Conference Agenda

Environmental change determines biodiversity loss and alters the natural dynamics of pathogen transmission within ecosystems, posing challenges to human and ecosystem health. About half of human infectious diseases which emerged in recent decades originated from wildlife, and many were triggered by the same pressures that determined rapid biodiversity loss (land-use change, wildlife trade, forest loss and degradation). The One Health approach has been promoted in response to this dual crisis, as a conceptual framework that recognizes the interconnected and interdependent nature of human, animal, and environmental health. These linkages are also recognised in the Kunming-Montreal Global Biodiversity Framework, under Target 11 “restore, maintain and enhance [...] ecosystem functions and services, such as […] reduction of disease risk”. Yet, despite increased interest in One Health science and policy, biodiversity-related aspects of zoonotic disease risk remain poorly integrated into epidemic preparedness plans. Conservation science can play an important role in facilitating this integration. This talk will introduce several of the topics discussed during the symposium such as the role of ecological modelling in zoonotic preparedness plans, the impact of biodiversity loss on ecosystem function, the role of ecosystem integrity, and the analysis of social factors associated with wildlife consumption.

Bovine tuberculosis (bTB) is a major disease affecting livestock, especially cattle, across Europe, but prominently in the UK and Ireland. The European badger (Meles meles) is a known wildlife host of this disease, and transmission occurs between badgers and cattle and vice versa. Recently, badger vaccination has been used to reduce disease incidence, and protect landscape biodiversity as an alternative to badger culling. Badger vaccination schemes require the permission and buy-in of farmers, and this research compares the approaches of schemes in England. We show that famers’ different motivations have an effect on how they are likely to view the success of schemes, and how they want schemes to be carried out. We show that the role of conservation groups to help carry out vaccination can be complex, with farmers either preferring this approach, or actively shying away from it, depending on contextual factors. Scheme monitoring is also of crucial importance: monitoring badger bTB is resource-intensive, but we show farmers are unlikely to be satisfied with schemes without conclusive empirical evidence the scheme is working. This research draws several important conclusions for how conservation can work with farming communities to undertake mutually beneficial action that preserves farm biodiversity.

Zoonotic outbreaks in recent decades have highlighted viruses as a group of pathogens with remarkable epidemic and pandemic potential. Among these, mammal-borne viral zoonoses have been recognised as a major threath for public health and targeted by surveillance strategies for zoonotic risk prevention. Still, the full spectrum of reservoir hosts of many viruses of primary public health concern remains severely underestimated. Here, we implemented a trait-based predictive pipeline to predict currently unknown wild mammals that may serve as reservoirs of high-risk viral zoonoses that require priority research attention according to the World Health Organisation (WHO)’s blueprint of infectious diseases. Using trait similarity and phylogenetic proximity with known virus-seropositive or virological-positive reservoir species, we predicted unrecognised viral reservoirs and mapped their geographical distribution to identify neglected hotspots of mammal-associated viral hazard at the global scale. We show that the overall diversity of viral reservoirs is currently underestimated in mammals, with direct implications for the establishment of critical control points for spillover prevention at the human-wildlife interface. We anticipate that our results will support the identification of shared targets between biodiversity conservation and zoonotic spillover prevention under a One Health approach.

The One Health approach accounts for the inextricable connection between environmental and human health. However, human-induced environmental change can severely alter this critical connection, leading to undesired outcomes, such as increased risk of zoonotic disease emergence. Such emergence is influenced by several processes which occur at different scales and levels, making it highly challenging to determine . As a matter of fact, the ecological dynamics relating environmental changes, especially the human-induces ones, to the zoonotic risk remain unexplored. Therefore, we align spatial information on anthropogenic factors influencing natural ecosystems with environmental correlates of spillover risk, to draw a standardized framework for modeling human exposure to zoonotic disease emergence. We combine multiple drivers of human exposure to zoonotic viral spillover through machine learning approaches, which allow to account for non-linear and non-intuitive relationships between the predictive variables and the response one. Our framework could represent a useful approach for analyzing, and potentially mitigating, the risk from emerging zoonotic diseases. Moreover, when coupled with the data on the costs for zoonotic disease monitoring, our outcomes could help decision-makers defining effective monitoring strategies and management actions.

Background:

West Nile Virus (WNV) is a zoonotic arbovirus that is maintained in a transmission circle between Culex mosquitoes and birds, and occasionally spills over into the human host. Current knowledge of the link between WNV circulation and avian biodiversity has been inconsistent, with dilution effects (negative correlations) and amplification effects (positive correlations) observed in different parts of the world. However, this relationship remains largely unexplored in European countries including regions with high WNV circulation, such as Emilia-Romagna in Italy.

Method:

We explored the statistical association between WNV circulation from mosquito surveillance data collected in Emilia-Romagna (Italy) from 2013 to 2018 and observed avian biodiversity data collected in the region during the study period as part of the MITO 2000 project. Regression models were employed to test the association between multiple measures of WNV circulation and of avian biodiversity, including presence/absence and relative species abundance in combination with climate and environmental data.

Significance:

This study has the potential to unveil local effects of avian biodiversity on WNV transmission, which can inform surveillance programmes as well as policies integrating biodiversity protection into public health planning for WNV prevention in Emilia-Romagna and beyond.

One side of preparing to the probability, rather than the possibility, of a next pandemic caused by a still unknown "Disease X" is to pre-allocate funds for vaccines or design pandemic treaties, as recently discussed at the World Economic Forum. Another side consists in projecting plausible scenarios of disease transmission, based on (i) information about pathogens' emergence risk, and (ii) the combined modelling of credible local-scale dynamics with data-driven, large-scale diffusion mechanisms. Here we first identify a taxonomy of prototypical epidemiological models that could promisingly describe infections of Disease X, which is expected to come out of the WHO Blueprint list of priority diseases. This includes zoonotic viruses spilled over from animals but then circulating mainly in humans (e.g. SARS), carried by vectors (e.g. Zika), or co-circulating in humans and animals (e.g. RVF). Depending upon their major environmental transmission pathways, we then discuss the relevant modelling approaches to anticipate possible geographies of pandemic spread, as potentially emerging from the diversified connectivity networks between communities (e.g. air flights vs cars vs cargos). We finally discuss the advantages of building an intermediate-complexity framework to hierarchically model the spread of "Disease X" at different spatiotemporal scales.