## 3 Automatic Analysis and Reporting  
The key for a getting a quick feedback and, as a consequence, a quick support for the decision-making is to have the technological infrastructure ready to collect data as well as to analyze the data as soon as the collection period is closed. Data arrives to the on-line questionnaire automatically as soon as participants provide their responses without additional intervention of the research team, other than ensuring the permanent availability of the server. At any moment, data can be downloaded from the on-line questionnaire in form of a csv file, so several waves can be treated as well to form a continuous panel if required. The contents of the csv file represents the several questions from the questionnaire following the formats described in Figure 7 according to the type of the variables representing the different questions. Given a certain questionnaire, a metadata file can be linked with it, by indicating which type correspond to each variable, and which columns contains the information relative to that variable in the csv. Each questionnaire requires its own metadata file. Changing questionnaire is relatively simple, so that modifications in the corresponding digital questionnaire can be easily done, and the corresponding metadata file must be modified accordingly. The analysis of the data collected in the questionnaire is automatically processed through some R and Rmarkdown scripts, which inputs both the dataset in csv format and the corresponding metadata file. A knowledge component is also implemented, so the procedures know in each moment which kind of analysis is appropriated for each variable, according to its type. This gives the intelligence to the system and is able to manage exceptions. In addition, it can be modified to add new data types including other analysis tools when required. This component is the one including all the guidelines that guarantee the preservation of the statistical secrecy in front of small samples mentioned in previous sections. In addition, a very important part of the procedure is that Rmarkdown has been designed for automatic reporting in such a way that it produces a formatted Word document with the results. So, the result of the analysis is an editable Word file ready to be read, commented, and post-processed in a very easy way by the decision-maker itself, just requiring specific domain expertise to select the relevant results, to add complementary explanations for the analytical findings, to synthesis the findings in a short overview or to reorder them in a rational that makes sense for the communication of results. When the analysis must be repeated periodically (every six months for example), the system is also prepared to add those reordering and selection criteria into the automatic reporting part, thus producing a results document much closer to what the expert need to communicate results. As said before, the INESES-COVID19 questionnaire is generating a csv file with 195 columns representing 25 blocks of information. Some of the variables split in many columns by internal representation, as explained before. The total elapsed time between downloading the csv file from the questionnaire (located in the server) and getting the Word file containing the results of the analysis by using the scripts designed in the project is about 15 min on average.