Numbers of non–indigenous species—species introduced from elsewhere—are increasing rapidly worldwide, causing both environmental and economic damage. Rigorous quantitative risk–analysis frameworks, however, for invasive species are lacking. We need to evaluate the risks posed by invasive species and quantify the relative merits of different management strategies (e.g. allocation of resources between prevention and control). We present a quantitative bioeconomic modelling framework to analyse risks from non–indigenous species to economic activity and the environment. The model identifies the optimal allocation of resources to prevention versus control, acceptable invasion risks and consequences of invasion to optimal investments (e.g. labour and capital). We apply the model to zebra mussels (Dreissena polymorpha), and show that society could benefit by spending up to US$324 000 year⁻¹ to prevent invasions into a single lake with a power plant. By contrast, the US Fish and Wildlife Service spent US$825 000 in 2001 to manage all aquatic invaders in all US lakes. Thus, greater investment in prevention is warranted.

We examine the net benefits of social distancing to slow the spread of COVID-19 in USA. Social distancing saves lives but imposes large costs on society due to reduced economic activity. We use epidemiological and economic forecasting to perform a rapid benefit–cost analysis of controlling the COVID-19 outbreak. Assuming that social distancing measures can substantially reduce contacts among individuals, we find net benefits of about $5.2 trillion in our benchmark case. We examine the magnitude of the critical parameters that might imply negative net benefits, including the value of statistical life and the discount rate. A key unknown factor is the speed of economic recovery with and without social distancing measures in place. A series of robustness checks also highlight the key role of the value of mortality risk reductions and discounting in the analysis and point to a need for effective economic stimulus when the outbreak has passed.

International commerce in live organisms presents a policy challenge for trade globalization; sales of live organisms create wealth, but some nonindigenous species cause harm. To reduce damage, some countries have implemented species screening to limit the introduction of damaging species. Adoption of new risk assessment (RA) technologies has been slowed, however, by concerns that RA accuracy remains insufficient to produce positive net economic benefits. This concern arises because only a small proportion of all introduced species escape, spread, and cause harm (i.e., become invasive), so a RA will exclude many noninvasive species (which provide a net economic benefit) for every invasive species correctly identified. Here, we develop a simple cost:benefit bioeconomic framework to quantify the net benefits from applying species prescreening. Because invasive species are rarely eradicated, and their damages must therefore be borne for long periods, we have projected the value of RA over a suitable range of policy time horizons (10–500 years). We apply the model to the Australian plant quarantine program and show that this RA program produces positive net economic benefits over the range of reasonable assumptions. Because we use low estimates of the financial damage caused by invasive species and high estimates of the value of species in the ornamental trade, our results underestimate the net benefit of the Australian plant quarantine program. In addition, because plants have relatively low rates of invasion, applying screening protocols to animals would likely demonstrate even greater benefits.

Significance: Emerging pandemics are increasing in frequency, threatening global health and economic growth. Global strategies to thwart pandemics can be classed as adaptive (reducing impact after a disease emerges) or mitigation (reducing the causes of pandemics). Our economic analysis shows that the optimal time to implement a globally coordinated adaptive policy is within 27 y and that given geopolitical challenges around pandemic control, these should be implemented urgently. Furthermore, we find that mitigation policies, those aimed at reducing the likelihood of an emerging disease originating, are more cost effective, saving between $344.0 billion and $360.8 billion over the next 100 y if implemented today.

Mathematical epidemiology, one of the oldest and richest areas in mathematical biology, has significantly enhanced our understanding of how pathogens emerge, evolve, and spread. Classical epidemiological models, the standard for predicting and managing the spread of infectious disease, assume that contacts between susceptible and infectious individuals depend on their relative frequency in the population. The behavioral factors that underpin contact rates are not generally addressed. There is, however, an emerging a class of models that addresses the feedbacks between infectious disease dynamics and the behavioral decisions driving host contact. Referred to as “economic epidemiology” or “epidemiological economics,” the approach explores the determinants of decisions about the number and type of contacts made by individuals, using insights and methods from economics. We show how the approach has the potential both to improve predictions of the course of infectious disease, and to support development of novel approaches to infectious disease management.