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Enregistrement W6901661964 · doi:10.60692/fcf1d-apr61

Towards a coordinated strategy for intercepting human disease emergence in Africa

2021· article· en· W6901661964 sur OpenAlexaffabout

Notice bibliographique

RevueGreater South Information System · 2021
Typearticle
Langueen
DomaineMedicine
ThématiqueZoonotic diseases and public health
Établissements canadiensUniversity of ManitobaCanadian Food Inspection AgencyUniversity of SaskatchewanIzaak Walton Killam Health CentreDalhousie University
Organismes subventionnairesnon disponible
Mots-clésScopusHuman viromePandemicPublic healthWildlife tradeConvention on Biological DiversityGlobal health

Résumé

récupéré en direct d'OpenAlex

Emerging zoonotic viruses are one of the greatest threats to human health and security, as evidenced by the increasing frequency of disease outbreaks.1Morens DM Fauci AS Emerging pandemic diseases: how we got to COVID-19.Cell. 2020; 182: 1077-1092Summary Full Text Full Text PDF PubMed Scopus (165) Google Scholar To date, the main pre-emptive response to these outbreaks has been extensive, cost-heavy efforts to document virus diversity in wildlife (eg, PREDICT and the Global Virome Projects).2Morse SS Mazet JAK Woolhouse M et al.Prediction and prevention of the next pandemic zoonosis.Lancet. 2012; 380: 1956-1965Summary Full Text Full Text PDF PubMed Scopus (499) Google Scholar, 3Carroll D Daszak P Wolfe ND et al.The Global Virome Project.Science. 2018; 359: 872-874Crossref PubMed Scopus (189) Google Scholar Although these efforts have resulted in the identification of thousands of novel viruses, fewer than 1% are described to date, substantial challenges remain around access and benefit sharing from viral discovery programmes, and—perhaps most problematic for public health application—the spillover hazard of these viruses can only be coarsely inferred at present.4Rourke M Viruses for sale – all viruses are subject to access and benefit sharing obligations under the convention on biological diversity. Griffith University Law School Research Paper No. 17-14.https://papers.ssrn.com/sol3/papers.cfm?abstract_id=2984046Date: June 17, 2017Date accessed: November 30, 2020Google Scholar, 5Carlson CJ Zipfel CM Garnier R Bansal S Global estimates of mammalian viral diversity accounting for host sharing.Nat Ecol Evol. 2019; 3: 1070-1075Crossref PubMed Scopus (45) Google Scholar, 6Carlson CJ From PREDICT to prevention, one pandemic later.Lancet Microbe. 2020; 1: e6-e7Summary Full Text Full Text PDF PubMed Google Scholar Our ability to control and restrict the spread of infectious diseases is critically dependent on early detection. This vigilance should include viruses that might not pose immediate or widespread public health threats, but where repeated spillover or persistent, unchecked transmission chains in humans provides latitude for the evolution of increased pathogenicity, host immune evasion mechanisms, and efficient human-to-human transmission.7Holmes EC On the origin and evolution of the human immunodeficiency virus (HIV).Biol Rev Camb Philos Soc. 2001; 76: 239-254Crossref PubMed Scopus (50) Google Scholar Building on existing research, here we emphasise the importance of a coordinated and targeted strategy for early detection of virus spillover and emergence in humans. This model is based on inter-related study or evidence types and is a collaborative framework geared towards African and other low-income and middle-income countries where risk of disease emergence is often great, infectious disease-related morbidity and mortality are over-represented compared with in high-income regions, undescribed virus diversity is high, and resources are constrained. For this strategy we highlight four complementary study or evidence types indicative of past or current unknown infection: procurement and screening of diagnostic samples from undiagnosed patients, analysis of samples from suspicious fatalities of unknown cause, serosurveys of high-risk or sentinel groups, and analysis of archived samples (appendix p 1). Approaches might overlap (eg, death and post-mortem analysis of undiagnosed patients) but are independently capable of detecting separate evidence for pathogen spillover and novel disease emergence. Their concurrent implementation heightens detectability. Collecting and screening samples from patients with undiagnosed febrile illness provides an efficient means to target the subset of populations most likely to have novel infectious diseases. With properly trained staff and systems, sample collections can be implemented at the point of care for continuous monitoring. When possible, collecting and screening samples from people with an unknown cause of death can identify cases of severe disease that might not remain in hospitals sufficiently long for inclusion in a monitoring strategy, present with unusual symptoms, or develop severe disease but not present to a hospital or clinic, as is likely to occur in low-income and middle-income countries where traditional medicine is practiced. Although new technologies such as next-generation sequencing are increasingly available for detection of unknown viruses, linking clinical findings to disease aetiology can be a challenge. Due consideration must also be given to sample types for collection and their appropriate storage. By contrast, serosurveys or screening of sentinel groups are proactive studies implemented by researchers to collect blood samples from individuals at greatest risk of exposure to zoonotic viruses. Such individuals include pastoralists, agriculture workers, game hunters, traders, or others working in close contact with wildlife. Studies can detect antibodies indicative of spillover events, including asymptomatic cases, and use increasingly efficient and cost-effective screening methods. Likewise, archived samples provide varied, potentially copious, and readily available sample sources that are appropriate for detection of viruses and antibodies indicative of spillover across longer timescales. Like focused serosurveys, archived samples can be especially powerful for identifying viruses that are silently circulating among humans or rare spillover events that could have future implications. These samples also provide important datapoints for efforts to track the effect of global environmental changes on virus spillover, given that most forecasting efforts do not have empirical real-time validation. An important limitation of antibody surveillance is the inability to identify active infections or specific viruses. But these approaches can prompt and guide focused investigation and are integral to a comprehensive strategy. None of the methods or evidence types that we describe here are novel tools and each has limitations;8Wolfe ND Heneine W Carr JK et al.Emergence of unique primate T-lymphotropic viruses among central African bushmeat hunters.Proc Natl Acad Sci USA. 2005; 102: 7994-7999Crossref PubMed Scopus (326) Google Scholar, 9Forbes KM Webala PW Jääskeläinen AJ et al.Bombali virus in Mops condylurus bat, Kenya.Emerg Infect Dis. 2019; 25: 955-957Crossref PubMed Scopus (44) Google Scholar, 10Steffen I Lu K Hoff NA et al.Seroreactivity against Marburg or related filoviruses in west and central Africa.Emerg Microbes Infect. 2020; 9: 124-128Crossref PubMed Scopus (3) Google Scholar however, we highlight the value of a cohesive, targeted, and widespread approach for maximising the likelihood of detecting novel infections (appendix p 1). We acknowledge that in some settings or situations some of the proposed approaches might not be appropriate or might need to be adapted. Ongoing research aims to develop predictive tools so that we might be able to infer the zoonotic potential of the ever-increasing number of newly identified wildlife viruses from their genetic sequence. Meanwhile, comprehensive systems for early detection and containment of wildlife virus spillover and emergence remain one of our strongest responses against the threat posed by zoonotic viruses. We declare no competing interests. We are members of the new Consortium for Intercepting Emerging Diseases in Africa. KMF is supported by grants from the National Science Foundation (NSF; grant number DEB 1911925) and the Arkansas Biosciences Institute. JK is supported by a Tier 2 Canada Research Chair in the Molecular Pathogenesis of Emerging and Re-Emerging Viruses provided by the Canadian Institutes of Health Research (grant number 950-231498). CJC is supported by NSF (grant number BII 2021909) through the Verena Consortium, and thanks the consortium for formative discussions. Download .pdf (.16 MB) Help with pdf files Supplementary appendix

Récupéré en direct depuis OpenAlex et désinversé. Les résumés ne sont pas conservés dans cette base de données : les index inversés représentent 8,6 Go des 9,3 Go de texte de la base, et le serveur dispose de 13 Go libres.

Comment cette classification a été obtenuedéplier

Prédiction machine sur la base complète

Imitation des enseignants

Ni 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.

score de la tête « metaresearch » (Codex)0,012
score de la tête « metaresearch » (Gemma)0,024
Version: metacan-v3-hybrid-931329e0061cStatut de validation: machine_predicted_unvalidated
Catégories candidatesaucune
Catégories consensuellesaucune
DomaineSignal candidat: aucune · Signal consensuel: aucune
Devis d'étudeSignal candidat: Théorique ou conceptuel · Signal consensuel: aucune
GenreSignal candidat: Empirique · Signal consensuel: aucune
Score de désaccord entre enseignants0,017
Score d'incertitude au seuil0,065

Scores du classifieur distillé par catégorie (deux têtes)

CatégorieCodexGemma
Métarecherche0,0120,024
Méta-épidémiologie (sens strict)0,0020,001
Méta-épidémiologie (sens large)0,0010,001
Bibliométrie0,0050,002
Études des sciences et des technologies0,0030,004
Communication savante0,0080,009
Science ouverte0,0040,021
Intégrité de la recherche0,0050,005
Charge utile insuffisante (le modèle a refusé de juger)0,0170,003

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.

Tête enseignante Opus0,097
Tête enseignante GPT0,308
Écart entre enseignants0,211 · la distance entre les deux têtes enseignantes sur ce seul travail
Statut de validationscore_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 découle

Classification

machine, non validée

Prédiction automatique; un appel candidat d’une seule source (Gemma direct ou Codex distillé), pas un consensus.

Les modèles n’ont appliqué aucune catégorie : rien dans la taxonomie ne correspondait à ce travail.
Devis d'étudeThéorique ou conceptuel
Domainenon disponible
GenreEmpirique

Le détail, modèle par modèle et score par score, se trouve en fin de page sous « Comment cette classification a été obtenue ».

En bref

Citations0
Publié2021
Routes d'admission2
Résumé présentoui

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