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.

Identifying importation points of the SARS-CoV-2 Omicron variant into the USA and potential locations of early domestic spread and impact.

Highlight: We identified the US airports and metropolitan areas, particularly New York City, Miami and Los Angeles, that were the most likely locations of importation and domestic spread of Omicron from South Africa. Vaccination coverage suggested that several cities in GA, TX and UT were particularly vulnerable to public health impacts.

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.

Potential for inter-state spread of Covid-19 from Arizona, USA: analysis of mobile device location and commercial flight data.

A significant rise of SARS-CoV-2 transmission in Arizona in June 2020 prompted the need to evaluate potential dispersion to other regions in the United States. We evaluate the potential for domestic dissemination of SARS-CoV-2 from Arizona using mobile device-location and scheduled flights data.

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.