## The Portuguese pandemic model updated results

After some days of model development and calibration it is now time to see if our work can predict the outbreak in Portugal.

Using yesterday’s data of the Portuguese outbreak we can see if the model can keep up with the outbreak evolution.

The new infections per day report:

The results show that there is a diference from the predicted infections compared to the actual measured infections.

From the graph we can see that the model can still predict the evolution up to now.

From the close up image we can see a horizontal offset. We will need more time to evaluate if this will continue on probably due to mitigation and suppression activities or if it is simply a counting issue.

The way we affect our model may change from a simple decay function to something really messy to represent reality.

Model details can be seen on a previous post:

COVID-19 pandemic growth in Portuguese territory

## COVID-19 pandemic growth in Portuguese territory

All over the world we start to get richer datasets of the pandemic evolution in each country. Portugal is a small country on the western extremity of Europe next to Spain. Let us examine the numbers.

The results are based on the python code shared by: https://scipython.com/book/chapter-8-scipy/additional-examples/the-sir-epidemic-model/

With some modifications to take into account transmission rate and mean recovery time variation through time.

I also have to share some good resources from Stanford University and the Imperial College of London:

On a previous post I introduced the equations:

Why the Western approach to COVID-19 pandemic may be dangerous. Mitigation and Suppression effects seen on the models.

On my model Instead of having a constant β and γ we have time dependent functions B and G based on the decay function:

Measured data was gather from HDX compiled by Johns Hopkins University Center for Systems Science and Engineering (JHU CCSE).

Here we can see the daily infection cases in Portugal which is following an exponential.

And also observe the current pandemic infection count in Portugal:

The resulting B and G estimation curves can be seen bellow:

The model seems to fit well to the measured data at least for now.

Zooming out we get:

And yet another view:

The model predicts the following:

If the prediction is correct we should expect to have at the peek of the crises 331388 reported infection cases. Hopefully less than 2% of these will require a ventilator (6627 ventilators). My concern is that the usage time of ventilators can be greater than the 14 days estimated.

The ability to compensate the model curve based on time dependent factors is efficient to adapt the curve to experimental results.

We can see that the mitigation and suppressive actions have payed off giving a softer but longer epidemic curve.

As we get new measured data, the model precision increases for we refit and generate a new model. If the mitigation actions continue it is expected that the peek value continues to decrease, however time will increase.

My final remarks: there will be no guaranty we can lift the heavy restrictions to contain the spread. Humanity will live in fear and alert until we find a vaccine or get herd immunity.