Evidence map›Paper›PMID 42120899›Full record

ArticleScientific reports2026

Investigating a deterministic canonical model for tropical influenza.

Joseph L Servadio, Maciej F Boni

Abstract read
In one paragraph

Article in Scientific reports, 2026. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 2 papers.

0numbers the graph read from it
0cells of the map it votes in
2citing 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

2 citing papers in PubMed.

  1. Article
  2. Noisy periodicity in tropical respiratory disease dynamics.medRxiv : the preprint server for health sciences · 2026
    Article
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.

Joseph L ServadioDepartment of Biology, Institute for Genomics and Evolutionary Medicine, Temple University, Philadelphia, PA, 19122, USA. jlservadio@temple.edu.
Maciej F BoniDepartment of Biology, Institute for Genomics and Evolutionary Medicine, Temple University, Philadelphia, PA, 19122, USA.

Funding

National Institute of Allergy and Infectious Diseases F32AI167600
6 · The paper itself

Abstract

Mechanistic infectious disease models are crucial for understanding the forces driving epidemics. Many epidemic models naturally produce equilibrium or stationary behaviors readily seen in epidemiological surveillance. However, respiratory viruses in tropical locations exhibit non-equilibrium behavior without consistent timing. This prevents standard models that commonly produce cycles and equilibria from describing their true dynamics. To evaluate deterministic model structures that can create asynchronous nonannual epidemic patterns seen with tropical influenza, we parameterized 30 variations of a respiratory disease model incorporating three influenza (sub)types and evaluated their abilities to emulate nonannual behaviors. Variations across models included immune-waning dynamics, subpopulations, and periodic case introduction. We defined seven criteria describing irregular epidemic behaviors and conducted parameter-space searches to find parameter sets meeting these criteria. Small parameter perturbations (1%) often led to violations of these criteria, indicating a lack of robustness in the parameter sets’ abilities to predict nonannual influenza dynamics. The models were also unstable to traditional perturbation in state space. We were unable to find evidence of a deterministic epidemic model with a stable parameterization or structure that can robustly produce asynchronous behaviors. It remains unknown whether a canonical deterministic epidemic model exists for the observed asynchronous nonstationary dynamics of tropical influenza.

Indexed as

Epidemiological ModelsInfluenza, HumanTropical ClimateEpidemicsHumansModels, BiologicalCanonical modelComputational epidemiologyEpidemic modelInfluenzaParameter-space search

Identifiers

PMID42120899
PMCPMC13168500

What OpenQuestion holds

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LicenceCC BY-NC-ND
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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.