Estimation of SIR model’s parameters of COVID-19 in Algeria

Lounis, Mohamed; Bagal, Dilip Kumar

doi:10.1186/s42269-020-00434-5

Cited by 20 publications

(15 citation statements)

References 5 publications

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“…Table 3 shows the values of R 0 obtained for each network. For COVID-19 different researchers find multiple values of R 0 between 1.3 and 3.5 [ 2 , 24 , 44 , 45 ].…”

Section: Resultsmentioning

confidence: 99%

Managing school interaction networks during the COVID-19 pandemic: Agent-based modeling for evaluating possible scenarios when students go back to classrooms

Hernández-Hernández

Huerta-Quintanilla

2021

PLoS ONE

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The most unexpected and toughest phenomenon that has occurred in recent times is the global COVID-19 pandemic. One of the first measures to prevent the spread of the disease was to close educational institutions. The students were forced to start a learning process through social networks and web platforms. In some countries, a return to face-to-face classes was established. However, weeks later, some of them had to return to virtual activities due to an upswing in the COVID-19 cases. In Mexico, classes have been held virtually, with face-to-face activities only re-established in two of the 32 states. In our state, Yucatan, scholarly activities are still virtual. In this work, the dispersion of COVID-19 at different academic establishments in Yucatan was simulated. Networks of Friendship, noncordial treatment, family ties and study groups were considered. Based on these networks, we evaluated the possibility of returning to school without inducing a rebound in the COVID-19 cases in the state. Agent-based simulations were used, with each student as an agent. Interaction rules were established based on international research regarding good practices in times of COVID-19. We used seven networks from different academic institutions, ranging from primary through college level. As a result, possible contagion curves were obtained for different scenarios, which leads to a discussion about the measures that would be relevant once a return to face-to-face classes is overseen. Simulations show that isolating students and reducing the number of students in the same classroom are good strategies and substantially reduce the possible contagiousness.

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“…Table 3 shows the values of R 0 obtained for each network. For COVID-19 different researchers find multiple values of R 0 between 1.3 and 3.5 [ 2 , 24 , 44 , 45 ].…”

Section: Resultsmentioning

confidence: 99%

Managing school interaction networks during the COVID-19 pandemic: Agent-based modeling for evaluating possible scenarios when students go back to classrooms

Hernández-Hernández

Huerta-Quintanilla

2021

PLoS ONE

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“…The SIR model created by Kermack and McKendrick [1,2] is a representative compartment model for infectious diseases and is now commonly used even for COVID-19 [3][4][5][6][7][8][9][10][11][12][13]. The model consists of three compartments of 'Susceptible', 'Infected' and 'Removed' and is expressed by the following differential equations, which have not changed since Kermack and McKendrick [1]: dS(t)/dt= -βS(t) I(t)/N…”

Section: Contact Rate Between Infected and Susceptible In The Communitymentioning

confidence: 99%

A flexible compartment model for simulation specific to COVID-19

Ohmori

2022

Preprint

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The dynamic relation among ‘Susceptible’, ‘Infected’, ‘Removed (Recovered)’, ‘Death’ and others for COVID-19 disease is a kind of multibody problem. It has been simulated mainly by the compartment models, of which the representative is the SIR model. For the SIR model, ‘Infected’ infects ‘Susceptible’ through the recovery period, and ‘Infected’ is removed as ‘Removed’ not only from the disease but also from the community after the recovery period is ended. For COVID-19, however, the infected individuals should be isolated from the community when they become symptomatic after the latent period is ended. Thus, the infected individuals do not infect susceptible individuals in the community after the latent period, even during the recovery period. Additionally, the infection has occurred in the community even during the latent period before the infected individuals are isolated due to being symptomatic. These two facts for COVID-19 suggest that the simulation by the SIR model would be less accurate in calculating the number of infected individuals and that the results might mislead political and medical interventions. For the model proposed here, the infected individuals are isolated from the community when they become symptomatic after the latent period is ended, but the recovered individuals who have medically recovered and have immunity return to the community, and the infection occurs even during the latent period. The model shows remarkably different results from those simulated by the SIR model. The model also provides the processes evaluating the political and social countermeasures against COVID-19.

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“…Kami menggunakan nilai awal variabel s(0) = 0, 997, v(0) = 0, i(0) = 0, 47 × 10 −3 , dan r(0) = 0, 27 × 10 −2 . Estimasi parameter dapat dilakukan dengan menggunakan pendekatan yang terdapat pada [28]. Nilai awal parameter yang digunakan dalam melakukan simulasi numerik dapat dilihat pada Tabel 1.…”

Section: Simulasi Numerikunclassified

Analisis Model Matematika Penyebaran Covid-19 Dengan Intervensi Vaksinasi Dan Pengobatan

Nuha¹,

Achmad²,

SUPU³

2021

JMU

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Coronavirus Disease-2019 (COVID-19) merupakan suatu virus yang dapat menyebabkan penyakit menular dengan gejala batuk, demam, hilangnya indra perasa maupun penciuman, hingga sesak napas. Proses penyebaran penyakit terjadi ketika adanya kontak dari individu terinfeksi dengan individu rentan, baik secara langsung maupun tidak langsung. Penelitian ini dilakukan untuk merumuskan model matematika penyebaran COVID-19, melakukan analisis kestabilan model, dan simulasi numerik. Berdasarkan asumsi serta pertimbangan dalam membangun model diperoleh suatu model matematika penyebaran Covid-19 tipe SVIR yang terdiri atas empat kelas populasi, yaitu kelas populasi; individu rentan (S), individu tervaksin (V), individu terinfeksi (I), dan individu sembuh dari COVID-19 (R). Sifat kestabilan titik kesetimbangan model bergantung pada perubahan nilai bilangan reproduksi dasar (<0). Titik kesetimbangan bebas penyakit E0 bersifat stabil asimtotik lokal jika <0 < 1, serta tidak stabil jika <0 > 1. Titik kesetimbangan bebas penyakit E0 akan selalu ada, serta titik kesetimbangan endemik E∗ unik dan positif jika dan hanya jika <0 > 1. Simulasi numerik menggambarkan bahwa sistem berada pada kondisi stabil disekitar titik kesetimbangan endemik. Peningkatan laju vaksinasi dan laju efektivitas pengobatan masing-masing dapat menekan jumlah kasus infeksi COVID-19. Sedangkan peningkatan laju penyusutan vaksin dan laju penurunan efektivitas vaksin dapat mengakibatkan jumlah kasus infeksi COVID-19 terus meningkat.Kata Kunci: COVID-19, Model SVIR, Titik Kesetimbangan

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Estimation of SIR model’s parameters of COVID-19 in Algeria

Cited by 20 publications

References 5 publications

Managing school interaction networks during the COVID-19 pandemic: Agent-based modeling for evaluating possible scenarios when students go back to classrooms

Managing school interaction networks during the COVID-19 pandemic: Agent-based modeling for evaluating possible scenarios when students go back to classrooms

A flexible compartment model for simulation specific to COVID-19

Analisis Model Matematika Penyebaran Covid-19 Dengan Intervensi Vaksinasi Dan Pengobatan

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