Evidence map›Paper›PMID 35757097›Full record

ArticleNonlinear dynamics2023

When might host heterogeneity drive the evolution of asymptomatic, pandemic coronaviruses?

Kenichi W Okamoto, Virakbott Ong, Robert Wallace, Rodrick Wallace, Luis Fernando Chaves

Abstract read
In one paragraph

Article in Nonlinear dynamics, 2023. 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
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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

5 authors.

Kenichi W OkamotoDepartment of Biology, University of St. Thomas, St. Paul, MN 55105 USA.ORCID 0000-0002-1889-4538
Virakbott OngDepartment of Biology, University of St. Thomas, St. Paul, MN 55105 USA.
Robert WallaceAgroecology and Rural Economics Research Corps, St. Paul, MN USA.
Rodrick WallaceNew York State Psychiatric Institute, NYC, New York, NY USA.
Luis Fernando ChavesInstituto Conmemorativo Gorgas de Estudios de la Salud (ICGES), Avenida Justo Arosemena, Panama, Panama.ORCID 0000-0002-5301-2764

Funding

No grant is acknowledged in the PubMed record.

6 · The paper itself

Abstract

Controlling many infectious diseases, including SARS-Coronavirus-2 (SARS-CoV-2), requires surveillance followed by isolation, contact-tracing and quarantining. These interventions often begin by identifying symptomatic individuals. However, actively removing pathogen strains causing symptomatic infections may inadvertently select for strains less likely to cause symptomatic infections. Moreover, a pathogen's fitness landscape is structured around a heterogeneous host pool; uneven surveillance efforts and distinct transmission risks across host classes can meaningfully alter selection pressures. Here, we explore this interplay between evolution caused by disease control efforts and the evolutionary consequences of host heterogeneity. Using an evolutionary epidemiology model parameterized for coronaviruses, we show that intense symptoms-driven disease control selects for asymptomatic strains, particularly when these efforts are applied unevenly across host groups. Under these conditions, increasing quarantine efforts have diverging effects. If isolation alone cannot eradicate, intensive quarantine efforts combined with uneven detections of asymptomatic infections (e.g., via neglect of some host classes) can favor the evolution of asymptomatic strains. We further show how, when intervention intensity depends on the prevalence of symptomatic infections, higher removal efforts (and isolating symptomatic cases in particular) more readily select for asymptomatic strains than when these efforts do not depend on prevalence. The selection pressures on pathogens caused by isolation and quarantining likely lie between the extremes of no intervention and thoroughly successful eradication. Thus, analyzing how different public health responses can select for asymptomatic pathogen strains is critical for identifying disease suppression efforts that can effectively manage emerging infectious diseases. Supplementary Information: The online version contains supplementary material available at 10.1007/s11071-022-07548-7.

Indexed as

CoronavirusCOVID-19Evolutionary epidemiologyHost heterogeneityMathematical modelingPublic health

Identifiers

PMID35757097
PMCPMC9207439

What OpenQuestion holds

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Registered trials

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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.