A Stochastic Model for Meningococcal Disease
(2017) In Master's Theses in Mathematical Sciences FMN820 20161Mathematics (Faculty of Engineering)
- Abstract
- This paper presents a stochastic model for Meningococcal disease. Mathematical models for diseases are an important tool in population dynamics, and their results carry real-world implications as they guide policies and vaccination strategies. While the systems being modelled are generally stochastic in nature, deterministic models are often used, possibly to the detriment of accuracy. In this project, a stochastic model is developed for Meningococcal disease in the African Meningitis belt, a region plagued by recurring epidemics, a high incidence rate of disease and significant seasonal variations. The model uses the Feller-Kendall algorithm, and is able to accurately emulate some of the specificities present in the region. Simulation... (More)
- This paper presents a stochastic model for Meningococcal disease. Mathematical models for diseases are an important tool in population dynamics, and their results carry real-world implications as they guide policies and vaccination strategies. While the systems being modelled are generally stochastic in nature, deterministic models are often used, possibly to the detriment of accuracy. In this project, a stochastic model is developed for Meningococcal disease in the African Meningitis belt, a region plagued by recurring epidemics, a high incidence rate of disease and significant seasonal variations. The model uses the Feller-Kendall algorithm, and is able to accurately emulate some of the specificities present in the region. Simulation results suggest that the seasonality cannot be exclusively explained by behavioural variations, but does not dismiss its influence entirely. Furthermore, there are signs that the system in a non-epidemic state can be modelled using a deterministic framework. (Less)
- Popular Abstract
- Bacterial Meningitis is a severe disease that often leads to death or permanent disabilities. It is globally infrequent, but in the region of sub-Saharan Africa known as the Meningitis belt, the disease rates are several times higher and characterised by a strong seasonality. In order to bring light to the underlying mechanisms to the seasonality, a mathematical model has been developed. It finds that it is plausible that the dynamics depend on both social and biochemical factors. These results imply that both social and medical interventions are useful in counteracting and eradicating the disease.
Please use this url to cite or link to this publication:
http://lup.lub.lu.se/student-papers/record/8924218
- author
- Autio, Hanna LU
- supervisor
- organization
- course
- FMN820 20161
- year
- 2017
- type
- H2 - Master's Degree (Two Years)
- subject
- keywords
- population dynamics, stochastic modelling, African meningitis belt, meningococcal meningitis, Feller-Kendall algorithm
- publication/series
- Master's Theses in Mathematical Sciences
- report number
- LUTFNA-3401-2017
- ISSN
- 1404-6342
- other publication id
- 2017:E48
- language
- English
- id
- 8924218
- date added to LUP
- 2017-09-04 14:51:41
- date last changed
- 2017-09-04 14:51:41
@misc{8924218, abstract = {{This paper presents a stochastic model for Meningococcal disease. Mathematical models for diseases are an important tool in population dynamics, and their results carry real-world implications as they guide policies and vaccination strategies. While the systems being modelled are generally stochastic in nature, deterministic models are often used, possibly to the detriment of accuracy. In this project, a stochastic model is developed for Meningococcal disease in the African Meningitis belt, a region plagued by recurring epidemics, a high incidence rate of disease and significant seasonal variations. The model uses the Feller-Kendall algorithm, and is able to accurately emulate some of the specificities present in the region. Simulation results suggest that the seasonality cannot be exclusively explained by behavioural variations, but does not dismiss its influence entirely. Furthermore, there are signs that the system in a non-epidemic state can be modelled using a deterministic framework.}}, author = {{Autio, Hanna}}, issn = {{1404-6342}}, language = {{eng}}, note = {{Student Paper}}, series = {{Master's Theses in Mathematical Sciences}}, title = {{A Stochastic Model for Meningococcal Disease}}, year = {{2017}}, }