Age-stratified transmission model of COVID-19 in Ontario with human mobility during pandemic's first wave

Abstract

In this work, we employ a data-fitted compartmental model to visualize the progression and behavioural response to COVID-19 that match provincial case data in Ontario, Canada from February to June of 2020. This is a “rear-view mirror” glance at how this region has responded to the 1st wave of the pandemic, when testing was sparse and NPI measures were the only remedy to stave off the pandemic. We use an SEIR-type model with age-stratified subpopulations and their corresponding contact rates and asymptomatic rates in order to incorporate heterogeneity in our population and to calibrate the time-dependent reduction of Ontario-specific contact rates to reflect intervention measures in the province throughout lockdown and various stages of social-distancing measures. Cellphone mobility data taken from Google, combining several mobility categories, allows us to investigate the effects of mobility reduction and other NPI measures on the evolution of the pandemic. Of interest here is our quantification of the effectiveness of Ontario's response to COVID-19 before and after provincial measures and our conclusion that the sharp decrease in mobility has had a pronounced effect in the first few weeks of the lockdown, while its effect is harder to infer once other NPI measures took hold.

Human behaviour, NPI and mobility reduction effects on COVID-19 transmission in different countries of the world

“…Notably, face masks are found to be useful in both preventing asymptomatic transmission and illness in healthy persons. We adapt our previously developed SEIRL model [56] for transmission of COVID-19 with the impact of public use of face masks. Moreover, varying efficacy and compliance of masks have an impact on the transmission dynamics and control of the COVID-19 pandemic [4].…”

“…Prem et al created synthetic contact matrices to represent the effect of intervention measures to reduce social mixing on outcomes of the COVID-19 epidemic in Wuhan, China [29, 30]. The age-specific social contact characterization also supports the possibility of suspecting differences in transmission patterns of COVID-19 outbreak among different age-groups [31, 32, 33, 20]. In this work, projected contact matrices provided by Prem et al [21] are used.…”

Studying the mixed transmission in a community with age heterogeneity: COVID-19 as a case study

“…The aforementioned studies focus on modeling the initial wave of the pandemic and provide valuable insights in short-term predictions and disease intervention strategies. Age-stratified models were proposed to study the heterogeneity in social contact patterns among a susceptible population in early stage of the disease ( Davies et al., 2020 ; Fields et al., 2021 ; Gabriele-Rivet et al., 2021 ; McCarthy et al., 2020 ). Statistical analyses were applied in studying various aspects of the disease spread, such as identifying the impact of undiagnosed cases in COVID-19 ( Hamadeh et al., 2021 ), the importance of household transmission ( Paul et al., 2021 ), and suggestions for testing policy ( Lawless & Yan, 2021 ).…”

Study of COVID-19 epidemiological evolution in India with a multi-wave SIR model

“…Case studies on few countries are available. [14][15][16][17][18][19][20] Hence, a multi-wave SIR (MWSIR) model is formulated by modifying the SWSIR model in order to explain the dynamics of epidemiological infections including the origin of multiple waves found in the infection pattern. This is a fact that the same individual has been infected repeatedly during this pandemic period.…”