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From the start of the pandemic, questions were raised concerning how the pandemic could change or even transform relationships to our living environments. COVID-19 has had a disproportionate impact on the health and well-being of older people due to the increased risk of severity of the disease with both advancing age and associated co-morbidities. Restrictions on the movement of older people have also been more severe, with many countries imposing restrictions based on chronological age. In Portugal, confinement measures were targeted at older persons in terms of sheltering-at-home orders for those over 70. This paper looks at the impact of these restrictions on the lives of older people and asks what we can learn from the pandemic about the concept of age-friendly cities (WHO, 2007a). We look at the lived experience of older people to understand how their well-being and mobility were impacted during the crisis and its aftermath. The study was undertaken in four urban areas: Aveiro, Coimbra, Lisbon, and Faro. Data was collected using semi-structured interviews analysed using a process of thematic coding based on the eight pillars of the WHO's Age-Friendly City Checklist (WHO, 2007b). The results are discussed using conceptualisations from the new mobilities paradigm: existential mobility, connection and (im)mobility governance, offering new ways of thinking about age-friendliness in and out of crisis. • Measures to contain COVID-19 were aimed at older people. • Interview data was analysed to understand the impact in Portugal. • Participants reported an existential need to move and connect. • These findings are essential to understanding age-friendly places. • A multi-dimensional framework of mobility is a necessary starting point for AFCC. [ABSTRACT FROM AUTHOR]

Community vulnerability is widely viewed as an important aspect to consider when modeling disease. Although COVID-19 does disproportionately impact vulnerable populations, human behavior as measured by community mobility is equally influential in understanding disease spread. In this research, we seek to understand which of four composite measures perform best in explaining disease spread and mortality, and we explore the extent to which mobility account for variance in the outcomes of interest. We compare two community mobility measures, three composite measures of community vulnerability, and one composite measure that combines vulnerability and human behavior to assess their relative feasibility in modeling the US COVID-19 pandemic. Extensions – via temporally dependent fixed effect coefficients – of the commonly used Bayesian spatio-temporal Poisson disease mapping models are implemented and compared in terms of goodness of fit as well as estimate precision and viability. A comparison of goodness of fit measures nearly unanimously suggests the human behavior-based models are superior. The duration at residence mobility measure indicates two unique and seemingly inverse relationships between mobility and the COVID-19 pandemic: the findings indicate decreased COVID-19 presence with decreased mobility early in the pandemic and increased COVID-19 presence with decreased mobility later in the pandemic. The early indication is likely influenced by a large presence of state-issued stay at home orders and self-quarantine, while the later indication likely emerges as a consequence of holiday gatherings in a country under limited restrictions. This study implements innovative statistical methods and furnishes results that challenge the generally accepted notion that vulnerability and deprivation are key to understanding disparities in health outcomes. We show that human behavior is equally, if not more important to understanding disease spread. We encourage researchers to build upon the work we start here and continue to explore how other behaviors influence the spread of COVID-19. • Shows vulnerability coupled with mobility are most effective for modeling COVID-19. • Demonstrates social distancing limits the spread of COVID-19. • Challenges using vulnerability and deprivation to explain disparities in health. • Confirms that capturing human behavior is crucial for understanding disease spread. • Elucidates the value of using novel spatiotemporal methods to model disease spread. [ABSTRACT FROM AUTHOR]