How to read the data
While we think that our reports on the projected spreading of the new H1N1 Flu can be useful when considering possible scenarios for the spreading of the disease, we want to underline a number of aspects of the presented analysis and some disclaimers. We hope that this will help in framing the presented results in the right context.
- The map reporting the number of cases are showing the expected number of cases. These values are obtained over many statistical outcomes and are therefore only a good indicator for large urban areas and when the number of cases is appreciable. For this reason the maps may not show some areas with only a few cases. This might be also due to the resolution of the image.
- We do have a full statistical treatment of the data including confidence intervals and maximum and minimum values for the quantities of interest. We have got several requests for more detailed statistical analysis. For a number of reasons, including the sake of readability of the postings, we cannot answer positively to these requests.
- Data coming from different regions of the world have to be validated in most of the cases. We feed the model with what appears plausible and confirmed at the moment. As a result, the scenarios are subject to changes depending on the available data.
- We consider both a worse case and a best base scenario. Both are based on a certain number of assumptions on the data and what we find from the information gathered so far.
- The model is mostly used to project first time arrival of infected individuals and provides risk maps telling the probability of infected case detection at a given date. At this early stage of the disease, the number of predicted cases is subject to large fluctuations as freshly available data in integrated in the model. These numbers should thus be considered as representing trends rather than exact predictions.
- The results are based on computational models and have to be considered only as an extra source of information and not as the reality of the epidemic unfolding.