Using daily data on vaccinations, disease spread and measures of social interaction from Google Mobility reports aggregated at the country level for 112 countries, we present estimates of behavioural responses to the global rollout of COVID-19 vaccines. We first estimate correlates of the timing and intensity of the vaccination rollout, finding that countries which vaccinated more of their population earlier strongly tended to be richer, whereas measures of the state of pandemic or its death toll up to the time of the initial vaccine rollout had little predictive ability after controlling for income. Estimates of models of social distancing and disease spread suggest that countries which vaccinated more quickly also experienced decreases in some measures of social distancing, yet also lower incidence of disease, and in these countries, policy-makers relaxed social distancing measures relative to countries which rolled out vaccinations more slowly.

An often overlooked strategy for fighting the COVID-19 pandemic is group testing. Its main advantage is that it can scale, enabling the regular testing of the whole population. We argue that another advantage is that it can induce social distancing. Using a simple model, we show that if a group tests positive and its members are in close social proximity, then they will rationally choose not to meet. The driving force is the uncertainty about who has the virus and the fact that the group cares about its collective welfare. We therefore propose identifying socially connected groups, such as colleagues, friends and neighbours, and testing them regularly.

In 2020–2021, the world has been gripped by a pandemic that no living person has ever known. The coronavirus pandemic is undoubtedly the greatest challenge the world has faced in over a generation. The imperative of statistical modelling is not only to manage the short-run crisis for the health services, but also to explain the pandemic’s course and establish the effectiveness of different policies, both non-pharmaceutical and with vaccines. This difficult task has been undertaken by the epidemiologists and others in the face of measurement data problems, behavioural complications and endogeneity issues. This paper proposes a simple taxonomy of the alternative different models and suggests how they may be used together to overcome limitations. This perspective may have important implications for how policy-makers cope with future waves or strains in the current pandemic, or future pandemics.

This article shows how new time series models can be used to track the progress of an epidemic, forecast key variables and evaluate the effects of policies. The univariate framework of Harvey and Kattuman (2020, Harvard Data Science Review, Special Issue 1—COVID-19, https://hdsr.mitpress.mit.edu/pub/ozgjx0yn) is extended to model the relationship between two or more series and the role of common trends is discussed. Data on daily deaths from COVID-19 in Italy and the UK provides an example of leading indicators when there is a balanced growth. When growth is not balanced, the model can be extended by including a non-stationary component in one of the series. The viability of this model is investigated by examining the relationship between new cases and deaths in the Florida second wave of summer 2020. The balanced growth framework is then used as the basis for policy evaluation by showing how some variables can serve as control groups for a target variable. This approach is used to investigate the consequences of Sweden’s soft lockdown coronavirus policy in the spring of 2020.

Can smart containment policies crowd out private efforts at social distancing? We analyse this question from the perspective of network formation theory. We focus in particular on the role of externalities in social distancing choices. We also look at how these choices are affected by factors such as the agents’ risk perception, the speed of the policy intervention, the structure of the underlying network and the presence of strategic complementarities. We argue that crowding out is a problem when the probability that an outbreak may spread undetected is relatively high (either because testing is too infrequent or because tests are highly inaccurate). This is also the case where the choice of relaxing social distancing generates the largest negative externalities. Simulations on a real-world network suggest that crowding out is more likely to occur when, in the absence of interventions, face-to-face contacts are perceived to carry relatively high risk.

Britain, like many other societies within the OECD, has been facing cumulative and interdependent social, political, and economic crises which came to a head shortly before COVID. The shock of COVID has accentuated these crises, creating a state of policy flux in which all long-established intellectual frameworks have proved inadequate: across the OECD, public policy has largely abandoned them. Fortunately, across the social sciences, history and philosophy there have been important new advances by major scholars which cohere and provide a more sophisticated account of society. While they will ultimately prove inadequate as new complexities emerge, for the present that offer the best guide available for policy. This essay provides an integrated review of this recent literature and relates it to some of the key policy problems.