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A statistically sound design of an early warning system for nosocomial infection

Student:Magnus van Niekerk
Timeline:February 2017 - 14 February 2021

Healthcare-associated infections (HAI) are responsible for social and economic burdens. Risk factors are used to identify the relationship between the occurrence of HAI and a susceptible subject to better understand and prevent the future occurrence of HAI. Traditional risk factors do not consider the spatiotemporal nature in which HAI can be transmitted in healthcare settings, which is a fundamental part of the transmission process. Using data collected using innovative RFID technology and improved hospital bed logistic systems, it is possible to collect and incorporate spatiotemporal data to better understand the occurrence and spread of harmful microorganism in healthcare settings.

We will answer the following questions during this research:

1.     How do risk factors for surgical site infection differ for major groups of surgeries?

2.     What risk factors can be used to predict the occurrence of surgical site infection?

3.     How do spatiotemporal movement data and social mixing patterns of healthcare workers affect the transmission and spread of harmful microorganisms in a hospital ward?

4.     How can traditional risk factors for HAI be combined with spatiotemporal risk factors to better predict the occurrence and spread of HAI across hospital departments?

This project is funded by the INTERREG VA project under the name “EurHealth-1Health – Euregional Antibiotic Resistance and Infection Prevention”

Meet the team

J.M. van Niekerk MSc (Magnus)
Graduate Student A. Stein (Alfred)
Prof.dr. Lisette van Gemert-Pijnen
Research theme
Acquisition and quality of geo-spatial information

Developments in sensor and web technology have led to a vast increase in earth observation data. Advanced methodology is needed for interpretation and integration of such big geo-data to support decision making.

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