ArticleComputer methods and programs in biomedicine2020
The influence of immune individuals in disease spread evaluated by cellular automaton and genetic algorithm.
Article in Computer methods and programs in biomedicine, 2020. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 8 papers.
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.
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.
Who cites it
8 citing papers in PubMed, 28 citations in OpenAlex.
- Structural edge damage detection based on wavelet transform and immune genetic algorithm.Scientific reports · 2025Article
- Optimizing Contact Network Topological Parameters of Urban Populations Using the Genetic Algorithm.Entropy (Basel, Switzerland) · 2024Article
- Article
- A new method for spatio-temporal transmission prediction of COVID-19.Chaos, solitons, and fractals · 2023Article
- Simulation and forecasting models of COVID-19 taking into account spatio-temporal dynamic characteristics: A review.Frontiers in public health · 2022Review
- Estimates of the collective immunity to COVID-19 derived from a stochastic cellular automaton based framework.Natural computing · 2022Article
- A GIS-aided cellular automata system for monitoring and estimating graph-based spread of epidemics.Natural computing · 2022Article
- Stochastic formulation of multiwave pandemic: decomposition of growth into inherent susceptibility and external infectivity distributions.Journal of chemical sciences (Bangalore, India) · 2021Article
Corrections and comments
PubMed lists nothing against this paper. Absence here is not a guarantee, only a check that was made.
Authors and funding
3 authors at 3 institutions in 1 country.
Funding
No grant is acknowledged in the PubMed record.
Abstract
BACKGROUND AND
objectiveOne of the main goals of epidemiological studies is to build models capable of forecasting the prevalence of a contagious disease, in order to propose public health policies for combating its propagation. Here, the aim is to evaluate the influence of immune individuals in the processes of contagion and recovery from varicella. This influence is usually neglected.
methodsAn epidemic model based on probabilistic cellular automaton is introduced. By using a genetic algorithm, the values of three parameters of this model are determined from data of prevalence of varicella in Belgium and Italy, in a pre-vaccination period.
resultsThis methodology can predict the varicella prevalence (with average relative error of 2%-4%) in these two European countries. Belgium data can be explained by ignoring the role of immune individuals in the infection propagation; however, Italy data can be explained by considering contagion exclusively mediated by immune individuals.
conclusionsThe role of immune individuals should be accurately delineated in investigations on the dynamics of disease propagation. In addition, the proposed methodology can be adapted for evaluating, for instance, the role of asymptomatic carriers in the novel coronavirus spread.
Indexed as
Identifiers
What OpenQuestion holds
Registered trials
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.