Extended Bayesian endemic-epidemic models to incorporate mobility data into COVID-19 forecasting.

Forecasting the number of daily COVID-19 cases is critical in the short-term planning of hospital and other public resources. One potentially important piece of information for forecasting COVID-19 cases is mobile device location data that measure the amount of time an individual spends at home. Endemic-epidemic (EE) time series models are recently proposed autoregressive models where the current mean case count is modelled as a weighted average of past case counts multiplied by an autoregressive rate, plus an endemic component. We extend EE models to include a distributed-lag model in order to investigate the association between mobility and the number of reported COVID-19 cases; we additionally include a weekly first-order random walk to capture additional temporal variation. Further, we introduce a shifted negative binomial weighting scheme for the past counts that is more flexible than previously proposed weighting schemes. We perform inference under a Bayesian framework to incorporate parameter uncertainty into model forecasts. We illustrate our methods using data from four US counties.

La prévision du nombre de cas quotidiens de COVID­19 est cruciale pour la planification à court terme de ressources hospitalières et d'autres ressources publiques. Les données de localisation des téléphones mobiles qui mesurent le temps passé à la maison peuvent constituer un élément d'information important pour prédire les cas de COVID­19. Les modèles de séries chronologiques endémiques­épidémiques sont des modèles auto­régressifs récents où le nombre moyen de cas en cours est modélisé comme une moyenne pondérée du nombre de cas antérieurs multipliée par un taux auto­régressif (reproductif), plus une composante endémique. Les auteurs de ce travail généralisent les modèles endémiques­épidémiques pour y inclure un modèle à décalage distribué, et ce, dans le but de tenir compte du lien entre la mobilité et le nombre de cas de COVID­19 enregistrés. Pour saisir les variations de temps supplémentaires, ils y incorporent une marche hebdomadaire aléatoire d'ordre supérieur. De plus, ils proposent un schéma de pondération binomiale négative décalée pour les dénombrements passés, qui est plus flexible que les schémas de pondération existants. Ils utilisent l'inférence bayésienne afin d'intégrer l'incertitude des paramètres aux prédictions du modèle et ils illustrent les méthodes proposées avec des données provenant de quatre comtés américains.

A joint hierarchical model for the number of cases and deaths due to COVID-19 across the boroughs of Montreal.

As of July 2021, Montreal is the epicentre of the COVID-19 pandemic in Canada with highest number of deaths. We aim to investigate the spatial distribution of the number of cases and deaths due to COVID-19 across the boroughs of Montreal. To this end, we propose that the cumulative numbers of cases and deaths in the 33 boroughs of Montreal are modelled through a bivariate hierarchical Bayesian model using Poisson distributions. The Poisson means are decomposed in the log scale as the sums of fixed effects and latent effects. The areal median age, the educational level, and the number of beds in long-term care homes are included in the fixed effects. To explore the correlation between cases and deaths inside and across areas, three different bivariate models are considered for the latent effects, namely an independent one, a conditional autoregressive model, and one that allows for both spatially structured and unstructured sources of variability. As the inclusion of spatial effects change some of the fixed effects, we extend the Spatial+ approach to a Bayesian areal set up to investigate the presence of spatial confounding. We find that the model which includes independent latent effects across boroughs performs the best among the ones considered, there appears to be spatial confounding with the diploma and median age variables, and the correlation between the cases and deaths across and within boroughs is always negative.

Commentary: "How's It Going?" Training Experiences of Pediatric Postdoctoral Fellows During COVID-19.

The COVID-19 pandemic has impacted the lives and workplaces of individuals across the world substantially, in ways that are yet largely unknown. This commentary aims to provide an early snapshot of the experiences of pediatric postdoctoral fellows in academic medical settings; specifically, we will explore the impact of the pandemic on developing mastery within several competencies (e.g., research, professional development, clinical, interdisciplinary). These competencies are critical elements to fellowship to prepare for independent practice. Several models of training competencies for professional psychology and pediatric psychology exist, which focus on trainee skill development. Measures taken to minimize the spread of COVID-19 have directly impacted hospital systems and training, requiring programs to adapt competencies in various domains, such as increased familiarity with telehealth and virtual supervision. Additionally, fellows experienced an impact of the pandemic on securing employment following fellowship, conducting research and program development activities, and on cognitive flexibility and self-care. Governing bodies, such as the APA and Council of Chairs of Training Councils, have released statements and guidelines on addressing training of postdoctoral fellows including increasing flexibility of training methods, limiting in-person contact, and adjusting educational and licensing requirements. This paper offers informed commentary and diverse perspectives from current postdoctoral fellows engaged in a variety of clinical and research responsibilities regarding how the COVID-19 pandemic has impacted their training. We hope this paper will provide important insight into the unique experiences of postdoctoral fellows during the capstone year(s) of training prior to independent work and inform recommendations for postdoctoral training programs.

The role of case importation in explaining differences in early SARS-CoV-2 transmission dynamics in Canada-A mathematical modeling study of surveillance data.

OBJECTIVE: The North American coronavirus disease-2019 (COVID-19) epidemic exhibited distinct early trajectories. In Canada, Quebec had the highest COVID-19 burden and its earlier March school break, taking place two weeks before those in other provinces, could have shaped early transmission dynamics. METHODS: We combined a semi-mechanistic model of SARS-CoV-2 transmission with detailed surveillance data from Quebec and Ontario (initially accounting for 85% of Canadian cases) to explore the impact of case importation and timing of control measures on cumulative hospitalizations. RESULTS: A total of 1544 and 1150 cases among returning travelers were laboratory-confirmed in Quebec and Ontario, respectively (symptoms onset ≤03-25-2020). Hospitalizations could have been reduced by 55% (95% CrI: 51%-59%) if no cases had been imported after Quebec's March break. However, if Quebec had experienced Ontario's number of introductions, hospitalizations would have only been reduced by 12% (95% CrI: 8%-16%). Early public health measures mitigated the epidemic spread as a one-week delay could have resulted in twice as many hospitalizations (95% CrI: 1.7-2.1). CONCLUSION: Beyond introductions, factors such as public health preparedness, responses and capacity could play a role in explaining interprovincial differences. In a context where regions are considering lifting travel restrictions, coordinated strategies and proactive measures are to be considered.