3 research outputs found
A Method for Reducing the Severity of Epidemics by Allocating Vaccines According to Centrality
One long-standing question in epidemiological research is how best to
allocate limited amounts of vaccine or similar preventative measures in order
to minimize the severity of an epidemic. Much of the literature on the problem
of vaccine allocation has focused on influenza epidemics and used mathematical
models of epidemic spread to determine the effectiveness of proposed methods.
Our work applies computational models of epidemics to the problem of
geographically allocating a limited number of vaccines within several Texas
counties. We developed a graph-based, stochastic model for epidemics that is
based on the SEIR model, and tested vaccine allocation methods based on
multiple centrality measures. This approach provides an alternative method for
addressing the vaccine allocation problem, which can be combined with more
conventional approaches to yield more effective epidemic suppression
strategies. We found that allocation methods based on in-degree and inverse
betweenness centralities tended to be the most effective at containing
epidemics.Comment: 10 pages, accepted to ACM BCB 201
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Simulation of Dengue Outbreak in Thailand
The dengue virus has become widespread worldwide in recent decades. It has no specific treatment and affects more than 40% of the entire population in the world. In Thailand, dengue has been a health concern for more than half a century. The highest number of cases in one year was 174,285 in 1987, leading to 1,007 deaths. In the present day, dengue is distributed throughout the entire country. Therefore, dengue has become a major challenge for public health in terms of both prevention and control of outbreaks. Different methodologies and ways of dealing with dengue outbreaks have been put forward by researchers. Computational models and simulations play an important role, as they have the ability to help researchers and officers in public health gain a greater understanding of the virus's epidemic activities.
In this context, this dissertation presents a new framework, Modified Agent-Based Modeling (mABM), a hybrid platform between a mathematical model and a computational model, to simulate a dengue outbreak in human and mosquito populations. This framework improves on the realism of former models by utilizing the reported data from several Thai government organizations, such as the Thai Ministry of Public Health (MoPH), the National Statistical Office, and others. Additionally, its implementation takes into account the geography of Thailand, as well as synthetic mosquito and synthetic human populations. mABM can be used to represent human behavior in a large population across variant distances by specifying demographic factors and assigning mobility patterns for weekdays, weekends, and holidays for the synthetic human population. The mosquito dynamic population model (MDP), which is a component of the mABM framework, is used for representing the synthetic mosquito population dynamic and their ecology by integrating the regional model to capture the effect of dengue outbreak. The two synthetic populations can be linked to each other for the purpose of presenting their interactions, and the Local Stochastic Contact Model for Dengue (LSCM-DEN) is utilized. For validation, the number of cases from the experiment is compared to reported cases from the Thailand Vector Borne Disease Bureau for the selected years.
This framework facilitates model configuration for sensitivity analysis by changing parameters, such as travel routes and seasonal temperatures. The effects of these parameters were studied and analyzed for an improved understanding of dengue outbreak dynamics
Un système de surveillance de maladies infectieuses utilisant la technologie sans fils et réseau de capteurs
La surveillance des maladies infectieuses est l'un des moyens efficaces permettant de prendre des mesures en vue de limiter l'expansion d'une épidémie. Des outils technologiques pour collecter des données de santé sur les individus, ainsi que des mécanismes pour capter et gérer ces informations sont essentiels à la surveillance épidémiologique. Avec l'avènement des capteurs intégrés dans les appareils mobiles intelligents combinés avec les objets connectés à l'Internet, une nouvelle forme de collection de données a pris naissance, dont le crowdsensing mobile, qui permet d'utiliser les téléphones munis de capteurs comme sources de données pour accomplir des tâches telles que la collecte d'informations biologiques et médicales. L'accès aux différents services offerts par ces systèmes de surveillance se fait par l'intermédiaire d'une plateforme Web, communément appelé crowdsourcing, à travers laquelle les tâches et les critères de ce processus de collection sont publiés. En vue de comprendre le phénomène de propagation des maladies infectieuses, nous avons simulé la progression de certaines maladies. Nous avons utilisé des modèles de mobilité employés dans les réseaux ad-hoc comme approche pour simuler le déplacement des individus dans une région. Les résultats de la simulation sont accessibles via un service web qui permet d'interpréter les données simulées. En vue de faire une surveillance en temps réel afin de prendre les mesures qui s'imposent, nous proposons une architecture de crowdsourcing/crowdsensing permettant de suivre en temps réel l'évolution d'une épidémie dans une région à l'aide des capteurs de localisation intégrés dans les téléphones et d'autres capteurs médicaux permettant d'avoir des informations sur l'état de santé des individus. Les résultats permettront aux institutions de santé de prendre à temps les mesures préventives nécessaires pour limiter la propagation de la maladie.\ud
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MOTS-CLÉS DE L’AUTEUR : maladies infectieuses, crowdsourcing, crowdsensing, réseaux MANETS, modèles de mobilité ad-hoc, capteurs médicaux, gestion de propagation de maladies contagieuses