Estimating the instantaneous reproduction number (
COVID-19
Compartment model
Effective reproduction number
Particle filter
Sequential Monte Carlo
Transmission model
Journal
Infectious Disease Modelling
ISSN: 2468-0427
Titre abrégé: Infect Dis Model
Pays: China
ID NLM: 101692406
Informations de publication
Date de publication:
Dec 2023
Dec 2023
Historique:
received:
19
06
2023
revised:
29
07
2023
accepted:
08
08
2023
medline:
31
8
2023
pubmed:
31
8
2023
entrez:
31
8
2023
Statut:
epublish
Résumé
Monitoring the transmission of coronavirus disease 2019 (COVID-19) requires accurate estimation of the effective reproduction number ( To include real-world factors, we expanded the susceptible-exposed-infectious-recovered (SEIR) model by incorporating pre-symptomatic (P) and asymptomatic (A) states, creating the SEPIAR model. By utilizing both stochastic and deterministic versions of the model, and incorporating predetermined time series of The particle filtering method accurately estimated The SEPIAR model, in conjunction with the particle filtering method, offers a reliable tool for predicting the transmission trend of COVID-19 and assessing the impact of intervention strategies. This approach enables enhanced monitoring of COVID-19 transmission and can inform public health policies aimed at controlling the spread of the disease.
Sections du résumé
Background
UNASSIGNED
Monitoring the transmission of coronavirus disease 2019 (COVID-19) requires accurate estimation of the effective reproduction number (
Method
UNASSIGNED
To include real-world factors, we expanded the susceptible-exposed-infectious-recovered (SEIR) model by incorporating pre-symptomatic (P) and asymptomatic (A) states, creating the SEPIAR model. By utilizing both stochastic and deterministic versions of the model, and incorporating predetermined time series of
Results
UNASSIGNED
The particle filtering method accurately estimated
Conclusions
UNASSIGNED
The SEPIAR model, in conjunction with the particle filtering method, offers a reliable tool for predicting the transmission trend of COVID-19 and assessing the impact of intervention strategies. This approach enables enhanced monitoring of COVID-19 transmission and can inform public health policies aimed at controlling the spread of the disease.
Identifiants
pubmed: 37649793
doi: 10.1016/j.idm.2023.08.003
pii: S2468-0427(23)00079-9
pmc: PMC10463196
doi:
Types de publication
Journal Article
Langues
eng
Pagination
1002-1014Informations de copyright
© 2023 The Authors.
Déclaration de conflit d'intérêts
The authors declare that they have no known competing financial interests or personal relationships that could have appeared to influence the work reported in this paper.
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