Arguably one of the most important effects of climate change is the potential impact on human health. While this is likely to take many forms, the implications for future transmission of vector-borne diseases (VBDs), given their ongoing contribution to global disease burden, are both extremely important and highly uncertain. In part, this is owing not only to data limitations and methodological challenges when integrating climate-driven VBD models and climate change projections, but also, perhaps most crucially, to the multitude of epidemiological, ecological and socio-economic factors that drive VBD transmission, and this complexity has generated considerable debate over the past 10–15 years. In this review, we seek to elucidate current knowledge around this topic, identify key themes and uncertainties, evaluate ongoing challenges and open research questions and, crucially, offer some solutions for the field. Although many of these challenges are ubiquitous across multiple VBDs, more specific issues also arise in different vector–pathogen systems.

In the modeling of infectious disease spread within explicit social contact networks, previous studies have predominantly assumed that the effects of shifting social associations within groups are small. These models have utilized static approximations of contact networks. We examine this assumption by modeling disease spread within dynamic networks where associations shift according to individual preference based on three different measures of network centrality. The results of our investigations clearly show that this assumption may not hold in many cases. We demonstrate that these differences in association dynamics do yield significantly different disease outcomes both from each other and also from models using graph-theoretically accurate static network approximations. Further work is therefore needed to explore under which circumstances static models accurately reflect constantly shifting natural populations.

Analysing social networks is challenging. Key features of relational data require the use of non‐standard statistical methods such as developing system‐specific null, or reference, models that randomize one or more components of the observed data. Here we review a variety of randomization procedures that generate reference models for social network analysis. Reference models provide an expectation for hypothesis testing when analysing network data. We outline the key stages in producing an effective reference model and detail four approaches for generating reference distributions: permutation, resampling, sampling from a distribution, and generative models. We highlight when each type of approach would be appropriate and note potential pitfalls for researchers to avoid. Throughout, we illustrate our points with examples from a simulated social system. Our aim is to provide social network researchers with a deeper understanding of analytical approaches to enhance their confidence when tailoring reference models to specific research questions.