Risk factors for serological evidence of MERS-CoV in camels, Kenya, 2016-2017

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

INTRODUCTION

Emerging and/or re-emerging 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 socioeconomic factors that increase 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.

METHODS

An 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.

RESULTS

Of the total number of studies retrieved from database searches (n=18 075) 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 10 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 modelling (ENM) for environmental monitoring of potential transmission pathways of EIDs and other biological threats.

CONCLUSION

This integrative review recommends the adoption of specialised risk mapping approaches such as ENM for environmental monitoring of EIDs under IRA processes. Findings from the review were used for the conceptualisation of an IRA framework for addressing environmentally driven EIDs.

The intersection of land use and human behavior as risk factors for zoonotic pathogen exposure in Laikipia County, Kenya

A majority of emerging infectious diseases (EIDs) are zoonotic, mainly caused through spillover events linked to human-animal interactions. We conducted a survey-based human behavioral study in Laikipia County, Kenya, which is characterized by a dynamic human-wildlife-livestock interface. Questionnaires that assessed human-animal interactions, sanitation, and illnesses experienced within the past year were distributed to 327 participants among five communities in Laikipia. This study aimed to 1) describe variation in reported high-risk behaviors by community type and 2) assess the relationship between specific behaviors and self-reported illnesses. Behavioral trends were assessed in R via Fisher’s exact tests. A generalized linear mixed model with Lasso penalization (GLMMLasso) was used to assess correlations between behaviors and participants’ self-reported illness within the past year, with reported behaviors as independent variables and reported priority symptoms as the outcome. Reported behaviors varied significantly among the study communities. Participants from one community (Pastoralist-1) were significantly more likely to report eating a sick animal in the past year (p< 0.001), collecting an animal found dead to sell in the past year (p<0.0001), and not having a designated location for human waste (p<0.0001) when compared to participants from other communities. The GLMMLasso revealed that reports of an ill person in the household in the past year was significantly associated with self-reported illness. Sixty-eight percent of participants reported that bushmeat is available within the communities. Our study demonstrates community-level variation in behaviors that may influence zoonotic pathogen exposure. We further recommend development of targeted studies that explore behavioral variations among land use systems in animal production contexts.

Many infectious diseases, such as H1N1, Ebola, and COVID-19, can be spread to humas from animals. In order to reduce the risk of disease “spillover” (disease transmission from an animal to a person), it is important to understand how interactions between humans and animals can influence spread. Certain behaviors, such as eating raw meat, hunting, or sharing drinking water with animals can put people at greater risk of contacting bacteria and viruses that can cause these diseases. Globally, communities that depend on animal production are at heightened risk due to increased contact with animals. In this study, the authors conducted human behavioral surveys among different communities in Kenya that raise livestock. Results show that reported behaviors varied greatly by community. One of the communities reported a significantly higher proportion of behaviors, such as eating raw meat or eating animals found dead. Communities that show high prevalence of these behaviors may be at greater risk for contracting diseases from animals. Understanding this variation is important for developing plans for community outreach and addressing behaviors that can influence risk of disease spread.