ern: An [Formula: see text] package to estimate the effective reproduction number using clinical and wastewater surveillance data.

The effective reproduction number, [Formula: see text], is an important epidemiological metric used to assess the state of an epidemic, as well as the effectiveness of public health interventions undertaken in response. When [Formula: see text] is above one, it indicates that new infections are increasing, and thus the epidemic is growing, while an [Formula: see text] is below one indicates that new infections are decreasing, and so the epidemic is under control. There are several established software packages that are readily available to statistically estimate [Formula: see text] using clinical surveillance data. However, there are comparatively few accessible tools for estimating [Formula: see text] from pathogen wastewater concentration, a surveillance data stream that cemented its utility during the COVID-19 pandemic. We present the [Formula: see text] package ern that aims to perform the estimation of the effective reproduction number from real-world wastewater or aggregated clinical surveillance data in a user-friendly way.

A wastewater-based risk index for SARS-CoV-2 infections among three cities on the Canadian Prairie.

Wastewater surveillance (WWS) is useful to better understand the spreading of coronavirus disease 2019 (COVID-19) in communities, which can help design and implement suitable mitigation measures. The main objective of this study was to develop the Wastewater Viral Load Risk Index (WWVLRI) for three Saskatchewan cities to offer a simple metric to interpret WWS. The index was developed by considering relationships between reproduction number, clinical data, daily per capita concentrations of virus particles in wastewater, and weekly viral load change rate. Trends of daily per capita concentrations of SARS-CoV-2 in wastewater for Saskatoon, Prince Albert, and North Battleford were similar during the pandemic, suggesting that per capita viral load can be useful to quantitatively compare wastewater signals among cities and develop an effective and comprehensible WWVLRI. The effective reproduction number (Rt) and the daily per capita efficiency adjusted viral load thresholds of 85 × 106 and 200 × 106 N2 gene counts (gc)/population day (pd) were determined. These values with rates of change were used to categorize the potential for COVID-19 outbreaks and subsequent declines. The weekly average was considered 'low risk' when the per capita viral load was 85 × 106 N2 gc/pd. A 'medium risk' occurs when the per capita copies were between 85 × 106 and 200 × 106 N2 gc/pd. with a rate of change <100 %. The start of an outbreak is indicated by a 'medium-high' risk classification when the week-over-week rate of change was >100 %, and the absolute magnitude of concentrations of viral particles was >85 × 106 N2 gc/pd. Lastly, a 'high risk' occurs when the viral load exceeds 200 × 106 N2 gc/pd. This methodology provides a valuable resource for decision-makers and health authorities, specifically given the limitation of COVID-19 surveillance based on clinical data.

cchsflow: an open science approach to transform and combine population health surveys.

SETTING: The Canadian Community Health Survey (CCHS) is one of the world's largest ongoing cross-sectional population health surveys, with over 130,000 respondents every two years or over 1.1 million respondents since its inception in 2001. While the survey remains relatively consistent over the years, there are differences between cycles that pose a challenge to analyze the survey over time. INTERVENTION: A program package called cchsflow was developed to transform and harmonize CCHS variables to consistent formats across multiple survey cycles. An open science approach was used to maintain transparency, reproducibility and collaboration. OUTCOMES: The cchsflow R package uses CCHS survey data between 2001 and 2014. Worksheets were created that identify variables, their names in previous cycles, their category structure, and their final variable names. These worksheets were then used to recode variables in each CCHS cycle into consistently named and labelled variables. Following, survey cycles can be combined. The package was then added as a GitHub repository to encourage collaboration with other researchers. IMPLICATION: The cchsflow package has been added to the Comprehensive R Archive Network (CRAN) and contains support for over 160 CCHS variables, generating a combined data set of over 1 million respondents. By implementing open science practices, cchsflow aims to minimize the amount of time needed to clean and prepare data for the many CCHS users across Canada.

RéSUMé: CONTEXTE: L'Enquête sur la santé dans les collectivités canadiennes (ESCC) est l'une des plus grandes enquêtes transversales sur la santé de la population, avec plus de 130 000 sondés tous les deux ans et plus de 1,1 million de sondés depuis son début en 2001. Tant que l'enquête reste relativement cohérent, il y a des différences entre des cycles qui posent une challenge majeure pour analyser l'enquête au fil du temps. INTERVENTION: Un paquet de programme appelé cchsflow a été développé pour transformer et harmoniser les variables CCHS aux formats cohérents à travers plusieurs cycles de sondage. Une approche de science ouverte était utilisée pour maintenir la transparence, la reproductibilité et la collaboration. RéSULTATS: Le paquet cchsflow R développé utilisait les données d'enquête de l'ESCC entre 2001 et 2014. Les feuilles de calcul ont été créées pour identifier des variables, leurs noms dans des cycles précédents, leurs structures de catégories et leurs noms de variables finales. Ces feuilles de calcul ont ensuite été utilisées pour recoder les variables dans chaque cycle de l'ESCC pour générer les ensembles de données harmonisés qui peuvent être combiner dans un ensemble de données constamment étiqueté pour l'analyse. Le paquet a ensuite été ajouté comme un entrepôt de GitHub pour encourager la collaboration avec les autres chercheurs. IMPLICATION: Le paquet cchsflow a été ajouté au Comprehensive R Archive Network (CRAN) et contient un appui pour plus de 160 variables de l'ESCC, générant un ensemble de données de plus d'un million de sondés. En exécutant les pratiques de sciences ouvertes, cchsflow vise à minimiser le temps requis pour nettoyer et préparer les données pour les plusieurs utilisateurs du CCHS à travers le Canada.