scholarly journals Mathematical Epidemiology: Possible Improvements to Alert systems

2018 ◽  
Vol 03 (01) ◽  
Author(s):  
Carlos Polanco
Math Horizons ◽  
2018 ◽  
Vol 25 (3) ◽  
pp. 8-11
Author(s):  
Meredith Greer ◽  
Ella Livesay

2019 ◽  
Vol 81 (11) ◽  
pp. 4311-4312
Author(s):  
Julien Arino ◽  
James Watmough

2021 ◽  
Vol 14 (1) ◽  
pp. 1-21
Author(s):  
Francesca Bernardi

We suggest the use of historical documents and primary sources, as well as data and articles from recent events, to teach students about mathematical epidemiology. We propose a project suitable -- in different versions -- as part of a class syllabus, as an undergraduate research project, and as an extra credit assignment. Throughout this project, students explore mathematical, historical, and sociological aspects of the SIR model and approach data analysis and interpretation. Based on their work, students form opinions on public health decisions and related consequences. Feedback from students has been encouraging. We begin our project by having students read excerpts of documents from the early 1900s discussing the Indian plague epidemic. We then guide students through the derivation of the SIR model by analyzing the seminal 1927 Kermack and McKendrick paper, which is based on data from the Indian epidemiological event they have studied. After understanding the historical importance of the SIR model, we consider its modern applications focusing on the Ebola outbreak of 2014-2016 in West Africa. Students fit SIR models to available compiled data sets. The subtleties in the data provide opportunities for students to consider the data and SIR model assumptions critically. Additionally, social attitudes of the outbreak are explored; in particular, local attitudes towards government health recommendations.


2019 ◽  
Vol 12 (3) ◽  
pp. 834-845
Author(s):  
Hay Yoba Talkibing ◽  
Barro Diakarya ◽  
Ouoba Fabrice

The study of infectious diseases represents one of the oldest and richest sectors of biomathematics. The transmission dynamics of these diseases are still a major problem in mathematical epidemiology. In this work, we propose a stochastic version of a SEIRS epidemiological model for infectious diseases evolving in a random environment for the propagation of infectious diseases. This random model takes into account the rates of immigration and mortality in each compartment and the spread of these diseases follows a four-state Makovian process. We first study the stability of the model and then estimate the marginal parameters of each disease state over time. Real measles data are applied to the model.


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