Voir la notice de l'article provenant de la source EDP Sciences
Sarbaz H. A. Khoshnaw 1 ; Rizgar H. Salih 1 ; Sadegh Sulaimany 2
@article{MMNP_2020_15_a8, author = {Sarbaz H. A. Khoshnaw and Rizgar H. Salih and Sadegh Sulaimany}, title = {Mathematical modelling for coronavirus disease {(COVID-19)} in predicting future behaviours and sensitivity analysis}, journal = {Mathematical modelling of natural phenomena}, eid = {33}, publisher = {mathdoc}, volume = {15}, year = {2020}, doi = {10.1051/mmnp/2020020}, language = {en}, url = {https://geodesic-test.mathdoc.fr/articles/10.1051/mmnp/2020020/} }
TY - JOUR AU - Sarbaz H. A. Khoshnaw AU - Rizgar H. Salih AU - Sadegh Sulaimany TI - Mathematical modelling for coronavirus disease (COVID-19) in predicting future behaviours and sensitivity analysis JO - Mathematical modelling of natural phenomena PY - 2020 VL - 15 PB - mathdoc UR - https://geodesic-test.mathdoc.fr/articles/10.1051/mmnp/2020020/ DO - 10.1051/mmnp/2020020 LA - en ID - MMNP_2020_15_a8 ER -
%0 Journal Article %A Sarbaz H. A. Khoshnaw %A Rizgar H. Salih %A Sadegh Sulaimany %T Mathematical modelling for coronavirus disease (COVID-19) in predicting future behaviours and sensitivity analysis %J Mathematical modelling of natural phenomena %D 2020 %V 15 %I mathdoc %U https://geodesic-test.mathdoc.fr/articles/10.1051/mmnp/2020020/ %R 10.1051/mmnp/2020020 %G en %F MMNP_2020_15_a8
Sarbaz H. A. Khoshnaw; Rizgar H. Salih; Sadegh Sulaimany. Mathematical modelling for coronavirus disease (COVID-19) in predicting future behaviours and sensitivity analysis. Mathematical modelling of natural phenomena, Tome 15 (2020), article no. 33. doi : 10.1051/mmnp/2020020. https://geodesic-test.mathdoc.fr/articles/10.1051/mmnp/2020020/
[1] Mathematical Model for the Ebola Virus Disease J. Adv. Phys 2018 190 198
, ,[2] Topological sensitivity analysis for systems biology Proc. Natl. Acad. Sci 2014 18507 18512
, ,[3] A mathematical model for simulating the phase–based transmissibility of a novel coronavirus Infect. Dis. Poverty 2020 1 8
, , , , ,[4] A discrete stochastic model of the COVID–19 outbreak: Forecast and control Math. Biosci. Eng 2020 13
, ,[5] Modeling the dynamics of novel coronavirus (2019-nCov) with fractional derivative Alex. Eng. J. 2020
,[6] Ph.D. thesis 2015
[7] A mathematical modelling approach for childhood vaccination with some computational simulations AIP Conf. Proc 2019 020022
[8] Identifying critical parameters in SIR model for spread of disease Open J. Model. Simul 2017 32 46
, ,[9] Global sensitivity analysis challenges in biological systems modeling Ind. Eng. Chem. Res 2009 7168 7180
, , ,[10] Early dynamics of transmission and control of COVID–19: a mathematical modelling study Lancet Infect. Dis 2020 553
, , , , , ,[11] Global dynamics of a SEIR model with varying total population size Math. Biosci 1999 191 213
, , ,[12] 2019 novel coronavirus patients’ clinical characteristics, dischargerate and fatality rate of meta–analysis J. Med. Virol 2020 577 583
, , , , , ,[13] Sensitivity analysis of an infectious disease model Proceedings of the International System Dynamics Conference 2005
, , , ,[14] Sensitivity analysis in chemical kinetics Ann. Rev. Phys. Chem 1983 419 461
, ,[15] Estimation of the transmission risk of the 2019–nCoV and its implication for public health interventions J. Clin. Med 2020 462
, , , , , ,[16] An updated estimation of the risk of transmission of the novel coronavirus (2019–nCov) Infect. Dis. Model 2020 248 255
, , , , ,[17] On a quarantine model of coronavirus infection and data analysis MMNP 2020 24
, ,[18] WHO, Novel coronavirus (COVID–19) situation. Available from https://experience.arcgis.com/experience/685d0ace521648f8a5beeeee1b9125cd (2020).
[19] Sensitivity analysis approaches applied to systems biology models IET Syst. Biol 2011 336 346
Cité par Sources :