COVID-19 case rates, spatial mobility, and neighbourhood socioeconomic characteristics in Toronto: a spatial-temporal analysis.

OBJECTIVES: This study has two primary research objectives: (1) to investigate the spatial clustering pattern of mobility reductions and COVID-19 cases in Toronto and their relationships with marginalized populations, and (2) to identify the most relevant socioeconomic characteristics that relate to human mobility and COVID-19 case rates in Toronto's neighbourhoods during five distinct time periods of the pandemic. METHODS: Using a spatial-quantitative approach, we combined hot spot analyses, Pearson correlation analyses, and Wilcoxon two-sample tests to analyze datasets including COVID-19 cases, a mobile device-derived indicator measuring neighbourhood-level time away from home (i.e., mobility), and socioeconomic data from 2016 census and Ontario Marginalization Index. Temporal variations among pandemic phases were examined as well. RESULTS: The paper identified important spatial clustering patterns of mobility reductions and COVID-19 cases in Toronto, as well as their relationships with marginalized populations. COVID-19 hot spots were in more materially deprived neighbourhood clusters that had more essential workers and people who spent more time away from home. While the spatial pattern of clusters of COVID-19 cases and mobility shifted slightly over time, the group socioeconomic characteristics that clusters shared remained similar in all but the first time period. A series of maps and visualizations were created to highlight the dynamic spatiotemporal patterns. CONCLUSION: Toronto's neighbourhoods have experienced the COVID-19 pandemic in significantly different ways, with hot spots of COVID-19 cases occurring in more materially and racially marginalized communities that are less likely to reduce their mobility. The study provides solid evidence in a Canadian context to enhance policy making and provide a deeper understanding of the social determinants of health in Toronto during the COVID-19 pandemic.

RéSUMé: OBJECTIFS: Cette étude a deux grands objectifs de recherche : 1) examiner les schémas d'agrégation spatiale des baisses de mobilité et des cas de COVID-19 à Toronto et leurs liens avec les populations marginalisées; et 2) cerner les caractéristiques socioéconomiques les plus pertinentes liées à la mobilité humaine et aux taux de cas de COVID-19 dans les quartiers de Toronto au cours de cinq périodes distinctes de la pandémie. MéTHODE: À l'aide d'une approche spatio-quantitative, nous avons combiné des analyses de points chauds, des analyses de corrélation de Pearson et des tests de Wilcoxon à deux échantillons pour analyser des ensembles de données incluant : les cas de COVID-19, un indicateur dérivé d'appareils mobiles pour mesurer le temps passé à l'extérieur du domicile au niveau du quartier (c.-à-d. la mobilité), ainsi que les données socioéconomiques du recensement de 2016 et de l'indice de marginalisation ontarien. Nous avons aussi examiné les variations temporelles entre les phases de la pandémie. RéSULTATS: Nous avons repéré d'importants schémas d'agrégation spatiale des baisses de mobilité et des cas de COVID-19 à Toronto, ainsi que leurs liens avec les populations marginalisées. Les points chauds de la COVID-19 se trouvaient dans des grappes de quartiers plus défavorisés sur le plan matériel, où il y avait davantage de travailleurs essentiels et de personnes passant du temps à l'extérieur de leur domicile. La structure spatiale des grappes de cas de COVID-19 et de la mobilité a légèrement changé au fil du temps, mais les caractéristiques des groupes socioéconomiques communes à toutes les grappes sont restées semblables durant toutes les périodes sauf la première. Nous avons créé une série de cartes et de visualisations pour faire ressortir les schémas spatio-temporels dynamiques. CONCLUSION: Les quartiers de Toronto ont vécu la pandémie de COVID-19 de façons très différentes : les points chauds des cas de COVID-19 sont survenus dans des communautés plus marginalisées sur le plan matériel et racial et moins susceptibles de réduire leur mobilité. L'étude fournit des preuves solides dans un contexte canadien pour améliorer l'élaboration des politiques et approfondir la compréhension des déterminants sociaux de la santé à Toronto pendant la pandémie de COVID-19.

Impact of a Public Policy Restricting Staff Mobility Between Nursing Homes in Ontario, Canada During the COVID-19 Pandemic.

OBJECTIVES: To assess changes in the mobility of staff between nursing homes in Ontario, Canada, before and after enactment of public policy restricting staff from working at multiple homes. DESIGN: Pre-post observational study. SETTING AND PARTICIPANTS: 623 nursing homes in Ontario, Canada, between March 2020 and June 2020. METHODS: We used GPS location data from mobile devices to approximate connectivity between all 623 nursing homes in Ontario during the 7 weeks before (March 1-April 21) and after (April 22-June 13) the policy restricting staff movement was implemented. We constructed a network diagram visualizing connectivity between nursing homes in Ontario and calculated the number of homes that had a connection with another nursing home and the average number of connections per home in each period. We calculated the relative difference in these mobility metrics between the 2 time periods and compared within-home changes using McNemar test and the Wilcoxon rank-sum test. RESULTS: In the period preceding restrictions, 266 (42.7%) nursing homes had a connection with at least 1 other home, compared with 79 (12.7%) homes during the period after restrictions, a drop of 70.3% (P < .001). Including all homes, the average number of connections in the before period was 3.90 compared to 0.77 in the after period, a drop of 80.3% (P < .001). In both periods, mobility between nursing homes was higher in homes located in larger communities, those with higher bed counts, and those part of a large chain. CONCLUSIONS AND IMPLICATIONS: Mobility between nursing homes in Ontario fell sharply after an emergency order by the Ontario government limiting long-term care staff to a single home, though some mobility persisted. Reducing this residual mobility should be a focus of efforts to reduce risk within the long-term care sector during the COVID-19 pandemic.

Development of a global infectious disease activity database using natural language processing, machine learning, and human expertise.

OBJECTIVE: We assessed whether machine learning can be utilized to allow efficient extraction of infectious disease activity information from online media reports. MATERIALS AND METHODS: We curated a data set of labeled media reports (n = 8322) indicating which articles contain updates about disease activity. We trained a classifier on this data set. To validate our system, we used a held out test set and compared our articles to the World Health Organization Disease Outbreak News reports. RESULTS: Our classifier achieved a recall and precision of 88.8% and 86.1%, respectively. The overall surveillance system detected 94% of the outbreaks identified by the WHO covered by online media (89%) and did so 43.4 (IQR: 9.5-61) days earlier on average. DISCUSSION: We constructed a global real-time disease activity database surveilling 114 illnesses and syndromes. We must further assess our system for bias, representativeness, granularity, and accuracy. CONCLUSION: Machine learning, natural language processing, and human expertise can be used to efficiently identify disease activity from digital media reports.