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Improving risk analysis of the environmental drivers of the spillover, emergence/reemergence, and spread of Crimean-Congo haemorrhagic fever virus, Marburg virus, and Middle East respiratory syndrome coronavirus in the East Africa Region

Ajumobi, O.; Davis, M.; George, C. M.; Rosman, L.; von DobSchuetz, S.; Watson, C.; Nuzzo, J.

2024-11-08 infectious diseases
10.1101/2024.11.07.24316934 medRxiv
Show abstract

IntroductionEmerging and/or reemerging infectious diseases (EIDs) in the East Africa region are associated with climate change induced environmental drivers. There is a need for a comprehensive understanding of these environmental drivers and to adopt an integrated risk analysis (IRA) framework for addressing a combination of the biological, environmental, and socio-economic factors that increases population vulnerabilities to EID risks, to inform biological risk mitigation and cross-sectoral decision-making. The aim of this integrative review was to identify knowledge gaps and contribute to a holistic understanding about the environmental drivers of Crimean-Congo haemorrhagic fever virus (CCHFV), Marburg virus (MARV), and Middle East respiratory syndrome coronavirus (MERS-CoV) infections in the East Africa Region, to improve IRA processes at the environment-animal-human exposure interface MethodsAn integrative review search was carried out to identify relevant studies and reports from 2000 to 2024. Searches were conducted in bibliographic databases and global institutional websites. Inclusion criteria were studies and reports (in English) addressing environmental drivers of CCHFV, MARV, and MERS-CoV infections across countries in the East Africa region, existing risk frameworks/methodological tools, and/or One Health policy recommendations for risk analysis of environmentally driven biological threats. ResultsOf the total number of studies retrieved from database searches (N = 18075) and website searches (N= 44), 242 studies and reports combined were included in the review with the majority covering the environmental drivers (N = 137), the risk frameworks/methodological tools (N=73), and the policy recommendations (N = 32). We identified ten categories of environmental drivers, four thematic groups of risk frameworks, and three categories of policy recommendations. Overall, many of the included records on the risk frameworks/methodological tools expounded on the adoption of ecological niche modeling (ENM) for environmental monitoring of potential transmission pathways of EIDs and other biological threats. ConclusionThis integrative review recommends the adoption of specialized risk mapping approaches such as ENM for environmental monitoring of EIDs under IRA processes. Findings from the review were used for the conceptualization of an IRA framework for addressing environmentally driven EIDs. What is already knownOutbreaks of emerging and/or reemerging infectious diseases (EIDs) have been increasing in frequency and severity over the last few decades and the risk of severe outbreaks are further aggravated by climate change and other anthropogenic factors. What this study addsOur review study adds to knowledge about context-specific environmental drivers and other related cascading risk factors that facilitate the transmission of EIDs with epidemic and pandemic potential in the East Africa region. In recognition of the need to address gaps in existing risk assessment processes, findings from the review were used for the conceptualization and initial development of an integrated risk analysis (IRA) framework that accounts for environmental drivers of pathogen spillover, disease emergence and spread across the entire spectrum of risk analysis; and to provide practical policy recommendations for enhancing the integration of the environment sector in the implementation of risk-based One Health interventions. How this study might affect research, practice or policyThis study highlights the importance of incorporating environmental monitoring techniques under IRA processes to fill surveillance data gaps and to inform risk-based decision making for pandemic prevention, outbreak preparedness, and biological risk reduction.

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