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In early 2015, an outbreak of 10 confirmed measles cases occurred in Toronto, Ontario. As part of the outbreak response, the Toronto Public Health staff conducted both traditional and supplementary case investigation activities. Despite this extensive effort, and unlike many previous measles outbreaks in Canada, neither the source case nor any confirmed epidemiologic links between cases were identified. The outbreak investigation brought to light potential gaps in the current measles surveillance and suggested approaches to future investigations: routine use of social media and other time-stamped resources to enhance case investigation; early and repeated targeted communication with primary care partners to improve case detection; and continued efforts to increase and maintain sufficient immunization coverage to interrupt transmission. [ABSTRACT FROM AUTHOR]

Background: Syndromic surveillance through web or phone-based polling has been used to track the course of infectious diseases worldwide. Our study objective was to describe the characteristics, symptoms, and self-reported testing rates of respondents in three different COVID-19 symptom surveys in Canada., Methods: This was a cross-sectional study using three distinct Canada-wide web-based surveys, and phone polling in Ontario. All three sources contained self-reported information on COVID-19 symptoms and testing. In addition to describing respondent characteristics, we examined symptom frequency and the testing rate among the symptomatic, as well as rates of symptoms and testing across respondent groups., Results: We found that over March- April 2020, 1.6% of respondents experienced a symptom on the day of their survey, 15% of Ontario households had a symptom in the previous week, and 44% of Canada-wide respondents had a symptom in the previous month. Across the three surveys, SARS-CoV-2-testing was reported in 2-9% of symptomatic responses. Women, younger and middle-aged adults (versus older adults) and Indigenous/First nations/Inuit/Métis were more likely to report at least one symptom, and visible minorities were more likely to report the combination of fever with cough or shortness of breath., Interpretation: The low rate of testing among those reporting symptoms suggests significant opportunity to expand testing among community-dwelling residents of Canada. Syndromic surveillance data can supplement public health reports and provide much-needed context to gauge the adequacy of SARS-CoV-2 testing rates., Competing Interests: WJ Schatten is a paid employee of Forum Research. This commercial affiliation does not alter our adherence to PLOS ONE policies on sharing data and materials. II Bogoch has consulted to BlueDot, a social benefit corporation that tracks the spread of emerging infectious diseases. The remaining authors have no disclosures.

BackgroundGiven that measles is eliminated in Canada and measles immunisation coverage in Ontario is high, it has been questioned whether Ontario's measles outbreak response is worthwhile.AimOur objective was to determine cost-effectiveness of measles containment protocols in Ontario from the healthcare payer perspective.MethodsWe developed a decision-analysis model comparing Ontario's measles containment strategy (based on actual 2015 outbreak data) with a hypothetical 'modified response'. The modified scenario assumed 10% response costs with reduced case and contact tracing and no outbreak-associated vaccinations; it was based on local and provincial administrative and laboratory data and parameters from peer-reviewed literature. Short- and long-term health outcomes, quality-adjusted life years (QALYs) and costs discounted at 1.5%, were estimated. We conducted one- and two-way sensitivity analyses.ResultsThe 2015 outbreak in Ontario comprised 16 measles cases and an estimated 3,369 contacts. Predictive modelling suggested that the outbreak response prevented 16 outbreak-associated cases at a cost of CAD 1,213,491 (EUR 861,579). The incremental cost-effectiveness ratio was CAD 739,063 (EUR 524,735) per QALY gained for the outbreak response vs modified response. To meet the commonly accepted cost-effectiveness threshold of CAD 50,000 (EUR 35,500) per QALY gained, the outbreak response would have to prevent 94 measles cases. In sensitivity analyses, the findings were robust.ConclusionsOntario's measles outbreak response exceeds generally accepted cost-effectiveness thresholds and may not be the most efficient use of public health resources from a healthcare payer perspective. These findings should be balanced against benefits of increased vaccine coverage and maintaining elimination status.

Objectives: This study examined Twitter for public health surveillance during a mass gathering in Canada with two objectives: to explore the feasibility of acquiring, categorizing and using geolocated Twitter data and to compare Twitter data against other data sources used for Pan Parapan American Games (P/PAG) surveillance., Methods: Syndrome definitions were created using keyword categorization to extract posts from Twitter. Categories were developed iteratively for four relevant syndromes: respiratory, gastrointestinal, heat-related illness, and influenza-like illness (ILI). All data sources corresponded to the location of Toronto, Canada. Twitter data were acquired from a publicly available stream representing a 1% random sample of tweets from June 26 to September 10, 2015. Cross-correlation analyses of time series data were conducted between Twitter and comparator surveillance data sources: emergency department visits, telephone helpline calls, laboratory testing positivity rate, reportable disease data, and temperature., Results: The frequency of daily tweets that were classified into syndromes was low, with the highest mean number of daily tweets being for ILI and respiratory syndromes (22.0 and 21.6, respectively) and the lowest, for the heat syndrome (4.1). Cross-correlation analyses of Twitter data demonstrated significant correlations for heat syndrome with two data sources: telephone helpline calls (r = 0.4) and temperature data (r = 0.5)., Conclusion: Using simple syndromes based on keyword classification of geolocated tweets, we found a correlation between tweets and two routine data sources for heat alerts, the only public health event detected during P/PAG. Further research is needed to understand the role for Twitter in surveillance.

Objectives: The adverse effects of hot weather on public health are of increasing concern. A surveillance system using 911 medical dispatch data for the detection of heat-related illness (HRI) could provide new information on the impact of excessive heat on the population. This paper describes how we identified medical dispatch call codes, called "determinants", that could represent HRI events., Methods: Approximately 500 medical dispatch determinants were reviewed in focus groups composed of Emergency Medical Services (EMS) paramedics, dispatchers, physicians, and public health epidemiologists. Each group was asked to select those determinants that might adequately represent HRI. Selections were then assessed empirically using correlations with daily mean temperature over the study period (June 1-August 31,2005)., Results: The focus groups identified 12 determinant groupings and ranked them according to specificity for HRI. Of these, "Heat/cold exposure" was deemed the most specific. The call determinant groupings with the clearest positive associations with daily mean temperature empirically were "Heat/cold exposure" (Spearman's correlation coefficient (SCC) 0.71, p < 0.0001) and "Unknown problem (man down)" (SCC 0.21, p = 0.04). Within each grouping, the determinant "Unknown status (3rd party caller)" showed significant associations, SCC = 0.34 (p = 0.001) and SCC = 0.22 (p = 0.03) respectively., Conclusions: Clinically-informed expertise and empirical evidence both contributed to identification of a group of 911 medical dispatch call determinants that plausibly represent HRI events. Once evaluated prospectively, these may be used in public health surveillance to better understand environmental health impacts on human populations and inform targeted public health interventions.