Do regionally targeted lockdowns alter movement to non-lockdown regions? Evidence from Ontario, Canada.

Regionally targeted interventions are being used by governments to slow the spread of COVID-19. In areas where free movement is not being actively restricted, there is uncertainty about how effective such regionally targeted interventions are due to the free movement of people between regions. We use mobile-phone network mobility data to test two hypotheses: 1) do regions targeted by exhibit increased outflows into other regions and 2) do regions targeted by interventions increase outflows specifically into areas with lesser restrictions. Our analysis focuses on two well-defined regionally targeted interventions in Ontario, Canada the first intervention as the first wave subsided (July 17, 2020) and the second intervention as we entered into new restrictions during the onset of the second wave (November 23, 2020). We use a difference-in-difference model to investigate hypothesis 1 and an interrupted time series model to investigate hypothesis 2, controlling for spatial effects (using a spatial-error model) in both cases. Our findings suggest that there that the regionally targeted interventions had a neutral effect (or no effect) on inter-regional mobility, with no significant differences associated with the interventions. We also found that overall inter-regional mobility was associated with socio-economic factors and the distance to the boundary of the intervention region. These findings are important as they should guide how governments design regionally targeted interventions (from a geographical perspective) considering observed patterns of mobility.

Digital technology and disease surveillance in the COVID-19 pandemic: a scoping review protocol.

INTRODUCTION: Infectious diseases pose a risk to public health, requiring efficient strategies for disease prevention. Digital health surveillance technologies provide new opportunities to enhance disease prevention, detection, tracking, reporting and analysis. However, in addition to concerns regarding the effectiveness of these technologies in meeting public health goals, there are also concerns regarding the ethics, legality, safety and sustainability of digital surveillance technologies. This scoping review examines the literature on digital surveillance for public health purposes during the COVID-19 pandemic to identify health-related applications of digital surveillance technologies, and to highlight discussions of the implications of these technologies. METHODS AND ANALYSIS: The scoping review will be guided by the framework proposed by Arksey and O'Malley and the guidelines outlined by Colquhoun et al and Levac et al. We will search Medline (Ovid), PsycInfo, PubMed, Scopus, CINAHL (EBSCOhost), ACM Digital Library, Google Scholar and IEEE Explore for relevant studies published between December 2019 and December 2020. The review will also include grey literature. Data will be managed and analysed through an extraction table and thematic analysis. ETHICS AND DISSEMINATION: Findings will be disseminated through traditional academic channels, as well as social media channels and research briefs and infographics. We will target our dissemination to provincial and federal public health organisations, as well as technology companies and community-based organisations managing the public response to the COVID-19 pandemic.

Associations between mobility and socio-economic indicators vary across the timeline of the Covid-19 pandemic.

Covid-19 interventions are greatly affecting patterns of human mobility. Changes in mobility during Covid-19 have differed across socio-economic gradients during the first wave. We use fine-scale network mobility data in Ontario, Canada to study the association between three different mobility measures and four socio-economic indicators throughout the first and second wave of Covid-19 (January to December 2020). We find strong associations between mobility and the socio-economic indicators and that relationships between mobility and other socio-economic indicators vary over time. We further demonstrate that understanding how mobility has changed in response to Covid-19 varies considerably depending on how mobility is measured. Our findings have important implications for understanding how mobility data should be used to study interventions across space and time. Our results support that Covid-19 non-pharmaceutical interventions have resulted in geographically disparate responses to mobility and quantifying mobility changes at fine geographical scales is crucial to understanding the impacts of Covid-19.