A new coronavirus, called COVID-19, appeared in the Chinese region of Wuhan at the end of last year; since then the virus spread to other countries, including most of Europe. We propose a differential equation governing the evolution of the COVID-19. This dynamic equation also describes the evolution of the number of infected people for 13 common respiratory viruses (including the COVID-19). We validate our theoretical predictions with experimental data for Italy and Belgium , and compare them with the predictions of the logistic model. We find that our predictions are in good agreement with the real world since the beginning of the appearance of the COVID-19; this is not the case for the logistic model that only applies to the first days.  We use our differential equation parametrised with experimental data to make several predictions, such as the date when Italy and Belgium will reach a peak number of COVID-19 infected people  .
A new coronavirus, called COVID-19, appeared in the Chinese region of Wuhan at the end of last year; since then the virus spread to other countries, including most of Europe. We propose a differential equation governing the evolution of the COVID-19. This dynamic equation also describes the evolution of the number of infected people for 13 common respiratory viruses (including the COVID-19). We validate our theoretical predictions with experimental data for Italy , Belgium and Luxembourg , and compare them with the predictions of the logistic model. We find that our predictions are in good agreement with the real world since the beginning of the appearance of the COVID-19; this is not the case for the logistic model that only applies to the first days. The second part of the work is devoted to modeling the descending phase, i.e. the decrease of the number of people tested positive for COVID-19. Also in this case, we propose a new set of dynamic differential equations that we solved numerically. We use our differential equations parametrized with experimental data to make several predictions, such as the date when Italy , Belgium and Luxembourg will reach a peak number of COVID-19 infected people . We shall see that the descending curves provide valuable information such as the duration of the COVID-19 epidemic in a given Country (and therefore when it will be possible to return to normal life) or how long we will still have to undergo the lockdown measures .
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and Belgium
, Belgium and Luxembourg
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The second part of the work is devoted to modeling the descending phase, i.e. the decrease of the number of people tested positive for COVID-19. Also in this case, we propose a new set of dynamic differential equations that we solved numerically.
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equation parametrised
equations parametrized
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and Belgium
, Belgium and Luxembourg
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. We shall see that the descending curves provide valuable information such as the duration of the COVID-19 epidemic in a given Country (and therefore when it will be possible to return to normal life) or how long we will still have to undergo the lockdown measures