Transmission dynamics of COVID-19 outbreak in India and effectiveness of self-quarantine: a phase-wise data driven analysis

2021 ◽  
Vol 66 (3/4) ◽  
pp. 389
Author(s):  
Shweta Sankhwar ◽  
Ravins Dohare ◽  
Narender Kumar ◽  
Sahil Khan ◽  
Md. Wasim Khan
Keyword(s):  
Transmission Dynamics ◽  
Data Driven

scholarly journals A Time-Dependent SEIRD Model for Forecasting the COVID-19 Transmission Dynamics

2020 ◽  
Author(s):  
Taarak Rapolu ◽  
Brahmani Nutakki ◽  
T. Sobha Rani ◽  
S. Durga Bhavani
Keyword(s):  
Mathematical Model ◽  
Time Dependent ◽  
Transmission Dynamics ◽  
Data Driven ◽  
Disease Spread ◽  
Main Principle ◽  
Epidemiological Models ◽  
Dynamic Nature ◽  
Human Contact ◽  
Medical Emergencies

AbstractThe spread of a disease caused by a virus can happen through human to human contact or could be from the environment. A mathematical model could be used to capture the dynamics of the disease spread to estimate the infections, recoveries, and fatalities that may result from the disease. An estimation is crucial to make policy decisions and for the alerts for the medical emergencies that may arise. Many epidemiological models are being used to make such an estimation. One major factor that is important in the forecasts using the models is the dynamic nature of the disease spread. Unless we can come up with a way of estimating the parameters that guide this dynamic spread, the models may not give accurate forecasts. The main principle is to keep the model generic while making minimal assumptions. In this work, we have derived a data-driven model from SEIRD, where we attempt to forecast Infected, Recovered and Deceased rates of COVID-19 up to a week. A method of estimating the parameters of the model is also discussed thoroughly in this work. The model is tested for India at a district level along with the most affected foreign cities like Lombardia from Italy and Moscow from Russia.The forecasts can help the governments in planning for emergencies such as ICU requirements, PPEs, hospitalizations, and so on as the infection is going to be prevalent for some time until a vaccine or cure is invented.

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