Population-level impact of mass drug administration against schistosomiasis with anthelmintic drugs targeting juvenile schistosomes: a modelling study
Notice bibliographique
Résumé
BACKGROUND: Schistosomiasis is a neglected disease caused by parasitic flatworms of the genus Schistosoma and affects more than 150 million people worldwide. Praziquantel, the drug used in public health control programmes, has minimal activity against juvenile schistosomes (within 6 weeks of infection) and imperfect cure rates. We aimed to model the population-level impact of hypothetical novel drug candidates, targeting both juvenile and adult schistosomes with various efficacies, across a range of baseline epidemiological settings. METHODS: In this modelling study, we used a stochastic, individual-based mechanistic model of Schistosoma mansoni infection and simulated mass drug administration control programmes in diverse epidemiological environments. These programmes involved the administration, over a 5-year period at 75% coverage, of praziquantel (single-dose or two-dose regimens) or hypothetical novel drugs with various assumed efficacies against adult and juvenile schistosome parasites: novel drug A, with equivalent efficacy to praziquantel against adult schistosomes plus perfect (100%) efficacy against juvenile schistosomes; novel drug B, with higher efficacy than praziquantel against adult schistosomes and no activity against juveniles; and novel drug C, with higher efficacy than praziquantel against adult schistosomes plus perfect efficacy against juveniles. The outcomes were median observed S mansoni infection prevalence and infection intensity over time in simulated populations. FINDINGS: In a simulated high-endemicity setting (baseline prevalence of S mansoni infection of 53%), modelled prevalence after a single treatment was 20·8% (uncertainty interval 15·8-23·6) for single-dose praziquantel, 17·8% (15·2-19·8) for two-dose praziquantel, 18·4% (13·4-21·4) for novel drug A, 16·0% (15·0-16·8) for novel drug B, and 13·4% (12·6-14·0) for novel drug C; at year 5, modelled prevalence was 14·6% (12·2-16·4) for single-dose praziquantel, 13·6% (11·6-14·6) for two-dose praziquantel, 11·8% (9·4-13·4) for novel drug A, 12·6% (11·6-13·4) for novel drug B, and 9·6% (9·0-10·4) for novel drug C. In a simulated low-endemicity setting (baseline prevalence 15%), modelled prevalence after a single treatment was 4·8% (3·6-5·8) for single-dose praziquantel, 4·2% (3·6-5·0) for two-dose praziquantel, 4·6% (3·2-5·4) for novel drug A, 4·0% (3·4-4·6) for novel drug B, and 3·6% (3·2-4·2) for novel drug C; at year 5, modelled prevalence was 3·0% (2·2-3·6) for single-dose praziquantel, 2·8% (2·2-3·4) for two-dose praziquantel, 2·6% (1·8-3·2) for novel drug A, 2·7% (2·2-3·2) for novel drug B, and 2·2% (1·8-2·6) for novel drug C. INTERPRETATION: This study provides policy-relevant data that could help to guide the development and selection of novel drugs for schistosomiasis. Novel anthelmintic drugs that can kill both adult and juvenile schistosomes with higher efficacy than praziquantel could have some public health gains in control programmes for schistosomiasis, especially in high-burden settings. Novel drugs with increased efficacy against adult schistosomes are likely to have an initial and larger impact on disease control, whereas targeting juveniles could moderately improve longer-term control outcomes. FUNDING: US National Institutes of Health.
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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 | 0,002 | 0,004 |
| Méta-épidémiologie (sens strict) | 0,001 | 0,000 |
| Méta-épidémiologie (sens large) | 0,001 | 0,002 |
| Bibliométrie | 0,001 | 0,001 |
| Études des sciences et des technologies | 0,000 | 0,001 |
| Communication savante | 0,001 | 0,001 |
| Science ouverte | 0,002 | 0,001 |
| Intégrité de la recherche | 0,002 | 0,001 |
| Charge utile insuffisante (le modèle a refusé de juger) | 0,003 | 0,000 |
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 ».