tetano
Editor, Senior Moderator
Can J Stat
. 2022 Apr 15;10.1002/cjs.11698.
doi: 10.1002/cjs.11698. Online ahead of print.
Characterizing the COVID-19 dynamics with a new epidemic model: Susceptible-exposed-asymptomatic-symptomatic-active-removed
Grace Y Yi[SUP] 1 2 [/SUP], Pingbo Hu[SUP] 1 [/SUP], Wenqing He[SUP] 1 [/SUP]
Affiliations
Abstract
in English, French
The coronavirus disease 2019 (COVID-19), caused by the severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2), has spread stealthily and presented a tremendous threat to the public. It is important to investigate the transmission dynamics of COVID-19 to help understand the impact of the disease on public health and the economy. In this article, we develop a new epidemic model that utilizes a set of ordinary differential equations with unknown parameters to delineate the transmission process of COVID-19. The model accounts for asymptomatic infections as well as the lag between symptom onset and the confirmation date of infection. To reflect the transmission potential of an infected case, we derive the basic reproduction number from the proposed model. Using the daily reported number of confirmed cases, we describe an estimation procedure for the model parameters, which involves adapting the iterated filter-ensemble adjustment Kalman filter (IF-EAKF) algorithm. To illustrate the use of the proposed model, we examine the COVID-19 data from Quebec for the period from 2 April 2020 to 10 May 2020 and carry out sensitivity studies under a variety of assumptions. Simulation studies are used to evaluate the performance of the proposed model under a variety of settings.
Keywords: Basic reproduction number; COVID‐19; IF‐EAKF algorithm; epidemic model; ordinary differential equations; transmission.
. 2022 Apr 15;10.1002/cjs.11698.
doi: 10.1002/cjs.11698. Online ahead of print.
Characterizing the COVID-19 dynamics with a new epidemic model: Susceptible-exposed-asymptomatic-symptomatic-active-removed
Grace Y Yi[SUP] 1 2 [/SUP], Pingbo Hu[SUP] 1 [/SUP], Wenqing He[SUP] 1 [/SUP]
Affiliations
- PMID: 35573897
- PMCID: PMC9087003
- DOI: 10.1002/cjs.11698
Abstract
in English, French
The coronavirus disease 2019 (COVID-19), caused by the severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2), has spread stealthily and presented a tremendous threat to the public. It is important to investigate the transmission dynamics of COVID-19 to help understand the impact of the disease on public health and the economy. In this article, we develop a new epidemic model that utilizes a set of ordinary differential equations with unknown parameters to delineate the transmission process of COVID-19. The model accounts for asymptomatic infections as well as the lag between symptom onset and the confirmation date of infection. To reflect the transmission potential of an infected case, we derive the basic reproduction number from the proposed model. Using the daily reported number of confirmed cases, we describe an estimation procedure for the model parameters, which involves adapting the iterated filter-ensemble adjustment Kalman filter (IF-EAKF) algorithm. To illustrate the use of the proposed model, we examine the COVID-19 data from Quebec for the period from 2 April 2020 to 10 May 2020 and carry out sensitivity studies under a variety of assumptions. Simulation studies are used to evaluate the performance of the proposed model under a variety of settings.
Keywords: Basic reproduction number; COVID‐19; IF‐EAKF algorithm; epidemic model; ordinary differential equations; transmission.