A Model for HIV Spread in a South African Village
Identifieur interne : 005408 ( Main/Exploration ); précédent : 005407; suivant : 005409A Model for HIV Spread in a South African Village
Auteurs : Shah Jamal Alam [Royaume-Uni] ; Ruth Meyer [Royaume-Uni] ; Emma Norling [Royaume-Uni]Source :
- Lecture Notes in Computer Science [ 0302-9743 ] ; 2009.
Descripteurs français
- Wicri :
- topic : Simulation.
English descriptors
- KwdEn :
- African village, Alam, Anecdotal evidence, Aspiration level, Average percentage, Better understanding, Case study area, Coital, Concurrent partners, Condom usage, Constant transmission rate, Courtship, Courtship duration, Courtship offer, Different probabilities, Different settings, Different stages, Different transmission rates, Epidemiological component, Exogenous, Exogenous incidence, Female agent, Female agents, Friendship links, Health status, Heterosexual community, Individual agents, Last stage, Likoma island, Limpopo province, Male agents, Maximum number, Migrant agents, Model parameters, Modelling, Network characteristics, Next steps, Norling, Other stds, Previous section, Sekhukhune district, Several studies, Sexual activities, Sexual interaction, Sexual intercourse, Sexual network, Sexual networks, Sexual partner, Sexual partners, Sexual partnerships, Sexual transmission, Simulation, Simulation proceeds, Social network, Social networks, Social norms, Socioeconomic stressors, Time step, Time steps, Transmission probabilities, Transmission probability, Transmission rates, Vertical transmission.
- Teeft :
- African village, Alam, Anecdotal evidence, Aspiration level, Average percentage, Better understanding, Case study area, Coital, Concurrent partners, Condom usage, Constant transmission rate, Courtship, Courtship duration, Courtship offer, Different probabilities, Different settings, Different stages, Different transmission rates, Epidemiological component, Exogenous, Exogenous incidence, Female agent, Female agents, Friendship links, Health status, Heterosexual community, Individual agents, Last stage, Likoma island, Limpopo province, Male agents, Maximum number, Migrant agents, Model parameters, Modelling, Network characteristics, Next steps, Norling, Other stds, Previous section, Sekhukhune district, Several studies, Sexual activities, Sexual interaction, Sexual intercourse, Sexual network, Sexual networks, Sexual partner, Sexual partners, Sexual partnerships, Sexual transmission, Simulation, Simulation proceeds, Social network, Social networks, Social norms, Socioeconomic stressors, Time step, Time steps, Transmission probabilities, Transmission probability, Transmission rates, Vertical transmission.
Abstract
Abstract: This paper describes an agent-based simulation model of the spread of HIV/AIDS in the Sub-Saharan region. The model is part of our studying social complexity in the Sekhukhune district of the Limpopo province in South Africa. The model presents a coherent framework and identifies the essential factors agent-based modellers need to take into account when modelling HIV spread. The necessary empirical data are drawn from the villagers’ accounts during our fieldtrip to the case study region and reports from the available epidemiological and demographic literature. The results presented here demonstrate how agent-based simulation can aid in a better understanding of this complex interplay of various factors responsible for the spread of the epidemic. Although the model is specific to the case study area, the general framework described in this paper can easily be extended and adapted for other regions.
Url:
DOI: 10.1007/978-3-642-01991-3_3
Affiliations:
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area</term><term>Coital</term><term>Concurrent partners</term><term>Condom usage</term><term>Constant transmission rate</term><term>Courtship</term><term>Courtship duration</term><term>Courtship offer</term><term>Different probabilities</term><term>Different settings</term><term>Different stages</term><term>Different transmission rates</term><term>Epidemiological component</term><term>Exogenous</term><term>Exogenous incidence</term><term>Female agent</term><term>Female agents</term><term>Friendship links</term><term>Health status</term><term>Heterosexual community</term><term>Individual agents</term><term>Last stage</term><term>Likoma island</term><term>Limpopo province</term><term>Male agents</term><term>Maximum number</term><term>Migrant agents</term><term>Model parameters</term><term>Modelling</term><term>Network characteristics</term><term>Next steps</term><term>Norling</term><term>Other stds</term><term>Previous section</term><term>Sekhukhune district</term><term>Several studies</term><term>Sexual activities</term><term>Sexual interaction</term><term>Sexual intercourse</term><term>Sexual network</term><term>Sexual networks</term><term>Sexual partner</term><term>Sexual partners</term><term>Sexual partnerships</term><term>Sexual transmission</term><term>Simulation</term><term>Simulation proceeds</term><term>Social network</term><term>Social networks</term><term>Social norms</term><term>Socioeconomic stressors</term><term>Time step</term><term>Time steps</term><term>Transmission probabilities</term><term>Transmission probability</term><term>Transmission rates</term><term>Vertical transmission</term></keywords><keywords scheme="Teeft" xml:lang="en"><term>African village</term><term>Alam</term><term>Anecdotal evidence</term><term>Aspiration level</term><term>Average percentage</term><term>Better understanding</term><term>Case study area</term><term>Coital</term><term>Concurrent partners</term><term>Condom usage</term><term>Constant transmission rate</term><term>Courtship</term><term>Courtship duration</term><term>Courtship offer</term><term>Different probabilities</term><term>Different settings</term><term>Different stages</term><term>Different transmission rates</term><term>Epidemiological component</term><term>Exogenous</term><term>Exogenous incidence</term><term>Female agent</term><term>Female agents</term><term>Friendship links</term><term>Health status</term><term>Heterosexual community</term><term>Individual agents</term><term>Last stage</term><term>Likoma island</term><term>Limpopo province</term><term>Male agents</term><term>Maximum number</term><term>Migrant agents</term><term>Model parameters</term><term>Modelling</term><term>Network characteristics</term><term>Next steps</term><term>Norling</term><term>Other stds</term><term>Previous section</term><term>Sekhukhune district</term><term>Several studies</term><term>Sexual activities</term><term>Sexual interaction</term><term>Sexual intercourse</term><term>Sexual network</term><term>Sexual networks</term><term>Sexual partner</term><term>Sexual partners</term><term>Sexual partnerships</term><term>Sexual transmission</term><term>Simulation</term><term>Simulation proceeds</term><term>Social network</term><term>Social networks</term><term>Social norms</term><term>Socioeconomic stressors</term><term>Time step</term><term>Time steps</term><term>Transmission probabilities</term><term>Transmission probability</term><term>Transmission rates</term><term>Vertical transmission</term></keywords><keywords scheme="Wicri" type="topic" xml:lang="fr"><term>Simulation</term></keywords></textClass><langUsage><language ident="en">en</language></langUsage></profileDesc></teiHeader><front><div type="abstract" xml:lang="en">Abstract: This paper describes an agent-based simulation model of the spread of HIV/AIDS in the Sub-Saharan region. The model is part of our studying social complexity in the Sekhukhune district of the Limpopo province in South Africa. The model presents a coherent framework and identifies the essential factors agent-based modellers need to take into account when modelling HIV spread. The necessary empirical data are drawn from the villagers’ accounts during our fieldtrip to the case study region and reports from the available epidemiological and demographic literature. The results presented here demonstrate how agent-based simulation can aid in a better understanding of this complex interplay of various factors responsible for the spread of the epidemic. Although the model is specific to the case study area, the general framework described in this paper can easily be extended and adapted for other regions.</div></front></TEI><affiliations><list><country><li>Royaume-Uni</li></country><region><li>Angleterre</li><li>Grand Manchester</li></region><settlement><li>Manchester</li></settlement></list><tree><country name="Royaume-Uni"><region name="Angleterre"><name sortKey="Alam, Shah Jamal" sort="Alam, Shah Jamal" uniqKey="Alam S" first="Shah Jamal" last="Alam">Shah Jamal Alam</name></region><name sortKey="Meyer, Ruth" sort="Meyer, Ruth" uniqKey="Meyer R" first="Ruth" last="Meyer">Ruth Meyer</name><name sortKey="Norling, Emma" sort="Norling, Emma" uniqKey="Norling E" first="Emma" last="Norling">Emma Norling</name></country></tree></affiliations></record>Pour manipuler ce document sous Unix (Dilib)
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