Efficacy of a "stay-at-home" policy on SARS-CoV-2 transmission in Toronto, Canada: a mathematical modelling study

Pei Yuan1, Juan Li1, Elena Aruffo1

  • 1Canadian Centre for Disease Modeling (Yuan, Juan Li, Aruffo, Q. Li, Zheng, Heffernan, Tan, Jun Li, Arino, Bélair, Watmough, Kong, Moyles, Zhu), and Department of Mathematics and Statistics (Yuan, Aruffo, Heffernan, Tan, Kong, Moyles, Zhu), York University, Toronto, Ont.; Complex Systems Research Center (Juan Li), Shanxi University, Taiyuan, Shanxi, China; Toronto Public Health (Gatov, Collier), City of Toronto, Toronto, Ont.; Department of Mathematics (Q. Li), Shanghai Normal University, Shanghai, China; College of Mathematics and System Science (Zheng), Xinjiang University, Urumqi, Xinjiang, China; Public Health Risk Sciences Division (Ogden), National Microbiology Laboratory, Public Health Agency of Canada, Sainte-Hyacinthe, Que.; Toronto Health Economics and Technology Assessment (THETA) Collaborative (Sander), University Health Network; Dalla Lana School of Public Health (Sander), University of Toronto, Toronto, Ont.; School of Mathematics and Statistics (Jun Li), Xidian University, Xi'an, Shaanxi, China; Department of Mathematics (Arino), University of Manitoba, Winnipeg, Man.; Département de mathématiques et de statistique (Bélair), Université de Montréal, Montréal, Que.; Department of Mathematics and Statistics (Watmough), University of New Brunswick, Fredericton, NB.

CMAJ Open
|April 20, 2022
PubMed
Abstract

Related Concept Videos

Steps in Outbreak Investigation01:18

Steps in Outbreak Investigation

In the ever-evolving field of public health, statistical analysis serves as a cornerstone for understanding and managing disease outbreaks. By leveraging various statistical tools, health professionals can predict potential outbreaks, analyze ongoing situations, and devise effective responses to mitigate impact. For that to happen, there are a few possible stages of the analysis:
232
Causality in Epidemiology01:21

Causality in Epidemiology

Causality or causation is a fundamental concept in epidemiology, vital for understanding the relationships between various factors and health outcomes. Despite its importance, there's no single, universally accepted definition of causality within the discipline. Drawing from a systematic review, causality in epidemiology encompasses several definitions, including production, necessary and sufficient, sufficient-component, counterfactual, and probabilistic models. Each has its strengths and...
971
Statistical Methods for Analyzing Epidemiological Data01:25

Statistical Methods for Analyzing Epidemiological Data

Epidemiological data primarily involves information on specific populations' occurrence, distribution, and determinants of health and diseases. This data is crucial for understanding disease patterns and impacts, aiding public health decision-making and disease prevention strategies. The analysis of epidemiological data employs various statistical methods to interpret health-related data effectively. Here are some commonly used methods:
576
Residuals and Least-Squares Property01:11

Residuals and Least-Squares Property

The vertical distance between the actual value of y and the estimated value of y. In other words, it measures the vertical distance between the actual data point and the predicted point on the line
If the observed data point lies above the line, the residual is positive, and the line underestimates the actual data value for y. If the observed data point lies below the line, the residual is negative, and the line overestimates the actual data value for y.
The process of fitting the best-fit...
7.9K