Congruent epidemic models for unstructured and structured populations: Analytical reconstruction of a 2003 SARS outbreak
Identifieur interne : 004148 ( Main/Merge ); précédent : 004147; suivant : 004149Congruent epidemic models for unstructured and structured populations: Analytical reconstruction of a 2003 SARS outbreak
Auteurs : John N. BombardtSource :
- Mathematical Biosciences [ 0025-5564 ] ; 2006.
Descripteurs français
- KwdFr :
- Dynamique des populations, Flambées de maladies, Humains, Modèles biologiques, Syndrome respiratoire aigu sévère (), Syndrome respiratoire aigu sévère (transmission), Syndrome respiratoire aigu sévère (épidémiologie), Taïwan (épidémiologie), Transmission de maladie infectieuse (), Virus du SRAS (croissance et développement).
- MESH :
- croissance et développement : Virus du SRAS.
- épidémiologie : Syndrome respiratoire aigu sévère, Taïwan.
- Dynamique des populations, Flambées de maladies, Humains, Modèles biologiques, Syndrome respiratoire aigu sévère, Transmission de maladie infectieuse.
- Wicri :
- geographic : Taïwan.
English descriptors
- KwdEn :
- Disease Outbreaks, Disease Transmission, Infectious (prevention & control), Humans, Models, Biological, Population Dynamics, SARS Virus (growth & development), Severe Acute Respiratory Syndrome (epidemiology), Severe Acute Respiratory Syndrome (prevention & control), Severe Acute Respiratory Syndrome (transmission), Taiwan (epidemiology).
- MESH :
- geographic , epidemiology : Taiwan.
- epidemiology : Severe Acute Respiratory Syndrome.
- growth & development : SARS Virus.
- prevention & control : Disease Transmission, Infectious, Severe Acute Respiratory Syndrome.
- transmission : Severe Acute Respiratory Syndrome.
- Disease Outbreaks, Humans, Models, Biological, Population Dynamics.
Abstract
Both the threat of bioterrorism and the natural emergence of contagious diseases underscore the importance of quantitatively understanding disease transmission in structured human populations. Over the last few years, researchers have advanced the mathematical theory of scale-free networks and used such theoretical advancements in pilot epidemic models. Scale-free contact networks are particularly interesting in the realm of mathematical epidemiology, primarily because these networks may allow meaningfully structured populations to be incorporated in epidemic models at moderate or intermediate levels of complexity. Moreover, a scale-free contact network with node degree correlation is in accord with the well-known preferred mixing concept. The present author describes a semi-empirical and deterministic epidemic modeling approach that (a) focuses on time-varying rates of disease transmission in both unstructured and structured populations and (b) employs probability density functions to characterize disease progression and outbreak controls. Given an epidemic curve for a historical outbreak, this modeling approach calls for Monte Carlo calculations (that define the average new infection rate) and solutions to integro-differential equations (that describe outbreak dynamics in an aggregate population or across all network connectivity classes). Numerical results are obtained for the 2003 SARS outbreak in Taiwan and the dynamical implications of time-varying transmission rates and scale-free contact networks are discussed in some detail.
Url:
DOI: 10.1016/j.mbs.2006.05.004
PubMed: 16904134
PubMed Central: 7094332
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PMC:7094332Le document en format XML
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<term>Models, Biological</term>
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<term>SARS Virus (growth & development)</term>
<term>Severe Acute Respiratory Syndrome (epidemiology)</term>
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<term>Syndrome respiratoire aigu sévère (épidémiologie)</term>
<term>Taïwan (épidémiologie)</term>
<term>Transmission de maladie infectieuse ()</term>
<term>Virus du SRAS (croissance et développement)</term>
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<front><div type="abstract" xml:lang="en"><p>Both the threat of bioterrorism and the natural emergence of contagious diseases underscore the importance of quantitatively understanding disease transmission in structured human populations. Over the last few years, researchers have advanced the mathematical theory of scale-free networks and used such theoretical advancements in pilot epidemic models. Scale-free contact networks are particularly interesting in the realm of mathematical epidemiology, primarily because these networks may allow meaningfully structured populations to be incorporated in epidemic models at moderate or intermediate levels of complexity. Moreover, a scale-free contact network with node degree correlation is in accord with the well-known preferred mixing concept. The present author describes a semi-empirical and deterministic epidemic modeling approach that (a) focuses on time-varying rates of disease transmission in both unstructured and structured populations and (b) employs probability density functions to characterize disease progression and outbreak controls. Given an epidemic curve for a historical outbreak, this modeling approach calls for Monte Carlo calculations (that define the average new infection rate) and solutions to integro-differential equations (that describe outbreak dynamics in an aggregate population or across all network connectivity classes). Numerical results are obtained for the 2003 SARS outbreak in Taiwan and the dynamical implications of time-varying transmission rates and scale-free contact networks are discussed in some detail.</p>
</div>
</front>
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