tetano
Editor, Senior Moderator
Heliyon
. 2021 Sep;7(9):e07905.
doi: 10.1016/j.heliyon.2021.e07905. Epub 2021 Sep 2.
Age-stratified transmission model of COVID-19 in Ontario with human mobility during pandemic's first wave
R Fields[SUP] 1 [/SUP], L Humphrey[SUP] 1 [/SUP], D Flynn-Primrose[SUP] 1 [/SUP], Z Mohammadi[SUP] 1 [/SUP], M Nahirniak[SUP] 1 [/SUP], E W Thommes[SUP] 2 [/SUP], M G Cojocaru[SUP] 1 [/SUP]
Affiliations
Abstract
In this work, we employ a data-fitted compartmental model to visualize the progression and behavioral 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.
Keywords: Age stratification; Age stratified; COVID-19; Compartmental model; Coronavirus; Google mobility; Infectious disease modeling; Mobility; Ontario; SEIR.
. 2021 Sep;7(9):e07905.
doi: 10.1016/j.heliyon.2021.e07905. Epub 2021 Sep 2.
Age-stratified transmission model of COVID-19 in Ontario with human mobility during pandemic's first wave
R Fields[SUP] 1 [/SUP], L Humphrey[SUP] 1 [/SUP], D Flynn-Primrose[SUP] 1 [/SUP], Z Mohammadi[SUP] 1 [/SUP], M Nahirniak[SUP] 1 [/SUP], E W Thommes[SUP] 2 [/SUP], M G Cojocaru[SUP] 1 [/SUP]
Affiliations
- PMID: 34514179
- PMCID: PMC8419869
- DOI: 10.1016/j.heliyon.2021.e07905
Abstract
In this work, we employ a data-fitted compartmental model to visualize the progression and behavioral 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.
Keywords: Age stratification; Age stratified; COVID-19; Compartmental model; Coronavirus; Google mobility; Infectious disease modeling; Mobility; Ontario; SEIR.