The SISA dataset was used for the identification of clinical biomarkers that can predict the severity of the arboviral infection severity using SIMON analysis, as described (https://doi.org/10.1101/2020.08.16.252767). The dataset contains clinical data from 543 individuals hospitalized due to the arboviral infection with Dengue, chikungunya, or Zika viruses from a surveillance study in Ecuador collected from 2013 to 2017 (Suppy et al., 2020, doi: 10.1371/journal.pntd.0007969). In the SISA dataset, we have excluded columns with high level of missing values (pregnancy, WomPreg and complete blood count test which was not performed for all donors and includes columns: PLT_count, Lymphocytes, CBC_N%, WBC_calc and CBC_HCT). Additionally, 9 donors with missing values were removed and SIMON analysis was performed with this final dataset.

The clinical study and the initial data is described in the original publication: Sippy, R., Farrell, D.F., Lichtenstein, D.A., Nightingale, R., Harris, M.A., Toth, J., Hantztidiamantis, P., Usher, N., Cueva Aponte, C., Barzallo Aguilar, J., et al. (2020). Severity Index for Suspected Arbovirus (SISA): Machine learning for accurate prediction of hospitalization in subjects suspected of arboviral infection. PLoS neglected tropical diseases 14, e0007969.