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Record W3115333674

Disease Modelling on Measles Immunity: Theoretical and Numerical Analyses

2020· dissertation· en· W3115333674 on OpenAlexaboutno aff
Elena Aruffo

Bibliographic record

VenueYork University Digital Library (York University) · 2020
Typedissertation
Languageen
FieldMathematics
TopicCOVID-19 epidemiological studies
Canadian institutionsnot available
Fundersnot available
KeywordsHerd immunityMeaslesImmunityVaccinationImmunologyOutbreakHygiene hypothesisBiologyInfectious disease (medical specialty)DiseaseVirologyMedicineImmune system
DOInot available

Abstract

fetched live from OpenAlex

Although measles vaccine is considered safe and highly effective, cases continue to be reported globally, even in countries, such as Canada, where herd immunity (a form of indirect protection provided by immunized individuals) threshold is reached. Biological processes and social behaviours are fundamental factors in understanding the re-emergence of this childhood disease in highly vaccinated populations. In the past decades, the assumption that vaccine-induced immunity is life long has started to vacillate and many studies show how measles antibodies wane over time. However, the time needed to wane immunity partially, or fully, is still unknown. During this waning stage, immunity can experience a boosting process, if an encounter with the pathogen occurs. However, in absence of virus, immunity can wane until individuals return fully susceptible. In a society where mobility, travel and immigration are a daily routine, infections stages and levels of immunity are important factors to potentially increase or reduce the spread of a virus. In particular, with the assumption that measles-induced immunity is lifelong, immigrants immunity provides an increase of
\nprotection in the host country. On the other hand, immunity heterogeneity in a community creates pockets of individuals vulnerable to the infection, and movement of infectious cases might lead to relatively big outbreaks. In this thesis, we investigate how waning immunity, boosting and vaccination processes, immigration and migration affect the achievement of herd immunity and the spread of the infection. We propose different compartmental models described by systems of ordinary and partial differential equations, following, and extending, the Susceptible-Exposed-Infectious-Recovered framework. We employ both deterministic and stochastic models in order to capture those factors which mostly affect the infection dynamics and immunity of individuals as well as to investigate the probability of extinction or outbreak. Since measles vaccine is given at different ages, from 12 months up to 6 years, we also employ age structured models, discrete and continuous, to capture the age groups which mostly experience waning immunity and infection. Meta-population models are also used to investigate the effect of mobility on the spread of measles infection. We derive expressions for the basic and control reproduction numbers as well as performing sensitivity analysis on the model parameters and its outcomes.

Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.

How this classification was reachedexpand

Full frame distilled prediction

Teacher imitation

Not calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.

metaresearch head score (Codex)0.000
metaresearch head score (Gemma)0.000
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesMeta-epidemiology (narrow)
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Theoretical or conceptual · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: none
Teacher disagreement score0.693
Threshold uncertainty score1.000

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0000.000
Meta-epidemiology (narrow)0.0010.001
Meta-epidemiology (broad)0.0010.001
Bibliometrics0.0010.001
Science and technology studies0.0010.000
Scholarly communication0.0000.001
Open science0.0010.001
Research integrity0.0000.001
Insufficient payload (model declined to judge)0.0000.000

Machine scores (provisional)

The two teacher heads of the student model, read on this work. A score orders the frame for review; it never asserts a category, and the validation status ships verbatim with every row.

Baseline scores from an immature model (maturity gate not passed, 7 training rounds). Scores rank; they never assert a category.

Opus teacher head0.134
GPT teacher head0.303
Teacher spread0.169 · how far apart the two teachers sit on this one work
Validation statusscore_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from it

Classification

machine, unvalidated

Machine predicted; a candidate call from one teacher head, not a consensus.

Study designTheoretical or conceptual
Domainnot available
GenreEmpirical

How this classification was reached, model by model and score by score, is at the end of the page under "How this classification was reached".

Quick stats

Citations0
Published2020
Admission routes1
Has abstractyes

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