Stochastic and nonstochastic descriptions of the 2019-2020 measles outbreak worldwide with an emphasis in Mexico
Measles is an infectious disease caused by the Morbilivirus Measles Virus which has accompanied the human race since the 4th millennium BC, it is a disease usually concerning the paediatric population and in the past, before the advent of vaccination, almost all the population suffered from it, and in some cases the complications derived from this disease, such as central nervous involvement. Vaccination changed the course of the disease worldwide and diminished the associated comorbidities and mortality; in Mexico the vaccination program commenced in the decade of the 1970s and was successful in preventing peaks of infection. Nevertheless, due to various factors, has the world seen measles outbreaks once more, this commencing in the year 2019 and extending towards the year 2020. Here we make account of the biology and the pathophysiology of the viral infection, and present three models: one concerning the dynamics of the cases by means of a continuous method and a discrete stochastic model; one concerning the cellular compartmentalization behaviour of the virus, that is the viral tropism towards certain cell types in the host and the tendencies in extended or complicated infection; the last one concerning geographic behaviour of the virus, regarding in particular the tendencies in Mexico City, those involved at a global scale, and finally a model providing a prediction of the viral genotypes' distribution worldwide.
Code (0)
등록된 구현이 없습니다.
Similar Papers 제목 키워드 기반
Likelihood-based estimation and prediction for a measles outbreak in Samoa
Prediction of the progression of an infectious disease outbreak is important for planning and coordinating a response. Differential equations are often used to model an epidemic outbreak's behaviour but are challenging t…
Uncertainty QuantificationStatistical Models for Outbreak Detection of Measles in North Cotabato, Philippines
A measles outbreak occurs when the number of cases of measles in the population exceeds the typical level. Outbreaks that are not detected and managed early can increase mortality and morbidity and incur costs from activ…
EpidemiologyMeasles Rash Identification Using Residual Deep Convolutional Neural Network
Measles is extremely contagious and is one of the leading causes of vaccine-preventable illness and death in developing countries, claiming more than 100,000 lives each year. Measles was declared eliminated in the US in …
SpecificityUnsupervised Pre-trainingAn AI-enabled Agent-Based Model and Its Application in Measles Outbreak Simulation for New Zealand
Agent Based Models (ABMs) have emerged as a powerful tool for investigating complex social interactions, particularly in the context of public health and infectious disease investigation. In an effort to enhance the conv…
Graph Neural NetworkA Labelled Dataset for Sentiment Analysis of Videos on YouTube, TikTok, and Other Sources about the 2024 Outbreak of Measles
The work of this paper presents a dataset that contains the data of 4011 videos about the ongoing outbreak of measles published on 264 websites on the internet between January 1, 2024, and May 31, 2024. The dataset is av…
Sentiment AnalysisSubjectivity AnalysisVideo Description