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A Role For Ecohealth Approaches In Pandemic Prevention

2006· article· en· W2078339958 on OpenAlexaff
Dominique Charron

Bibliographic record

VenueEpidemiology · 2006
Typearticle
Languageen
FieldEconomics, Econometrics and Finance
TopicCOVID-19 Pandemic Impacts
Canadian institutionsInternational Development Research Centre
Fundersnot available
KeywordsPandemicMalariaInfectious disease (medical specialty)CholeraDengue feverEnvironmental healthSanitationSmallpoxMedicineDiseaseVirologyGeographyVaccinationImmunologyCoronavirus disease 2019 (COVID-19)

Abstract

fetched live from OpenAlex

SAB1-O-06 The failure to control infectious diseases is a major international health issue. Long-standing problems such as infectious gastroenteritis, malaria, cholera, and dengue fever exact an enormous toll on the world's poorest populations. Eradication earlier this century of smallpox and the development of increasingly potent antimicrobials contributed to the belief that most major infectious diseases could eventually be controlled. Yet infectious diseases continue to be a scourge worldwide. Rich industrialized countries again fear pandemic disease like severe acute respiratory syndrome (SARS) and influenza. Many other pandemic diseases are a daily burden for developing countries. Pandemic spread of infectious diseases is characterized by the large number of people infected, contagion and rapid spread, difficult treatment and containment, and occurrence in many geographic loci. Pandemic influenza and SARS are perhaps best known because of their high mortality and very rapid spread around the world from one geographic source. Cholera, dengue, and HIV/AIDS easily qualify as pandemics, as do childhood infectious gastroenteritis, malaria, and hepatitis B virus. Pandemics are distinguishable by our inability or failure to contain disease spread. Pandemics appear to emerge and persist in developing countries more so than in richer countries, this for several reasons. First, many diseases capable of pandemic spread are zoonoses (transmissible between animals and people) that emerge in populations in intimate contact with livestock or wildlife. Second, densely populated human settlements with poor sanitation and health care fuel pandemics. Third, pandemics spread best if neglected for a time, this being more likely to happen outside of rich developed countries. Finally, new diseases appear to emerge most readily in areas undergoing rapid environmental change, such change also preventing communities from being resilient to the population-level impacts of disease. Much health research into pandemics is based on biomedical control strategies: vaccination, containment, quarantine, and eradication. These measures are proven effective in certain circumstances (smallpox), but they do not favour upstream investigations into the emergence of disease. Ecohealth approaches tackle all drivers of pandemic spread: social, economic, and environmental. Ecohealth approaches could be used to understand how social conditioning surrounding animal rearing may favor the evolution of influenza. Participatory methods typical of ecohealth elucidate new approaches that will be culturally appropriate yet effective in stopping the transmission of disease at some critical juncture (say, interrupting spread of influenza from chickens to pigs and then to people). These and other strengths of the approach are discussed.

Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.

How this classification was reachedexpand

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)0.004
metaresearch head score (Gemma)0.002
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Observational · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.456
Threshold uncertainty score0.602

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0040.002
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0010.000
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)0.0000.000

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 head0.244
GPT teacher head0.348
Teacher spread0.104 · 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

Classification

machine, unvalidated

Machine predicted; a candidate call from one teacher head, not a consensus.

The models applied no category: nothing in the taxonomy fit this work.
Study designObservational
Domainnot available
GenreEmpirical

How this classification was reached, model by model and score by score, is at the end of the page under "How this classification was reached".

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Citations0
Published2006
Admission routes1
Has abstractyes

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