Project Overview

Invasive bacterial infections account for a significant portion of neonatal deaths, particularly in low- and middle-income countries (LMICs). A critical, yet poorly defined, contributor to this burden is healthcare-associated transmission, infections passed from one person to another via contaminated hands of healthcare workers or the hospital environment.

Traditional surveillance methods rely on simple temporal and spatial links or basic antibiotic susceptibility testing (AST) patterns to flag an outbreak. These methods often fail to detect complex or hidden transmission chains, allowing outbreaks to continue undetected and compromising infant safety. The Baby GERMS Outbreak study aims to overcome these limitations by demonstrating the powerful added value of Whole Genome Sequencing (WGS) in neonatal infection surveillance within low-resource settings. We will retrospectively analyse data from the Baby GERMS-SA Surveillance Program (October 2019 to September 2020) and combine three data streams:

1. Epidemiological Data: the who, when, and where of the infections.
2. Laboratory Data (AST): conventional antibiotic resistance profiles.
3. Genomic Data (WGS): granular, high-resolution genetic fingerprints of the pathogens.

Our goal is to retrospectively identify clusters and outbreaks of neonatal invasive healthcare-associated infections (HAIs) that were potentially missed by conventional methods at Baby GERMS-SA sentinel hospitals. By comparing the clusters detected by each method, and their respective estimated healthcare transmission rates, we will demonstrate the superior accuracy and added value of integrating genomic data into standard surveillance protocols.