On the Potential of Relational Databases for the Detection of Clusters of Infection and Antibiotic Resistance Patterns

Dear colleagues,

We are glad to announce that a new scientific paper has been published:

Gelfusa, Michela, Andrea Murari, Gian Marco Ludovici, Cristiano Franchi, Claudio Gelfusa, Andrea Malizia, Pasqualino Gaudio, Giovanni Farinelli, Giacinto Panella, Carla Gargiulo, and Katia Casinelli (2023). "On the Potential of Relational Databases for the Detection of Clusters of Infection and Antibiotic Resistance Patterns" Antibiotics 12, no. 4: 784. https://doi.org/10.3390/antibiotics12040784

The paper is a work based on a collaboration between University of Rome Tor Vergata, ASL and and Fabrizio Spaziani, Frosinone Hospital and CNR.

We want to thanks all the author with a mention to Gian Marco Ludovici that has created this network.

STAY TUNED!

Abstract:

In recent years, several bacterial strains have acquired significant antibiotic resistance and can, therefore, become difficult to contain. To counteract such trends, relational databases can be a powerful tool for supporting the decision-making process. The case of Klebsiella pneumoniae diffusion in a central region of Italy was analyzed as a case study. A specific relational database is shown to provide very detailed and timely information about the spatial–temporal diffusion of the contagion, together with a clear assessment of the multi-drug resistance of the strains. The analysis is particularized for both internal and external patients. Tools such as the one proposed can, therefore, be considered important elements in the identification of infection hotspots, a key ingredient of any strategy to reduce the diffusion of an infectious disease at the community level and in hospitals. These types of tools are also very valuable in the decision-making process related to antibiotic prescription and to the management of stockpiles. The application of this processing technology to viral diseases such as COVID-19 is under investigation.