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Record W3112997935 · doi:10.1016/s2666-5247(20)30220-2

Towards a coordinated strategy for intercepting human disease emergence in Africa

2020· article· en· W3112997935 on OpenAlex

Why this work is in the frame

A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.

affAt least one author lists a Canadian institution in the pinned OpenAlex snapshot.
fundA Canadian funder is recorded on the work.
aboutThe title or abstract carries a Canadian signal from the geographic lexicon.

Bibliographic record

VenueThe Lancet Microbe · 2020
Typearticle
Languageen
FieldMedicine
TopicZoonotic diseases and public health
Canadian institutionsCanadian Food Inspection AgencyUniversity of SaskatchewanCanadian Science Centre for Human and Animal HealthUniversity of ManitobaIzaak Walton Killam Health CentreInternational Centre for Infectious DiseasesDalhousie University
FundersCanadian Institutes of Health ResearchArkansas Biosciences InstituteNational Science Foundation
KeywordsScopusHuman viromeConvention on Biological DiversityPandemicPolitical scienceWildlife tradeGlobal healthGeographyLibrary scienceBiologyWildlifeMetagenomicsMEDLINECoronavirus disease 2019 (COVID-19)DiseaseHealth careBiodiversityMedicineGeneticsComputer scienceInfectious disease (medical specialty)EcologyLaw

Abstract

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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

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Full frame distilled prediction

Teacher imitation

Not calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.

metaresearch head score (Codex)0.000
metaresearch head score (Gemma)0.000
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Not applicable · Consensus signal: Not applicable
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.227
Threshold uncertainty score0.619

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0000.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.000
Science and technology studies0.0000.000
Scholarly communication0.0000.000
Open science0.0000.000
Research integrity0.0000.000
Insufficient payload (model declined to judge)0.0010.000

Machine scores (provisional)

The two teacher heads of the student model, read on this work. A score orders the frame for review; it never asserts a category, and the validation status ships verbatim with every row.

Baseline scores from an immature model (maturity gate not passed, 7 training rounds). Scores rank; they never assert a category.

Opus teacher head0.155
GPT teacher head0.369
Teacher spread0.214 · how far apart the two teachers sit on this one work
Validation statusscore_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from it