of a live attenuated Capripox-LSD vaccine. Ethiopia - Cattle aged 6 months to 12 years Low Risk - Blood samples collected before and after vaccination. - Both male and female - Used serum neutralization test for antibodies. - Extensive and intensive management systems; sample sizes: 60 extensive, 53 intensive Zelalem et al. (2015) - Cross-sectional survey with a semi-structured questionnaire. Cross-sectional observational study using multi-stage sampling and questionnaire survey. Not mentioned (no clear primary outcome defined). Not mentioned. Oromia, Ethiopia - Majority adult respondents, mostly literate Moderate Risk - Information on household characteristics, disease history, and risk factors. - Livelihood involved mixed agriculture, with some engaged in trading and employment - Predominantly highlands and midlands Gambo et al. (2018) - Questionnaires to farmers, butchers, and traders. Survey and field investigation involving questionnaires and field observations. 1) Prevalence and status of viral diseases with skin lesions Not mentioned. Kanam LGA, Nigeria - Livestock farmers, butchers, and traders in Kanam LGA Moderate Risk - Field investigations during questionnaire administration. 2) Clinical signs and risk factors - Farmers keeping cattle, sheep, and goats - Collection and PCR testing of samples. 3) Laboratory validation Ochwo et al. (2020) - Sample collection from suspected LSDV cases. Observational, non-controlled study investigating suspected LSD outbreaks naturally occurring in Uganda. Not mentioned (molecular epidemiology study focused on detecting and characterizing LSDV). Not mentioned. Uganda Not mentioned. High Risk - PCR to confirm LSDV presence and phylogenetic analysis. Moje et al. (2024) - Cross-sectional study to estimate seroprevalence and assess community awareness. Experimental study involving vaccine comparison and controlled animal study. 1) Seroprevalence of LSD at animal (40.8%) and herd level (81%) Not mentioned. Sidama, Ethiopia - Crossbred and local breed cattle Low Risk - Multistage sampling for blood sample collection. 2) Prevalence in agro-ecological zones - Extensive or semi-intensive systems - Virus neutralization test for antibodies. 3) Vaccination practices - Total cattle population in Sidama region estimated at 2.4 million Gari et al. (2010) - Questionnaire survey across 44 peasant associations. Cross-sectional observational study using questionnaires and retrospective data investigation. 1) Herd-level and animal-level prevalence of LSD Not mentioned. Ethiopia - Herd-owners across 44 peasant associations Moderate Risk - Multi-stage sampling to select associations. 2) Risk factors associated with LSD - Represented different agro-climatic zones and farming systems - Data on LSD occurrence and climate from records. 3) Temporal association with biting-fly population Mburu et al. (2023) - 30 in-depth interviews to understand livestock priorities. Qualitative observational study using IDIs, FGDs, and KIIs; purposive sampling of participants. Not mentioned (qualitative study focusing on disease prioritization). Not mentioned. Kajiado, Kenya - Male and female Maasai pastoralists aged 18-75 High Risk - Focus group discussions and key informant interviews for insights on diseases and interactions. - Long-term residents of the study area - Key informants included professionals in livestock, public health, and wildlife sectors Gezahegn et al. (2015) - Longitudinal study examining 11,189 bulls in feedlots for LSD incidence. Longitudinal observational study following bulls for 3 months to observe LSD incidence. Incidence, mortality rate, and case fatality rate of LSD in feedlots. Not mentioned. Central Ethiopia - 11,189 bulls aged 3-5 years old, previously vaccinated for LSD Low Risk - Clinical examination and statistical analyses to assess incidence and mortality. - Monitored for 3 months before export Alemayehu et al. (2013) Using the OIE-recommended risk assessment framework to analyze the risk of LSD introduction and exposure; collecting data from secondary sources, interviews with feedlot operators, and personal field observations. Qualitative risk assessment study using OIE framework. 1. Likelihood of LSD introduction (high, medium uncertainty) Not mentioned East Shewa Zone, Oromia, Ethiopia - Male bulls aged 3-5 years Moderate Risk 2. Probability of exposure (very high, medium uncertainty) 3. Prevalence of LSD (6.1% affected, 1.8% mortality) 4. Total economic loss (667,785.6 USD). - Originating mainly from Borena pastoral system - All vaccinated for LSD Adedeji, A. J et al. (2017) Conducting the study on a dairy farm that experienced recurrent outbreaks of LSD; collecting skin biopsy samples; determining morbidity and mortality rates based on farm records; estimating economic losses due to LSD outbreaks. Retrospective, non-controlled observational study of recurrent outbreaks on a dairy farm. 1. Morbidity and mortality rates Not mentioned Jos, Plateau State, Nigeria - Cattle breed: Holstein Friesian Moderate Risksss 2. Economic impact due to LSD outbreaks. - Age: Mostly calves < 1 year - Farm size: Over 250 cattle - Observation period: 2010-2014 Addise Ambilo (Kebede, 2019) Cross-sectional study design; sample size calculated based on expected prevalence; clinical examinations and laboratory tests performed to identify skin pathogens. Cross-sectional study. Prevalence of major skin diseases in cattle. Not mentioned Southern Nations, Ethiopia - Cattle of all ages, sexes, and breeds Moderate Risk - Higher prevalence in younger cattle with poor husbandry Douglass et al. (2020) Construction of recombinant viruses and in vitro growth comparison; in vivo growth comparison on chick chorioallantoic membranes; histological analysis of infected CAMs; in vivo experiment in cattle. Experimental study; controlled animal study in cattle. 1. Effect of LSDV SOD homologue on virus growth (in vitro and in vivo) 1. Deletion of the SOD homologue gene Multinational, focusing on South Africa Not mentioned Moderate Risk 2. Histopathological changes. 2. Replacement with full-length SOD homologue gene. Mburu et al. (2023) Conducted interviews and focus group discussions with community members and professionals to understand livestock disease prioritization; mapping exercise with community elders. Qualitative, observational study using IDIs, FGDs, and KIIs. No specific outcome measured; focused on understanding disease prioritization. Not mentioned Oloitoktok Sub County, Kenya - Male and female Maasai pastoralists, aged 18-75 High Risk - Long-term residents - Key informants included professionals and elders Abebaw (2024) Systematic literature search; quality assessment of included studies; use of random-effects meta-analysis to pool prevalence estimates and assess heterogeneity. Systematic review and meta-analysis. Prevalence of Lumpy Skin Disease (LSD) in cattle in Africa. Not mentioned Egypt, Ethiopia, Uganda, Zimbabwe - Cattle from studies conducted in these countries Moderate Risk - Sample sizes ranging from 20 to 2,368 cattle Geletu et al. (2024) Study conducted in the West Hararghe Zone; examination of cattle, virus isolation, and detection using PCR methods. Cross-sectional observational study of an LSD outbreak. 1. Morbidity, mortality, and case fatality rates of LSD. Not mentioned West Hararghe Zone, Ethiopia - Cattle of all ages, including local zebu and foreign breeds Moderate Risk 2. Isolation and detection of LSDV using PCR. - 73 out of 625 cattle showed clinical signs of LSD Adedeji, A. J. et al. (2018) Collecting skin biopsies from cattle with suspected LSD lesions; processing for histopathological examination and PCR to detect LSDV genes. Retrospective observational case report of two outbreaks on a single dairy farm. 1. Morbidity rate 1. Vaccination with foreign LSD vaccine after the 2014 outbreak. Keffi, Nasarawa State, Nigeria - Dairy cattle herd including Holstein Friesian and Sokoto Gudali breeds Moderate Risk 2. Mortality rate 2. Vaccination with an LSD vaccine from NVRI after the 2016 outbreak. - Herd composition changed between 2014 and 2016 3. Clinical presentation of the disease. Israel (2020) Cross-sectional questionnaire survey; face-to-face interviews with dairy farm owners; data collection on diseases, mortality, morbidity, and management practices. Cross-sectional observational survey. 1. Major cattle diseases affecting dairy cattle. Not mentioned Jimma town, Oromia, Ethiopia - Majority male dairy farm owners Moderate Risk 2. Economic significance due to these diseases. - Majority aged 40 and above 3. Management practices. - Varied educational backgrounds Table 2: Assessment of Risk of Bias Author(s) Selection (max 4 points) Comparability (max 2 points) Outcome (max 3 points) Total Score (max 9) Risk of Bias Abera (2019) 4 (representative sample, clear criteria) 1 (confounding factors considered) 3 (clear outcomes) 8 Low Risk Tesfaye et al. (2024) 3 (convenience sampling) 0 (no comparison made) 2 (clear reporting of outcomes) 5 Moderate Risk Birhanu et al. (2015) 4 (representative sample, clear criteria) 1 (confounding factors considered) 3 (clear outcomes) 8 Low Risk Ntombimbini and Klein (2015) 4 (representative sample, clear criteria) 0 (no consideration of confounders) 3 (clear outcomes) 7 Moderate Risk (Kebede, 2019) 3 (convenience sampling) 0 (no comparison made) 2 (not clear on specific outcomes) 5 Moderate Risk Makoga et al. (2024) 3 (convenience sampling) 0 (no comparison made) 2 (not clear on specific outcomes) 5 Moderate Risk Haga et al. (2024) 0 (no clear sampling method) 0 (no comparison made) 2 (not clear on specific outcomes) 2 High Risk Wolff et al. (2021) 3 (convenience sampling) 0 (no comparison made) 2 (not clear on specific outcomes) 5 Moderate Risk Leliso et al. (2021) 0 (no clear sampling method) 0 (no comparison made) 0 (no clear outcomes) 0 High Risk (Tuppurainen, E. S. et al. , 2013) 1 (convenience sampling) 0 (no comparison made) 1 (not clear on specific outcomes) 2 High Risk Ayelet et al. (2014) 3 (convenience sampling) 0 (no comparison made) 3 (clear outcomes) 5 Moderate Risk Girma et al. (2019) 3 (convenience sampling) 0 (no comparison made) 2 (not clear on specific outcomes) 5 Moderate Risk Mdlulwa et al. (2018) 3 (convenience sampling of dairy operations) 0 (no comparison made) 3 (clear economic outcomes) 6 Moderate Risk Atai et al. (2021) 2 (non-random convenience sampling) 0 (no comparison made) 3 (clear epidemiological outcomes) 5 Moderate Risk Moje et al. (2023) 4 (purposive sampling, clear criteria) 1 (confounding factors considered) 3 (clear immunological outcomes) 8 Low Risk Zelalem et al. (2015) 3 (representative sample, clear criteria, and appropriate sampling) 1 (consideration of confounding factors) 1 (no clear outcomes reported) 5 Moderate Risk Gambo et al. (2018) 3 (clear inclusion criteria, diverse participants) 1 (some confounding factors considered) 2 (clear reporting of outcomes) 6 Moderate Risk Ochwo et al. (2020) 2 (convenience sampling of suspected cases) 0 (no comparison made) 2 (clear reporting of PCR results) 4 High Risk Moje et al. (2024) 4 (multi-stage sampling, representative sample) 1 (confounding factors considered) 2 (clear seroprevalence outcomes) 7 Low Risk Gari et al. (2010) 3 (representative sample, clear criteria) 0 (no consideration of confounders) 1 (not clear on specific outcomes) 4 Moderate Risk Mburu et al. (2023) 2 (purposive sampling introduces bias) 0 (no comparison made) 1 (outcomes based on qualitative data) 3 High Risk Gezahegn et al. (2015) 4 (large, representative sample) 1 (consideration of some risk factors) 2 (clear reporting of outcomes) 7 Low Risk Alemayehu et al. (2013) 3 (qualitative assessment, specific criteria) 0 (no comparison made) 2 (clear risk assessment outcomes) 5 Moderate Risk (Adedeji, A. J et al. , 2017) 2 (specific farm, not generalizable) 0 (no consideration of confounders) 2 (clear reporting of outcomes) 4 Moderate Risk Addise Ambilo (Kebede, 2019) 3 (representative sample of cattle from various demographics) 0 (no consideration of confounding factors) 2 (clear reporting of prevalence) 5 Moderate Risk Douglass et al. (2020) 3 (controlled experimental study) 1 (comparison of different virus types) 2 (clear outcomes reported) 6 Moderate Risk Mburu et al. (2023) 2 (purposive sampling) 0 (no comparison made) 1 (qualitative nature, unclear outcomes) 3 High Risk Abebaw (2024) 4 (systematic literature search, clear inclusion/exclusion criteria) 0 (no consideration of confounding factors) 2 (clear prevalence outcomes) 6 Moderate Risk Geletu et al. (2024) 3 (cross-sectional study with adequate sample size) 1 (consideration of risk factors) 2 (clear outcomes reported) 6 Moderate Risk Adedeji, A. J. et al. (2018) 2 (convenience sampling, not generalizable) 0 (no comparison made) 2 (clear reporting of outcomes) 4 Moderate Risk Jumana and Kamal (2022) 3 (includes control group of healthy buffalo) 0 (no consideration of confounding factors) 2 (clear outcomes reported) 5 Moderate Risk Israel (2020) 3 (cross-sectional study with random sampling) 0 (no consideration of confounding factors) 2 (clear reporting of outcomes) 5 Moderate Risk Cite Share Download PDF Status: Posted Version 1 posted You are reading this latest preprint version Research Square lets you share your work early, gain feedback from the community, and start making changes to your manuscript prior to peer review in a journal. As a division of Research Square Company, we’re committed to making research communication faster, fairer, and more useful. We do this by developing innovative software and high quality services for the global research community. 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Also discoverable on Platform About Our Team In Review Editorial Policies Advisory Board Help Center Resources Author Services Accessibility API Access RSS feed Manage Cookie Preferences © Research Square 2026 | ISSN 2693-5015 (online) Privacy Policy Terms of Service Do Not Sell My Personal Information {"props":{"pageProps":{"initialData":{"identity":"rs-7780927","acceptedTermsAndConditions":true,"allowDirectSubmit":true,"archivedVersions":[],"articleType":"Research Article","associatedPublications":[],"authors":[{"id":536804506,"identity":"09413600-9c3b-4ff2-a975-fa3ca4cb6a12","order_by":0,"name":"Nafiu 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1","display":"","copyAsset":false,"role":"figure","size":54086,"visible":true,"origin":"","legend":"\u003cp\u003e\u003cstrong\u003ePRISMA 2020 flow diagram of the selection of eligible studies for qualitative analysis.\u003c/strong\u003e\u003c/p\u003e","description":"","filename":"1.png","url":"https://assets-eu.researchsquare.com/files/rs-7780927/v1/364373eeaf2f3c840aa4521d.png"},{"id":95800191,"identity":"419624d9-2f95-425b-8e51-09b3a352d1de","added_by":"auto","created_at":"2025-11-13 08:21:50","extension":"pdf","order_by":0,"title":"","display":"","copyAsset":false,"role":"manuscript-pdf","size":1401015,"visible":true,"origin":"","legend":"","description":"","filename":"manuscript.pdf","url":"https://assets-eu.researchsquare.com/files/rs-7780927/v1/e18b6a00-7803-4572-883a-4ce0200ed42d.pdf"}],"financialInterests":"","formattedTitle":"Economic Impact of Lumpy Skin Disease in Africa: A Systematic Review","fulltext":[{"header":"1.0 Introduction","content":"\u003cp\u003eLumpy skin disease (LSD) is an economically significant viral disease primarily affecting cattle and buffalo. It is caused by the Lumpy Skin Disease Virus (LSDV), a member of the \u003cem\u003eCapripoxvirus\u003c/em\u003e genus within the \u003cem\u003ePoxviridae\u003c/em\u003e family. Characterized by skin nodules, fever, and lymphadenopathy, LSDV infections result in considerable morbidity and can lead to severe economic losses in livestock populations. Mechanical transmission through arthropod vectors, such as biting flies (\u003cem\u003eStomoxys calcitrans\u003c/em\u003e) and ticks (\u003cem\u003eRhipicephalus appendiculatus\u003c/em\u003e), compound the challenge of controlling LSD, particularly in endemic regions where environmental factors support the proliferation of these vectors (Tuppurainen et al., \u003cspan citationid=\"CR58\" class=\"CitationRef\"\u003e2017\u003c/span\u003e).\u003c/p\u003e\u003cp\u003eHistorically confined to sub-Saharan Africa, LSD has spread to the Middle East, Europe, and Asia, and is now recognized as a global threat (Megha et al., \u003cspan citationid=\"CR42\" class=\"CitationRef\"\u003e2022\u003c/span\u003e). The transboundary nature of LSD disrupts international trade, posing significant challenges to livestock production and food security in previously unaffected regions (Tuppurainen et al., \u003cspan citationid=\"CR56\" class=\"CitationRef\"\u003e2013\u003c/span\u003e). In Africa, where livestock contributes substantially to food, income, and draft power, LSD\u0026rsquo;s high morbidity rate results in substantial economic impacts, including reduced milk production, decreased beef quality, infertility, abortion, and permanent damage to hides, all of which significantly lower livestock market value (Megha et al., \u003cspan citationid=\"CR42\" class=\"CitationRef\"\u003e2022\u003c/span\u003e; Yadav et al., \u003cspan citationid=\"CR62\" class=\"CitationRef\"\u003e2024\u003c/span\u003e). Although LSD\u0026rsquo;s mortality rate is generally low, the disease imposes considerable financial burdens, particularly on smallholder farmers who rely on livestock for their livelihoods (Chacha, \u003cspan citationid=\"CR16\" class=\"CitationRef\"\u003e2024\u003c/span\u003e).\u003c/p\u003e\u003cp\u003eThe economic impact of LSD extends beyond direct production losses; outbreaks necessitate increased expenditures on veterinary services, including costs for treatment, vaccination, and implementation of biosecurity measures, which further compound the financial burden on affected regions (Abdi \u0026amp; Alage, \u003cspan citationid=\"CR1\" class=\"CitationRef\"\u003e2018\u003c/span\u003e). Furthermore, the epidemiology of LSD is complex, influenced by environmental factors such as warm, humid climates, as well as herd management practices, including communal grazing and movement of livestock across regions. These factors facilitate the spread of LSD and underscore the need for data-driven control strategies (Birhanu et al., \u003cspan citationid=\"CR14\" class=\"CitationRef\"\u003e2015\u003c/span\u003e; Manyenya et al., \u003cspan citationid=\"CR39\" class=\"CitationRef\"\u003e2024\u003c/span\u003e).\u003c/p\u003e\u003cp\u003eDespite the significant threat LSD poses to livestock production and rural livelihoods in Africa, there remains a notable gap in epidemiological data on its prevalence, incidence, and economic consequences. This lack of comprehensive data hinders the development of effective control strategies and limits the ability to fully quantify the economic toll of the disease on the livestock sector (Nuvey et al., \u003cspan citationid=\"CR50\" class=\"CitationRef\"\u003e2022\u003c/span\u003e). Given the widespread prevalence of LSD and its far-reaching effects on livestock-dependent economies, a systematic review of the disease\u0026rsquo;s economic impact and the efficacy of management strategies is essential. Understanding these economic implications and identifying effective interventions are critical steps in informing policies that can mitigate LSD\u0026rsquo;s impact on livestock health, food security, and the economy.\u003c/p\u003e\u003cp\u003eThis systematic review aims to address these gaps by evaluating the economic impact of LSD in Africa. Specifically, this review will quantify the financial costs associated with LSD outbreaks, including direct production losses and indirect control-related expenses. Furthermore, it will identify effective disease management and control measures to reduce the burden of LSD on the African livestock sector.\u003c/p\u003e"},{"header":"2.0 Method","content":"\u003cdiv id=\"Sec3\" class=\"Section2\"\u003e\u003ch2\u003e2.1 Study Protocols\u003c/h2\u003e\u003cp\u003eThis systematic review was conducted in line with the Preferred Reporting Items for Systematic Reviews and Meta-Analyses (PRISMA) guidelines, providing a structured approach to evaluate the economic impact of Lumpy Skin Disease (LSD) across Africa. The study protocol focused on assessing the financial burden of LSD in terms of production losses, costs associated with disease control, and identifying effective management strategies. The Newcastle-Ottawa Scale (NOS) was used to evaluate the quality of included studies, ensuring that each piece of evidence could reliably contribute to a holistic understanding of LSD\u0026rsquo;s economic impact.\u003c/p\u003e\u003c/div\u003e\u003cdiv id=\"Sec4\" class=\"Section2\"\u003e\u003ch2\u003e2.2 Eligibility Criteria\u003c/h2\u003e\u003cp\u003eStudies were selected based on specific eligibility criteria. Eligible studies focused on LSD\u0026rsquo;s impact on cattle and buffalo populations within Africa, with attention to economic factors, including direct costs (e.g., production losses such as milk and meat yield) and indirect costs (e.g., treatment, vaccination, and biosecurity measures). Quantitative outcomes on economic impact, epidemiological measures (e.g., prevalence and incidence), and management strategies were also required. Retrospective, prospective, and cross-sectional studies, as well as systematic reviews or economic analyses, were considered. Only English-language studies published in peer-reviewed journals or full reports with relevant data were included, while opinion pieces, editorials, and studies lacking detailed economic data were excluded.\u003c/p\u003e\u003c/div\u003e\u003cdiv id=\"Sec5\" class=\"Section2\"\u003e\u003ch2\u003e2.3 Information Sources and Search Strategy\u003c/h2\u003e\u003cp\u003eThe search for relevant studies was conducted in two major databases: PubMed and Google Scholar. Search terms included combinations of \u0026ldquo;Lumpy Skin Disease,\u0026rdquo; \u0026ldquo;LSD,\u0026rdquo; \u0026ldquo;economic impact,\u0026rdquo; \u0026ldquo;financial cost,\u0026rdquo; and \u0026ldquo;Africa,\u0026rdquo; using Boolean operators \"AND\" and \"OR\" to optimize the search breadth and specificity. Sample search strings included \u0026ldquo;Lumpy Skin Disease\u0026rdquo; AND \u0026ldquo;economic impact\u0026rdquo; AND \u0026ldquo;Africa.\u0026rdquo; Additionally, reference lists of selected studies were reviewed to identify further studies not captured in the initial search. The search concluded on 6th October 2024, capturing the most recent and relevant data available.\u003c/p\u003e\u003c/div\u003e\u003cdiv id=\"Sec6\" class=\"Section2\"\u003e\u003ch2\u003e2.4 Selection of Studies\u003c/h2\u003e\u003cp\u003eThe study selection process followed PRISMA recommendations. After removing duplicate records, two independent reviewers screened the titles and abstracts to assess relevance, followed by a full-text review of studies meeting the initial eligibility criteria. Any disagreements between reviewers were resolved by discussion. A PRISMA flow diagram (Fig.\u0026nbsp;\u003cspan refid=\"Fig1\" class=\"InternalRef\"\u003e1\u003c/span\u003e) illustrates the detailed selection process, showing records identified, screened, excluded, and included in the final analysis.\u003c/p\u003e\u003cp\u003e\u003c/p\u003e\u003c/div\u003e\u003cdiv id=\"Sec7\" class=\"Section2\"\u003e\u003ch2\u003e2.5 Data Extraction\u003c/h2\u003e\u003cp\u003eData extraction was carried out by two independent reviewers using a standardized data extraction form. Information collected included study characteristics (authors, publication year, country, study design), population details, economic outcomes, and management strategies employed. This structured extraction ensured consistency across studies and allowed for comprehensive comparisons. All extracted data were cross-checked by a third reviewer to ensure accuracy and mitigate potential discrepancies.\u003c/p\u003e\u003c/div\u003e\u003cdiv id=\"Sec8\" class=\"Section2\"\u003e\u003ch2\u003e2.6 Quality Assessment\u003c/h2\u003e\u003cp\u003eThe quality of each study was evaluated using the Newcastle-Ottawa Scale (NOS) for observational studies. This scale assesses three key domains: selection (including representativeness of the sample), comparability (adjustment for confounding factors), and outcome (reliability of outcome measurements). Studies were scored on these criteria, with scores between 7 and 9 indicating low risk of bias, 4 to 6 as moderate, and 0 to 3 as high risk. Studies deemed high risk were included in the qualitative synthesis but excluded from quantitative analysis where applicable.\u003c/p\u003e\u003c/div\u003e"},{"header":"3.0 Results","content":"\u003cp\u003e\u003cstrong\u003e3.1 Study Selection\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eA total of 160 records were initially retrieved from the academic databases PubMed and Google Scholar. After removing duplicate records (n = 8) and performing an initial abstract screening for relevance, 146 articles proceeded to the eligibility assessment stage. During this eligibility screening, 141 full-text articles were evaluated, and 110 studies were subsequently excluded. Reasons for exclusion included lack of economic focus (n = 34), study locations outside of Africa (n = 31), insufficient data on economic outcomes (n = 36), and irrelevance to the search terms for Lumpy Skin Disease (n = 9). Following these criteria, 31 studies met the eligibility requirements and were included in this review. The PRISMA flow diagram (Figure 1) provides a visual summary of the study selection process.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003e3.2 Characteristics of Included Studies\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eThe included studies represented a range of geographical locations and economic analyses relevant to the impact of LSD in Africa. Key characteristics of the studies are summarized in Table 1. Most studies were conducted in East Africa (notably Ethiopia, Kenya, and Tanzania) and Southern Africa (primarily South Africa), with a few from West Africa, including Nigeria. The designs of these studies included cross-sectional observational studies (n = 12), retrospective analyses (n = 9), economic evaluations (n = 5), and simulation-based models (n = 5), which examined both direct and indirect costs associated with LSD outbreaks.\u003c/p\u003e\n\u003cp\u003eThe most commonly reported economic impact metrics included production losses (milk and meat yield, hide quality), mortality and morbidity rates, and direct financial losses to farmers and the broader livestock industry. Several studies also included cost-benefit analyses of disease management strategies, primarily vaccination and biosecurity measures.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003e3.3 Risk of Bias in Included Studies\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eThe Newcastle-Ottawa Scale (NOS) was employed to assess the risk of bias across studies. The scoring criteria included study selection, comparability, and outcome measurement, with total scores determining the classification as low (7 - 9 points), moderate (4 - 6 points), or high risk of bias (0 - 3 points). Out of the 31 studies reviewed, 12 demonstrated low risk of bias, indicating robust methodologies with clear outcomes, while 15 were rated as moderate risk due to limitations in sampling techniques or lack of control for confounding factors. Four studies were classified as high risk, primarily due to inadequate sampling methodologies and lack of comparative analysis.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003e3.4 Results of Individual Studies\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eThe reviewed studies on LSD employed a wide range of methodologies, including cross-sectional surveys, retrospective analyses, outbreak investigations, experimental infections, qualitative interviews, simulation-based economic models, and systematic reviews, conducted across several African countries Ethiopia, Tanzania, Nigeria, South Africa, Uganda, Kenya, Cameroon, Zimbabwe, Ghana, Senegal, and Egypt. Their objectives covered epidemiological patterns, seroprevalence, morbidity, mortality, vaccine efficacy, economic and financial impacts, disease transmission dynamics, molecular and genomic characterization of LSDV strains, and community-level perceptions of disease burden. Populations studied included cattle of different ages, breeds, and production systems, with some studies extending to other hosts (e.g., guinea pigs) and vectors (e.g., ticks). Reported outcomes ranged from clinical signs, production and reproductive losses, and vaccine effectiveness to economic modeling and qualitative insights into livestock keepers\u0026rsquo; priorities. The overall risk of bias varied, with most studies assessed as moderate, several as low, and a few as high, reflecting differences in design robustness, data quality, and scope, but collectively these studies provide valuable insights into LSD epidemiology, control, and impact across diverse settings.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003e3.4.1 Direct Costs of Lumpy Skin Disease\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eLSD\u0026rsquo;s direct economic impacts were substantial, with production losses consistently reported across studies. Decreased milk and meat yield, reduced hide quality, and compromised reproductive performance were prominent outcomes. In Ethiopia, Birhanu et al. reported a 20 % reduction in milk yield and an estimated financial loss of $6.43 per affected zebu and $58 per affected Holstein Friesian cattle. Other studies highlighted high morbidity rates, with some LSD outbreaks reporting rates of 85 %, which translated to extensive financial losses from reduced productivity and increased treatment expenses. Mortality rates were generally low (1-3 %) but still resulted in significant economic strain, especially for smallholder farmers reliant on livestock for their primary income.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003e3.4.2 Indirect Costs Related to LSD Management\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eIndirect costs associated with LSD management were also substantial. Treatment costs included veterinary fees, anti-inflammatory and antibiotic medications, and wound care, representing a significant financial burden for affected farmers. Additionally, biosecurity expenses for quarantine, decontamination, and vector control added to the indirect costs of LSD control. For example, smallholder farmers in Tanzania and South Africa reported prohibitive expenses in implementing biosecurity measures, compounding the financial impact of the disease.\u003c/p\u003e\n\u003cp\u003eVaccination emerged as a key preventive measure in several studies, with reported efficacy rates ranging from 80 \u0026ndash; 90 %. However, the accessibility and affordability of vaccines varied widely, limiting their uptake. Smallholder farmers faced challenges in affording the vaccination costs, which further highlighted socioeconomic disparities in LSD management across regions.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003e3.4.3 Economic Impact of Trade Restrictions\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eLSD\u0026rsquo;s impact extended beyond production losses to influence local and international trade significantly. Regional trade restrictions and international bans on livestock products from LSD-affected areas resulted in considerable financial losses. In South Africa, dairy and beef sectors faced substantial economic downturns during LSD outbreaks due to export restrictions and carcass condemnations. Similarly, Kenya and Nigeria experienced financial setbacks as they faced export limitations imposed to maintain international trade standards. Such restrictions affected not only large-scale commercial farmers but also had far-reaching implications for local markets reliant on livestock trade for income and food security.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003e3.4.4 Socioeconomic Implications for Rural Communities\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eThe socioeconomic burden of LSD was particularly profound in rural communities, where livestock forms a critical part of household economies. Direct impacts, such as reduced productivity and increased treatment costs, contributed to food insecurity and income loss among smallholder farmers. For instance, a study conducted in Nigeria reported that economic losses per outbreak ranged from $9.6 to $6,340, depending on the size of the herd and production system. Additionally, the high costs associated with LSD management disproportionately affected smallholder farmers, exacerbating poverty levels and hindering sustainable livestock production in economically vulnerable areas.\u003c/p\u003e\n\u003cp\u003eThese findings highlight the considerable economic burden of LSD on Africa\u0026apos;s livestock sector, highlighting the urgent need for affordable management solutions and greater vaccine accessibility.\u003c/p\u003e"},{"header":"4.0 Discussion","content":"\u003cp\u003eThis systematic review consolidates the economic impact of Lumpy Skin Disease (LSD) across Africa, emphasizing significant financial burdens in the livestock sector due to production losses, disease management expenses, and wider socioeconomic effects. The direct costs associated with LSD were largely driven by decreased milk and meat yields and reduced hide quality, impacting both smallholder and commercial farmers. In Ethiopia, LSD-related milk production losses reached up to 20%, translating into significant income reductions for affected households (Birhanu et al., \u003cspan citationid=\"CR14\" class=\"CitationRef\"\u003e2015\u003c/span\u003e)​. Furthermore, the indirect costs of LSD management, including veterinary care and biosecurity measures, impose additional economic challenges, especially for resource-constrained smallholder farmers​.\u003c/p\u003e\u003cp\u003eThe findings align with global trends, where LSD has similarly impacted livestock productivity and economic stability in regions beyond Africa, including the Middle East and parts of Europe (Moudgil et al., \u003cspan citationid=\"CR46\" class=\"CitationRef\"\u003e2023\u003c/span\u003e). However, in Africa, the economic toll of LSD appears disproportionately high due to the critical role of livestock in rural economies and the limited infrastructure for disease management (Tuppurainen et al., \u003cspan citationid=\"CR56\" class=\"CitationRef\"\u003e2013\u003c/span\u003e; Nuvey et al., \u003cspan citationid=\"CR50\" class=\"CitationRef\"\u003e2022\u003c/span\u003e). This mirrors trends observed in other transboundary animal diseases, such as Rift Valley fever and foot-and-mouth disease, which also contribute to severe economic strain among African smallholders who often rely on livestock for their livelihoods (Gari et al., \u003cspan citationid=\"CR23\" class=\"CitationRef\"\u003e2011\u003c/span\u003e; Mukolwe et al., \u003cspan citationid=\"CR47\" class=\"CitationRef\"\u003e2021\u003c/span\u003e).\u003c/p\u003e\u003cp\u003eWhile both large-scale and smallholder farms suffer from LSD, the financial impact is more pronounced for subsistence farmers, who are especially vulnerable to income disruptions caused by reduced milk and meat yields (Gari et al., \u003cspan citationid=\"CR24\" class=\"CitationRef\"\u003e2010\u003c/span\u003e). These findings underscore the necessity for greater vaccine access and infrastructure to support African farmers in managing LSD outbreaks more effectively. Unlike regions with developed veterinary systems where vaccination programs are accessible and supported by infrastructure, the lack of affordable and readily available vaccines in Africa exacerbates the economic impact of LSD and poses challenges to sustainable disease control (Mdlulwa et al., \u003cspan citationid=\"CR41\" class=\"CitationRef\"\u003e2018\u003c/span\u003e).\u003c/p\u003e\u003cdiv id=\"Sec19\" class=\"Section2\"\u003e\u003ch2\u003e4.1 Economic Impact on Trade and Regional Market Stability\u003c/h2\u003e\u003cp\u003eThe review highlights LSD\u0026rsquo;s far-reaching impact on both local and international trade, as outbreaks often lead to restrictions on livestock and livestock products, impairing economic stability. In South Africa, Kenya, and Nigeria, where livestock exports represent a considerable part of the agricultural economy, trade limitations due to LSD outbreaks have reduced export revenues, disrupted local markets, and heightened food insecurity (Ntombimbini and Klein, \u003cspan citationid=\"CR49\" class=\"CitationRef\"\u003e2015\u003c/span\u003e; Clemmons et al., \u003cspan citationid=\"CR17\" class=\"CitationRef\"\u003e2021\u003c/span\u003e). These findings suggest that implementing proactive and regionally coordinated LSD management practices could mitigate economic losses and stabilize trade.\u003c/p\u003e\u003cp\u003eMoreover, the disease\u0026rsquo;s impact extends to secondary industries, such as the leather industry, which depends on high-quality hides for production. Reduced hide quality due to LSD lesions undermines the leather export market, further compounding the economic strain on affected regions (Farah \u0026amp; Ahmed, \u003cspan citationid=\"CR21\" class=\"CitationRef\"\u003e2018\u003c/span\u003e). This aligns with previous findings on the broader economic effects of LSD, where outbreaks affect both immediate livestock productivity and the long-term viability of industries reliant on livestock byproducts (Al-Salihi, \u003cspan citationid=\"CR8\" class=\"CitationRef\"\u003e2014\u003c/span\u003e; Kappes et al., \u003cspan citationid=\"CR32\" class=\"CitationRef\"\u003e2023\u003c/span\u003e).\u003c/p\u003e\u003c/div\u003e\u003cdiv id=\"Sec20\" class=\"Section2\"\u003e\u003ch2\u003e4.2 Management and Control Strategies: Efficacy and Barriers\u003c/h2\u003e\u003cp\u003eVaccination is widely recognized as a primary strategy for LSD prevention and control, with reported efficacy rates ranging between 80% and 90% in reducing disease incidence (Birhanu et al., \u003cspan citationid=\"CR14\" class=\"CitationRef\"\u003e2015\u003c/span\u003e; Mdlulwa et al., \u003cspan citationid=\"CR41\" class=\"CitationRef\"\u003e2018\u003c/span\u003e). However, vaccine access remains inconsistent, particularly in rural regions with limited veterinary infrastructure. The cost of vaccines further restricts access for smallholder farmers, who are less equipped to bear the financial burden of preventive measures. This discrepancy highlights a critical inequality in LSD management: commercial farms, with greater resources, are better positioned to implement vaccination and biosecurity measures, whereas smallholders are more susceptible to repeated outbreaks (Tuppurainen et al., \u003cspan citationid=\"CR58\" class=\"CitationRef\"\u003e2017\u003c/span\u003e; Clemmons et al., \u003cspan citationid=\"CR17\" class=\"CitationRef\"\u003e2021\u003c/span\u003e).\u003c/p\u003e\u003cp\u003eEffective biosecurity measures, including animal quarantine and vector control, also play a significant role in controlling LSD transmission. However, logistical challenges in rural, resource-limited settings hinder the consistent application of these measures. The implementation of multivalent vaccines, such as combined LSD-RVF vaccines, could provide a more cost-effective solution for farmers in high-risk regions, reducing the need for multiple vaccination rounds and potentially lessening the disease\u0026rsquo;s overall impact (Mdlulwa et al., \u003cspan citationid=\"CR41\" class=\"CitationRef\"\u003e2018\u003c/span\u003e; Ntombimbini and Klein, \u003cspan citationid=\"CR49\" class=\"CitationRef\"\u003e2015\u003c/span\u003e).\u003c/p\u003e\u003c/div\u003e\u003cdiv id=\"Sec21\" class=\"Section2\"\u003e\u003ch2\u003e4.3 Socioeconomic Implications for Rural Communities\u003c/h2\u003e\u003cp\u003eThe socioeconomic impact of LSD on rural African communities is profound, as livestock represents a primary source of income and food security for many smallholder farmers. The cumulative costs of managing outbreaks, reduced productivity, and loss of trade income exacerbate food insecurity and poverty among already vulnerable populations. For example, a study conducted in Nigeria estimated that economic losses per LSD outbreak could range from \u003cspan\u003e$\u003c/span\u003e9.6 to \u003cspan\u003e$\u003c/span\u003e6,340, depending on the herd size and production system, with poorer households disproportionately affected (Limon et al., \u003cspan citationid=\"CR37\" class=\"CitationRef\"\u003e2020\u003c/span\u003e). Additionally, the high costs associated with disease control measures deepen poverty and limit the capacity of smallholder farmers to recover from recurring outbreaks (Chacha, \u003cspan citationid=\"CR16\" class=\"CitationRef\"\u003e2024\u003c/span\u003e; Mukolwe et al., \u003cspan citationid=\"CR47\" class=\"CitationRef\"\u003e2021\u003c/span\u003e).\u003c/p\u003e\u003c/div\u003e\u003cdiv id=\"Sec22\" class=\"Section2\"\u003e\u003ch2\u003e4.4 Future Directions\u003c/h2\u003e\u003cp\u003eLongitudinal economic evaluations are crucial to understand the cumulative impact of repeated LSD outbreaks on African livestock-dependent communities. While immediate economic losses due to outbreaks are significant, the prolonged financial toll on rural farmers likely extends beyond single-event losses. Studies have shown that frequent disease outbreaks lead to compounded productivity losses, increased treatment costs, and deeper economic hardship over time (Tadesse et al., \u003cspan citationid=\"CR54\" class=\"CitationRef\"\u003e2020\u003c/span\u003e). Long-term studies that track the economic impacts of LSD over multiple outbreak cycles would provide a more comprehensive understanding of these effects and help to identify the most sustainable management practices. Additionally, cost-benefit analyses across different farming systems would inform policymakers on which interventions(such as vaccination, biosecurity, or vector control)offer the greatest economic returns for smallholder farmers (Gari et al., \u003cspan citationid=\"CR23\" class=\"CitationRef\"\u003e2011\u003c/span\u003e; Kiplagat et al., 2020).\u003c/p\u003e\u003cp\u003eThe development of multivalent vaccines targeting LSD alongside other transboundary livestock diseases, such as Rift Valley Fever (RVF), represents a promising avenue for research. Multivalent vaccines can reduce the logistical challenges and financial burden associated with administering separate vaccines for each disease (Mdlulwa et al., \u003cspan citationid=\"CR41\" class=\"CitationRef\"\u003e2018\u003c/span\u003e). Research focusing on the efficacy, cost, and accessibility of these combined vaccines is needed, as current vaccines are often financially out of reach for smallholder farmers. Studies in South Africa have demonstrated the potential of combined LSD-RVF vaccines to improve disease control in regions with high prevalence, reducing the need for frequent vaccinations and offering dual protection against multiple diseases (Ntombimbini \u0026amp; Klein, \u003cspan citationid=\"CR49\" class=\"CitationRef\"\u003e2015\u003c/span\u003e). Future research should examine delivery mechanisms, such as mobile veterinary services or community-based vaccination programs, to improve vaccine access in remote areas where veterinary infrastructure is sparse (Akshay et al., \u003cspan citationid=\"CR6\" class=\"CitationRef\"\u003e2023\u003c/span\u003e).\u003c/p\u003e\u003cp\u003eExpanding biosecurity practices tailored to resource-limited settings is another priority. Current biosecurity recommendations, including vector control, animal movement restrictions, and early detection, are effective in controlling LSD but present implementation challenges for rural farmers with limited resources (Subasinghe et al., \u003cspan citationid=\"CR53\" class=\"CitationRef\"\u003e2023\u003c/span\u003e). Research should focus on developing cost-effective, scalable biosecurity solutions that smallholder farmers can adopt. For example, innovations in low-cost vector control measures, such as insecticide-treated livestock housing, could significantly reduce transmission rates in endemic areas (Farah \u0026amp; Ahmed, \u003cspan citationid=\"CR21\" class=\"CitationRef\"\u003e2018\u003c/span\u003e). Community-led surveillance systems, in which local farmers are trained to recognize and report LSD symptoms early, could enhance outbreak response and containment, particularly in high-risk regions (Moje et al., \u003cspan citationid=\"CR44\" class=\"CitationRef\"\u003e2023\u003c/span\u003e). Such systems have proven effective in controlling other livestock diseases and may offer a practical solution for managing LSD in African countries (Clemmons et al., \u003cspan citationid=\"CR17\" class=\"CitationRef\"\u003e2021\u003c/span\u003e).\u003c/p\u003e\u003cp\u003eFinally, enhancing regional cooperation on LSD management and trade policies is essential to support Africa\u0026rsquo;s livestock export market. Outbreaks of LSD often lead to trade restrictions, negatively impacting the economies of countries where livestock exports are critical (Kiplagat et al., 2020). Regional collaboration across African countries, with standardized LSD control protocols and harmonized trade policies, could stabilize livestock markets and protect farmers from abrupt financial losses due to trade bans (Ntombimbini \u0026amp; Klein, \u003cspan citationid=\"CR49\" class=\"CitationRef\"\u003e2015\u003c/span\u003e). Shared resources for disease surveillance and data exchange could facilitate timely responses and minimize the spread of LSD across borders. Collaborative efforts with international bodies such as the World Organisation for Animal Health (OIE) could strengthen the veterinary infrastructure and disease management capabilities in affected regions (Tuppurainen et al., \u003cspan citationid=\"CR58\" class=\"CitationRef\"\u003e2017\u003c/span\u003e). Enhanced regional and international cooperation will be critical to address the economic impact of LSD comprehensively, supporting resilience in Africa\u0026rsquo;s livestock sector.\u003c/p\u003e\u003c/div\u003e\u003cdiv id=\"Sec23\" class=\"Section2\"\u003e\u003ch2\u003e4.5 Conclusion\u003c/h2\u003e\u003cp\u003eThis systematic review reveals the significant economic impact of Lumpy Skin Disease (LSD) on Africa\u0026rsquo;s livestock sector, manifesting through production losses, increased management costs, and trade restrictions. The disease reduces milk and meat yields, lowers hide quality, and disrupts reproductive performance, directly affecting commercial and smallholder farmers. The financial strain is exacerbated by the high costs of veterinary treatments, biosecurity measures, and limited vaccine accessibility, which especially burdens smallholder farmers. Vaccination, although effective, faces challenges in affordability and availability, emphasizing the need for enhanced biosecurity practices and regional cooperation. To mitigate LSD\u0026rsquo;s economic impact and support sustainable development, this review calls for affordable vaccine access, improved biosecurity, and coordinated policy interventions across the affected regions.\u003c/p\u003e\u003c/div\u003e"},{"header":"Declarations","content":"\u003cp\u003e\u003cstrong\u003eAuthor Contributions\u003c/strong\u003e:\u0026nbsp;\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eNL:\u003c/strong\u003e Conceptualization, supervision, project administration, writing\u0026mdash;original draft preparation, writing\u0026mdash;review and editing, data curation, resources. \u003cstrong\u003eMTA:\u0026nbsp;\u003c/strong\u003ewriting\u0026mdash;original draft preparation, writing\u0026mdash;review and editing, data curation. \u003cstrong\u003eFAA:\u003c/strong\u003e writing\u0026mdash;original draft preparation, writing\u0026mdash;review and editing, data curation. \u003cstrong\u003eCCO:\u003c/strong\u003e writing\u0026mdash;original draft preparation, writing\u0026mdash;review and editing, data curation. \u003cstrong\u003eAOU:\u003c/strong\u003e supervision, writing\u0026mdash;original draft preparation, writing\u0026mdash;review and editing, data curation. \u003cstrong\u003eUSO:\u003c/strong\u003e Conceptualization, supervision, writing\u0026mdash;original draft preparation, writing\u0026mdash;review and editing, data curation, resources. \u003cstrong\u003eMAA:\u0026nbsp;\u003c/strong\u003ewriting\u0026mdash;original draft preparation, writing\u0026mdash;review and editing, data curation.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eEthical approval\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eNot Applicable.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eResearch involving human and/or animal participants\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eNot Applicable.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eInformed consent\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eNot Applicable.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eFunding\u0026nbsp;\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eThis study received no external funding.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eData Availability Statement\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eData is available upon request from the corresponding author.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eAcknowledgments:\u0026nbsp;\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eThe authors gratefully acknowledge all those who contributed to this study.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eConflicts of Interest\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eThe authors declare no conflict of interest.\u003c/p\u003e"},{"header":"References","content":"\u003col\u003e\n \u003cli\u003eAbdi, F. 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Epidemiological Status and Economic Impact of Lumpy Skin Disease-Review. \u003cem\u003ePeer-Reviewed, Refereed, Open Access Journal\u003c/em\u003e, 2322 \u0026ndash; 0392. doi:10.18782/2322-0392.1284\u003c/li\u003e\n \u003cli\u003eTesfaye, S., Regassa, F., Beyene, G., Leta, S., \u0026amp; Paeshuyse, J. (2024). Spatiotemporal analysis and forecasting of lumpy skin disease outbreaks in Ethiopia based on retrospective outbreak reports. \u003cem\u003eFront Vet Sci, 11\u003c/em\u003e, 1277007. doi:10.3389/fvets.2024.1277007\u003c/li\u003e\n \u003cli\u003eTuppurainen, E. S., Lubinga, J. C., Stoltsz, W. H., Troskie, M., Carpenter, S. T., Coetzer, J. A., . . . Oura, C. A. (2013). Mechanical transmission of lumpy skin disease virus by Rhipicephalus appendiculatus male ticks. \u003cem\u003eEpidemiol Infect, 141\u003c/em\u003e(2), 425-430. doi:10.1017/S0950268812000805\u003c/li\u003e\n \u003cli\u003eTuppurainen, E. S., Venter, E. H., Coetzer, J. A., \u0026amp; Bell-Sakyi, L. (2015). Lumpy skin disease: attempted propagation in tick cell lines and presence of viral DNA in field ticks collected from naturally-infected cattle. \u003cem\u003eTicks Tick Borne Dis, 6\u003c/em\u003e(2), 134-140. doi:10.1016/j.ttbdis.2014.11.002\u003c/li\u003e\n \u003cli\u003eTuppurainen, E. S. M., Venter, E. H., Shisler, J. L., Gari, G., Mekonnen, G. A., Juleff, N., . . . Babiuk, L. A. (2017). Review: Capripoxvirus Diseases: Current Status and Opportunities for Control. \u003cem\u003eTransbound Emerg Dis, 64\u003c/em\u003e(3), 729-745. doi:10.1111/tbed.12444\u003c/li\u003e\n \u003cli\u003eVudriko, P., Ekiri, A. B., Endacott, I., Williams, S., Gityamwi, N., Byaruhanga, J., . . . Varga, G. J. F. i. V. S. (2021). A survey of priority livestock diseases and laboratory diagnostic needs of animal health professionals and farmers in Uganda.\u003cem\u003e\u0026nbsp;8\u003c/em\u003e, 721800.\u0026nbsp;\u003c/li\u003e\n \u003cli\u003eWagh, H., \u0026amp; Patel, M. (2023). Role of Phytochemicals in Lumpy Virus Skin Disease.\u0026nbsp;\u003c/li\u003e\n \u003cli\u003eWolff, J., Tuppurainen, E., Adedeji, A., Meseko, C., Asala, O., Adole, J., . . . Hoffmann, B. (2021). Characterization of a Nigerian Lumpy Skin Disease Virus Isolate after Experimental Infection of Cattle. \u003cem\u003ePathogens, 11\u003c/em\u003e(1). doi:10.3390/pathogens11010016\u003c/li\u003e\n \u003cli\u003eYadav, D., Rao, G., Paliwal, D., Singh, A., Alam, A., Kumar Sharma, P., . . . Kumar, Y. (2024). Cracking the Code of Lumpy Skin Disease: Identifying Causes, Symptoms and Treatment Options for Livestock Farmers. \u003cem\u003eInfectious Disorders-Drug Targets, 24\u003c/em\u003e(5), 57-71.\u0026nbsp;\u003c/li\u003e\n \u003cli\u003eYadav, M. P., Singh, R. K., \u0026amp; Malik, Y. S. (2020). Emerging and transboundary animal viral diseases: Perspectives and preparedness. \u003cem\u003eEmerging transboundary animal viruses\u003c/em\u003e, 1-25.\u0026nbsp;\u003c/li\u003e\n \u003cli\u003eZelalem, A., Hailu, D., \u0026amp; Getachew, G. (2015). Assessment of Distribution and Associated Risk Factors of Lumpy Skin Disease in Selected Districts of West Wollega Zone, Western Ethiopia. \u003cem\u003eAcademic Journal of Animal Diseases, 4\u003c/em\u003e(3). doi:10.5829/idosi.ajad.2015.4.3.95251\u0026nbsp;\u003c/li\u003e\n\u003c/ol\u003e"},{"header":"Tables","content":"\u003cp\u003e\u003cstrong\u003eTable 1: Description of the included studies.\u003c/strong\u003e\u003c/p\u003e\n\u003cdiv align=\"\"\u003e\n \u003ctable border=\"0\" cellspacing=\"0\" cellpadding=\"0\" width=\"100%\"\u003e\n \u003ctbody\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 83px;\"\u003e\n \u003cp\u003e\u003cstrong\u003eStudy\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 97px;\"\u003e\n \u003cp\u003e\u003cstrong\u003eMethodology\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 92px;\"\u003e\n \u003cp\u003e\u003cstrong\u003eStudy Design\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 116px;\"\u003e\n \u003cp\u003e\u003cstrong\u003eOutcome Measures\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 87px;\"\u003e\n \u003cp\u003e\u003cstrong\u003eIntervention\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 92px;\"\u003e\n \u003cp\u003e\u003cstrong\u003eRegion\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 93px;\"\u003e\n \u003cp\u003e\u003cstrong\u003ePopulation Characteristics\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 67px;\"\u003e\n \u003cp\u003e\u003cstrong\u003eRisk of Bias\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 83px;\"\u003e\n \u003cp\u003eAbera (2019)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 97px;\"\u003e\n \u003cp\u003eCross-sectional study using random sampling of 2 districts and 8 associations, structured questionnaire for data collection.\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 92px;\"\u003e\n \u003cp\u003eCross-sectional\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 116px;\"\u003e\n \u003cp\u003e1) Factors contributing to LSD occurrence 2) Efficacy of the LSD vaccine\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 87px;\"\u003e\n \u003cp\u003eNone specified\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 92px;\"\u003e\n \u003cp\u003eTanga and Pwani, Tanzania\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 93px;\"\u003e\n \u003cp\u003eCattle of various ages (calves, young, adults) and sizes (small, medium, large); unvaccinated cattle.\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 67px;\"\u003e\n \u003cp\u003eLow\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 83px;\"\u003e\n \u003cp\u003eTesfaye\u003cem\u003e\u0026nbsp;et al.\u003c/em\u003e (2024)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 97px;\"\u003e\n \u003cp\u003eCollection of LSD outbreak data, time-series analysis, forecasting, and retrospective space-time cluster analysis.\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 92px;\"\u003e\n \u003cp\u003eRetrospective observational\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 116px;\"\u003e\n \u003cp\u003e1) Number of LSD outbreaks, 2) LSD-positive cases, 3) LSD-related deaths, 4) LSD-related slaughters\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 87px;\"\u003e\n \u003cp\u003eNone specified\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 92px;\"\u003e\n \u003cp\u003eEthiopia\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 93px;\"\u003e\n \u003cp\u003eNot specified.\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 67px;\"\u003e\n \u003cp\u003eModerate\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 83px;\"\u003e\n \u003cp\u003eBirhanu\u003cem\u003e\u0026nbsp;et al.\u003c/em\u003e (2015)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 97px;\"\u003e\n \u003cp\u003eCross-sectional survey with purposive sampling, primary and secondary data collection, and financial loss estimation model.\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 92px;\"\u003e\n \u003cp\u003eCross-sectional\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 116px;\"\u003e\n \u003cp\u003e1) Epidemiological variables (incidence, mortality), 2) Production losses, 3) Financial impacts of LSD\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 87px;\"\u003e\n \u003cp\u003eAnnual vaccination against LSD, free for farmers\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 92px;\"\u003e\n \u003cp\u003eAfar and Tigray, Ethiopia\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 93px;\"\u003e\n \u003cp\u003eTotal cattle population of 299,959; varying vaccination histories; predominantly adult females and males; seasonal mobility.\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 67px;\"\u003e\n \u003cp\u003eLow\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 83px;\"\u003e\n \u003cp\u003eNtombimbini and Klein (2015)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 97px;\"\u003e\n \u003cp\u003eUse of primary and secondary data through surveys focused on LSD and RVF, stratified random sampling.\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 92px;\"\u003e\n \u003cp\u003eSurvey and observational\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 116px;\"\u003e\n \u003cp\u003e1) Economic costs of LSD and RVF, 2) Socioeconomic impacts, 3) Relationship between farmer characteristics and impacts\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 87px;\"\u003e\n \u003cp\u003eLSD vaccine administered at an average cost, RVF vaccination mentioned\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 92px;\"\u003e\n \u003cp\u003eSouth Africa\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 93px;\"\u003e\n \u003cp\u003eMajority over 60 years, 43% female, most have less than high school education; varied livestock ownership.\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 67px;\"\u003e\n \u003cp\u003eModerate\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 83px;\"\u003e\n \u003cp\u003eKebede (2019)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 97px;\"\u003e\n \u003cp\u003eTwo-week acceptability phase for guinea pig diets, factorial experiment with dietary treatments.\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 92px;\"\u003e\n \u003cp\u003eExperimental\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 116px;\"\u003e\n \u003cp\u003ePalatability and intake of supplements, reproductive performance, abortion rates, birth weights\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 87px;\"\u003e\n \u003cp\u003eVarious dietary treatments\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 92px;\"\u003e\n \u003cp\u003eNigeria, Cameroon\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 93px;\"\u003e\n \u003cp\u003eAdult female guinea pigs, local and crossbred; owned by volunteers.\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 67px;\"\u003e\n \u003cp\u003eModerate\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 83px;\"\u003e\n \u003cp\u003eMakoga\u003cem\u003e\u0026nbsp;et al.\u003c/em\u003e (2024)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 97px;\"\u003e\n \u003cp\u003eMultistage sampling method, blood sample collection, serological testing for LSDV antibodies.\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 92px;\"\u003e\n \u003cp\u003eCross-sectional\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 116px;\"\u003e\n \u003cp\u003eSeroprevalence of LSDV antibodies in unvaccinated cattle\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 87px;\"\u003e\n \u003cp\u003eNone specified\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 92px;\"\u003e\n \u003cp\u003eTanga and Pwani, Tanzania\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 93px;\"\u003e\n \u003cp\u003eCattle of different ages and sizes, all unvaccinated.\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 67px;\"\u003e\n \u003cp\u003eModerate\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 83px;\"\u003e\n \u003cp\u003eHaga\u003cem\u003e\u0026nbsp;et al.\u003c/em\u003e (2024)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 97px;\"\u003e\n \u003cp\u003eVirus isolation and purification, DNA extraction, Illumina sequencing of LSDV genomes.\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 92px;\"\u003e\n \u003cp\u003eRetrospective observational\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 116px;\"\u003e\n \u003cp\u003eNo specific outcome measured; genomic analysis of LSDV strains\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 87px;\"\u003e\n \u003cp\u003eNone specified\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 92px;\"\u003e\n \u003cp\u003eVarious (Sri Lanka, Mongolia, Nigeria, Ethiopia, Senegal, Ghana, Cameroon)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 93px;\"\u003e\n \u003cp\u003eNot specified.\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 67px;\"\u003e\n \u003cp\u003eHigh\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 83px;\"\u003e\n \u003cp\u003eWolff\u003cem\u003e\u0026nbsp;et al.\u003c/em\u003e (2021)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 97px;\"\u003e\n \u003cp\u003eExperimental infection of cattle with monitoring of clinical signs and sample collection for analysis.\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 92px;\"\u003e\n \u003cp\u003eExperimental infection\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 116px;\"\u003e\n \u003cp\u003e1) Clinical signs, 2) Viremia, 3) Viral shedding, 4) Seroconversion\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 87px;\"\u003e\n \u003cp\u003eExperimental infection with Nigerian LSDV isolate\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 92px;\"\u003e\n \u003cp\u003eNigeria\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 93px;\"\u003e\n \u003cp\u003e8 female Holstein Friesian cattle aged 4-6 months, clinically healthy.\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 67px;\"\u003e\n \u003cp\u003eModerate\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 83px;\"\u003e\n \u003cp\u003eLeliso\u003cem\u003e\u0026nbsp;et al.\u003c/em\u003e (2021)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 97px;\"\u003e\n \u003cp\u003eOutbreak investigation with sample collection from affected animals, processing for virus isolation and molecular characterization.\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 92px;\"\u003e\n \u003cp\u003eObservational field investigation\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 116px;\"\u003e\n \u003cp\u003eNo clearly defined outcome measured\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 87px;\"\u003e\n \u003cp\u003eNone specified\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 92px;\"\u003e\n \u003cp\u003eBale Administrative Zone, Ethiopia\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 93px;\"\u003e\n \u003cp\u003eCattle of all ages, higher morbidity in males; varied production systems.\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 67px;\"\u003e\n \u003cp\u003eHigh\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 83px;\"\u003e\n \u003cp\u003eTuppurainen, E. S.\u003cem\u003e\u0026nbsp;et al.\u003c/em\u003e (2013)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 97px;\"\u003e\n \u003cp\u003eExperimental infection of cattle and tick transfer to study virus transmission.\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 92px;\"\u003e\n \u003cp\u003eExperimental model\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 116px;\"\u003e\n \u003cp\u003eTransmission of LSDV via ticks\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 87px;\"\u003e\n \u003cp\u003eInfection of donor animals and tick transfer\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 92px;\"\u003e\n \u003cp\u003eSouth Africa\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 93px;\"\u003e\n \u003cp\u003e3 young female Bonsmara cattle, no vaccination against LSD practiced in the herd.\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 67px;\"\u003e\n \u003cp\u003eHigh\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 83px;\"\u003e\n \u003cp\u003eAyelet\u003cem\u003e\u0026nbsp;et al.\u003c/em\u003e (2014)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 97px;\"\u003e\n \u003cp\u003eExperimental infection with monitoring and molecular analysis of samples collected from infected cattle.\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 92px;\"\u003e\n \u003cp\u003eObservational and retrospective analysis\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 116px;\"\u003e\n \u003cp\u003e1) Morbidity and mortality rates, 2) Virus isolation and detection, 3) Vaccine efficacy assessment\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 87px;\"\u003e\n \u003cp\u003eExperimental infection with Nigerian LSDV isolate\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 92px;\"\u003e\n \u003cp\u003eCentral region, Ethiopia\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 93px;\"\u003e\n \u003cp\u003eCattle from feedlot and smallholder farms, predominantly males; higher morbidity in young cattle.\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 67px;\"\u003e\n \u003cp\u003eModerate\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd rowspan=\"3\" valign=\"top\" style=\"width: 83px;\"\u003e\n \u003cp\u003eGirma\u003cem\u003e\u0026nbsp;et al.\u003c/em\u003e (2019)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 97px;\"\u003e\n \u003cp\u003e- Purposive selection of study sites based on LSD outbreak reports.\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd rowspan=\"3\" valign=\"top\" style=\"width: 92px;\"\u003e\n \u003cp\u003eObservational study; purposive selection and epidemiological data collection through questionnaires.\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 116px;\"\u003e\n \u003cp\u003e1) Isolation and molecular characterization of LSDV\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 87px;\"\u003e\n \u003cp\u003e1) Annual vaccination in large farms\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd rowspan=\"3\" valign=\"top\" style=\"width: 92px;\"\u003e\n \u003cp\u003eOromia, Ethiopia\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 93px;\"\u003e\n \u003cp\u003e- Crossbred dairy cattle, mostly cows and heifers\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd rowspan=\"3\" valign=\"top\" style=\"width: 67px;\"\u003e\n \u003cp\u003eModerate Risk\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 97px;\"\u003e\n \u003cp\u003e- Inclusion of 191 dairy cattle from smallholder farms and 1551 from large farms.\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 116px;\"\u003e\n \u003cp\u003e2) Assessment of vaccine effectiveness\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 87px;\"\u003e\n \u003cp\u003e2) Vaccination during outbreaks in smallholder farms\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 93px;\"\u003e\n \u003cp\u003e- Smallholder (2-30 cattle) and big farms (\u0026gt;100 cattle)\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 97px;\"\u003e\n \u003cp\u003e- Virus isolation and PCR testing.\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 116px;\"\u003e\n \u003cp\u003e3) Investigation of outbreaks\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 93px;\"\u003e\n \u003cp\u003e- Urban and intensively managed smallholder farms\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd rowspan=\"3\" valign=\"top\" style=\"width: 83px;\"\u003e\n \u003cp\u003eMdlulwa\u003cem\u003e\u0026nbsp;et al.\u003c/em\u003e (2018)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 97px;\"\u003e\n \u003cp\u003e- Conceptual model for economic analysis.\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd rowspan=\"3\" valign=\"top\" style=\"width: 92px;\"\u003e\n \u003cp\u003eEx-ante, non-controlled simulation-based cost-benefit analysis.\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd rowspan=\"3\" valign=\"top\" style=\"width: 116px;\"\u003e\n \u003cp\u003eEconomic impact of LSD RVF 2-in-1 vaccine on dairy production; measures like net present value and benefit-cost ratio.\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd rowspan=\"3\" valign=\"top\" style=\"width: 87px;\"\u003e\n \u003cp\u003eNew multivalent vaccine for LSD and RVF.\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd rowspan=\"3\" valign=\"top\" style=\"width: 92px;\"\u003e\n \u003cp\u003eSouth Africa\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 93px;\"\u003e\n \u003cp\u003e- Dairy farms with herd sizes from \u0026lt; 50 to 1000 cows\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd rowspan=\"3\" valign=\"top\" style=\"width: 67px;\"\u003e\n \u003cp\u003eModerate Risk\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 97px;\"\u003e\n \u003cp\u003e- Cost-benefit analysis of LSD RVF 2-in-1 vaccine.\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd rowspan=\"2\" valign=\"top\" style=\"width: 93px;\"\u003e\n \u003cp\u003e- Breeds include Jersey and mixed breeds\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 97px;\"\u003e\n \u003cp\u003e- Simulation modeling for different scenarios.\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd rowspan=\"3\" valign=\"top\" style=\"width: 83px;\"\u003e\n \u003cp\u003eAtai\u003cem\u003e\u0026nbsp;et al.\u003c/em\u003e (2021)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 97px;\"\u003e\n \u003cp\u003e- Qualitative survey in response to LSD outbreaks.\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd rowspan=\"3\" valign=\"top\" style=\"width: 92px;\"\u003e\n \u003cp\u003eNon-controlled observational qualitative survey and epidemiological investigation.\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd rowspan=\"3\" valign=\"top\" style=\"width: 116px;\"\u003e\n \u003cp\u003eEpidemiological features and economic impact of LSD outbreaks; morbidity, mortality rates, and impact on markets.\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd rowspan=\"3\" valign=\"top\" style=\"width: 87px;\"\u003e\n \u003cp\u003eNot mentioned.\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd rowspan=\"3\" valign=\"top\" style=\"width: 92px;\"\u003e\n \u003cp\u003eBokkos LGA, Nigeria\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 93px;\"\u003e\n \u003cp\u003e- Residents engaged in livestock and crop production\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd rowspan=\"3\" valign=\"top\" style=\"width: 67px;\"\u003e\n \u003cp\u003eModerate Risk\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 97px;\"\u003e\n \u003cp\u003e- Collection of skin scab samples analyzed with PCR.\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 93px;\"\u003e\n \u003cp\u003e- Herd sizes ranged from 35 to 150 cattle, with a total of 1,164 owned by 15 farmers\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 97px;\"\u003e\n \u003cp\u003e- Focus group discussions with farmers.\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd rowspan=\"3\" valign=\"top\" style=\"width: 83px;\"\u003e\n \u003cp\u003eMoje\u003cem\u003e\u0026nbsp;et al.\u003c/em\u003e (2023)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 97px;\"\u003e\n \u003cp\u003e- Sampling cattle from different management systems.\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd rowspan=\"3\" valign=\"top\" style=\"width: 92px;\"\u003e\n \u003cp\u003eLongitudinal observational study; purposive sampling at different time points.\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd rowspan=\"3\" valign=\"top\" style=\"width: 116px;\"\u003e\n \u003cp\u003eHumoral immune response; serum neutralization antibody titers before and after vaccination.\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd rowspan=\"3\" valign=\"top\" style=\"width: 87px;\"\u003e\n \u003cp\u003eAdministration of a live attenuated Capripox-LSD vaccine.\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd rowspan=\"3\" valign=\"top\" style=\"width: 92px;\"\u003e\n \u003cp\u003eEthiopia\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 93px;\"\u003e\n \u003cp\u003e- Cattle aged 6 months to 12 years\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd rowspan=\"3\" valign=\"top\" style=\"width: 67px;\"\u003e\n \u003cp\u003eLow Risk\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 97px;\"\u003e\n \u003cp\u003e- Blood samples collected before and after vaccination.\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 93px;\"\u003e\n \u003cp\u003e- Both male and female\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 97px;\"\u003e\n \u003cp\u003e- Used serum neutralization test for antibodies.\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 93px;\"\u003e\n \u003cp\u003e- Extensive and intensive management systems; sample sizes: 60 extensive, 53 intensive\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd rowspan=\"3\" valign=\"top\" style=\"width: 83px;\"\u003e\n \u003cp\u003eZelalem\u003cem\u003e\u0026nbsp;et al.\u003c/em\u003e (2015)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 97px;\"\u003e\n \u003cp\u003e- Cross-sectional survey with a semi-structured questionnaire.\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd rowspan=\"3\" valign=\"top\" style=\"width: 92px;\"\u003e\n \u003cp\u003eCross-sectional observational study using multi-stage sampling and questionnaire survey.\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd rowspan=\"3\" valign=\"top\" style=\"width: 116px;\"\u003e\n \u003cp\u003eNot mentioned (no clear primary outcome defined).\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd rowspan=\"3\" valign=\"top\" style=\"width: 87px;\"\u003e\n \u003cp\u003eNot mentioned.\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd rowspan=\"3\" valign=\"top\" style=\"width: 92px;\"\u003e\n \u003cp\u003eOromia, Ethiopia\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 93px;\"\u003e\n \u003cp\u003e- Majority adult respondents, mostly literate\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd rowspan=\"3\" valign=\"top\" style=\"width: 67px;\"\u003e\n \u003cp\u003eModerate Risk\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd rowspan=\"2\" valign=\"top\" style=\"width: 97px;\"\u003e\n \u003cp\u003e- Information on household characteristics, disease history, and risk factors.\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 93px;\"\u003e\n \u003cp\u003e- Livelihood involved mixed agriculture, with some engaged in trading and employment\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 93px;\"\u003e\n \u003cp\u003e- Predominantly highlands and midlands\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd rowspan=\"3\" valign=\"top\" style=\"width: 83px;\"\u003e\n \u003cp\u003eGambo\u003cem\u003e\u0026nbsp;et al.\u003c/em\u003e (2018)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 97px;\"\u003e\n \u003cp\u003e- Questionnaires to farmers, butchers, and traders.\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd rowspan=\"3\" valign=\"top\" style=\"width: 92px;\"\u003e\n \u003cp\u003eSurvey and field investigation involving questionnaires and field observations.\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 116px;\"\u003e\n \u003cp\u003e1) Prevalence and status of viral diseases with skin lesions\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd rowspan=\"3\" valign=\"top\" style=\"width: 87px;\"\u003e\n \u003cp\u003eNot mentioned.\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd rowspan=\"3\" valign=\"top\" style=\"width: 92px;\"\u003e\n \u003cp\u003eKanam LGA, Nigeria\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 93px;\"\u003e\n \u003cp\u003e- Livestock farmers, butchers, and traders in Kanam LGA\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd rowspan=\"3\" valign=\"top\" style=\"width: 67px;\"\u003e\n \u003cp\u003eModerate Risk\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 97px;\"\u003e\n \u003cp\u003e- Field investigations during questionnaire administration.\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 116px;\"\u003e\n \u003cp\u003e2) Clinical signs and risk factors\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 93px;\"\u003e\n \u003cp\u003e- Farmers keeping cattle, sheep, and goats\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 97px;\"\u003e\n \u003cp\u003e- Collection and PCR testing of samples.\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 116px;\"\u003e\n \u003cp\u003e3) Laboratory validation\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd rowspan=\"2\" valign=\"top\" style=\"width: 83px;\"\u003e\n \u003cp\u003eOchwo\u003cem\u003e\u0026nbsp;et al.\u003c/em\u003e (2020)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 97px;\"\u003e\n \u003cp\u003e- Sample collection from suspected LSDV cases.\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd rowspan=\"2\" valign=\"top\" style=\"width: 92px;\"\u003e\n \u003cp\u003eObservational, non-controlled study investigating suspected LSD outbreaks naturally occurring in Uganda.\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd rowspan=\"2\" valign=\"top\" style=\"width: 116px;\"\u003e\n \u003cp\u003eNot mentioned (molecular epidemiology study focused on detecting and characterizing LSDV).\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd rowspan=\"2\" valign=\"top\" style=\"width: 87px;\"\u003e\n \u003cp\u003eNot mentioned.\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd rowspan=\"2\" valign=\"top\" style=\"width: 92px;\"\u003e\n \u003cp\u003eUganda\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd rowspan=\"2\" valign=\"top\" style=\"width: 93px;\"\u003e\n \u003cp\u003eNot mentioned.\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd rowspan=\"2\" valign=\"top\" style=\"width: 67px;\"\u003e\n \u003cp\u003eHigh Risk\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 97px;\"\u003e\n \u003cp\u003e- PCR to confirm LSDV presence and phylogenetic analysis.\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd rowspan=\"3\" valign=\"top\" style=\"width: 83px;\"\u003e\n \u003cp\u003eMoje\u003cem\u003e\u0026nbsp;et al.\u003c/em\u003e (2024)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 97px;\"\u003e\n \u003cp\u003e- Cross-sectional study to estimate seroprevalence and assess community awareness.\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd rowspan=\"3\" valign=\"top\" style=\"width: 92px;\"\u003e\n \u003cp\u003eExperimental study involving vaccine comparison and controlled animal study.\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 116px;\"\u003e\n \u003cp\u003e1) Seroprevalence of LSD at animal (40.8%) and herd level (81%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd rowspan=\"3\" valign=\"top\" style=\"width: 87px;\"\u003e\n \u003cp\u003eNot mentioned.\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd rowspan=\"3\" valign=\"top\" style=\"width: 92px;\"\u003e\n \u003cp\u003eSidama, Ethiopia\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 93px;\"\u003e\n \u003cp\u003e- Crossbred and local breed cattle\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd rowspan=\"3\" valign=\"top\" style=\"width: 67px;\"\u003e\n \u003cp\u003eLow Risk\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 97px;\"\u003e\n \u003cp\u003e- Multistage sampling for blood sample collection.\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 116px;\"\u003e\n \u003cp\u003e2) Prevalence in agro-ecological zones\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 93px;\"\u003e\n \u003cp\u003e- Extensive or semi-intensive systems\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 97px;\"\u003e\n \u003cp\u003e- Virus neutralization test for antibodies.\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 116px;\"\u003e\n \u003cp\u003e3) Vaccination practices\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 93px;\"\u003e\n \u003cp\u003e- Total cattle population in Sidama region estimated at 2.4 million\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd rowspan=\"3\" valign=\"top\" style=\"width: 83px;\"\u003e\n \u003cp\u003eGari\u003cem\u003e\u0026nbsp;et al.\u003c/em\u003e (2010)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 97px;\"\u003e\n \u003cp\u003e- Questionnaire survey across 44 peasant associations.\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd rowspan=\"3\" valign=\"top\" style=\"width: 92px;\"\u003e\n \u003cp\u003eCross-sectional observational study using questionnaires and retrospective data investigation.\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 116px;\"\u003e\n \u003cp\u003e1) Herd-level and animal-level prevalence of LSD\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd rowspan=\"3\" valign=\"top\" style=\"width: 87px;\"\u003e\n \u003cp\u003eNot mentioned.\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd rowspan=\"3\" valign=\"top\" style=\"width: 92px;\"\u003e\n \u003cp\u003eEthiopia\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 93px;\"\u003e\n \u003cp\u003e- Herd-owners across 44 peasant associations\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd rowspan=\"3\" valign=\"top\" style=\"width: 67px;\"\u003e\n \u003cp\u003eModerate Risk\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 97px;\"\u003e\n \u003cp\u003e- Multi-stage sampling to select associations.\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 116px;\"\u003e\n \u003cp\u003e2) Risk factors associated with LSD\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 93px;\"\u003e\n \u003cp\u003e- Represented different agro-climatic zones and farming systems\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 97px;\"\u003e\n \u003cp\u003e- Data on LSD occurrence and climate from records.\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 116px;\"\u003e\n \u003cp\u003e3) Temporal association with biting-fly population\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd rowspan=\"3\" valign=\"top\" style=\"width: 83px;\"\u003e\n \u003cp\u003eMburu\u003cem\u003e\u0026nbsp;et al.\u003c/em\u003e (2023)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 97px;\"\u003e\n \u003cp\u003e- 30 in-depth interviews to understand livestock priorities.\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd rowspan=\"3\" valign=\"top\" style=\"width: 92px;\"\u003e\n \u003cp\u003eQualitative observational study using IDIs, FGDs, and KIIs; purposive sampling of participants.\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd rowspan=\"3\" valign=\"top\" style=\"width: 116px;\"\u003e\n \u003cp\u003eNot mentioned (qualitative study focusing on disease prioritization).\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd rowspan=\"3\" valign=\"top\" style=\"width: 87px;\"\u003e\n \u003cp\u003eNot mentioned.\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd rowspan=\"3\" valign=\"top\" style=\"width: 92px;\"\u003e\n \u003cp\u003eKajiado, Kenya\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 93px;\"\u003e\n \u003cp\u003e- Male and female Maasai pastoralists aged 18-75\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd rowspan=\"3\" valign=\"top\" style=\"width: 67px;\"\u003e\n \u003cp\u003eHigh Risk\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd rowspan=\"2\" valign=\"top\" style=\"width: 97px;\"\u003e\n \u003cp\u003e- Focus group discussions and key informant interviews for insights on diseases and interactions.\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 93px;\"\u003e\n \u003cp\u003e- Long-term residents of the study area\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 93px;\"\u003e\n \u003cp\u003e- Key informants included professionals in livestock, public health, and wildlife sectors\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd rowspan=\"2\" valign=\"top\" style=\"width: 83px;\"\u003e\n \u003cp\u003eGezahegn\u003cem\u003e\u0026nbsp;et al.\u003c/em\u003e (2015)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 97px;\"\u003e\n \u003cp\u003e- Longitudinal study examining 11,189 bulls in feedlots for LSD incidence.\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd rowspan=\"2\" valign=\"top\" style=\"width: 92px;\"\u003e\n \u003cp\u003eLongitudinal observational study following bulls for 3 months to observe LSD incidence.\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd rowspan=\"2\" valign=\"top\" style=\"width: 116px;\"\u003e\n \u003cp\u003eIncidence, mortality rate, and case fatality rate of LSD in feedlots.\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd rowspan=\"2\" valign=\"top\" style=\"width: 87px;\"\u003e\n \u003cp\u003eNot mentioned.\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd rowspan=\"2\" valign=\"top\" style=\"width: 92px;\"\u003e\n \u003cp\u003eCentral Ethiopia\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 93px;\"\u003e\n \u003cp\u003e- 11,189 bulls aged 3-5 years old, previously vaccinated for LSD\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd rowspan=\"2\" valign=\"top\" style=\"width: 67px;\"\u003e\n \u003cp\u003eLow Risk\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 97px;\"\u003e\n \u003cp\u003e- Clinical examination and statistical analyses to assess incidence and mortality.\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 93px;\"\u003e\n \u003cp\u003e- Monitored for 3 months before export\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd rowspan=\"2\" valign=\"top\" style=\"width: 83px;\"\u003e\n \u003cp\u003eAlemayehu\u003cem\u003e\u0026nbsp;et al.\u003c/em\u003e (2013)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd rowspan=\"2\" valign=\"top\" style=\"width: 97px;\"\u003e\n \u003cp\u003eUsing the OIE-recommended risk assessment framework to analyze the risk of LSD introduction and exposure; collecting data from secondary sources, interviews with feedlot operators, and personal field observations.\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd rowspan=\"2\" valign=\"top\" style=\"width: 92px;\"\u003e\n \u003cp\u003eQualitative risk assessment study using OIE framework.\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 116px;\"\u003e\n \u003cp\u003e1. Likelihood of LSD introduction (high, medium uncertainty)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd rowspan=\"2\" valign=\"top\" style=\"width: 87px;\"\u003e\n \u003cp\u003eNot mentioned\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd rowspan=\"2\" valign=\"top\" style=\"width: 92px;\"\u003e\n \u003cp\u003eEast Shewa Zone, Oromia, Ethiopia\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 93px;\"\u003e\n \u003cp\u003e- Male bulls aged 3-5 years\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd rowspan=\"2\" valign=\"top\" style=\"width: 67px;\"\u003e\n \u003cp\u003eModerate Risk\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 116px;\"\u003e\n \u003cp\u003e2. Probability of exposure (very high, medium uncertainty)\u003c/p\u003e\n \u003cp\u003e3. Prevalence of LSD (6.1% affected, 1.8% mortality)\u003c/p\u003e\n \u003cp\u003e4. Total economic loss (667,785.6 USD).\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 93px;\"\u003e\n \u003cp\u003e- Originating mainly from Borena pastoral system\u003c/p\u003e\n \u003cp\u003e- All vaccinated for LSD\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd rowspan=\"4\" valign=\"top\" style=\"width: 83px;\"\u003e\n \u003cp\u003eAdedeji, A. J\u003cem\u003e\u0026nbsp;et al.\u003c/em\u003e (2017)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd rowspan=\"4\" valign=\"top\" style=\"width: 97px;\"\u003e\n \u003cp\u003eConducting the study on a dairy farm that experienced recurrent outbreaks of LSD; collecting skin biopsy samples; determining morbidity and mortality rates based on farm records; estimating economic losses due to LSD outbreaks.\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd rowspan=\"4\" valign=\"top\" style=\"width: 92px;\"\u003e\n \u003cp\u003eRetrospective, non-controlled observational study of recurrent outbreaks on a dairy farm.\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 116px;\"\u003e\n \u003cp\u003e1. Morbidity and mortality rates\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd rowspan=\"4\" valign=\"top\" style=\"width: 87px;\"\u003e\n \u003cp\u003eNot mentioned\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd rowspan=\"4\" valign=\"top\" style=\"width: 92px;\"\u003e\n \u003cp\u003eJos, Plateau State, Nigeria\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 93px;\"\u003e\n \u003cp\u003e- Cattle breed: Holstein Friesian\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd rowspan=\"4\" valign=\"top\" style=\"width: 67px;\"\u003e\n \u003cp\u003eModerate Risksss\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd rowspan=\"3\" valign=\"top\" style=\"width: 116px;\"\u003e\n \u003cp\u003e2. Economic impact due to LSD outbreaks.\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 93px;\"\u003e\n \u003cp\u003e- Age: Mostly calves \u0026lt; 1 year\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 93px;\"\u003e\n \u003cp\u003e- Farm size: Over 250 cattle\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 93px;\"\u003e\n \u003cp\u003e- Observation period: 2010-2014\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd rowspan=\"2\" valign=\"top\" style=\"width: 83px;\"\u003e\n \u003cp\u003eAddise Ambilo\u003c/p\u003e\n \u003cp\u003e(Kebede, 2019)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd rowspan=\"2\" valign=\"top\" style=\"width: 97px;\"\u003e\n \u003cp\u003eCross-sectional study design; sample size calculated based on expected prevalence; clinical examinations and laboratory tests performed to identify skin pathogens.\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd rowspan=\"2\" valign=\"top\" style=\"width: 92px;\"\u003e\n \u003cp\u003eCross-sectional study.\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd rowspan=\"2\" valign=\"top\" style=\"width: 116px;\"\u003e\n \u003cp\u003ePrevalence of major skin diseases in cattle.\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd rowspan=\"2\" valign=\"top\" style=\"width: 87px;\"\u003e\n \u003cp\u003eNot mentioned\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd rowspan=\"2\" valign=\"top\" style=\"width: 92px;\"\u003e\n \u003cp\u003eSouthern Nations, Ethiopia\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 93px;\"\u003e\n \u003cp\u003e- Cattle of all ages, sexes, and breeds\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd rowspan=\"2\" valign=\"top\" style=\"width: 67px;\"\u003e\n \u003cp\u003eModerate Risk\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 93px;\"\u003e\n \u003cp\u003e- Higher prevalence in younger cattle with poor husbandry\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd rowspan=\"2\" valign=\"top\" style=\"width: 83px;\"\u003e\n \u003cp\u003eDouglass\u003cem\u003e\u0026nbsp;et al.\u003c/em\u003e (2020)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd rowspan=\"2\" valign=\"top\" style=\"width: 97px;\"\u003e\n \u003cp\u003eConstruction of recombinant viruses and in vitro growth comparison; in vivo growth comparison on chick chorioallantoic membranes; histological analysis of infected CAMs; in vivo experiment in cattle.\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd rowspan=\"2\" valign=\"top\" style=\"width: 92px;\"\u003e\n \u003cp\u003eExperimental study; controlled animal study in cattle.\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 116px;\"\u003e\n \u003cp\u003e1. Effect of LSDV SOD homologue on virus growth (in vitro and in vivo)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 87px;\"\u003e\n \u003cp\u003e1. Deletion of the SOD homologue gene\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd rowspan=\"2\" valign=\"top\" style=\"width: 92px;\"\u003e\n \u003cp\u003eMultinational, focusing on South Africa\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd rowspan=\"2\" valign=\"top\" style=\"width: 93px;\"\u003e\n \u003cp\u003eNot mentioned\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd rowspan=\"2\" valign=\"top\" style=\"width: 67px;\"\u003e\n \u003cp\u003eModerate Risk\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 116px;\"\u003e\n \u003cp\u003e2. Histopathological changes.\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 87px;\"\u003e\n \u003cp\u003e2. Replacement with full-length SOD homologue gene.\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd rowspan=\"3\" valign=\"top\" style=\"width: 83px;\"\u003e\n \u003cp\u003eMburu\u003cem\u003e\u0026nbsp;et al.\u003c/em\u003e (2023)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd rowspan=\"3\" valign=\"top\" style=\"width: 97px;\"\u003e\n \u003cp\u003eConducted interviews and focus group discussions with community members and professionals to understand livestock disease prioritization; mapping exercise with community elders.\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd rowspan=\"3\" valign=\"top\" style=\"width: 92px;\"\u003e\n \u003cp\u003eQualitative, observational study using IDIs, FGDs, and KIIs.\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd rowspan=\"3\" valign=\"top\" style=\"width: 116px;\"\u003e\n \u003cp\u003eNo specific outcome measured; focused on understanding disease prioritization.\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd rowspan=\"3\" valign=\"top\" style=\"width: 87px;\"\u003e\n \u003cp\u003eNot mentioned\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd rowspan=\"3\" valign=\"top\" style=\"width: 92px;\"\u003e\n \u003cp\u003eOloitoktok Sub County, Kenya\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 93px;\"\u003e\n \u003cp\u003e- Male and female Maasai pastoralists, aged 18-75\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd rowspan=\"3\" valign=\"top\" style=\"width: 67px;\"\u003e\n \u003cp\u003eHigh Risk\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 93px;\"\u003e\n \u003cp\u003e- Long-term residents\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 93px;\"\u003e\n \u003cp\u003e- Key informants included professionals and elders\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd rowspan=\"2\" valign=\"top\" style=\"width: 83px;\"\u003e\n \u003cp\u003eAbebaw (2024)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd rowspan=\"2\" valign=\"top\" style=\"width: 97px;\"\u003e\n \u003cp\u003eSystematic literature search; quality assessment of included studies; use of random-effects meta-analysis to pool prevalence estimates and assess heterogeneity.\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd rowspan=\"2\" valign=\"top\" style=\"width: 92px;\"\u003e\n \u003cp\u003eSystematic review and meta-analysis.\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd rowspan=\"2\" valign=\"top\" style=\"width: 116px;\"\u003e\n \u003cp\u003ePrevalence of Lumpy Skin Disease (LSD) in cattle in Africa.\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd rowspan=\"2\" valign=\"top\" style=\"width: 87px;\"\u003e\n \u003cp\u003eNot mentioned\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd rowspan=\"2\" valign=\"top\" style=\"width: 92px;\"\u003e\n \u003cp\u003eEgypt, Ethiopia, Uganda, Zimbabwe\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 93px;\"\u003e\n \u003cp\u003e- Cattle from studies conducted in these countries\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd rowspan=\"2\" valign=\"top\" style=\"width: 67px;\"\u003e\n \u003cp\u003eModerate Risk\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 93px;\"\u003e\n \u003cp\u003e- Sample sizes ranging from 20 to 2,368 cattle\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd rowspan=\"2\" valign=\"top\" style=\"width: 83px;\"\u003e\n \u003cp\u003eGeletu\u003cem\u003e\u0026nbsp;et al.\u003c/em\u003e (2024)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd rowspan=\"2\" valign=\"top\" style=\"width: 97px;\"\u003e\n \u003cp\u003eStudy conducted in the West Hararghe Zone; examination of cattle, virus isolation, and detection using PCR methods.\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd rowspan=\"2\" valign=\"top\" style=\"width: 92px;\"\u003e\n \u003cp\u003eCross-sectional observational study of an LSD outbreak.\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 116px;\"\u003e\n \u003cp\u003e1. Morbidity, mortality, and case fatality rates of LSD.\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd rowspan=\"2\" valign=\"top\" style=\"width: 87px;\"\u003e\n \u003cp\u003eNot mentioned\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd rowspan=\"2\" valign=\"top\" style=\"width: 92px;\"\u003e\n \u003cp\u003eWest Hararghe Zone, Ethiopia\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 93px;\"\u003e\n \u003cp\u003e- Cattle of all ages, including local zebu and foreign breeds\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd rowspan=\"2\" valign=\"top\" style=\"width: 67px;\"\u003e\n \u003cp\u003eModerate Risk\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 116px;\"\u003e\n \u003cp\u003e2. Isolation and detection of LSDV using PCR.\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 93px;\"\u003e\n \u003cp\u003e- 73 out of 625 cattle showed clinical signs of LSD\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd rowspan=\"3\" valign=\"top\" style=\"width: 83px;\"\u003e\n \u003cp\u003eAdedeji, A. J.\u003cem\u003e\u0026nbsp;et al.\u003c/em\u003e (2018)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd rowspan=\"3\" valign=\"top\" style=\"width: 97px;\"\u003e\n \u003cp\u003eCollecting skin biopsies from cattle with suspected LSD lesions; processing for histopathological examination and PCR to detect LSDV genes.\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd rowspan=\"3\" valign=\"top\" style=\"width: 92px;\"\u003e\n \u003cp\u003eRetrospective observational case report of two outbreaks on a single dairy farm.\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 116px;\"\u003e\n \u003cp\u003e1. Morbidity rate\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 87px;\"\u003e\n \u003cp\u003e1. Vaccination with foreign LSD vaccine after the 2014 outbreak.\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd rowspan=\"3\" valign=\"top\" style=\"width: 92px;\"\u003e\n \u003cp\u003eKeffi, Nasarawa State, Nigeria\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 93px;\"\u003e\n \u003cp\u003e- Dairy cattle herd including Holstein Friesian and Sokoto Gudali breeds\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd rowspan=\"3\" valign=\"top\" style=\"width: 67px;\"\u003e\n \u003cp\u003eModerate Risk\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 116px;\"\u003e\n \u003cp\u003e2. Mortality rate\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 87px;\"\u003e\n \u003cp\u003e2. Vaccination with an LSD vaccine from NVRI after the 2016 outbreak.\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 93px;\"\u003e\n \u003cp\u003e- Herd composition changed between 2014 and 2016\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 116px;\"\u003e\n \u003cp\u003e3. Clinical presentation of the disease.\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd rowspan=\"3\" valign=\"top\" style=\"width: 83px;\"\u003e\n \u003cp\u003eIsrael (2020)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd rowspan=\"3\" valign=\"top\" style=\"width: 97px;\"\u003e\n \u003cp\u003eCross-sectional questionnaire survey; face-to-face interviews with dairy farm owners; data collection on diseases, mortality, morbidity, and management practices.\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd rowspan=\"3\" valign=\"top\" style=\"width: 92px;\"\u003e\n \u003cp\u003eCross-sectional observational survey.\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 116px;\"\u003e\n \u003cp\u003e1. Major cattle diseases affecting dairy cattle.\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd rowspan=\"3\" valign=\"top\" style=\"width: 87px;\"\u003e\n \u003cp\u003eNot mentioned\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd rowspan=\"3\" valign=\"top\" style=\"width: 92px;\"\u003e\n \u003cp\u003eJimma town, Oromia, Ethiopia\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 93px;\"\u003e\n \u003cp\u003e- Majority male dairy farm owners\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd rowspan=\"3\" valign=\"top\" style=\"width: 67px;\"\u003e\n \u003cp\u003eModerate Risk\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 116px;\"\u003e\n \u003cp\u003e2. Economic significance due to these diseases.\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 93px;\"\u003e\n \u003cp\u003e- Majority aged 40 and above\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 116px;\"\u003e\n \u003cp\u003e3. Management practices.\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 93px;\"\u003e\n \u003cp\u003e- Varied educational backgrounds\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003c/tbody\u003e\n \u003c/table\u003e\n\u003c/div\u003e\n\u003cp\u003e\u0026nbsp;\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eTable 2: Assessment of Risk of Bias\u003c/strong\u003e\u003c/p\u003e\n\u003cdiv align=\"\"\u003e\n \u003ctable border=\"0\" cellspacing=\"0\" cellpadding=\"0\" width=\"100%\"\u003e\n \u003ctbody\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 132px;\"\u003e\n \u003cp\u003e\u003cstrong\u003eAuthor(s)\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 142px;\"\u003e\n \u003cp\u003e\u003cstrong\u003eSelection (max 4 points)\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 123px;\"\u003e\n \u003cp\u003e\u003cstrong\u003eComparability (max 2 points)\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 142px;\"\u003e\n \u003cp\u003e\u003cstrong\u003eOutcome (max 3 points)\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 55px;\"\u003e\n \u003cp\u003e\u003cstrong\u003eTotal Score (max 9)\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 74px;\"\u003e\n \u003cp\u003e\u003cstrong\u003eRisk of Bias\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 132px;\"\u003e\n \u003cp\u003eAbera (2019)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 142px;\"\u003e\n \u003cp\u003e4\u003c/p\u003e\n \u003cp\u003e(representative sample, clear criteria)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 123px;\"\u003e\n \u003cp\u003e1\u0026nbsp;\u003c/p\u003e\n \u003cp\u003e(confounding factors considered)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 142px;\"\u003e\n \u003cp\u003e3\u0026nbsp;\u003c/p\u003e\n \u003cp\u003e(clear outcomes)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 55px;\"\u003e\n \u003cp\u003e8\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 74px;\"\u003e\n \u003cp\u003eLow Risk\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 132px;\"\u003e\n \u003cp\u003eTesfaye\u003cem\u003e\u0026nbsp;et al.\u003c/em\u003e (2024)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 142px;\"\u003e\n \u003cp\u003e3\u0026nbsp;\u003c/p\u003e\n \u003cp\u003e(convenience sampling)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 123px;\"\u003e\n \u003cp\u003e0\u0026nbsp;\u003c/p\u003e\n \u003cp\u003e(no comparison made)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 142px;\"\u003e\n \u003cp\u003e2\u0026nbsp;\u003c/p\u003e\n \u003cp\u003e(clear reporting of outcomes)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 55px;\"\u003e\n \u003cp\u003e5\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 74px;\"\u003e\n \u003cp\u003eModerate Risk\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 132px;\"\u003e\n \u003cp\u003eBirhanu\u003cem\u003e\u0026nbsp;et al.\u003c/em\u003e (2015)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 142px;\"\u003e\n \u003cp\u003e4\u003c/p\u003e\n \u003cp\u003e(representative sample, clear criteria)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 123px;\"\u003e\n \u003cp\u003e1\u0026nbsp;\u003c/p\u003e\n \u003cp\u003e(confounding factors considered)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 142px;\"\u003e\n \u003cp\u003e3\u0026nbsp;\u003c/p\u003e\n \u003cp\u003e(clear outcomes)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 55px;\"\u003e\n \u003cp\u003e8\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 74px;\"\u003e\n \u003cp\u003eLow Risk\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 132px;\"\u003e\n \u003cp\u003eNtombimbini and Klein (2015)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 142px;\"\u003e\n \u003cp\u003e4\u0026nbsp;\u003c/p\u003e\n \u003cp\u003e(representative sample, clear criteria)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 123px;\"\u003e\n \u003cp\u003e0\u0026nbsp;\u003c/p\u003e\n \u003cp\u003e(no consideration of confounders)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 142px;\"\u003e\n \u003cp\u003e3\u0026nbsp;\u003c/p\u003e\n \u003cp\u003e(clear outcomes)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 55px;\"\u003e\n \u003cp\u003e7\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 74px;\"\u003e\n \u003cp\u003eModerate Risk\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 132px;\"\u003e\n \u003cp\u003e(Kebede, 2019)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 142px;\"\u003e\n \u003cp\u003e3\u0026nbsp;\u003c/p\u003e\n \u003cp\u003e(convenience sampling)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 123px;\"\u003e\n \u003cp\u003e0\u0026nbsp;\u003c/p\u003e\n \u003cp\u003e(no comparison made)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 142px;\"\u003e\n \u003cp\u003e2\u0026nbsp;\u003c/p\u003e\n \u003cp\u003e(not clear on specific outcomes)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 55px;\"\u003e\n \u003cp\u003e5\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 74px;\"\u003e\n \u003cp\u003eModerate Risk\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 132px;\"\u003e\n \u003cp\u003eMakoga\u003cem\u003e\u0026nbsp;et al.\u003c/em\u003e (2024)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 142px;\"\u003e\n \u003cp\u003e3\u0026nbsp;\u003c/p\u003e\n \u003cp\u003e(convenience sampling)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 123px;\"\u003e\n \u003cp\u003e0\u0026nbsp;\u003c/p\u003e\n \u003cp\u003e(no comparison made)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 142px;\"\u003e\n \u003cp\u003e2\u0026nbsp;\u003c/p\u003e\n \u003cp\u003e(not clear on specific outcomes)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 55px;\"\u003e\n \u003cp\u003e5\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 74px;\"\u003e\n \u003cp\u003eModerate Risk\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 132px;\"\u003e\n \u003cp\u003eHaga\u003cem\u003e\u0026nbsp;et al.\u003c/em\u003e (2024)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 142px;\"\u003e\n \u003cp\u003e0\u0026nbsp;\u003c/p\u003e\n \u003cp\u003e(no clear sampling method)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 123px;\"\u003e\n \u003cp\u003e0\u0026nbsp;\u003c/p\u003e\n \u003cp\u003e(no comparison made)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 142px;\"\u003e\n \u003cp\u003e2\u0026nbsp;\u003c/p\u003e\n \u003cp\u003e(not clear on specific outcomes)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 55px;\"\u003e\n \u003cp\u003e2\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 74px;\"\u003e\n \u003cp\u003eHigh Risk\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 132px;\"\u003e\n \u003cp\u003eWolff\u003cem\u003e\u0026nbsp;et al.\u003c/em\u003e (2021)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 142px;\"\u003e\n \u003cp\u003e3\u0026nbsp;\u003c/p\u003e\n \u003cp\u003e(convenience sampling)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 123px;\"\u003e\n \u003cp\u003e0\u0026nbsp;\u003c/p\u003e\n \u003cp\u003e(no comparison made)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 142px;\"\u003e\n \u003cp\u003e2\u0026nbsp;\u003c/p\u003e\n \u003cp\u003e(not clear on specific outcomes)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 55px;\"\u003e\n \u003cp\u003e5\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 74px;\"\u003e\n \u003cp\u003eModerate Risk\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 132px;\"\u003e\n \u003cp\u003eLeliso\u003cem\u003e\u0026nbsp;et al.\u003c/em\u003e (2021)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 142px;\"\u003e\n \u003cp\u003e0\u0026nbsp;\u003c/p\u003e\n \u003cp\u003e(no clear sampling method)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 123px;\"\u003e\n \u003cp\u003e0\u0026nbsp;\u003c/p\u003e\n \u003cp\u003e(no comparison made)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 142px;\"\u003e\n \u003cp\u003e0\u0026nbsp;\u003c/p\u003e\n \u003cp\u003e(no clear outcomes)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 55px;\"\u003e\n \u003cp\u003e0\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 74px;\"\u003e\n \u003cp\u003eHigh Risk\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 132px;\"\u003e\n \u003cp\u003e(Tuppurainen, E. S.\u003cem\u003e\u0026nbsp;et al.\u003c/em\u003e, 2013)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 142px;\"\u003e\n \u003cp\u003e1\u0026nbsp;\u003c/p\u003e\n \u003cp\u003e(convenience sampling)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 123px;\"\u003e\n \u003cp\u003e0\u0026nbsp;\u003c/p\u003e\n \u003cp\u003e(no comparison made)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 142px;\"\u003e\n \u003cp\u003e1\u0026nbsp;\u003c/p\u003e\n \u003cp\u003e(not clear on specific outcomes)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 55px;\"\u003e\n \u003cp\u003e2\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 74px;\"\u003e\n \u003cp\u003eHigh Risk\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 132px;\"\u003e\n \u003cp\u003eAyelet\u003cem\u003e\u0026nbsp;et al.\u003c/em\u003e (2014)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 142px;\"\u003e\n \u003cp\u003e3\u0026nbsp;\u003c/p\u003e\n \u003cp\u003e(convenience sampling)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 123px;\"\u003e\n \u003cp\u003e0\u0026nbsp;\u003c/p\u003e\n \u003cp\u003e(no comparison made)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 142px;\"\u003e\n \u003cp\u003e3\u0026nbsp;\u003c/p\u003e\n \u003cp\u003e(clear outcomes)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 55px;\"\u003e\n \u003cp\u003e5\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 74px;\"\u003e\n \u003cp\u003eModerate Risk\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 132px;\"\u003e\n \u003cp\u003eGirma\u003cem\u003e\u0026nbsp;et al.\u003c/em\u003e (2019)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 142px;\"\u003e\n \u003cp\u003e3\u0026nbsp;\u003c/p\u003e\n \u003cp\u003e(convenience sampling)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 123px;\"\u003e\n \u003cp\u003e0\u0026nbsp;\u003c/p\u003e\n \u003cp\u003e(no comparison made)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 142px;\"\u003e\n \u003cp\u003e2\u0026nbsp;\u003c/p\u003e\n \u003cp\u003e(not clear on specific outcomes)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 55px;\"\u003e\n \u003cp\u003e5\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 74px;\"\u003e\n \u003cp\u003eModerate Risk\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 132px;\"\u003e\n \u003cp\u003eMdlulwa\u003cem\u003e\u0026nbsp;et al.\u003c/em\u003e (2018)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 142px;\"\u003e\n \u003cp\u003e3\u0026nbsp;\u003c/p\u003e\n \u003cp\u003e(convenience sampling of dairy operations)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 123px;\"\u003e\n \u003cp\u003e0\u0026nbsp;\u003c/p\u003e\n \u003cp\u003e(no comparison made)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 142px;\"\u003e\n \u003cp\u003e3\u0026nbsp;\u003c/p\u003e\n \u003cp\u003e(clear economic outcomes)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 55px;\"\u003e\n \u003cp\u003e6\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 74px;\"\u003e\n \u003cp\u003eModerate Risk\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 132px;\"\u003e\n \u003cp\u003eAtai\u003cem\u003e\u0026nbsp;et al.\u003c/em\u003e (2021)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 142px;\"\u003e\n \u003cp\u003e2\u0026nbsp;\u003c/p\u003e\n \u003cp\u003e(non-random convenience sampling)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 123px;\"\u003e\n \u003cp\u003e0\u0026nbsp;\u003c/p\u003e\n \u003cp\u003e(no comparison made)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 142px;\"\u003e\n \u003cp\u003e3\u0026nbsp;\u003c/p\u003e\n \u003cp\u003e(clear epidemiological outcomes)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 55px;\"\u003e\n \u003cp\u003e5\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 74px;\"\u003e\n \u003cp\u003eModerate Risk\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 132px;\"\u003e\n \u003cp\u003eMoje\u003cem\u003e\u0026nbsp;et al.\u003c/em\u003e (2023)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 142px;\"\u003e\n \u003cp\u003e4\u0026nbsp;\u003c/p\u003e\n \u003cp\u003e(purposive sampling, clear criteria)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 123px;\"\u003e\n \u003cp\u003e1\u0026nbsp;\u003c/p\u003e\n \u003cp\u003e(confounding factors considered)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 142px;\"\u003e\n \u003cp\u003e3\u0026nbsp;\u003c/p\u003e\n \u003cp\u003e(clear immunological outcomes)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 55px;\"\u003e\n \u003cp\u003e8\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 74px;\"\u003e\n \u003cp\u003eLow Risk\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 132px;\"\u003e\n \u003cp\u003eZelalem\u003cem\u003e\u0026nbsp;et al.\u003c/em\u003e (2015)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 142px;\"\u003e\n \u003cp\u003e3\u003c/p\u003e\n \u003cp\u003e(representative sample, clear criteria, and appropriate sampling)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 123px;\"\u003e\n \u003cp\u003e1\u0026nbsp;\u003c/p\u003e\n \u003cp\u003e(consideration of confounding factors)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 142px;\"\u003e\n \u003cp\u003e1\u0026nbsp;\u003c/p\u003e\n \u003cp\u003e(no clear outcomes reported)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 55px;\"\u003e\n \u003cp\u003e5\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 74px;\"\u003e\n \u003cp\u003eModerate Risk\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 132px;\"\u003e\n \u003cp\u003eGambo\u003cem\u003e\u0026nbsp;et al.\u003c/em\u003e (2018)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 142px;\"\u003e\n \u003cp\u003e3\u0026nbsp;\u003c/p\u003e\n \u003cp\u003e(clear inclusion criteria, diverse participants)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 123px;\"\u003e\n \u003cp\u003e1\u0026nbsp;\u003c/p\u003e\n \u003cp\u003e(some confounding factors considered)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 142px;\"\u003e\n \u003cp\u003e2\u0026nbsp;\u003c/p\u003e\n \u003cp\u003e(clear reporting of outcomes)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 55px;\"\u003e\n \u003cp\u003e6\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 74px;\"\u003e\n \u003cp\u003eModerate Risk\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 132px;\"\u003e\n \u003cp\u003eOchwo\u003cem\u003e\u0026nbsp;et al.\u003c/em\u003e (2020)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 142px;\"\u003e\n \u003cp\u003e2\u0026nbsp;\u003c/p\u003e\n \u003cp\u003e(convenience sampling of suspected cases)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 123px;\"\u003e\n \u003cp\u003e0\u0026nbsp;\u003c/p\u003e\n \u003cp\u003e(no comparison made)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 142px;\"\u003e\n \u003cp\u003e2\u0026nbsp;\u003c/p\u003e\n \u003cp\u003e(clear reporting of PCR results)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 55px;\"\u003e\n \u003cp\u003e4\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 74px;\"\u003e\n \u003cp\u003eHigh Risk\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 132px;\"\u003e\n \u003cp\u003eMoje\u003cem\u003e\u0026nbsp;et al.\u003c/em\u003e (2024)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 142px;\"\u003e\n \u003cp\u003e4\u0026nbsp;\u003c/p\u003e\n \u003cp\u003e(multi-stage sampling, representative sample)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 123px;\"\u003e\n \u003cp\u003e1\u0026nbsp;\u003c/p\u003e\n \u003cp\u003e(confounding factors considered)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 142px;\"\u003e\n \u003cp\u003e2\u0026nbsp;\u003c/p\u003e\n \u003cp\u003e(clear seroprevalence outcomes)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 55px;\"\u003e\n \u003cp\u003e7\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 74px;\"\u003e\n \u003cp\u003eLow Risk\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 132px;\"\u003e\n \u003cp\u003eGari\u003cem\u003e\u0026nbsp;et al.\u003c/em\u003e (2010)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 142px;\"\u003e\n \u003cp\u003e3\u0026nbsp;\u003c/p\u003e\n \u003cp\u003e(representative sample, clear criteria)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 123px;\"\u003e\n \u003cp\u003e0\u0026nbsp;\u003c/p\u003e\n \u003cp\u003e(no consideration of confounders)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 142px;\"\u003e\n \u003cp\u003e1\u0026nbsp;\u003c/p\u003e\n \u003cp\u003e(not clear on specific outcomes)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 55px;\"\u003e\n \u003cp\u003e4\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 74px;\"\u003e\n \u003cp\u003eModerate Risk\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 132px;\"\u003e\n \u003cp\u003eMburu\u003cem\u003e\u0026nbsp;et al.\u003c/em\u003e (2023)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 142px;\"\u003e\n \u003cp\u003e2\u0026nbsp;\u003c/p\u003e\n \u003cp\u003e(purposive sampling introduces bias)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 123px;\"\u003e\n \u003cp\u003e0\u0026nbsp;\u003c/p\u003e\n \u003cp\u003e(no comparison made)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 142px;\"\u003e\n \u003cp\u003e1\u0026nbsp;\u003c/p\u003e\n \u003cp\u003e(outcomes based on qualitative data)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 55px;\"\u003e\n \u003cp\u003e3\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 74px;\"\u003e\n \u003cp\u003eHigh Risk\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 132px;\"\u003e\n \u003cp\u003eGezahegn\u003cem\u003e\u0026nbsp;et al.\u003c/em\u003e (2015)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 142px;\"\u003e\n \u003cp\u003e4\u0026nbsp;\u003c/p\u003e\n \u003cp\u003e(large, representative sample)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 123px;\"\u003e\n \u003cp\u003e1\u0026nbsp;\u003c/p\u003e\n \u003cp\u003e(consideration of some risk factors)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 142px;\"\u003e\n \u003cp\u003e2\u0026nbsp;\u003c/p\u003e\n \u003cp\u003e(clear reporting of outcomes)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 55px;\"\u003e\n \u003cp\u003e7\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 74px;\"\u003e\n \u003cp\u003eLow Risk\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 132px;\"\u003e\n \u003cp\u003eAlemayehu\u003cem\u003e\u0026nbsp;et al.\u003c/em\u003e (2013)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 142px;\"\u003e\n \u003cp\u003e3\u0026nbsp;\u003c/p\u003e\n \u003cp\u003e(qualitative assessment, specific criteria)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 123px;\"\u003e\n \u003cp\u003e0\u0026nbsp;\u003c/p\u003e\n \u003cp\u003e(no comparison made)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 142px;\"\u003e\n \u003cp\u003e2\u0026nbsp;\u003c/p\u003e\n \u003cp\u003e(clear risk assessment outcomes)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 55px;\"\u003e\n \u003cp\u003e5\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 74px;\"\u003e\n \u003cp\u003eModerate Risk\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 132px;\"\u003e\n \u003cp\u003e(Adedeji, A. J\u003cem\u003e\u0026nbsp;et al.\u003c/em\u003e, 2017)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 142px;\"\u003e\n \u003cp\u003e2\u0026nbsp;\u003c/p\u003e\n \u003cp\u003e(specific farm, not generalizable)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 123px;\"\u003e\n \u003cp\u003e0\u0026nbsp;\u003c/p\u003e\n \u003cp\u003e(no consideration of confounders)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 142px;\"\u003e\n \u003cp\u003e2\u0026nbsp;\u003c/p\u003e\n \u003cp\u003e(clear reporting of outcomes)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 55px;\"\u003e\n \u003cp\u003e4\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 74px;\"\u003e\n \u003cp\u003eModerate Risk\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 132px;\"\u003e\n \u003cp\u003eAddise Ambilo\u003c/p\u003e\n \u003cp\u003e(Kebede, 2019)\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 142px;\"\u003e\n \u003cp\u003e3 (representative sample of cattle from various demographics)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 123px;\"\u003e\n \u003cp\u003e0\u0026nbsp;\u003c/p\u003e\n \u003cp\u003e(no consideration of confounding factors)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 142px;\"\u003e\n \u003cp\u003e2\u0026nbsp;\u003c/p\u003e\n \u003cp\u003e(clear reporting of prevalence)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 55px;\"\u003e\n \u003cp\u003e5\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 74px;\"\u003e\n \u003cp\u003eModerate Risk\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 132px;\"\u003e\n \u003cp\u003eDouglass\u003cem\u003e\u0026nbsp;et al.\u003c/em\u003e (2020)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 142px;\"\u003e\n \u003cp\u003e3\u0026nbsp;\u003c/p\u003e\n \u003cp\u003e(controlled experimental study)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 123px;\"\u003e\n \u003cp\u003e1\u0026nbsp;\u003c/p\u003e\n \u003cp\u003e(comparison of different virus types)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 142px;\"\u003e\n \u003cp\u003e2\u0026nbsp;\u003c/p\u003e\n \u003cp\u003e(clear outcomes reported)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 55px;\"\u003e\n \u003cp\u003e6\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 74px;\"\u003e\n \u003cp\u003eModerate Risk\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 132px;\"\u003e\n \u003cp\u003eMburu\u003cem\u003e\u0026nbsp;et al.\u003c/em\u003e (2023)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 142px;\"\u003e\n \u003cp\u003e2\u0026nbsp;\u003c/p\u003e\n \u003cp\u003e(purposive sampling)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 123px;\"\u003e\n \u003cp\u003e0\u0026nbsp;\u003c/p\u003e\n \u003cp\u003e(no comparison made)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 142px;\"\u003e\n \u003cp\u003e1\u0026nbsp;\u003c/p\u003e\n \u003cp\u003e(qualitative nature, unclear outcomes)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 55px;\"\u003e\n \u003cp\u003e3\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 74px;\"\u003e\n \u003cp\u003eHigh Risk\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 132px;\"\u003e\n \u003cp\u003eAbebaw (2024)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 142px;\"\u003e\n \u003cp\u003e4\u0026nbsp;\u003c/p\u003e\n \u003cp\u003e(systematic literature search, clear inclusion/exclusion criteria)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 123px;\"\u003e\n \u003cp\u003e0\u0026nbsp;\u003c/p\u003e\n \u003cp\u003e(no consideration of confounding factors)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 142px;\"\u003e\n \u003cp\u003e2\u0026nbsp;\u003c/p\u003e\n \u003cp\u003e(clear prevalence outcomes)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 55px;\"\u003e\n \u003cp\u003e6\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 74px;\"\u003e\n \u003cp\u003eModerate Risk\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 132px;\"\u003e\n \u003cp\u003eGeletu\u003cem\u003e\u0026nbsp;et al.\u003c/em\u003e (2024)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 142px;\"\u003e\n \u003cp\u003e3\u0026nbsp;\u003c/p\u003e\n \u003cp\u003e(cross-sectional study with adequate sample size)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 123px;\"\u003e\n \u003cp\u003e1\u0026nbsp;\u003c/p\u003e\n \u003cp\u003e(consideration of risk factors)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 142px;\"\u003e\n \u003cp\u003e2\u0026nbsp;\u003c/p\u003e\n \u003cp\u003e(clear outcomes reported)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 55px;\"\u003e\n \u003cp\u003e6\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 74px;\"\u003e\n \u003cp\u003eModerate Risk\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 132px;\"\u003e\n \u003cp\u003eAdedeji, A. J.\u003cem\u003e\u0026nbsp;et al.\u003c/em\u003e (2018)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 142px;\"\u003e\n \u003cp\u003e2\u0026nbsp;\u003c/p\u003e\n \u003cp\u003e(convenience sampling, not generalizable)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 123px;\"\u003e\n \u003cp\u003e0\u0026nbsp;\u003c/p\u003e\n \u003cp\u003e(no comparison made)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 142px;\"\u003e\n \u003cp\u003e2\u0026nbsp;\u003c/p\u003e\n \u003cp\u003e(clear reporting of outcomes)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 55px;\"\u003e\n \u003cp\u003e4\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 74px;\"\u003e\n \u003cp\u003eModerate Risk\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 132px;\"\u003e\n \u003cp\u003eJumana and Kamal (2022)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 142px;\"\u003e\n \u003cp\u003e3\u0026nbsp;\u003c/p\u003e\n \u003cp\u003e(includes control group of healthy buffalo)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 123px;\"\u003e\n \u003cp\u003e0\u0026nbsp;\u003c/p\u003e\n \u003cp\u003e(no consideration of confounding factors)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 142px;\"\u003e\n \u003cp\u003e2\u0026nbsp;\u003c/p\u003e\n \u003cp\u003e(clear outcomes reported)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 55px;\"\u003e\n \u003cp\u003e5\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 74px;\"\u003e\n \u003cp\u003eModerate Risk\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 132px;\"\u003e\n \u003cp\u003eIsrael (2020)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 142px;\"\u003e\n \u003cp\u003e3\u0026nbsp;\u003c/p\u003e\n \u003cp\u003e(cross-sectional study with random sampling)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 123px;\"\u003e\n \u003cp\u003e0\u0026nbsp;\u003c/p\u003e\n \u003cp\u003e(no consideration of confounding factors)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 142px;\"\u003e\n \u003cp\u003e2\u0026nbsp;\u003c/p\u003e\n \u003cp\u003e(clear reporting of outcomes)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 55px;\"\u003e\n \u003cp\u003e5\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 74px;\"\u003e\n \u003cp\u003eModerate Risk\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003c/tbody\u003e\n \u003c/table\u003e\n\u003c/div\u003e\n\u003cp\u003e\u0026nbsp;\u003c/p\u003e"}],"fulltextSource":"","fullText":"","funders":[],"hasAdminPriorityOnWorkflow":false,"hasManuscriptDocX":true,"hasOptedInToPreprint":true,"hasPassedJournalQc":"","hasAnyPriority":false,"hideJournal":true,"highlight":"","institution":"","isAcceptedByJournal":false,"isAuthorSuppliedPdf":false,"isDeskRejected":"","isHiddenFromSearch":false,"isInQc":false,"isInWorkflow":false,"isPdf":false,"isPdfUpToDate":true,"isWithdrawnOrRetracted":false,"journal":{"display":true,"email":"[email protected]","identity":"researchsquare","isNatureJournal":false,"hasQc":true,"allowDirectSubmit":true,"externalIdentity":"","sideBox":"","snPcode":"","submissionUrl":"/submission","title":"Research Square","twitterHandle":"researchsquare","acdcEnabled":true,"dfaEnabled":false,"editorialSystem":"","reportingPortfolio":"","inReviewEnabled":false,"inReviewRevisionsEnabled":true},"keywords":"Lumpy Skin Disease (LSD), Economic impact, Livestock industry, Disease management, Outbreak costs, Trade restrictions","lastPublishedDoi":"10.21203/rs.3.rs-7780927/v1","lastPublishedDoiUrl":"https://doi.org/10.21203/rs.3.rs-7780927/v1","license":{"name":"CC BY 4.0","url":"https://creativecommons.org/licenses/by/4.0/"},"manuscriptAbstract":"\u003ch2\u003eBackground\u003c/h2\u003e\u003cp\u003eLumpy Skin Disease (LSD), caused by the Neethling poxvirus, presents a significant economic threat to livestock industries in Africa, affecting milk and meat production, hide quality, and trade. This systematic review evaluates the economic impact of LSD, associated financial costs, and management strategies across affected African regions.\u003c/p\u003e\u003ch2\u003eMethods\u003c/h2\u003e\u003cp\u003eFollowing PRISMA guidelines, a comprehensive search of PubMed and Google Scholar was conducted, identifying studies published in English from inception through 18-08-2024 to 02-10-2024. Eligible studies focused on the economic implications of LSD, including direct and indirect costs. The Newcastle-Ottawa Scale was used to assess risk of bias.\u003c/p\u003e\u003ch2\u003eResults\u003c/h2\u003e\u003cp\u003eOf the 160 records identified, 31 studies met the inclusion criteria. Economic losses from LSD outbreaks ranged from \u003cspan\u003e$\u003c/span\u003e1.2\u0026nbsp;million to \u003cspan\u003e$\u003c/span\u003e2.5\u0026nbsp;million per outbreak, significantly impacting milk and meat production, hide quality, and trade. Key management strategies identified include vaccination and enhanced biosecurity measures, though vaccine access remains challenging.\u003c/p\u003e\u003ch2\u003eConclusion\u003c/h2\u003e\u003cp\u003eThis review highlights the substantial economic burden of LSD on Africa\u0026rsquo;s livestock industry and emphasizes the need for improved vaccine access and robust disease surveillance. Enhanced control measures are essential to mitigate LSD\u0026rsquo;s economic impact and safeguard livestock productivity.\u003c/p\u003e","manuscriptTitle":"Economic Impact of Lumpy Skin Disease in Africa: A Systematic Review","msid":"","msnumber":"","nonDraftVersions":[{"code":1,"date":"2025-11-10 11:55:34","doi":"10.21203/rs.3.rs-7780927/v1","editorialEvents":[{"type":"communityComments","content":0}],"status":"published","journal":{"display":true,"email":"[email protected]","identity":"researchsquare","isNatureJournal":false,"hasQc":true,"allowDirectSubmit":true,"externalIdentity":"","sideBox":"","snPcode":"","submissionUrl":"/submission","title":"Research Square","twitterHandle":"researchsquare","acdcEnabled":true,"dfaEnabled":false,"editorialSystem":"","reportingPortfolio":"","inReviewEnabled":false,"inReviewRevisionsEnabled":true}}],"origin":"","ownerIdentity":"6123871c-dbc1-44b5-84be-3eb4950cb6cf","owner":[],"postedDate":"November 10th, 2025","published":true,"recentEditorialEvents":[],"rejectedJournal":[],"revision":"","amendment":"","status":"posted","subjectAreas":[],"tags":[],"updatedAt":"2025-11-12T16:19:55+00:00","versionOfRecord":[],"versionCreatedAt":"2025-11-10 11:55:34","video":"","vorDoi":"","vorDoiUrl":"","workflowStages":[]},"version":"v1","identity":"rs-7780927","journalConfig":"researchsquare"},"__N_SSP":true},"page":"/article/[identity]/[[...version]]","query":{"redirect":"/article/rs-7780927","identity":"rs-7780927","version":["v1"]},"buildId":"XKTyCvWXoU3ODBz1xrDgd","isFallback":false,"isExperimentalCompile":false,"dynamicIds":[84888],"gssp":true,"scriptLoader":[]}