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Modeling the effect of transient populations on epidemics in Washington DC.

Parikh N, Youssef M, Swarup S, Eubank S - Sci Rep (2013)

Bottom Line: We find that there are significantly more infections when transients are considered.Surprisingly, closing museums has no beneficial effect.However, promoting healthy behavior at the museums can both reduce and delay the epidemic peak.

View Article: PubMed Central - PubMed

Affiliation: Networks Dynamics and Simulation Science Laboratory, Virginia Bioinformatics Institute, Virginia Tech, USA.

ABSTRACT
Large numbers of transients visit big cities, where they come into contact with many people at crowded areas. However, epidemiological studies have not paid much attention to the role of this subpopulation in disease spread. We evaluate the effect of transients on epidemics by extending a synthetic population model for the Washington DC metro area to include leisure and business travelers. A synthetic population is obtained by combining multiple data sources to build a detailed minute-by-minute simulation of population interaction resulting in a contact network. We simulate an influenza-like illness over the contact network to evaluate the effects of transients on the number of infected residents. We find that there are significantly more infections when transients are considered. Since much population mixing happens at major tourism locations, we evaluate two targeted interventions: closing museums and promoting healthy behavior (such as the use of hand sanitizers, covering coughs, etc.) at museums. Surprisingly, closing museums has no beneficial effect. However, promoting healthy behavior at the museums can both reduce and delay the epidemic peak. We analytically derive the reproductive number and perform stability analysis using an ODE-based model.

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Related in: MedlinePlus

Evaluation of reproductive number as a function of the infection transmission rates.The circles represent the estimated infection transmission rate values based on the synthetic social network. The thin dash line represents the value of reproductive number Ro = 1.375, while the thick dash line represents reproductive number Ro = 1 below which the epidemic dies out. For every infection transmission rate, we sweep the transmission rate value between 0 and 2 and we evaluate the reproductive number Ro using eq. in the Supplementary Information, while the remaining transmission rates are fixed at their estimated values.
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f10: Evaluation of reproductive number as a function of the infection transmission rates.The circles represent the estimated infection transmission rate values based on the synthetic social network. The thin dash line represents the value of reproductive number Ro = 1.375, while the thick dash line represents reproductive number Ro = 1 below which the epidemic dies out. For every infection transmission rate, we sweep the transmission rate value between 0 and 2 and we evaluate the reproductive number Ro using eq. in the Supplementary Information, while the remaining transmission rates are fixed at their estimated values.

Mentions: To study the effect of each infection transmission rate on the reproductive number, we sweep the value of a single infection rate between 0 and 2, while the other infection rates are kept constant at their estimated values. In Figure 10, we show the reproductive number as a function of infection rates βrr→rr, βrt→tr, and βtr→rt (the four-letter subscripts indicate the two subpopulations that are coming into contact). The figure shows that reducing the infection transmission rates between residents who have contacts with transients (rt) and transients (tr), βrt→tr and βtr→rt, is the most effective strategy to reduce the reproductive number below 1. On the other hand, reducing the infection rate among residents who only have contacts with other residents βrr→rr slightly reduces the reproductive number, but it remains above 1. In the Supplementary Information, we also evaluate the reproductive number as a function of βrr→rt, βrt→rr, βrt→rt and βtr→tr. Epidemic results obtained from the ODE model using four subpopulation, using contact rates obtained from the synthetic social contact network, are qualitatively similar to the simulation results.


Modeling the effect of transient populations on epidemics in Washington DC.

Parikh N, Youssef M, Swarup S, Eubank S - Sci Rep (2013)

Evaluation of reproductive number as a function of the infection transmission rates.The circles represent the estimated infection transmission rate values based on the synthetic social network. The thin dash line represents the value of reproductive number Ro = 1.375, while the thick dash line represents reproductive number Ro = 1 below which the epidemic dies out. For every infection transmission rate, we sweep the transmission rate value between 0 and 2 and we evaluate the reproductive number Ro using eq. in the Supplementary Information, while the remaining transmission rates are fixed at their estimated values.
© Copyright Policy - open-access
Related In: Results  -  Collection

License
Show All Figures
getmorefigures.php?uid=PMC3818653&req=5

f10: Evaluation of reproductive number as a function of the infection transmission rates.The circles represent the estimated infection transmission rate values based on the synthetic social network. The thin dash line represents the value of reproductive number Ro = 1.375, while the thick dash line represents reproductive number Ro = 1 below which the epidemic dies out. For every infection transmission rate, we sweep the transmission rate value between 0 and 2 and we evaluate the reproductive number Ro using eq. in the Supplementary Information, while the remaining transmission rates are fixed at their estimated values.
Mentions: To study the effect of each infection transmission rate on the reproductive number, we sweep the value of a single infection rate between 0 and 2, while the other infection rates are kept constant at their estimated values. In Figure 10, we show the reproductive number as a function of infection rates βrr→rr, βrt→tr, and βtr→rt (the four-letter subscripts indicate the two subpopulations that are coming into contact). The figure shows that reducing the infection transmission rates between residents who have contacts with transients (rt) and transients (tr), βrt→tr and βtr→rt, is the most effective strategy to reduce the reproductive number below 1. On the other hand, reducing the infection rate among residents who only have contacts with other residents βrr→rr slightly reduces the reproductive number, but it remains above 1. In the Supplementary Information, we also evaluate the reproductive number as a function of βrr→rt, βrt→rr, βrt→rt and βtr→tr. Epidemic results obtained from the ODE model using four subpopulation, using contact rates obtained from the synthetic social contact network, are qualitatively similar to the simulation results.

Bottom Line: We find that there are significantly more infections when transients are considered.Surprisingly, closing museums has no beneficial effect.However, promoting healthy behavior at the museums can both reduce and delay the epidemic peak.

View Article: PubMed Central - PubMed

Affiliation: Networks Dynamics and Simulation Science Laboratory, Virginia Bioinformatics Institute, Virginia Tech, USA.

ABSTRACT
Large numbers of transients visit big cities, where they come into contact with many people at crowded areas. However, epidemiological studies have not paid much attention to the role of this subpopulation in disease spread. We evaluate the effect of transients on epidemics by extending a synthetic population model for the Washington DC metro area to include leisure and business travelers. A synthetic population is obtained by combining multiple data sources to build a detailed minute-by-minute simulation of population interaction resulting in a contact network. We simulate an influenza-like illness over the contact network to evaluate the effects of transients on the number of infected residents. We find that there are significantly more infections when transients are considered. Since much population mixing happens at major tourism locations, we evaluate two targeted interventions: closing museums and promoting healthy behavior (such as the use of hand sanitizers, covering coughs, etc.) at museums. Surprisingly, closing museums has no beneficial effect. However, promoting healthy behavior at the museums can both reduce and delay the epidemic peak. We analytically derive the reproductive number and perform stability analysis using an ODE-based model.

Show MeSH
Related in: MedlinePlus