Evidence map›Paper›PMID 38694617›Full record

ArticleOne health (Amsterdam, Netherlands)2024

Prediction of viral spillover risk based on the mass action principle.

Maryam Golchin, Moreno Di Marco, Paul F Horwood, Dean R Paini, Andrew J Hoskins, R I Hickson

Abstract read
In one paragraph

Article in One health (Amsterdam, Netherlands), 2024. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 4 papers.

0numbers the graph read from it
0cells of the map it votes in
4citing papers in PubMed
–field-weighted citation impact
1 · What the graph read from it

What it found

Each row is one number read from the abstract, on the scale the paper reported it, with its interval. Left of the dashed line favours the treatment, right favours the comparator. Under each row is the sentence it came from. New to these charts? A ten-minute tutorial.

The abstract states no effect estimate the extractor could read, or names no intervention and outcome on the map, so this paper lights no cell and moves no belief. It is still indexed, cited and linked below.

2 · The registry

The trial behind it

Trials whose registry record cites this paper, or whose number appears in the abstract. A trial that started after this paper was published is citing it as background, not reporting it.

Neither the registry nor the abstract names a trial number. If this is a trial report, that itself is worth knowing.

3 · Its place in the literature

Who cites it

4 citing papers in PubMed.

  1. Article
  2. Article
  3. Article
  4. Predicting pathogen evolution and immune evasion in the age of artificial intelligence.Computational and structural biotechnology journal · 2025
    Review
4 · The record

Corrections and comments

PubMed lists nothing against this paper. Absence here is not a guarantee, only a check that was made.

5 · Who and what money

Authors and funding

6 authors.

Maryam GolchinCommonwealth Scientific and Industrial Research Organisation (CSIRO), Townsville, QLD 4811, Australia.
Moreno Di MarcoDepartment of Biology and Biotechnologies, Sapienza University of Rome, 00185 Roma, RM, Italy.
Paul F HorwoodCollege of Public Health Medical and Veterinary Sciences, and Australian Institute of Tropical Health and Medicine, James Cook University, Townsville, QLD 4811, Australia.
Dean R PainiCollege of Public Health Medical and Veterinary Sciences, and Australian Institute of Tropical Health and Medicine, James Cook University, Townsville, QLD 4811, Australia.
Andrew J HoskinsCommonwealth Scientific and Industrial Research Organisation (CSIRO), Townsville, QLD 4811, Australia.
R I HicksonCommonwealth Scientific and Industrial Research Organisation (CSIRO), Townsville, QLD 4811, Australia.

Funding

No grant is acknowledged in the PubMed record.

6 · The paper itself

Abstract

Infectious zoonotic disease emergence, through spillover events, is of global concern and has the potential to cause significant harm to society, as recently demonstrated by COVID-19. More than 70% of the 400 infectious diseases that emerged in the past five decades have a zoonotic origin, including all recent pandemics. There have been several approaches used to predict the risk of spillover through some of the known or suspected infectious disease emergence drivers, largely using correlative approaches. Here, we predict the spatial distribution of spillover risk by approximating general transmission through animal and human interactions. These mass action interactions are approximated through the multiplication of the spatial distribution of zoonotic virus diversity and human population density. Although our results indicate higher risk in regions along the equator and in Southeast Asia where both virus diversity and human population density are high, it should be noted that this is primarily a conceptual exercise. We compared our spillover risk map to key factors, including the model inputs of zoonotic virus diversity estimate map, human population density map, and the spatial distribution of species richness. Despite the limitations of this approach, this viral spillover map is a step towards developing a more comprehensive spillover risk prediction system to inform global monitoring.

Indexed as

One healthPandemic riskSpillover riskVirus diversityZoonoses

Identifiers

PMID38694617
PMCPMC11061335

What OpenQuestion holds

Textmetadata
LicenceCC BY-NC
Read underepoch 390

Registered trials

None linked

Read under generation 80e0d062 · epoch 390. Bibliography from PubMed, PubMed Central and OpenAlex; grants from NIH RePORTER; trial links from ClinicalTrials.gov; estimates, votes and beliefs from the OpenQuestion graph.