scholarly journals A data driven analysis and forecast of an SEIARD epidemic model for COVID-19 in Mexico

2020 ◽  
Vol 5 (1) ◽  
pp. 14-28 ◽  
Author(s):  
Ugo Avila-Ponce de León ◽  
◽  
Ángel G. C. Pérez ◽  
Eric Avila-Vales ◽  
Keyword(s):  
Epidemic Model ◽  
Data Driven

scholarly journals A Data-Driven Air Transportation Delay Propagation Model Using Epidemic Process Models

2016 ◽  
Vol 2016 ◽  
pp. 1-11 ◽  
Author(s):  
B. Baspinar ◽  
E. Koyuncu
Keyword(s):  
Epidemic Model ◽  
Transportation Network ◽  
Air Transportation ◽  
Propagation Model ◽  
Process Models ◽  
Data Driven ◽  
Delay Propagation ◽  
Sis Epidemic Model ◽  
Definition Of ◽  
The Individual

In air transport network management, in addition to defining the performance behavior of the system’s components, identification of their interaction dynamics is a delicate issue in both strategic and tactical decision-making process so as to decide which elements of the system are “controlled” and how. This paper introduces a novel delay propagation model utilizing epidemic spreading process, which enables the definition of novel performance indicators and interaction rates of the elements of the air transportation network. In order to understand the behavior of the delay propagation over the network at different levels, we have constructed two different data-driven epidemic models approximating the dynamics of the system: (a) flight-based epidemic model and (b) airport-based epidemic model. The flight-based epidemic model utilizing SIS epidemic model focuses on the individual flights where each flight can be in susceptible or infected states. The airport-centric epidemic model, in addition to the flight-to-flight interactions, allows us to define the collective behavior of the airports, which are modeled as metapopulations. In network model construction, we have utilized historical flight-track data of Europe and performed analysis for certain days involving certain disturbances. Through this effort, we have validated the proposed delay propagation models under disruptive events.

scholarly journals A data driven epidemic model to analyse the lockdown effect and predict the course of COVID-19 progress in India

2020 ◽  
Author(s):  
B.K. Sahoo ◽  
B.K. Sapra
Keyword(s):  
Infection Rate ◽  
Epidemic Model ◽  
Current Trend ◽  
Real Data ◽  
Daily Basis ◽  
Least Square ◽  
Control Measures ◽  
Data Driven ◽  
Peak Time ◽  
The Real

AbstractWe propose a data driven epidemic model using the real data on the infection, recovery and death cases for the analysis of COVID-19 progression in India. The model assumes continuation of existing control measures such as lockdown and quarantines, the suspected and confirmed cases and does not consider the scenario of 2nd surge of the epidemic due to any reason. The model is arrived after least square fitting of epidemic behaviour model based on theoretical formulation to the real data of cumulative infection cases reported between 24 March 2020 and 15 May 2020. The predictive capability of the model has been validated with real data of infection cases reported during May 15–30, 2020. A detailed analysis of model predictions in terms of future trend of COVID-19 progress individually in 18 states of India and India as a whole has been attempted. Infection rate in India as a whole is continuously decreasing with time and has reached 3 times lower than the initial infection rate after 6 weeks of lock down suggesting the effectiveness of the lockdown in containing the epidemic. Results suggest that India as a whole could see the peak and end of the epidemic in the month of July 2020 and January 2021. As per the current trend in the data, active infected cases in India may reach 2 lakhs at the peak time and total infected cases may reach around 14 lakhs. State-wise results have been discussed in the manuscript. However, the prediction may deviate particularly for longer dates, as assumptions of model cannot be met always in a real scenario. In view of this, a real time application (COV-IND Predictor) has been developed which automatically syncs the latest data from COVID19 dash board on daily basis and update the model input parameters and predictions of relevant results on daily basis. This application can serve as a practical tool for epidemic management decisions

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