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BackgroundThe effectiveness of public health measures (PHMs) depends on population adherence. Social media were suggested as a tool to assess adherence, but representativeness and accuracy issues have been raised. ObjectiveThe objectives of this repeated cross-sectional study were to compare self-reported PHM adherence and sociodemographic characteristics between people who used Twitter (subsequently rebranded X) and people who did not use Twitter. MethodsRepeated Canada-wide web-based surveys were conducted every 14 days from September 2020 to March 2022. Weighted proportions were calculated for descriptive variables. Using Bayesian logistic regression models, we investigated associations between Twitter use, as well as opinions in tweets, and self-reported adherence with mask wearing and vaccination. ResultsData from 40,230 respondents were analyzed. As self-reported, Twitter was used by 20.6% (95% CI 20.1%-21.2%) of Canadians, of whom 29.9% (95% CI 28.6%-31.3%) tweeted about COVID-19. The sociodemographic characteristics differed across categories of Twitter use and opinions. Overall, 11% (95% CI 10.6%-11.3%) of Canadians reported poor adherence to mask-wearing, and 10.8% (95% CI 10.4%-11.2%) to vaccination. Twitter users who tweeted about COVID-19 reported poorer adherence to mask wearing than nonusers, which was modified by the age of the respondents and their geographical region (odds ratio [OR] 0.79, 95% Bayesian credibility interval [BCI] 0.18-1.69 to OR 4.83, 95% BCI 3.13-6.86). The odds of poor adherence to vaccination of Twitter users who tweeted about COVID-19 were greater than those of nonusers (OR 1.76, 95% BCI 1.48-2.07). English- and French-speaking Twitter users who tweeted critically of PHMs were more likely (OR 4.07, 95% BCI 3.38-4.80 and OR 7.31, 95% BCI 4.26-11.03, respectively) to report poor adherence to mask wearing than non–Twitter users, and those who tweeted in support were less likely (OR 0.47, 95% BCI 0.31-0.64 and OR 0.96, 95% BCI 0.18-2.33, respectively) to report poor adherence to mask wearing than non–Twitter users. The OR of poor adherence to vaccination for those tweeting critically about PHMs and for those tweeting in support of PHMs were 4.10 (95% BCI 3.40-4.85) and 0.20 (95% BCI 0.10-0.32), respectively, compared to non–Twitter users. ConclusionsOpinions shared on Twitter can be useful to public health authorities, as they are associated with adherence to PHMs. However, the sociodemographics of social media users do not represent the general population, calling for caution when using tweets to assess general population-level behaviors.

Contexte : Pour maintenir le contrôle de l’épidémie de maladie à coronavirus 2019 (COVID-19) lorsque les mesures de confinement seront levées, il sera essentiel de renforcer les mesures de santé publique alternatives. En ce qui concerne la surveillance, il sera nécessaire de détecter rapidement une grande proportion de tous nouveaux cas afin de pouvoir les isoler, et retrouver et mettre en quarantaine les personnes qui y ont été exposées. Nous présentons ici une approche mathématique qui peut être utilisée pour déterminer combien d’échantillons doivent être recueillis par unité de surface et par unité de temps pour détecter de nouveaux agrégats de cas de COVID-19 à un stade suffisamment précoce pour contrôler une éclosion. Méthodes : Nous présentons une méthode de détermination de la taille de l’échantillon qui utilise une approche de pondération relative. Compte tenu du fait que les résultats du test de diagnostic de la COVID-19 provenant de sous-populations ont contribué à détecter la maladie à un niveau de prévalence seuil pour contrôler l’éclosion, il s’agissait de 1) déterminer si le nombre prévu d’échantillons hebdomadaires fournis par la surveillance actuelle des infections virales respiratoires fondée sur les soins de santé peut fournir une taille d’échantillon déjà adéquate pour détecter de nouveaux agrégats de cas de COVID-19 et, dans le cas contraire, 2) de déterminer combien d’échantillons hebdomadaires supplémentaires étaient nécessaires à partir d’un échantillonnage volontaire. Résultats : Lors d’une démonstration de notre méthode à une fréquence hebdomadaire et à l’échelle des provinces et territoires canadiens, nous avons constaté que seuls les provinces et les territoires les plus peuplés disposaient d’un nombre suffisant de dépistages provenant des visites médicales en raison de maladies respiratoires pour détecter la COVID-19 à notre niveau de prévalence cible — qui doit être suffisamment élevé pour identifier et contrôler les nouveaux agrégats de cas. En outre, la détection de la COVID-19 est plus efficace (moins d’échantillons requis) lorsque la surveillance se concentre sur l’exigence de tests de dépistage de patients symptomatiques par les services des soins de santé. Dans les populations volontaires : plus les taux de contact sont élevés, plus le niveau de prévalence attendu est élevé, et moins il faut d’échantillons pour détecter la COVID-19 à un seuil prédéterminé. Conclusion : Cette étude présente une stratégie de surveillance ciblée, combinant des échantillons de surveillance passive et active, afin de déterminer le nombre d’échantillons à recueillir par unité de surface et par unité de temps pour détecter de nouveaux agrégats de cas de COVID-19. L’objectif de cette stratégie est de permettre une détection suffisamment précoce pour contrôler une éclosion.

Background: To maintain control of the coronavirus disease 2019 (COVID-19) epidemic as lockdowns are lifted, it will be crucial to enhance alternative public health measures. For surveillance, it will be necessary to detect a high proportion of any new cases quickly so that they can be isolated, and people who have been exposed to them traced and quarantined. Here we introduce a mathematical approach that can be used to determine how many samples need to be collected per unit area and unit time to detect new clusters of COVID-19 cases at a stage early enough to control an outbreak. Methods: We present a sample size determination method that uses a relative weighted approach. Given the contribution of COVID-19 test results from sub-populations to detect the disease at a threshold prevalence level to control the outbreak to 1) determine if the expected number of weekly samples provided from current healthcare-based surveillance for respiratory virus infections may provide a sample size that is already adequate to detect new clusters of COVID-19 and, if not, 2) to determine how many additional weekly samples were needed from volunteer sampling. Results: In a demonstration of our method at the weekly and Canadian provincial and territorial (P/T) levels, we found that only the more populous P/T have sufficient testing numbers from healthcare visits for respiratory illness to detect COVID-19 at our target prevalence level—assumed to be high enough to identify and control new clusters. Furthermore, detection of COVID-19 is most efficient (fewer samples required) when surveillance focuses on healthcare symptomatic testing demand. In the volunteer populations: the higher the contact rates; the higher the expected prevalence level; and the fewer the samples were needed to detect COVID-19 at a predetermined threshold level. Conclusion: This study introduces a targeted surveillance strategy, combining both passive and active surveillance samples, to determine how many samples to collect per unit area and unit time to detect new clusters of COVID-19 cases. The goal of this strategy is to allow for early enough detection to control an outbreak.

Advancements in artificial intelligence (AI), more precisely the subfield of machine learning, and their applications to open-source internet data, such as social media, are growing faster than the management of ethical issues for use in society. An ethical framework helps scientists and policy makers consider ethics in their fields of practice, legitimize their work and protect members of the data-generating public. A central question for advancing the ethical framework is whether or not Tweets, Facebook posts and other open-source social media data generated by the public represent a human or not. The objective of this paper is to highlight ethical issues that the public health sector will be or is already confronting when using social media data in practice. The issues include informed consent, privacy, anonymization and balancing these issues with the benefits of using social media data for the common good. Current ethical frameworks need to provide guidance for addressing issues arising from the use of social media data in the public health sector. Discussions in this area should occur while the application of open-source data is still relatively new, and they should also keep pace as other problems arise from ongoing technological change.

Climate change is driving emergence and establishment of Ixodes scapularis, the main vector of Lyme disease in Québec, Canada. As for the black-legged tick, I. scapularis Say, global warming may also favor northward expansion of other species of medically important ticks. The aims of this study were to determine (1) current diversity and abundance of ticks of public health significance other than I. scapularis, (2) sex and age of the human population bitten by these ticks (3), and the seasonal and geographic pattern of their occurrence. From 2007 to 2015, twelve tick species other than I. scapularis were submitted in the Québec passive tick surveillance program. Of these 9243 ticks, 91.2% were Ixodes cookei, 4.1% were Dermacentor variabilis, 4.0% were Rhipicephalus sanguineus and 0.7% were Amblyomma americanum. The combined annual proportion of submitted I. cookei, D. variabilis, R. sanguineus and A. americanum ticks in passive surveillance rose from 6.1% in 2007 to 16.0% in 2015 and an annual growing trend was observed for each tick species. The number of municipalities where I. cookei ticks were acquired rose from 104 to 197 during the same period. Of the 862 people bitten by these ticks, 43.3% were I. cookei ticks removed from children aged < 10 years. These findings demonstrate the need for surveillance of all the tick species of medical importance in Québec, particularly because climate may increase their abundance and geographic ranges, increasing the risk to the public of the diseases they transmit.

Abstract Background The severity of COVID-19 outbreaks is disproportionate across settings (e.g., long-term care facilities (LTCF), schools) across Canada. Few studies have examined factors associated with outbreak severity to inform prevention and response. Our study objective was to assess how outbreak severity, as measured using outbreak intensity and defined as number of outbreak-associated cases divided by outbreak duration, differed by setting and factors known to influence SARS-CoV-2 transmission. Methods We described outbreak intensity trends in 2021 using data from the Canadian COVID-19 Outbreak Surveillance System from seven provinces/territories, representing 93% of the Canadian population. A negative binomial fixed-effects model was used to assess for associations between the outcome, outbreak intensity, and characteristics of outbreaks: setting type, median age of cases, number at risk, and vaccination coverage of at least 1 dose. Also included were variables previously reported to influence SARS-CoV-2 transmission: stringency of non-pharmaceutical interventions (NPI) and the predominant SARS-CoV-2 variant detected by surveillance. Results The longest outbreaks occurred in LTCF (mean = 25.4 days) and correctional facilities (mean = 20.6 days) which also reported the largest outbreaks (mean = 29.6 cases per outbreak). Model results indicated that outbreak intensity was highest in correctional facilities. Relative to correctional facilities (referent), the second highest adjusted intensity ratio was in childcare centres (intensity ratio = 0.58 [95% CI: 0.51–0.66]), followed by LTCF (0.56 [95% CI: 0.51–0.66]). Schools had the lowest adjusted intensity ratio (0.46 [95% CI: 0.40–0.53]) despite having the highest proportion of outbreaks (37.5%). An increase in outbreak intensity was associated with increases in median age, the number at risk, and stringency of NPI. Greater vaccination coverage with at least 1 dose was associated with reduced outbreak intensity. Conclusion Descriptive and multivariable model results indicated that in Canada during 2021, outbreak intensity was greatest in closed congregate living facilities: correctional facilities and LTCF. Findings from this study support the importance of vaccination in reducing outbreak intensity when vaccines are effective against infection with circulating variants, which is especially important for closed congregate living facilities where NPIs are more challenging to implement.

In the Arctic, rabies is endemic in the Arctic fox (Vulpes lagopus), posing a significant and ongoing health risk for people and domestic animals. The mechanisms by which rabies is maintained within the low-density fox populations in the Arctic remain unclear. In this study, we developed a spatially explicit individual-based stochastic epidemiological model and performed an uncertainty analysis to better understand Arctic fox rabies dynamics. Rabies persisted in 25.68% of model simulations, with several variables having significant impact on rabies persistence: probability of rabies transmission, spatial and temporal distribution food resources, mean litter size and variability of rabies incubation periods. Where rabies is endemic, we identified 5 key parameters for rabies dynamics: spatiotemporal resource distribution, probability of birth for adult females, mean and standard deviation of litter size, and incubation period of rabies. Our study demonstrates that Arctic rabies can persist in its primary host under conditions consistent with existing empirical data in the literature and showed the important role played by the spatial and temporal distribution of resources. Finally, our results suggest that the ecological impacts of rapid climate warming could decrease the overall persistence of rabies in the Arctic and the associated health risk in Arctic communities.

Abstract Objectives In Canada, substance-related accidental acute toxicity deaths (AATDs) continue to rise at the national and sub-national levels. However, it is unknown if, where, when, and to what degree AATDs cluster in space, time, and space–time across the country. The objectives of this study were to 1) assess for clusters of AATDs that occurred in Canada during 2016 and 2017 at the national and provincial/territorial (P/T) levels, and 2) examine the substance types detected in AATD cases within each cluster. Methods Two years of person-level data on AATDs were abstracted from coroner and medical examiner files using a standardized data collection tool, including the decedent's postal code and municipality information on the places of residence, acute toxicity (AT) event, and death, and the substances detected in the death. Data were combined with Canadian census information to create choropleth maps depicting AATD rates by census division. Spatial scan statistics were used to build Poisson models to identify clusters of high rates (p

Abstract Background With increased attention to the importance of integrating the One Health approach into zoonotic disease surveillance and response, a greater understanding of the mechanisms to support effective communication and information sharing across animal and human health sectors is needed. The objectives of this qualitative case study were to describe the communication channels used between human and animal health stakeholders and to identify the elements that have enabled the integration of the One Health approach. Methods We combined documentary research with interviews with fifteen stakeholders to map the communication channels used in human and swine influenza surveillance in Alberta, Canada, as well as in the response to a human case of H1N2v in 2020. A thematic analysis of the interviews was also used to identify the barriers and facilitators to communication among stakeholders from the animal and human health sectors. Results When a human case of swine influenza emerged, the response led by the provincial Chief Medical Officer of Health involved players at various levels of government and in the human and animal health sectors. The collaboration of public and animal health laboratories and of the swine sector, in addition to the information available through the surveillance systems in place, was swift and effective. Elements identified as enabling smooth communication between the human and animal health systems included preexisting relationships between the various stakeholders, a relationship of trust between them (e.g., the swine sector and their perception of government structures), the presence of stakeholders acting as permanent liaisons between the ministries of health and agriculture, and stakeholders' understanding of the importance of the One Health approach. Conclusions Information flows through formal and informal channels and both structural and relational features that can support rapid and effective communication in infectious disease surveillance and outbreak response.

Abstract Background Estimating rates of disease importation by travellers is a key activity to assess both the risk to a country from an infectious disease emerging elsewhere in the world and the effectiveness of border measures. We describe a model used to estimate the number of travellers infected with SARS-CoV-2 into Canadian airports in 2021, and assess the impact of pre-departure testing requirements on importation risk. Methods A mathematical model estimated the number of essential and non-essential air travellers infected with SARS-CoV-2, with the latter requiring a negative pre-departure test result. The number of travellers arriving infected (i.e. imported cases) depended on air travel volumes, SARS-CoV-2 exposure risk in the departure country, prior infection or vaccine acquired immunity, and, for non-essential travellers, screening from pre-departure molecular testing. Importation risk was estimated weekly from July to November 2021 as the number of imported cases and percent positivity (PP; i.e. imported cases normalised by travel volume). The impact of pre-departure testing was assessed by comparing three scenarios: baseline (pre-departure testing of all non-essential travellers; most probable importation risk given the pre-departure testing requirements), counterfactual scenario 1 (no pre-departure testing of fully vaccinated non-essential travellers), and counterfactual scenario 2 (no pre-departure testing of non-essential travellers). Results In the baseline scenario, weekly imported cases and PP varied over time, ranging from 145 to 539 cases and 0.15 to 0.28%, respectively. Most cases arrived from the USA, Mexico, the United Kingdom, and France. While modelling suggested that essential travellers had a higher weekly PP (0.37 – 0.65%) than non-essential travellers (0.12 – 0.24%), they contributed fewer weekly cases (62 – 154) than non-essential travellers (84 – 398 per week) given their lower travel volume. Pre-departure testing was estimated to reduce imported cases by one third (counterfactual scenario 1) to one half (counterfactual scenario 2). Conclusions The model results highlighted the weekly variation in importation by traveller group (e.g., reason for travel and country of departure) and enabled a framework for measuring the impact of pre-departure testing requirements. Quantifying the contributors of importation risk through mathematical simulation can support the design of appropriate public health policy on border measures.

Abstract Background Zika virus (ZIKV) spread rapidly in the Americas in 2015. Targeting effective public health interventions for inhabitants of, and travellers to and from, affected countries depends on understanding the risk of ZIKV emergence (and re-emergence) at the local scale. We explore the extent to which environmental, social and neighbourhood disease intensity variables influenced emergence dynamics. Our objective was to characterise population vulnerability given the potential for sustained autochthonous ZIKV transmission and the timing of emergence. Logistic regression models estimated the probability of reporting at least one case of ZIKV in a given municipality over the course of the study period as an indicator for sustained transmission; while accelerated failure time (AFT) survival models estimated the time to a first reported case of ZIKV in week t for a given municipality as an indicator for timing of emergence. Results Sustained autochthonous ZIKV transmission was best described at the temporal scale of the study period (almost one year), such that high levels of study period precipitation and low mean study period temperature reduced the probability. Timing of ZIKV emergence was best described at the weekly scale for precipitation in that high precipitation in the current week delayed reporting. Both modelling approaches detected an effect of high poverty on reducing/slowing case detection, especially when inter-municipal road connectivity was low. We also found that proximity to municipalities reporting ZIKV had an effect to reduce timing of emergence when located, on average, less than 100 km away. Conclusions The different modelling approaches help distinguish between large temporal scale factors driving vector habitat suitability and short temporal scale factors affecting the speed of spread. We find evidence for inter-municipal movements of infected people as a local-scale driver of spatial spread. The negative association with poverty suggests reduced case reporting in poorer areas. Overall, relatively simplistic models may be able to predict the vulnerability of populations to autochthonous ZIKV transmission at the local scale.

Connectivity in an aquatic setting is determined by a combination of hydrodynamic circulation and the biology of the organisms driving linkages. These complex processes can be simulated in coupled biological-physical models. The physical model refers to an underlying circulation model defined by spatially-explicit nodes, often incorporating a particle-tracking model. The particles can then be given biological parameters or behaviors (such as maturity and/or survivability rates, diel vertical migrations, avoidance, or seeking behaviors). The output of the bio-physical models can then be used to quantify connectivity among the nodes emitting and/or receiving the particles. Here we propose a method that makes use of kernel density estimation (KDE) on the output of a particle-tracking model, to quantify the infection or infestation pressure (IP) that each node causes on the surrounding area. Because IP is the product of both exposure time and the concentration of infectious agent particles, using KDE (which also combine elements of time and space), more accurately captures IP. This method is especially useful for those interested in infectious agent networks, a situation where IP is a superior measure of connectivity than the probability of particles from each node reaching other nodes. Here we illustrate the method by modeling the connectivity of salmon farms via sea lice larvae in the Broughton Archipelago, British Columbia, Canada. Analysis revealed evidence of two sub-networks of farms connected via a single farm, and evidence that the highest IP from a given emitting farm was often tens of kilometers or more away from that farm. We also classified farms as net emitters, receivers, or balanced, based on their structural role within the network. By better understanding how these salmon farms are connected to each other via their sea lice larvae, we can effectively focus management efforts to minimize the spread of sea lice between farms, advise on future site locations and coordinated treatment efforts, and minimize any impact of farms on juvenile wild salmon. The method has wide applicability for any system where capturing infectious agent networks can provide useful guidance for management or preventative planning decisions.

The Centers for Disease Control (CDC) and USDA Wildlife Services (WS) have been involved in an oral rabies vaccination (ORV) program for raccoons (Procyon lotor) that has slowed the westward spread of raccoon rabies. The objective of this study was to forecast the spread of the disease if an ORV zone was not maintained. A group decision-making process was designed to address the forecasting problem and was implemented using a group of 15 experts and 4 support personnel at a meeting at the USDA National Wildlife Research Center. Ten expansion regions were constructed that described the spread of disease at 2-year intervals. This forecast may provide for more accurate cost-benefit analysis of the ORV barrier.