We study the epidemic patterns of the COVID-19 virus in Argentinafrom a mathematical modelling perspective . We implement an SEIR model , consisting of a set of first-order temporal differential equations (ODE's) to analyze the time evolution of the disease caused by the virus. The model is applied to the city of Buenos Aires and neighbouring cities (RMBA) with approximately 15 million inhabitants. The parameters of the model are calibrated by using as data the number of casualties officially reported. Since there are infinite solutions honouring the data, we show a set of cases by considering different situations. The first set of parameters yields initially a reproduction ratio R0 = 3.30 decreasing to 0.92 in April 8, after the lockdown, but increasing to 2.44 after April 27, most probably due to an increase of the contagion in highly populated slums. This case has incubation and infection periods of 11 and 7 days, approximately, and about 13 million people infected at the end of the epidemic. The infection fatality rate (IFR) is 1.88 \% and the predicted number of casualties is approximately 249000 deaths at the end of the epidemic. However, this death toll is highly affected by the evolution of the reproduction ratio, and keeping R0 = 0.92 after April 27, would cause 1321 casualties and only 66000 infected individuals. Other cases, assuming the present trend, predict smaller incubation periods ( between 4 and 5 days) and yield between 30000 and 90000 deaths and IFRs between 0.5 \% and 1 \%. This means that the intensity of the lockdown (and behaviour of the population) is essential and that the measures have to be guided by precise model predictions. We also consider doubling the number of casualties to date and, in this case, the death toll is almost 44000 individuals and about 5.1 million people being infected .
A pandemic caused by a new coronavirus has spread worldwide, causing an epidemic in Argentina . We implement an SEIR model to analyze the evolution of the disease in Buenos Aires and neighbouring cities (RMBA) with 15 million inhabitants. The parameters of the model are calibrated by using as data the number of casualties officially reported. Since infinite solutions honour the data, we show a set of cases by considering different situations. The first set of parameters yields initially a reproduction ratio R0 = 3.33 decreasing to 0.95 in April 8, after the lockdown, but increasing to 1.55 after April 27, most probably due to an increase of the contagion in highly populated slums. The infection fatality rate (IFR) is 1.88 \% and the predicted number of casualties is 173000 deaths with 9 million people infected at the end of the epidemic. However, keeping R_0 = 0.95 after April 27, would cause only 1881 casualties and 92955 infected individuals. Other cases, assuming the present trend, predict smaller incubation periods ( 4-5 days) and yield between 20000 and 70000 deaths and IFRs between 0.5 \% and 1.1 \%. We also consider doubling the number of casualties , with a death toll of 44000 individuals and 5.1 million infected individuals. Other choices of parameters also provide a good fit of the data, indicating the uncertainty of the results, which may differ from reported values. The analysis allows us to study how isolation and social distancing measures affect the time evolution of the epidemic .
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We study the epidemic patterns of the COVID-19 virus in Argentinafrom a mathematical modelling perspective
A pandemic caused by a new coronavirus has spread worldwide, causing an epidemic in Argentina
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, consisting of a set of first-order temporal differential equations (ODE's)
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time
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caused by the virus. The model is applied to the city of
in
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approximately
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there are infinite solutions honouring
infinite solutions honour
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3.30 decreasing to 0.92
3.33 decreasing to 0.95
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2.44
1.55
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This case has incubation and infection periods of 11 and 7 days, approximately, and about 13 million people infected at the end of the epidemic.
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approximately 249000 deaths
173000 deaths with 9 million people infected
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this death toll is highly affected by the evolution of the reproduction ratio, and keeping R0
keeping R_0
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0.92
0.95
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1321 casualties and
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66000
1881 casualties and 92955
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between 4 and 5
4-5
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30000 and 90000
20000 and 70000
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1 \%. This means that the intensity of the lockdown (and behaviour of the population) is essential and that the measures have to be guided by precise model predictions.
1.1 \%.
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to date and, in this case, the death toll is almost
, with a death toll of
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about
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people being infected
infected individuals. Other choices of parameters also provide a good fit of the data, indicating the uncertainty of the results, which may differ from reported values. The analysis allows us to study how isolation and social distancing measures affect the time evolution of the epidemic