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Soutenance de thèse de Patrick Obermeier

Patrick Obermeier soutiendra sa thèse de doctorat, à Besançon, mercredi 15 décembre, de 17h à 19h (UTC+1, heure de Besançon). Sa thèse s’intitule Standardized syndromic and virologic surveillance facilitating unbiased signal detection of emerging and re-emerging infectious diseases.
Au vue de la situation sanitaire, elle sera retransmise en visio. Pour obtenir le lien de connexion, il est nécessaire de s’inscrire (patrick.obermeier chez univ-fcomte.fr).

Résumé

Infectious diseases pose a global health threat with respiratory infections and infections of the central nervous system (CNS) ranking among the leading causes of morbidity and mortality, especially in children. Usually, we monitor (re-)emerging infectious diseases based on public health databases or regional, national, and international surveillance networks, all of which depend on reports from vigilant healthcare providers or patients themselves. The majority of public health data are not obtained at the individual patient level and stakeholders have repeatedly raised concerns about the timeliness and accuracy of disease burden estimates, especially in epi-/pandemic situations. Unless standardized criteria are applied and infectious diseases are recognized and diagnosed at the point-of-care, any association between a certain disease and a specific pathogen may be subject to bias and delays.
To investigate this topic, the Vienna Vaccine Safety Initiative, an international non-profit research organization, designed two quality improvement programs between 2009 and 2015 for the purpose of enhanced infection control and infectious disease surveillance in one of Europe’s largest pediatric tertiary care centers at the Charité University Medical Center in Berlin, Germany in collaboration with the Robert Koch Institute : clinical presentations and disease severity in patients with flu-like illness were assessed in real-time using mobile health technology. Mobile apps allowed for the application of standardized criteria for case classification, risk, and severity. Completeness of data according to standard operating procedures helped to diminish ascertainment bias. A similar program existed for patients with suspected CNS infection.
In the present PhD thesis project, we build on the lessons learned from these two quality improvement programs.

We will now link two highly standardized and granular clinical datasets from several hundred pediatric patients presenting with respiratory and CNS infection to a matching biobank for in-depth virological analysis and pattern recognition. Conventional PCR will be complemented by agnostic viral metagenomics.
In detail, we will discuss :
1) The utility of combining digital case classification with metagenomics in closing the ‘diagnostic gap’ in complex CNS infectious disease
2) The use of mobile health technology linked to PCR diagnostics and metagenomics to improve the clinical management of respiratory infections.
3) Molecular evolution of adenovirus species C based on whole-genome sequencing
4) A precision medicine approach to investigating disease severity and clinical patterns of respiratory adenovirus infection using machine learning methodology.

The results of the studies discussed herein demonstrate that
(a) standardized clinical case assessments, aided by mobile health technology, enable pattern recognition, machine learning, and the reliable computation of meta-analyzable disease severity measures and that
(b) combining (a) with comprehensive virological testing, such as PCR and agnostic next-generation sequencing, will facilitate unbiased signal detection of (re-)emerging infectious diseases, thus allowing for improved infectious disease management in acute patient care, surveillance, and research.

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