The global political economy is facing extreme challenges against a backdrop of large-scale expansion of human and domestic animal populations and related impacts on the biosphere. Significant global socio-ecological changes have occurred in the period of a single lifetime, driven by increased technology and access to physical and biological resources through open markets and globalization. Current resource consumption rates are not sustainable and ecological tipping points are being reached and one of the indicators of these may be a changing balance between hosts and pathogens. A period of extraordinary progress in reducing infection risk and disease impact on humans and domestic animals in the 20th Century is reversing in the 21st, but not always and not everywhere. Drivers for this shift are discussed in terms of demographics, agroecology, biodiversity decline and loss of resilience in ecosystems, climate change and increasing interconnectedness between species globally. Causality of disease emergence remains highly speculative, but patterns and data are emerging to commend a precautionary approach, while reassessing our global political, social and economic systems.

Before attributing cause and consequence to climate change, the precise patterns of change must be known. Ground records across much of Europe show a 1–2 °C rise in temperatures in 1989 with no significant rise since then. The timing and spatial uniformity of this pattern, relative to changes in the distribution and incidence of many vector-borne diseases, are sufficient to falsify most simple claims that climate change is the principal cause of disease emergence. Furthermore, age-specific increases in incidence indicate causes other than, or in addition to, climate change. Unfortunately, many public health professionals repeat the received wisdom that climate change is worsening the burden of indirectly transmitted infections; this ‘expert opinion’ soon becomes consensus dogma divorced from quantitative evidence. The pressing need is to gather appropriate data to test the simple concept that the composition and relative importance of disparate multifactorial factors, commonly integrated within a causal nexus, will inevitably vary with the geographical, cultural, socio-economical, wildlife, etc. context. The greatest impact of warming occurs at the geographical limits of current distributions, where low temperatures limit the hazard of infected vectors. Within core endemic regions, changing exposure of humans to this hazard, through changing socio-economic factors is evidently more important amongst both the poor and the wealthy.

Levels and seasonal patterns of parasite challenge to livestock are likely to be affected by climate change, through direct effects on life cycle stages outside the definitive host and through alterations in management that affect exposure and susceptibility. Net effects and options for adapting to them will depend very strongly on details of the system under consideration. This short paper is not a comprehensive review of climate change effects on parasites, but rather seeks to identify key areas in which detail is important and arguably under-recognized in supporting farmer adaptation. I argue that useful predictions should take fuller account of system-specific properties that influence disease emergence, and not just the effects of climatic variables on parasite biology. At the same time, excessive complexity is ill-suited to useful farm-level decision support. Dealing effectively with the ‘devil of detail’ in this area will depend on finding the right balance, and will determine our success in applying science to climate change adaptation by farmers.

Climate change is widely expected to cause the emergence and spread of vector-borne diseases, and predictive models are needed so that we can be prepared. We developed a climate-sensitive, predictive, model that describes the risk of bluetongue, an arboviral disease of ruminants, which has emerged dramatically in Europe. Developing the predictive bluetongue model led to the identification of numerous gaps in both the understanding and the availability of data. These mostly pertain to the vectors and their interaction with hosts. Closing these gaps will allow better models, with more precise predictions, to be produced. These research gaps apply to many other arboviral diseases as well. As a consequence, there needs to be an increase in research on the vectors that transmit arboviral diseases. Priorities are the training of a new generation of taxonomists, studies on the field biology of potential vectors, and increased coordination of vector surveillance and recording between countries facing similar threats.

Animal disease surveillance is watching an animal population closely to determine if a specific disease or a group of diseases makes an incursion so that a prior plan of action can be implemented. The purpose of this paper is to review existing tools and techniques for an animal disease-surveillance system that can incorporate the monitoring of climate factors and related data to enhance understanding of disease epidemiology. In recent decades, there has been interest in building information systems by combining various data sources for different purposes. Within the field of animal health, there have only been limited attempts at the integration of surveillance data with relevant climate conditions. Statistical techniques for data integration, however, have been explored and used by several disciplines. Clearly the application of available techniques for linking climate data with surveillance systems should be explored with the aim of facilitating prevention, mitigation, and adaptation responses in the surveillance setting around climate change and animal disease risks. Drawing on this wider body of work, three of the available techniques that can be utilized in the analysis of surveillance data with the available climate data sets are reviewed.

Conceptualizing climate as a distinct variable limits our understanding of the synergies and interactions between climate change and the range of abiotic and biotic factors, which influence animal health. Frameworks such as eco-epidemiology and the epi-systems approach, while more holistic, view climate and climate change as one of many discreet drivers of disease. Here, I argue for a new paradigmatic framework: climate-change syndemics. Climate-change syndemics begins from the assumption that climate change is one of many potential influences on infectious disease processes, but crucially is unlikely to act independently or in isolation; and as such, it is the inter-relationship between factors that take primacy in explorations of infectious disease and climate change. Equally importantly, as climate change will impact a wide range of diseases, the frame of analysis is at the collective rather than individual level (for both human and animal infectious disease) across populations.

As the demand for meat continues to grow in South Asia and Africa and access to communal sources of water and forage shrinks, intensification of small-scale livestock systems in peri-urban areas is expected to expand. In South East Asia, smallholder transition to livestock intensification has been transformative, increasing economic opportunities while also introducing new disease risks. While we have an understanding of the emerging disease burden from livestock intensification; we have just begun to understand the possible public health benefits of sustainable landscapes and the potential health savings accrued from disease avoidance. To date, few studies have attempted to quantify the health benefits attributable to sustainable agro-ecosystems, especially in regard to livestock systems. In this paper, I will examine what is needed to measure and communicate the public health benefits and cost-savings (from disease avoidance) of sustainable agro-ecosystems.

Global food security, livestock production and animal health are inextricably bound. However, our focus on the future tends to disaggregate food and health into largely separate domains. Indeed, much foresight work is either food systems or health-based with little overlap in terms of predictions or narratives. Work on animal health is no exception. Part of the problem is the fundamental misunderstanding of the role, nature and impact of the modern futures tool kit. Here, I outline three key issues in futures research ranging from methodological confusion over the application of scenarios to the failure to effectively integrate multiple methodologies to the gap between the need for more evidence and power and control over futures processes. At its core, however, a better understanding of the narrative and worldview framing much of the futures work in animal health is required to enhance the value and impact of such exercises.