As the COVID-19 pandemic lingers, the possibility of 'pandemic fatigue' has raised worldwide concerns. Here, we examine whether there was a gradual reduction in adherence to protective behaviours against COVID-19 from March through December 2020, as hypothesized in expectations of fatigue. We considered self-report behaviours from representative samples of the populations of 14 countries (N = 238,797), as well as mobility and policy data for 124 countries. Our results show that changes in adherence were empirically meaningful and geographically widespread. While a low-cost and habituating behaviour (mask wearing) exhibited a linear rise in adherence, high-cost and sensitizing behaviours (physical distancing) declined, but this decline decelerated over time, with small rebounds seen in later months. Reductions in adherence to physical distancing showed little difference across societal groups, but were less intense in countries with high interpersonal trust. Alternative underlying mechanisms and policy implications are discussed.

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This study presents an educational performance assessment of Brazilian state-level units using the technique for order preference by similarity to the ideal solution (TOPSIS). Although this technique is a well-established multi-criteria decision making (MCDM) model that can be applied across diverse areas, it has yet to be applied to an analysis of primary and secondary education. We assess how state-level units in Brazil perform in terms of primary and secondary educational inputs and outputs during the period from 2013 to 2017. We handle epistemic uncertainty with regard to weight definition using maximal information entropy. Additionally, a Tobit regression approach on performance scores is developed. Results show that region of the country plays an important role in determining educational performance. Moreover, results indicate that gross domestic product (GDP) is positively related to education scores while infant mortality is negatively associated with educational performance.

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The COVID-19 pandemic has led governments worldwide to impose extensive restrictions on citizens, some of which may have long-term impact after their removal. Education is arguably the policy domain where closure policies are anticipated to lead to greatest lasting loss, in this case learning loss. Currently, limited data exists from which researchers and practitioners can draw insightful conclusions about how to remedy the problem. In this paper, we outline the global pattern in pandemic school-closure periods and illustrate data needs through the examples drawn from Brazil and India, two large countries with decentralised education systems, and which experienced prolonged periods of school closures during the pandemic. We conclude with a series of recommendations for building an improved data environment at government, school and household levels, to serve the building back agenda in education.