Literature DB >> 17924718

Final and peak epidemic sizes for SEIR models with quarantine and isolation.

Zhilan Feng1.   

Abstract

Two SEIR models with quarantine and isolation are considered, in which the latent and infectious periods are assumed to have an exponential and gamma distribution, respectively. Previous studies have suggested (based on numerical observations) that a gamma distribution model (GDM) tends to predict a larger epidemic peak value and shorter duration than an exponential distribution model (EDM). By deriving analytic formulas for the maximum and final epidemic sizes of the two models, we demonstrate that either GDM or EDM may predict a larger epidemic peak or final epidemic size, depending on control measures. These formulas are helpful not only for understanding how model assumptions may affect the predictions, but also for confirming that it is important to assume realistic distributions of latent and infectious periods when the model is used for public health policy making.

Mesh:

Year:  2007        PMID: 17924718     DOI: 10.3934/mbe.2007.4.675

Source DB:  PubMed          Journal:  Math Biosci Eng        ISSN: 1547-1063            Impact factor:   2.080


  17 in total

1.  Threshold dynamics of a non-autonomous SEIRS model with quarantine and isolation.

Authors:  Mohammad A Safi; Mudassar Imran; Abba B Gumel
Journal:  Theory Biosci       Date:  2012-01-06       Impact factor: 1.919

2.  Investigating the trade-off between self-quarantine and forced quarantine provisions to control an epidemic: An evolutionary approach.

Authors:  Md Mamun-Ur-Rashid Khan; Md Rajib Arefin; Jun Tanimoto
Journal:  Appl Math Comput       Date:  2022-07-06       Impact factor: 4.397

3.  Theoretical basis to measure the impact of short-lasting control of an infectious disease on the epidemic peak.

Authors:  Ryosuke Omori; Hiroshi Nishiura
Journal:  Theor Biol Med Model       Date:  2011-01-26       Impact factor: 2.432

4.  Quantifying the relative effects of environmental and direct transmission of norovirus.

Authors:  S Towers; J Chen; C Cruz; J Melendez; J Rodriguez; A Salinas; F Yu; Y Kang
Journal:  R Soc Open Sci       Date:  2018-03-07       Impact factor: 2.963

5.  Lessons from being challenged by COVID-19.

Authors:  E Tagliazucchi; P Balenzuela; M Travizano; G B Mindlin; P D Mininni
Journal:  Chaos Solitons Fractals       Date:  2020-05-23       Impact factor: 5.944

6.  Mathematical assessment of the impact of non-pharmaceutical interventions on curtailing the 2019 novel Coronavirus.

Authors:  Calistus N Ngonghala; Enahoro Iboi; Steffen Eikenberry; Matthew Scotch; Chandini Raina MacIntyre; Matthew H Bonds; Abba B Gumel
Journal:  Math Biosci       Date:  2020-05-01       Impact factor: 2.144

7.  Mathematical epidemiology is not an oxymoron.

Authors:  Fred Brauer
Journal:  BMC Public Health       Date:  2009-11-18       Impact factor: 3.295

8.  The determinant of periodicity in Mycoplasma pneumoniae incidence: an insight from mathematical modelling.

Authors:  Ryosuke Omori; Yukihiko Nakata; Heidi L Tessmer; Satowa Suzuki; Keigo Shibayama
Journal:  Sci Rep       Date:  2015-09-28       Impact factor: 4.379

9.  Dynamics of Multi-stage Infections on Networks.

Authors:  N Sherborne; K B Blyuss; I Z Kiss
Journal:  Bull Math Biol       Date:  2015-09-24       Impact factor: 1.758

10.  COVID-19: Perturbation dynamics resulting chaos to stable with seasonality transmission.

Authors:  Saikat Batabyal
Journal:  Chaos Solitons Fractals       Date:  2021-02-12       Impact factor: 5.944

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