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A Stochastic Model for Meningococcal Disease

Autio, Hanna LU (2017) In Master's Theses in Mathematical Sciences FMN820 20161
Mathematics (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.
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author
Autio, Hanna LU
supervisor
organization
course
FMN820 20161
year
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},
  keyword      = {population dynamics,stochastic modelling,African meningitis belt,meningococcal meningitis,Feller-Kendall algorithm},
  language     = {eng},
  note         = {Student Paper},
  series       = {Master's Theses in Mathematical Sciences},
  title        = {A Stochastic Model for Meningococcal Disease},
  year         = {2017},
}