Using Predictive Model for Screening Bacterial Meningitis in National Surveillance System in Iran.

Bacterial meningitis is a dangerous infectious disease that the entire community can be influenced by its epidemics. The objective of this study is to develop a predictive model as a screening tool to accelerate distinguishing between patients with acute bacterial meningitis and non-bacterial ones to prevent bacterial meningitis epidemics in Iran. This study was conducted on Iranian meningitis registry, which consists of 7,945 suspected cases of the disease between 2009 and 2011. Each sample has 8 predictive and a target variables. The predictive model was developed by decision tree algorithm and, the overall accuracy was 78%, with a sensitivity of 87%, and a specificity of 70%, respectively. This model can help health policymakers and epidemiologists to identify bacterial meningitis outbreaks and support them to make a decision in infection dynamics. In conclusion, we developed and validated a predictive model that can be used in meningitis surveillance system in Iran. However, further research is needed to use the model in practice with different pathogen types of bacterial meningitis in order to proper antimicrobial therapy planning.