Artificial Intelligence is a tool which can be used in a variety of industry sectors to optimize processes, but also for monitoring and to gather useful data to predict outcomes. In these hard days caused by the epidemic outbreak, AI is providing substantial help to the Health sector to effectively fight COVID-19 and the workload that health facilities have to take on.
The role of Revelis
As stated in the past weeks, Revelis has formed an internal task-force to contribute to the development of Artificial Intelligence solutions against the COVID-19 outbreak. This is a joint work with researchers from the Italian Research Council ICAR-CNR(CS).
In this blog post, we want to show you a model to forecast new daily confirmed cases and ICU workload in Italy.
Our work takes inspiration from the paper by Arianna Agosto and Paolo Giudici, where a model for financial contagion is adapted to predict the daily new cases caused by COVID-19.
It is possible to summarize our contribution in two main points:
- providing a novel mathematical formulation.
- taking into account containment measures.
The Model
The model is a variant of the well-known Poisson Auto Regressive Model; we assume a Poisson distribution for the target variable and its intensity is estimated through a log-linear model containing:
- target variable values in the previous k days,
- containment measures in the previous k days,
- an estimate of the Poisson parameter form at the time instant t-k..
The model has been trained on official data from Italian Civil Protection from 24th of February until 8th of April. Prior to modelling, we have transformed the target variable using moving average of 3 days.
Outcomes
As time goes by, performances are subjected to deterioration and, for this reason, the model has been updated many times. The following models have been trained before:
- a first model using data until the 9th of April
- a second model using data until the 16th of April
For readiness purposes, only the last model trained using data until the 25th of April is shown. After breaking down the moving average values into point estimates for each day, the results are listed in the following table:
| Date | Prediction | Real value | Error |
| 2020-04-26 | 1989 | 2009 | 20 |
| 2020-04-27 | 1918 | 1956 | 38 |
| 2020-04-28 | 1856 | 1795 | 37 |
| 2020-04-29 | 1758 | 1956 | 38 |
| 2020-04-30 | 1693 | 1694 | 1 |
| 2020-05-01 | 1640 | 1578 | -62 |
| 2020-05-02 | 1551 | 1539 | -12 |
| 2020-05-03 | 1492 | 1501 | 9 |
| 2020-05-04 | 1448 | 1479 | 31 |
| 2020-05-05 | 1368 | 1427 | 59 |
| 2020-05-06 | 1318 | 1333 | 15 |
| 2020-05-07 | 1280 | 1311 | 31 |
| 2020-05-08 | 1206 | 1168 | -38 |
| 2020-05-09 | 1165 | 1034 | -131 |
| 2020-05-10 | 1130 | 1027 | -103 |
On a medium-term perspective, we predict that after the second week of May ICU workload will reach a value around 1k. Unfortunately, as you can observe in figure that follows, confidence intervals blows up at that time and therefore it’s risky for policy-makers to rely on these predictions.
Conclusions
Long-term predictions are not enough reliable because of the confidence interval width, this might have been expected because Poisson distribution has a variance equal to its expected value and, as times goes, uncertainty sums up. However, it is reasonable to use this model to perform a short-term forecasting and to get a rough idea of when containment measures will reach their maximum efficacy. It is crucial to say that this work has not been peer-reviewed and it has limits given by the official data reliability.
