PubMed · 8979761
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T L Jones. 1996. TB roars back.. https://pubmed.ncbi.nlm.nih.gov/8979761/
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INTRODUCTION: Outbreaks of healthcare-associated infections (HAI) result in substantial patient morbidity and mortality; mitigation efforts by infection prevention teams have the potential to curb outbreaks and prevent transmission to additional patients. The incorporation of whole genome sequencing (WGS) surveillance of suspected high-risk pathogens often identifies outbreaks that are not detected by traditional infection prevention methods and provides evidence for transmission. Our approach to real-time WGS surveillance, the Enhanced Detection System for Healthcare-Associated Transmission (EDS-HAT), has 1) identified serious outbreaks that were otherwise undetected and 2) shown the potential to be cost saving. METHODS: We describe our cost-efficient methods to perform WGS surveillance and data analysis of pathogens for institutions that are interested in expanding infection prevention surveillance. We provide an overview of the weekly workflow of EDS-HAT during two distinct phases over three years. RESULTS: In an average week at our tertiary healthcare system, we sequenced 60 samples at a cost of less than $100 each during Phase 1, and 80 samples for less than $70 each in Phase 2, inclusive of laboratory reagents and staff salaries. The average turnaround time, from sample collection to reporting data to infection prevention, was nine days. CONCLUSIONS: Performing EDS-HAT in real-time can be both feasible and time-efficient. Providing such timely information to aid in outbreak detection could identify transmission events sooner and thus could increase patient safety.
In late 2024, an outbreak of over 400 cases of undiagnosed febrile illness, predominantly presenting as fever and cough, was reported in Panzi Health Zone, southwestern Democratic Republic of the Congo. Here we conducted an epidemiological and laboratory investigation to determine the etiology of the outbreak. Clinical data and specimens were prospectively collected from 108 individuals, of whom 59/108 (54.6%) were female. Children aged <5 years were the most affected (47/108, 43.5%); 14/32 (43.7%) were malnourished. Oro/nasopharyngeal swabs from 96/108 individuals were PCR tested; 26 blood samples were sequenced. Plasmodium falciparum was detected in 56/108 (51.8%) individuals. Co-infections were also detected, with influenza A(H1N1)pdm09 virus in 16/56 (28.6%) and severe acute respiratory syndrome coronavirus 2 in 10/56 (17.9%) individuals. No novel pathogens were detected via metagenomics. Our findings suggest that the outbreak was primarily associated with a surge in malaria cases, with concurrent viral respiratory infections. Increasing decentralized laboratory capacity and strengthening broader health systems remain crucial for faster outbreak detection and investigation.
The response people have to vaccination varies because their immune systems differ and vaccine failures occur. Here we consider the effect that a random response, independent for each vaccinee, has on the vaccination coverage required to prevent epidemics in a large community. For a community of uniformly mixing individuals an explicit expression is found for the critical vaccination coverage (CVC) and the effect of the vaccine response is determined entirely by the mean E(AB), where A and B, respectively, reflect the infectivity and susceptibility of a vaccinated individual. This result shows that the usual concept of vaccine efficacy, which focuses on the amount of protection the vaccine provides the vaccinee against infection, is not adequate to describe the requirements for preventing epidemics when vaccination affect infectivity. The estimation of E(AB) poses a problem because A and B refer to the vaccine response of the same individual. Similar results are found when there are different types of individual, but now the mean E(AB) may differ between types. However, for a community made up of households it is shown that the CVC also depends on other characteristics of the vaccine response distribution. In practice this means that estimating a single measure of vaccine effectiveness is generally not enough to determine the CVC. For a specific community of households it is found that the vaccination coverage required to prevent epidemics decreases as the variation in the vaccine response increases.