Public Health Threat of New, Reemerging, and Neglected Zoonoses in the Industrialized World

Microbiologic infections acquired from animals, known as zoonoses, pose a risk to public health. An estimated 60% of emerging human pathogens are zoonotic. Of these pathogens, >71% have wildlife origins. These pathogens can switch hosts by acquiring new genetic combinations that have altered pathogenic potential or by changes in behavior or socioeconomic, environmental, or ecologic characteristics of the hosts. We discuss causal factors that influence the dynamics associated with emergence or reemergence of zoonoses, particularly in the industrialized world, and highlight selected examples to provide a comprehensive view of their range and diversity

Dynamical Patterns of Cattle Trade Movements

Despite their importance for the spread of zoonotic diseases, our understanding of the dynamical aspects characterizing the movements of farmed animal populations remains limited as these systems are traditionally studied as static objects and through simplified approximations. By leveraging on the network science approach, here we are able for the first time to fully analyze the longitudinal dataset of Italian cattle movements that reports the mobility of individual animals among farms on a daily basis. The complexity and inter-relations between topology, function and dynamical nature of the system are characterized at different spatial and time resolutions, in order to uncover patterns and vulnerabilities fundamental for the definition of targeted prevention and control measures for zoonotic diseases. Results show how the stationarity of statistical distributions coexists with a strong and non-trivial evolutionary dynamics at the node and link levels, on all timescales. Traditional static views of the displacement network hide important patterns of structural changes affecting nodes' centrality and farms' spreading potential, thus limiting the efficiency of interventions based on partial longitudinal information. By fully taking into account the longitudinal dimension, we propose a novel definition of dynamical motifs that is able to uncover the presence of a temporal arrow describing the evolution of the system and the causality patterns of its displacements, shedding light on mechanisms that may play a crucial role in the definition of preventive actions

Dynamical Patterns of Cattle Trade Movements

Despite their importance for the spread of zoonotic diseases, our understanding of the dynamical aspects characterizing the movements of farmed animal populations remains limited as these systems are traditionally studied as static objects and through simplified approximations. By leveraging on the network science approach, here we are able for the first time to fully analyze the longitudinal dataset of Italian cattle movements that reports the mobility of individual animals among farms on a daily basis. The complexity and inter-relations between topology, function and dynamical nature of the system are characterized at different spatial and time resolutions, in order to uncover patterns and vulnerabilities fundamental for the definition of targeted prevention and control measures for zoonotic diseases. Results show how the stationarity of statistical distributions coexists with a strong and non-trivial evolutionary dynamics at the node and link levels, on all timescales. Traditional static views of the displacement network hide important patterns of structural changes affecting nodes' centrality and farms' spreading potential, thus limiting the efficiency of interventions based on partial longitudinal information. By fully taking into account the longitudinal dimension, we propose a novel definition of dynamical motifs that is able to uncover the presence of a temporal arrow describing the evolution of the system and the causality patterns of its displacements, shedding light on mechanisms that may play a crucial role in the definition of preventive actions

Global shifts in mammalian population trends reveal key predictors of virus spillover risk.

Emerging infectious diseases in humans are frequently caused by pathogens originating from animal hosts, and zoonotic disease outbreaks present a major challenge to global health. To investigate drivers of virus spillover, we evaluated the number of viruses mammalian species have shared with humans. We discovered that the number of zoonotic viruses detected in mammalian species scales positively with global species abundance, suggesting that virus transmission risk has been highest from animal species that have increased in abundance and even expanded their range by adapting to human-dominated landscapes. Domesticated species, primates and bats were identified as having more zoonotic viruses than other species. Among threatened wildlife species, those with population reductions owing to exploitation and loss of habitat shared more viruses with humans. Exploitation of wildlife through hunting and trade facilitates close contact between wildlife and humans, and our findings provide further evidence that exploitation, as well as anthropogenic activities that have caused losses in wildlife habitat quality, have increased opportunities for animal-human interactions and facilitated zoonotic disease transmission. Our study provides new evidence for assessing spillover risk from mammalian species and highlights convergent processes whereby the causes of wildlife population declines have facilitated the transmission of animal viruses to humans

Towards an ecosystem model of infectious disease

Increasingly intimate associations between human society and the natural environment are driving the emergence of novel pathogens, with devastating consequences for humans and animals alike. Prior to emergence, these pathogens exist within complex ecological systems that are characterized by trophic interactions between parasites, their hosts and the environment. Predicting how disturbance to these ecological systems places people and animals at risk from emerging pathogens-and the best ways to manage this-remains a significant challenge. Predictive systems ecology models are powerful tools for the reconstruction of ecosystem function but have yet to be considered for modelling infectious disease. Part of this stems from a mistaken tendency to forget about the role that pathogens play in structuring the abundance and interactions of the free-living species favoured by systems ecologists. Here, we explore how developing and applying these more complete systems ecology models at a landscape scale would greatly enhance our understanding of the reciprocal interactions between parasites, pathogens and the environment, placing zoonoses in an ecological context, while identifying key variables and simplifying assumptions that underly pathogen host switching and animal-to-human spillover risk. As well as transforming our understanding of disease ecology, this would also allow us to better direct resources in preparation for future pandemics

Publisher Correction: Towards an ecosystem model of infectious disease

Correction to: Nature Ecology & Evolution https://doi.org/10.1038/s41559-021-01454-8, published online 17 May 2021

Dynamic and integrative approaches to understanding pathogen spillover

No abstract available

Mathematical modelling of the environmental and ecological drivers of zoonotic disease with an application to Lassa fever

Due to the essential role of zoonotic hosts, zoonotic spillover results from a complex system of environmentally-driven ecological and epidemiological processes. Despite being a global public health concern, zoonotic disease epidemiology is rarely viewed through the lens of disease ecology, meaning that the ecological factors driving zoonotic disease risk are typically not quantified. In this thesis I develop mathematical models to understand the zoonotic disease system from a process-based perspective informed by ecology, dependent on environmental variables, and tested using human health data. I focus these methods on a case study of Lassa fever which has historically been a neglected zoonosis but now may have improved opportunities for disease mitigation and surveillance. I present an overview of the topic in Chapter 1, outlining the challenges in zoonotic disease modelling and management. In Chapter 2, I find evidence of a severity bias in Lassa fever case data and estimate that infection incidence is likely on a much greater scale than previously thought. To elucidate environmental and ecological drivers of the Lassa virus system, in Chapter 3 I quantify the climatic dependence of reservoir host demographic processes. Along with strong seasonality, I estimate that year-on-year changes in precipitation can lead to substantial changes in the reservoir host population. In Chapter 4, I extend this population model to include pathogen transmission dynamics. Applying this model to states in Nigeria and linking reservoir host virus dynamics to observed human cases, I find that patterns of Lassa fever are significantly and positively correlated with predicted prevalence of infectious reservoir hosts. Finally, in Chapter 5 I summarise the findings and discuss future directions for the management and mitigation of zoonotic disease, concluding that ecological process-based modelling – facilitated by increased integration of knowledge, methods, and data – is essential for understanding zoonotic disease systems.Open Acces

Predicting wildlife reservoirs and global vulnerability to zoonotic Flaviviruses.

Flaviviruses continue to cause globally relevant epidemics and have emerged or re-emerged in regions that were previously unaffected. Factors determining emergence of flaviviruses and continuing circulation in sylvatic cycles are incompletely understood. Here we identify potential sylvatic reservoirs of flaviviruses and characterize the macro-ecological traits common to known wildlife hosts to predict the risk of sylvatic flavivirus transmission among wildlife and identify regions that could be vulnerable to outbreaks. We evaluate variability in wildlife hosts for zoonotic flaviviruses and find that flaviviruses group together in distinct clusters with similar hosts. Models incorporating ecological and climatic variables as well as life history traits shared by flaviviruses predict new host species with similar host characteristics. The combination of vector distribution data with models for flavivirus hosts allows for prediction of  global vulnerability to flaviviruses and provides potential targets for disease surveillance in animals and humans

Host phylogeny, geographic overlap, and roost sharing shape parasite communities in European bats

How multitrophic relationships between wildlife communities and their ectoparasitic vectors interact to shape the diversity of vector-borne microorganisms is poorly understood. Nested levels of dependence among microbes, vectors, and vertebrate hosts may have complicated effects on both microbial community assembly and evolution. We examined Bartonella sequences from European bats and their ectoparasites with a combination of network analysis, Bayesian phylogenetics, tip-association and cophylogeny tests, and linear regression to understand the ecological and evolutionary processes that shape parasite communities. We detected seven batectoparasite-Bartonella communities that can be differentiated based on bat families and roosting patterns. Tips of the Bartonella tree were significantly clustered by host taxonomy and geography. We also found significant evidence of evolutionary congruence between bat host and Bartonella phylogenies, indicating that bacterial species have evolved to infect related bat species. Exploring these ecological and evolutionary associations further, we found that sharing of Bartonella species among bat hosts was strongly associated with host phylogenetic distance and roost sharing and less strongly with geographic range overlap. Ectoparasite sharing between hosts was strongly predicted by host phylogenetic distance, roost sharing, and geographic overlap but had no additive effect on Bartonella sharing. Finally, historical Bartonella host-switching was more frequent for closely related bats after accounting for sampling bias among bat species. This study helps to disentangle the complex ecology and evolution of Bartonella bacteria in bat species and their arthropod vectors. Our work provides insight into the important mechanisms that partition parasite communities among hosts, particularly the effect of host phylogeny and roost sharing, and could help to elucidate the evolutionary patterns of other diverse vector-borne microorganisms