Stochastic epidemic metapopulation models on networks: SIS dynamics and control strategies
While deterministic metapopulation models for the spread of epidemics between populations have been well-studied in the literature, variability in disease transmission rates and interaction rates between individual agents or populations suggests the need to consider stochastic fluctuations in model...
| Main Authors: | , , , |
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| Format: | Journal article |
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Elsevier
2018
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| _version_ | 1797091936916471808 |
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| author | Krause, A Kurowski, L Yawar, K Van Gorder, R |
| author_facet | Krause, A Kurowski, L Yawar, K Van Gorder, R |
| author_sort | Krause, A |
| collection | OXFORD |
| description | While deterministic metapopulation models for the spread of epidemics between populations have been well-studied in the literature, variability in disease transmission rates and interaction rates between individual agents or populations suggests the need to consider stochastic fluctuations in model parameters in order to more fully represent realistic epidemics. In the present paper, we have extended a stochastic SIS epidemic model - which introduces stochastic perturbations in the form of white noise to the force of infection (the rate of disease transmission from classes of infected to susceptible populations) - to spatial networks, thereby obtaining a stochastic epidemic metapopulation model. We solved the stochastic model numerically and found that white noise terms do not drastically change the overall long-term dynamics of the system (for sufficiently small variance of the noise) relative to the dynamics of a corresponding deterministic system. The primary difference between the stochastic and deterministic metapopulation models is that for large time, solutions tend to quasi-stationary distributions in the stochastic setting, rather than to constant steady states in the deterministic setting. We then considered different approaches to controlling the spread of a stochastic SIS epidemic over spatial networks, comparing results for a spectrum of controls utilizing local to global information about the state of the epidemic. Variation in white noise was shown to be able to counteract the treatment rate (treated curing rate) of the epidemic, requiring greater treatment rates on the part of the control and suggesting that in real-life epidemics one should be mindful of such random variations in order for a treatment to be effective. Additionally, we point out some problems using white noise perturbations as a model, but show that a truncated noise process gives qualitatively comparable behaviors without these issues. |
| first_indexed | 2024-03-07T03:39:27Z |
| format | Journal article |
| id | oxford-uuid:bd619e06-ae8e-4376-9979-caabf7bb4ad9 |
| institution | University of Oxford |
| last_indexed | 2024-03-07T03:39:27Z |
| publishDate | 2018 |
| publisher | Elsevier |
| record_format | dspace |
| spelling | oxford-uuid:bd619e06-ae8e-4376-9979-caabf7bb4ad92022-03-27T05:31:28ZStochastic epidemic metapopulation models on networks: SIS dynamics and control strategiesJournal articlehttp://purl.org/coar/resource_type/c_dcae04bcuuid:bd619e06-ae8e-4376-9979-caabf7bb4ad9Symplectic Elements at OxfordElsevier2018Krause, AKurowski, LYawar, KVan Gorder, RWhile deterministic metapopulation models for the spread of epidemics between populations have been well-studied in the literature, variability in disease transmission rates and interaction rates between individual agents or populations suggests the need to consider stochastic fluctuations in model parameters in order to more fully represent realistic epidemics. In the present paper, we have extended a stochastic SIS epidemic model - which introduces stochastic perturbations in the form of white noise to the force of infection (the rate of disease transmission from classes of infected to susceptible populations) - to spatial networks, thereby obtaining a stochastic epidemic metapopulation model. We solved the stochastic model numerically and found that white noise terms do not drastically change the overall long-term dynamics of the system (for sufficiently small variance of the noise) relative to the dynamics of a corresponding deterministic system. The primary difference between the stochastic and deterministic metapopulation models is that for large time, solutions tend to quasi-stationary distributions in the stochastic setting, rather than to constant steady states in the deterministic setting. We then considered different approaches to controlling the spread of a stochastic SIS epidemic over spatial networks, comparing results for a spectrum of controls utilizing local to global information about the state of the epidemic. Variation in white noise was shown to be able to counteract the treatment rate (treated curing rate) of the epidemic, requiring greater treatment rates on the part of the control and suggesting that in real-life epidemics one should be mindful of such random variations in order for a treatment to be effective. Additionally, we point out some problems using white noise perturbations as a model, but show that a truncated noise process gives qualitatively comparable behaviors without these issues. |
| spellingShingle | Krause, A Kurowski, L Yawar, K Van Gorder, R Stochastic epidemic metapopulation models on networks: SIS dynamics and control strategies |
| title | Stochastic epidemic metapopulation models on networks: SIS dynamics and control strategies |
| title_full | Stochastic epidemic metapopulation models on networks: SIS dynamics and control strategies |
| title_fullStr | Stochastic epidemic metapopulation models on networks: SIS dynamics and control strategies |
| title_full_unstemmed | Stochastic epidemic metapopulation models on networks: SIS dynamics and control strategies |
| title_short | Stochastic epidemic metapopulation models on networks: SIS dynamics and control strategies |
| title_sort | stochastic epidemic metapopulation models on networks sis dynamics and control strategies |
| work_keys_str_mv | AT krausea stochasticepidemicmetapopulationmodelsonnetworkssisdynamicsandcontrolstrategies AT kurowskil stochasticepidemicmetapopulationmodelsonnetworkssisdynamicsandcontrolstrategies AT yawark stochasticepidemicmetapopulationmodelsonnetworkssisdynamicsandcontrolstrategies AT vangorderr stochasticepidemicmetapopulationmodelsonnetworkssisdynamicsandcontrolstrategies |