A mathematical model for COVID-19 transmission dynamics with a case study of India

Samui, Piu; Mondal, Jayanta; Khajanchi, Subhas

doi:10.1016/j.chaos.2020.110173

Cited by 246 publications

(196 citation statements)

References 29 publications

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“…We estimated that the mean R 0 for the COVID-19 ranges from 1.58 to 2.24 ( Table 2 ) and is significantly larger than 1 and is consistent with the other estimations for the human-to-human (direct) transmission ranged from 1.3 to 7.7 ( Campos et al, 2020 ; Dropkin, 2020 ; Samui et al, 2020 ; Temime et al, 2020 ; Wei et al, 2020 ; WHO, 2020b ; Yuan et al, 2020 ; Zhang et al, 2020 ). On the other hand, most of the early predictions of mean R 0 in the literature range from 2 to 5, and are largely inconsistent with our results ( Zhao et al, 2020 ).…”

Section: Discussionsupporting

confidence: 87%

COVID-19 in China: Risk Factors and R0 Revisited

et al. 2021

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Highlights Understanding the characteristics of spatiotemporal clustering of the COVID-19 epidemic and R0 is critical in effectively preventing and controlling the pandemic. Temperature profile played a significant role in the spatiotemporal clustering of the COVID-19 epidemic in China The increase in temperature increases the R0 value Temperatures had more contribution towards the transmission of COVID-19 than population age in China.

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Section: Discussionsupporting

confidence: 87%

COVID-19 in China: Risk Factors and R0 Revisited

et al. 2021

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“…However, we have not found any article that focuses specifically on nurses' mental health during the COVID-19 outbreak. Samui et al's (19) findings suggested that COVID-19 would persist for a long time. We sought to describe the mental health of nurses in China during the COVID-19 outbreak.…”

Section: Introductionmentioning

confidence: 99%

“…The 2019 outbreak of the new coronavirus disease (COVID- 19) in China is an epidemic threat and major public health issue (1). The World Health Organization (WHO) declared this outbreak a public health emergency of international concern on January 30, 2020 (2).…”

Section: Introductionmentioning

confidence: 99%

Psychological Impact of the COVID-19 Outbreak on Nurses in China: A Nationwide Survey During the Outbreak

et al. 2020

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Background: The COVID-19 pandemic is a major public health issue and challenge to health professionals. In similar epidemics, nurses experienced more distress than other providers.Methods: We surveyed both on-duty nurses caring for infected patients and second-line nurses caring for uninfected patients from Hubei and other provinces throughout China.Results: We received completed surveys from 1,364 nurses from 22 provinces: 658 front-line and 706 second-line nurses. The median (IQR) GHQ-28 score of all nurses was 17 (IQR 11–24). The overall incidence of mild-to-moderate distress (GHQ score > 5) was 28%; that for severe distress (GHQ score > 11) was 6%. The incidence of mild-to-moderate distress in the second-line nurses was higher than that in the front-line nurses (31 vs. 25%; OR, 0.74; 95 CI, 0.58–0.94). Living alone (OR, 0.62; 95% CI, 0.44–0.86) and feeling supported (OR, 0.82, 95% CI, 0.74–0.90) independently predicted lower anxiety.Conclusions: During the COVID-19 pandemic, the psychological problems of all nurses were generally serious. The interviewed second-line nurses face more serious issues than the front-line nurses.

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“…The conditions were; for R 0 < 1, the disease would stop spreading, for R 0 = 1, an infected person could infect a single person on an average and if R 0 > 1, the disease could become an epidemic (Khajanchi et al 2020a , b ). The local and global asymptotic stability analysis stated that the disease turned out to be an epidemic within a very short time and predicted that by the intervention methods of practicing social distancing, lockdown effect and use of precautionary measures like isolation, quarantine and hospitalization (Biswas et al 2020 ; Samui et al 2020 ) the transmission of the disease could be curbed. Sarkar et al ( 2020 ) concluded that the number of infected individuals might increase in India if human-to-human transmission and personal preventive measures were not applied, considering the existing rates of the testing which were lower than the required.…”

Section: Introductionmentioning

confidence: 99%

COVID-19 outbreak and Urban dynamics: regional variations in India

2021

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India was the second highest COVID-19 affected country in the world with 2.1 million cases by 11th August. This study focused on the spatial transmission of the pandemic among the 640 districts in India over time, and aimed to understand the urban-centric nature of the infection. The connectivity context was emphasized that possibly had inflicted the outbreak. Using the modes of transmission data for the available cases, the diffusion of this disease was explained. Metropolitans contributed three-fourths of total cases from the beginning. The transport networks attributed significantly in transmitting the virus from the urban containment zones. Later, there was a gradual shift of infections from urban to rural areas; however, the numbers kept increasing in the former. The massive reverse migration after lockdown spiked the infected cases further. Districts with airports reported more with influx of international passengers. A profound east–west division in April with higher infections in the southern and western districts existed. By mid-May eastern India saw a steep rise in active cases. Moran’s I analysis showed a low autocorrelation initially which increased over time. Hotspot clustering was observed in western Maharashtra, eastern Tamil Nadu, Gujarat and around Kolkata by the second week of August. The diffusion was due to travel, exposure to infected individuals and among the frontline workers. Spatial regression models confirmed that urbanization was positively correlated with higher incidences of infections. Transit mediums, especially rail and aviation were positively associated. These models validated the crucial role of spatial proximity in diffusion of the pandemic.

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A mathematical model for COVID-19 transmission dynamics with a case study of India

Cited by 246 publications

References 29 publications

COVID-19 in China: Risk Factors and R0 Revisited

COVID-19 in China: Risk Factors and R0 Revisited

Psychological Impact of the COVID-19 Outbreak on Nurses in China: A Nationwide Survey During the Outbreak

COVID-19 outbreak and Urban dynamics: regional variations in India

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