scholarly journals Principles of mathematical epidemiology and compartmental modelling application to COVID-19

2021 ◽  
pp. 101017
Author(s):  
Bastien Reyné ◽  
Nicolas Saby ◽  
Mircea T. Sofonea
Keyword(s):  
Mathematical Epidemiology ◽  
Compartmental Modelling ◽  
Modelling Application

Technological innovation and the future of predictive model of pandemics (Preprint)

2021 ◽  
Author(s):  
Xavier Dupont
Keyword(s):  
Machine Learning ◽  
Technological Innovation ◽  
Predictive Models ◽  
State Of The Art ◽  
Field Research ◽  
Sir Model ◽  
Rapid Progress ◽  
Compartmental Modelling ◽  
Research Interview ◽  
Modelling Techniques

BACKGROUND As of October 2020, the COVID-19 death toll has reached over one million with 38 million confirmed cases globally. This pandemic is shaking the foundations of economies and reminding us the fragility of our system. Epidemics have affected societies since biblical times, but the recent acceleration in science and technology, as well as global cooperation, has provided scientists and mathematicians new resources, they can use to anticipate how a pandemic will spread with mathematical modelling. Compartmental modelling techniques, such as the SIR model, have been well-established for more than a century and have proven efficient and reliable in helping governments decide what strategies to use to fight pandemics. OBJECTIVE State of the art report on predictive models and technology METHODS Field research, Interview, RESULTS More recently, digitalisation and rapid progress in fields such as Machine Learning, IoT and big data have brought new perspectives to predictive models that improve their ability to predict how a pandemic will unfold and therefore which actions should be taken to eradicate the disease. This report will first review how pandemic modelling works. CONCLUSIONS It will then discuss the benefits and limitations of those models before outlining how new initiatives in several fields of technology are being used to fight the virus that causes COVID-19.

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