A model for pandemic control through isolation policy
RAIRO - Operations Research - Recherche Opérationnelle, Tome 54 (2020) no. 6, pp. 1875-1890.

In this paper we model the dynamics of a spreading pandemic over a country using a new dynamical and decentralised differential model with the main objective of studying the effect of different policies of social isolation (social distancing) over the population to control the spread of the pandemic. A probabilistic infection process with time lags is introduced in the dynamics with the main contribution being the proposed model to explicitly look at levels of interaction between towns and regions within the considered country. We believe the strategies and findings here will help practitioners, planners and Governments to put in place better strategies to control the spread of pandemics, thus saving lives and minimizing the impact of pandemia on socio-economic development and the populations livelihood.

DOI : 10.1051/ro/2020133
Classification : 34A34, 49K30, 37M05
Mots-clés : Pandemic modelling, spatial dynamics, optimization, simulation 
@article{RO_2020__54_6_1875_0,
     author = {Moyo, Sibusiso and Cruz, Luis Gustavo Zelaya and Carvalho, Rafael Lima de and Faye, Roger Marcelin and Tabakov, Pavel Yaroslav and Mora-Camino, Felix},
     title = {A model for pandemic control through isolation policy},
     journal = {RAIRO - Operations Research - Recherche Op\'erationnelle},
     pages = {1875--1890},
     publisher = {EDP-Sciences},
     volume = {54},
     number = {6},
     year = {2020},
     doi = {10.1051/ro/2020133},
     mrnumber = {4186530},
     language = {en},
     url = {http://archive.numdam.org/articles/10.1051/ro/2020133/}
}
TY  - JOUR
AU  - Moyo, Sibusiso
AU  - Cruz, Luis Gustavo Zelaya
AU  - Carvalho, Rafael Lima de
AU  - Faye, Roger Marcelin
AU  - Tabakov, Pavel Yaroslav
AU  - Mora-Camino, Felix
TI  - A model for pandemic control through isolation policy
JO  - RAIRO - Operations Research - Recherche Opérationnelle
PY  - 2020
SP  - 1875
EP  - 1890
VL  - 54
IS  - 6
PB  - EDP-Sciences
UR  - http://archive.numdam.org/articles/10.1051/ro/2020133/
DO  - 10.1051/ro/2020133
LA  - en
ID  - RO_2020__54_6_1875_0
ER  - 
%0 Journal Article
%A Moyo, Sibusiso
%A Cruz, Luis Gustavo Zelaya
%A Carvalho, Rafael Lima de
%A Faye, Roger Marcelin
%A Tabakov, Pavel Yaroslav
%A Mora-Camino, Felix
%T A model for pandemic control through isolation policy
%J RAIRO - Operations Research - Recherche Opérationnelle
%D 2020
%P 1875-1890
%V 54
%N 6
%I EDP-Sciences
%U http://archive.numdam.org/articles/10.1051/ro/2020133/
%R 10.1051/ro/2020133
%G en
%F RO_2020__54_6_1875_0
Moyo, Sibusiso; Cruz, Luis Gustavo Zelaya; Carvalho, Rafael Lima de; Faye, Roger Marcelin; Tabakov, Pavel Yaroslav; Mora-Camino, Felix. A model for pandemic control through isolation policy. RAIRO - Operations Research - Recherche Opérationnelle, Tome 54 (2020) no. 6, pp. 1875-1890. doi : 10.1051/ro/2020133. http://archive.numdam.org/articles/10.1051/ro/2020133/

M. Abdullah , C. Cooper and M. Draief , Viral processes by random walks on random regular graphs. Ann. Appl. Probab. 25 (2015) 477–522. | DOI | MR | Zbl

G.G. Alcaraz and C. Vargas-De-Leon , Modeling control strategies for influenza H1N1 epidemics: SIR models. Rev. Mex. Fis. S 58 (2012) 37–43.

R. Anguelov , J. Banasiak , C. Bright and R. Ouifki , The big unknown: the asymptomatic spread of COVID-19. J. Biomath. 9 (2020) 2005103. | MR | Zbl

U.S. Basak , B.K. Datta and P.K. Ghose , Mathematical analysis of an HIV/AIDS epidemic model. Am. J. Math. Stat. 5 (2015) 253–258.

F. Brauer and C. Castillo-Chavez , Mathematical Models in Population Biology and Epidemiology. Springer (2012). | DOI | MR | Zbl

R.M. Faye and F. Mora-Camino , La Commande Optimale. L’Harmattan, Paris (2017).

A. Flahault , S. Deguen and A.-J. Valleron , A mathematical model for the European spread of influenza. Eur. J. Epidemiol. 10 (1994) 471–474. | DOI

H.W. Hethcote , The mathematics of infectious diseases. SIAM Rev. 42 (2000) 599–653. | DOI | MR | Zbl

A.D. Lewis , The maximum principle of pontryaginin in optimal control. Available from: https://www.ime.usp.br/ tonelli/pub/maximum-principle.pdf (2006).

Z. Liu , P. Magal , O. Seydi and G. Webb , Predicting the cumulative number of cases for the COVID-19 epidemic in China from early data Preprint medRXiv DOI: (2020). | DOI | MR

G.C.E. Mbah , D. Omale and B.O. Adejo , A SIR epidemic model for HIV/AIDS infection. Int. J. Sci. Eng. Res. 5 (2014) 479–484.

B. Mbaye Ndiaye , L. Tendeng and D. Seck , Analysis of the COVID-19 pandemic by SIR model and machine learning technics for forecasting. Preprint (2020). | arXiv

T.W. Ng , G. Turinici and A. Danchin , A double epidemic model for the SARS propagation. BMC Infectious Diseases 3 (2003) 19. | DOI

B.S. Pujari and S. Shekatka , Multi-city modeling of epidemics using spatial networks: application to 2019-nCov (COVID-19) coronavirus in India. Preprint medRxiv. DOI: (2020). | DOI

WHO, Available from: https://www.who.int/csr/disease (2010).

O. Zakary , M. Rachik and I. Elmouki , Multi-regions discrete SIR epidemic model: an optimal control approach. Int. J. Dyn. Control 5 (2017) 917–930. | DOI | MR

Cité par Sources :