Person:
López Herrero, María Jesús

First Name

María Jesús

Last Name

López Herrero

URI

https://hdl.handle.net/20.500.14352/77420

Affiliation

Universidad Complutense de Madrid

Faculty / Institute

Estudios estadísticos

Department

Estadística y Ciencia de los Datos

Area

Estadística e Investigación Operativa

Identifiers

Full item page

Search Results

Now showing 1 - 8 of 8

Measuring infection transmission in a stochastic SIV model with infection reintroduction and imperfect vaccine
(Acta Biotheoretica, 2020) Gamboa Pérez, María; López Herrero, María Jesús
An additional compartment of vaccinated individuals is considered in a SIS stochastic epidemic model with infection reintroduction. The quantification of the spread of the disease is modeled by a continuous time Markov chain. A well-known measure of the initial transmission potential is the basic reproduction number R, which determines the herd immunity threshold or the critical proportion of immune individuals required to stop the spread of a disease when a vaccine offers a complete protection. Due to repeated contacts between the typical infective and previously infected individuals, R overestimates the average number of secondary infections and leads to, perhaps unnecessary, high immunization coverage. Assuming that the vaccine is imperfect, alternative measures to R are defined in order to study the influence of the initial coverage and vaccine efficacy on the transmission of the epidemic.
Measures to assess a warning vaccination level in a stochastic SIV model with imperfect vaccine
(Studies in Applied Mathematics, 2022) Gamboa Pérez, María; López Herrero, María Jesús
A stochastic Markovian Susceptible-Infectious-Susceptible (SIS) model, with infection reintroduction is considered to represent the evolution of an epidemic process within a finite population. Disease is assumed to be a contact disease whose effect can be prevented by a vaccine. Before the epidemic process emerges, individuals got vaccinated to assure that the population is protected by herd immunity. In consequence, we formulate the model by adding a new compartment for vaccine protected individuals. The administered vaccine is not a perfect one and consequently it fails in a proportion of vaccinated individuals that are not protected against the vaccine preventable communicable disease. Hence, while the infectious process is in progress, the initial vaccine coverage declines and herd immunity could be lost. A threshold on the size of the vaccinated group is included as a warning measure on the protection of the community. Our objective is to define and study random characteristics, depending on the vaccination eligible group, that could advise health authorities when to launch a new vaccination program to recover the initial immunity level.
A stochastic SVIR model with imperfect vaccine and external source of infection
(Performance Engineering and Stochastic Modeling, 2021) Gamboa Pérez, María; López-García, Martín; López Herrero, María Jesús; Ballarini, Paolo
A stochastic SIR (Susceptible - Infected - Recovered) type model, with external source of infection, is considered for the spread of a disease in a finite population of constant size. Our interest is in studying this process in the situation where some individuals have been vaccinated prior to the start of the epidemic, but where the efficacy of the vaccine to prevent infection is not perfect. The evolution of the epidemic is represented by an absorbing three-dimensional continuous-time Markov chain. We focus on analysing the time for a threshold number of individuals to become infected, and carry out a global sensitivity analysis for the impact of varying model parameters on the summary statistic of interest.
On the Number of Periodic Inspections During Outbreaks of Discrete-Time Stochastic SIS Epidemic Models
(Mathematics, 2018) Gamboa Pérez, María; López Herrero, María Jesús
This paper deals with an infective process of type SIS, taking place in a closed population of moderate size that is inspected periodically. Our aim is to study the number of inspections that find the epidemic process still in progress. As the underlying mathematical model involves a discrete time Markov chain (DTMC) with a single absorbing state, the number of inspections in an outbreak is a first-passage time into this absorbing state. Cumulative probabilities are numerically determined from a recursive algorithm and expected values came from explicit expressions.
On the exact and population bi-dimensional reproduction numbers in a stochastic SVIR model with imperfect vaccine
(Applied Mathematics and Computation, 2024) Gamboa Pérez, María; López-García, M.; López Herrero, María Jesús; Simos, Theodore E.
We aim to quantify the spread of a direct contact infectious disease that confers permanent immunity after recovery, within a non-isolated finite and homogeneous population. Prior to the onset of the infection and to prevent the spread of this disease, a proportion of individuals was vaccinated. But the administered vaccine is imperfect and can fail, which implies that some vaccinated individuals get the infection when being in contact with infectious individuals. We study the evolution of the epidemic process over time in terms of a continuous-time Markov chain, which represents a general SIR model with an additional compartment for vaccinated individuals. In our stochastic framework, we study two bi-dimensional variables recording infection events, produced by a single infectious individual or by the whole infected group, taking into account if the newly infected individual was previously vaccinated or not. Theoretical schemes and recursive algorithms are derived in order to compute joint probability mass functions and factorial moments for these random variables. We illustrate the applicability of our techniques by means of a set of numerical experiments.
Project number: 4
Gamificación en el aula con Kahoot! en la Facultad de Estudios Estadísticos
(2020) Calviño Martínez, Aída; Rapado Vicente, María Eva; Peressini Álvarez, Melina; Cabrera Gómez, Gloria; Cáceres García, Inés María; Alonso Revenga, Juana María; Latorre Muñoz, María De La Concepción; Amador Pacheco, Julia; Rodríguez Cánovas, María Belén; Rodríguez Palanquex, María Cruz; Cintas Del Rio, María Del Rosario; Susi García, María Del Rosario; López Herrero, María Jesús; Alcón Giménez, María José; Pérez Pérez, Teresa; Gamboa Pérez, María
Memoria final del proyecto de innovación docente titulado: "Gamificación en el aula con Kahoot! en la Facultad de Estudios Estadísticos"
The Effect of Setting a Warning Vaccination Level on a Stochastic SIVS Model with Imperfect Vaccine
(Mathematics, 2020) Gamboa Pérez, María; López Herrero, María Jesús
This paper deals with a stochastic Susceptible-Infective-Vaccinated-Susceptible (SIVS) model with infection reintroduction. Health policies depend on vaccine coverage, v0, that guarantees herd immunity levels in the population. Vaccine failures occur when an organism develops a disease despite of being vaccinated against it. After vaccination, a proportion of healthy individuals unsuccessfully tries to increase antibody levels and, consequently these individuals are not immune to the vaccine preventable disease. When an infectious process is in progress, the initial vaccine coverage drops down and herd immunity will be lost. Our objective was to introduce a warning vaccination level and define random measures quantifying the time until the number of vaccinated descends to a warning vaccination level (i.e., the so-called sleeping period), and the epidemic size. A sensitivity analysis was performed to assess the influence of the model parameters on the variation and robustness of the sleeping period and the number of infections observed within it.
The number of inspections until the extinction of an epidemic in a discrete-time stochastic SIS-type model with some applications
(2023) Gamboa Pérez, María; López Herrero, María Jesús
This talk deals with an infective process of type SIS, taking place in a closed population of moderate size that is inspected periodically. Our purpose is to study the extinction time counterpart in discrete-time, that is the random variable that counts the total number of inspections that find an active epidemic process. As the underlying mathematical model involves a discrete-time Markov chain (DTMC) with a single absorbing state, the number of inspections in an outbreak is a first-passage time into this absorbing state. Cumulative probabilities are numerically determined from a recursive algorithm and expected values came from explicit expressions. Additionally, I provide several applications derived from the theoretical results. The talk is based on the paper: Gamboa M. and López-Herrero M.J. (2018). On the number of periodic inspections during outbreaks of discrete-time stochastic SIS epidemic models. Mathematics 6, article 128.DOI: 10.1007/s11538-013- 9836-3