Evidence map›Paper›PMID 29561846›Full record

ArticlePLoS computational biology2018

Role of genetic heterogeneity in determining the epidemiological severity of H1N1 influenza.

Narmada Sambaturu, Sumanta Mukherjee, Martín López-García, Carmen Molina-París, Gautam I Menon, Nagasuma Chandra

Abstract read
In one paragraph

Article in PLoS computational biology, 2018. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 10 papers.

0numbers the graph read from it
0cells of the map it votes in
10citing 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

10 citing papers in PubMed.

  1. Article
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  5. Review
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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

6 authors.

Narmada SambaturuIISc Mathematics Initiative, Indian Institute of Science, Bangalore, Karnataka, India.ORCID 0000-0002-2570-3699
Sumanta MukherjeeIISc Mathematics Initiative, Indian Institute of Science, Bangalore, Karnataka, India.
Martín López-GarcíaDepartment of Applied Mathematics, University of Leeds, Leeds, United Kingdom.
Carmen Molina-ParísDepartment of Applied Mathematics, University of Leeds, Leeds, United Kingdom.
Gautam I MenonComputational Biology and Theoretical Physics groups, The Institute of Mathematical Sciences, Chennai, Tamil Nadu, India.ORCID 0000-0001-5528-4002
Nagasuma ChandraIISc Mathematics Initiative, Indian Institute of Science, Bangalore, Karnataka, India.ORCID 0000-0002-9939-8439

Funding

Medical Research Council MR/N014855/1
6 · The paper itself

Abstract

Genetic differences contribute to variations in the immune response mounted by different individuals to a pathogen. Such differential response can influence the spread of infectious disease, indicating why such diseases impact some populations more than others. Here, we study the impact of population-level genetic heterogeneity on the epidemic spread of different strains of H1N1 influenza. For a population with known HLA class-I allele frequency and for a given H1N1 viral strain, we classify individuals into sub-populations according to their level of susceptibility to infection. Our core hypothesis is that the susceptibility of a given individual to a disease such as H1N1 influenza is inversely proportional to the number of high affinity viral epitopes the individual can present. This number can be extracted from the HLA genetic profile of the individual. We use ethnicity-specific HLA class-I allele frequency data, together with genome sequences of various H1N1 viral strains, to obtain susceptibility sub-populations for 61 ethnicities and 81 viral strains isolated in 2009, as well as 85 strains isolated in other years. We incorporate these data into a multi-compartment SIR model to analyse the epidemic dynamics for these (ethnicity, viral strain) epidemic pairs. Our results show that HLA allele profiles which lead to a large spread in individual susceptibility values can act as a protective barrier against the spread of influenza. We predict that populations skewed such that a small number of highly susceptible individuals coexist with a large number of less susceptible ones, should exhibit smaller outbreaks than populations with the same average susceptibility but distributed more uniformly across individuals. Our model tracks some well-known qualitative trends of influenza spread worldwide, suggesting that HLA genetic diversity plays a crucial role in determining the spreading potential of different influenza viral strains across populations.

Indexed as

Computer SimulationDisease OutbreaksDisease SusceptibilityEpidemicsEpitopesEthnicityHumansInfluenza A Virus, H1N1 SubtypeInfluenza, HumanEpitopes

Identifiers

PMID29561846
PMCPMC5880410

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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.