Evidence map›Paper›PMID 42879480›Full record

ArticleProceedings of the National Academy of Sciences of the United States of America2026

Drivers of the likelihood of evolutionary rescue in response to novel infectious diseases.

Laurinne J Balstad, Marissa L Baskett

Abstract read
In one paragraph

Article in Proceedings of the National Academy of Sciences of the United States of America, 2026. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Not yet cited in PubMed.

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0cells of the map it votes in
0citing papers in PubMed
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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

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3 · Its place in the literature

Who cites it

0 citing papers in PubMed.

No citing paper in PubMed yet.

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

2 authors.

Laurinne J BalstadDepartment of Environmental Science and Policy, University of California, Davis, CA 95616.ORCID 0000-0002-4615-529X
Marissa L BaskettDepartment of Environmental Science and Policy, University of California, Davis, CA 95616.ORCID 0000-0001-6102-1110

Funding

NSF | National Science Foundation Graduate Research Fellowship Program (GRFP) 1734999NSF | National Science Foundation Graduate Research Fellowship Program (GRFP) 2036201UC | UCD | College of Agricultural and Environmental Sciences, University of California, Davis (Ag&E) CA-D-ESP-2244-HUC | UCD | College of Agricultural and Environmental Sciences, University of California, Davis (Ag&E) Jastro-Shields fund
6 · The paper itself

Abstract

The introduction of novel infectious diseases has caused significant declines in many species, and in some cases, local extinction. Simultaneously, hosts might adapt to these novel pathogens, allowing population recovery, an example of evolutionary rescue (ER). Novel infectious diseases differ from sudden, abiotic stressors in that disease only impacts a subset of individuals within the population and individuals might be affected for a finite period. Whether ER occurs in an infectious disease context, and how likely it is relative to abiotic stress, might depend on both host and pathogen characteristics such as the scale of transmission (e.g., local density-dependent transmission only or additional infection through an external reservoir), the degree of fatality or immunity, and the adaptive pathway in terms of whether adaptive alleles reduce mortality, reduce likelihood of infection, or enhance recovery. Here, we use a stochastic mathematical model of host-pathogen dynamics with evolution of a novel allele conferring disease robustness to quantify how population trajectories, including the probability of and time to ER, vary across adaptive pathways and disease characteristics. We find that ER is more likely to occur with dynamical disease transmission compared to a lasting stressor that affects the entire population, indicating that protecting a subset of the population from stress facilitates ER. Among cases with disease transmission, ER is generally more likely to occur with the adaptation of mortality-blocking alleles and sources of transmission that include an external reservoir.

Indexed as

Biological EvolutionCommunicable DiseasesHost-Pathogen InteractionsAnimalsHumansModels, Biologicaladaptationdiseaseevolutionary rescuehost-pathogen systemmathematical model

Identifiers

PMID42879480
PMCPMC13684229

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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.