Inferring transmission trees of infectious disease outbreaks with multiple introductions and transmission routes
Notice bibliographique
Résumé
Like a detective in a murder mystery seeks the culprit, researchers studying infectious disease transmission aim to uncover who infected whom. Tracking links between infected individuals and identifying transmission events is essential for understanding many aspects of an outbreak. To solve this question, epidemiologists use a toolkit that includes two classes of methods: identifying transmission events through contact information and identifying them by tracing the evolution of the pathogen within the population. In densely sampled outbreaks, where genetic sequences are available for almost all cases, methods have been developed that infer transmission events from genetic and epidemiological information. Yet the assumptions behind these methods can limit their use across different outbreak settings. This thesis focuses on how infection of cases is represented in transmission models. One part examines the role of multiple introductions into a population. Another part studies the role of contact in transmission. Many current models assume a single introduction and treat all contact in the same way, which reduces their value for real outbreak analysis. To address these issues, this work expands the phybreak model to allow multiple introductions and to incorporate contact information, improving both practical use and accuracy. Multiple introductions are common in infectious disease outbreaks, yet many inference methods assume only one index case. To resolve this, I extended the transmission inference model in the R package phybreak with a “history host”, an artificial source for the index cases. This allows the entire outbreak to be represented in one phylogenetic tree. This approach successfully identified repeated introductions of SARS CoV 2 into mink farms (Chapter 2) and showed that ongoing introductions were a key driver of the 2022 Mpox outbreak in Slovenia (Chapter 3). The method also performed well during real time analysis, where strongly supported introductions matched those later confirmed. Recognizing repeated introductions is important for effective public health action, since outside sources often sustain an outbreak. Contact information is another essential source for understanding transmission, especially when used together with whole genome sequencing. In studies of Mycobacterium tuberculosis, cases are commonly grouped using SNP distance cutoffs, but these depend in part on the quality of contact tracing. In Chapter 4, phylodynamic methods were used to infer transmission events in the Netherlands. This supported the use of common SNP thresholds as a first step in identifying possible transmission pairs. Chapter 5 introduced another extension of phybreak that estimates the contribution of different types of contact to transmission. When applied to SARS CoV 2 in Dutch mink farms, the model showed that shared personnel accounted for most transmission whenever present, while other contact types were less influential. The phybreak model is best suited for outbreaks with relatively few cases, nearly complete observation, and genetic sequences for most individuals. It captures transmission, observation, within host processes, and mutation, and is particularly useful when multiple introductions occur. Although challenges remain, the extensions presented here strengthen the use of phybreak for reconstruction of transmission events and support informed public health decisions.
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Comment cette classification a été obtenuedéplier
Prédiction machine sur la base complète
Imitation des enseignantsNi prévalence calibrée, ni vérité terrain. Validation humaine à venir. Le volet Gemma est une étiquette directe du modèle pour chaque travail de la base, lue sur la notice réduite au titre. Le volet Codex est un classifieur appris des 10 348 étiquettes directes de Codex et calibré sur les taux pondérés de l'échantillon; les champs sans appui suffisant ne portent aucun appel Codex. Le mode candidate est l'union des deux volets; le consensus est leur intersection. Ces sorties portent le statut machine_predicted_unvalidated et ne sont pas des étiquettes humaines.
Scores du classifieur distillé par catégorie (deux têtes)
| Catégorie | Codex | Gemma |
|---|---|---|
| Métarecherche | 0,003 | 0,014 |
| Méta-épidémiologie (sens strict) | 0,001 | 0,001 |
| Méta-épidémiologie (sens large) | 0,001 | 0,002 |
| Bibliométrie | 0,003 | 0,002 |
| Études des sciences et des technologies | 0,001 | 0,001 |
| Communication savante | 0,002 | 0,003 |
| Science ouverte | 0,001 | 0,001 |
| Intégrité de la recherche | 0,001 | 0,002 |
| Charge utile insuffisante (le modèle a refusé de juger) | 0,003 | 0,001 |
Scores machine (provisoires)
Les deux têtes enseignantes du modèle étudiant, lues sur ce travail. Un score ordonne la base pour la relecture; il n'affirme jamais une catégorie, et le statut de validation accompagne chaque rangée tel quel.
Scores de référence d'un modèle non mature (critères de maturité non atteints, 7 itérations). Un score ordonne; il n'affirme jamais une catégorie.
score_only:v0-immature-baseline · tel quel depuis la passe de notation : score_only signifie que le nombre peut ordonner les travaux, et qu'aucune étiquette de catégorie n'en découleClassification
machine, non validéePrédiction automatique; un appel candidat d’une seule source (Gemma direct ou Codex distillé), pas un consensus.
Le détail, modèle par modèle et score par score, se trouve en fin de page sous « Comment cette classification a été obtenue ».