Prevention of zoonotic spillover: From relying on response to reducing the risk at source
Bibliographic record
Abstract
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.
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 imitationNot 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.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.001 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 0.001 |
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.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; a candidate call from one teacher head, not a consensus.
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".