MétaCan
Menu
Retour à la cohorte
Enregistrement W4387363415 · doi:10.1371/journal.ppat.1011504

Prevention of zoonotic spillover: From relying on response to reducing the risk at source

2023· article· en· W4387363415 sur OpenAlexaff
Wanda Markotter, Thomas C. Mettenleiter, Wiku Adisasmito, Salama Almuhairi, Casey Barton Behravesh, Pépé Bilivogui, Salome A. Bukachi, Natalia Casas, Natalia Cediel, Dominique Charron, Abhishek Chaudhary, J. R. C. Zanella, Andrew A. Cunningham, Osman Dar, Nitish Debnath, Baptiste Dungu, Elmoubasher Farag, George F. Gao, David T. S. Hayman, Margaret L. Khaitsa, Marion Koopmans, Catherine Machalaba, J. S. Mackenzie, Sergé Morand, Vyacheslav Smolenskiy, Lei Zhou

Notice bibliographique

RevuePLoS Pathogens · 2023
Typearticle
Langueen
DomaineMedicine
ThématiqueZoonotic diseases and public health
Établissements canadiensUniversity of Guelph
Organismes subventionnairesWorld Health Organization
Mots-clésSpillover effectBusinessRisk analysis (engineering)Environmental healthMedicineEconomics

Résumé

récupéré en direct d'OpenAlex

BACKGROUND AND CONTEXT : The devastating impact of Coronavirus Disease 2019 (COVID-19) on human health globally has prompted extensive discussions on how to better prepare for and safeguard against the next pandemic. Zoonotic spillover of pathogens from animals to humans is recognized as the predominant cause of emerging infectious diseases and as the primary cause of recent pandemics. This spillover risk is increased by a range of factors (called drivers) that impact the nature, frequency, and intensity of contact between humans and wild animals. Many of these drivers are related to human impact, for example, deforestation and changes in land use and agricultural practices. While it is clear that the triad of prevention-preparedness-response (P-P-R) is highly relevant, there is much discussion on which of these 3 strategic activities in the field of emerging infectious disease should be prioritized and how to optimally target resources. For this, it is important to understand the scope of the respective activity and the consequences of prioritization. Already, the World Bank Pandemic Fund and forthcoming global Pandemic instrument negotiated by the World Health Organization (WHO) appear primarily focused on the early detection, and reaction to the appearance of human illnesses, often with explicit focus only on action to be taken once pathogen spillover and spread have occurred. Strategies to reduce the probability of spillover events are under-prioritized and underutilized, as highlighted by recent infectious disease crises such as Ebola and Mpox epidemics, and have been lost in overall preparedness discussions and recovery financing. This “more of the same” focus suggests that it is politically more expedient to allocate financial resources to deal with a problem once it has arisen, rather than taking the steps necessary to reduce the risk of it occurring in the first place. It is often claimed that allocating resources to prevent something from happening is politically difficult as the value of prevention is largely “invisible” (prevention paradox) or it will take a long time to show effects. However, there are now several communications highlighting the economic benefits of prevention of spillover. If taken, actions to prevent spillover are estimated at $10 to 31 billion per year globally, as a cumulative investment from preventive actions achievable by specific industries. However, addressing the drivers of pathogen spillover through a One Health approach has significant subsequent economic co-benefits; for example, reducing deforestation is estimated to create $4 billion per year in social benefits from reduced greenhouse gas emissions. COVID-19 has demonstrated the immense burden of a pandemic, including significant mortality resulting in economic recession, with the global economy contracting by 4.4 percent in 2020. The expected economic losses from this pandemic are estimated at nearly $14 trillion up to 2024. These losses parallel those incurred by other infectious disease emergencies, including the 2003 SARS pandemic with an estimated economic loss of $52 billion; the Ebola virus disease outbreak in West Africa in 2014 to 2016 with a GDP loss of $2.8 to 32.6 billion and the comprehensive economic and social burden estimated to be $53.19 billion; and the 2015 to 2016 Zika virus disease outbreak with an estimated loss in the United States, Caribbean, and Latin America of $20 billion. If invested in, prevention strategies would reduce the likelihood of another pandemic substantially and likely generate sufficient return on investment over time while also having the potential to generate substantial co-benefits. Prevention is already valued in other sectors: policymakers and industries have led on prevention in other areas, such as expenditure on counter-terrorism, driving laws and insurance incentives to reduce the frequency of traffic accidents, on the nuclear deterrent, and in some cases on flood prevention and other water management measures, exemplifying a political willingness to spend vast sums of money to preempt a harmful event in certain areas or circumstances, but not on pandemic prevention.

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 distillée sur la base complète

Imitation des enseignants

Ni prévalence calibrée, ni vérité terrain. Validation humaine à venir. Apprise à partir de 10 348 étiquettes directes de Codex et de 10 348 étiquettes directes de Gemma. Le mode candidate est l'union des têtes enseignantes seuillées; le consensus est leur intersection. Ces sorties portent le statut machine_predicted_unvalidated et ne sont ni des étiquettes humaines ni des étiquettes directes de modèles de pointe.

score de la tête « metaresearch » (Codex)0,001
score de la tête « metaresearch » (Gemma)0,002
Version: codex-gemma-dda1882f352aStatut de validation: machine_predicted_unvalidated
Catégories candidatesaucune
Catégories consensuellesaucune
DomaineSignal candidat: aucune · Signal consensuel: aucune
Devis d'étudeSignal candidat: Observationnel · Signal consensuel: aucune
GenreSignal candidat: Empirique · Signal consensuel: Empirique
Score de désaccord entre enseignants0,792
Score d'incertitude au seuil0,692

Scores Codex et Gemma par catégorie

CatégorieCodexGemma
Métarecherche0,0010,002
Méta-épidémiologie (sens strict)0,0000,000
Méta-épidémiologie (sens large)0,0000,000
Bibliométrie0,0000,000
Études des sciences et des technologies0,0000,000
Communication savante0,0000,000
Science ouverte0,0000,000
Intégrité de la recherche0,0000,000
Charge utile insuffisante (le modèle a refusé de juger)0,0000,001

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.

Tête enseignante Opus0,041
Tête enseignante GPT0,300
Écart entre enseignants0,259 · 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 tête enseignante, pas un consensus.

Les modèles n’ont appliqué aucune catégorie : rien dans la taxonomie ne correspondait à ce travail.
Devis d'étudeObservationnel
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

Citations55
Publié2023
Routes d'admission1
Résumé présentoui

Explorer davantage

Même revuePLoS PathogensMême sujetZoonotic diseases and public healthTravaux en français237 207