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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

Comparison of various scenarios (residents only, residents + transients, and two intervention strategies, closing museums (four most-visited locations) and practice of healthy behavior (at these museums with the compliance rate of 50%), with 50 simulations for each case) in terms of the day of peak prevalence as shown in the box plot.The presence of a transient population in the city makes the outbreak peak earlier as compared to a scenario with residents only. Closing major museums does not delay the peak (both in the presence and absence of transients). However, using promoting healthy behavior at these museums could delay the outbreak considerably (2 to 5 weeks in the presence of transients and about 10 days to at least a couple of months (disease does not reach peak during the simulation period of 12 days when efficacy is assumed to be 40% and 20%) in the absence of transients).
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f6: Comparison of various scenarios (residents only, residents + transients, and two intervention strategies, closing museums (four most-visited locations) and practice of healthy behavior (at these museums with the compliance rate of 50%), with 50 simulations for each case) in terms of the day of peak prevalence as shown in the box plot.The presence of a transient population in the city makes the outbreak peak earlier as compared to a scenario with residents only. Closing major museums does not delay the peak (both in the presence and absence of transients). However, using promoting healthy behavior at these museums could delay the outbreak considerably (2 to 5 weeks in the presence of transients and about 10 days to at least a couple of months (disease does not reach peak during the simulation period of 12 days when efficacy is assumed to be 40% and 20%) in the absence of transients).

Mentions: As we are interested in the effect of transients on the number of residents being infected, Figures 1, 2, and 3 show scatter plots for the fraction of residents infected at peak, the fraction of residents infected cumulatively over the simulation period (120 days), and the day when the the disease peaks, respectively. This helps to show the differences in the variances of different scenarios and motivates our choice of statistical tests (see Supplementary information for details on statistical tests). We also create box plots for the fraction of the resident population currently infected at peak (Figure 4), the fraction of the resident population infected cumulatively over 120 days (Figure 5), and the day of peak (Figure 6). The simulations show that the disease peaks about 10 days earlier and there are about 23% more resident infections on average at peak when the transients are considered. Over the period of 120 days, 9% more residents are infected. All these differences are statistically significant (t-test, α = 0.05, Supplementary Information). Specifically the difference in the number of the number of infections at peak is very important from a public health perspective because it determines elements of response such as the number of beds required in hospitals.


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

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

Comparison of various scenarios (residents only, residents + transients, and two intervention strategies, closing museums (four most-visited locations) and practice of healthy behavior (at these museums with the compliance rate of 50%), with 50 simulations for each case) in terms of the day of peak prevalence as shown in the box plot.The presence of a transient population in the city makes the outbreak peak earlier as compared to a scenario with residents only. Closing major museums does not delay the peak (both in the presence and absence of transients). However, using promoting healthy behavior at these museums could delay the outbreak considerably (2 to 5 weeks in the presence of transients and about 10 days to at least a couple of months (disease does not reach peak during the simulation period of 12 days when efficacy is assumed to be 40% and 20%) in the absence of transients).
© Copyright Policy - open-access
Related In: Results  -  Collection

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

f6: Comparison of various scenarios (residents only, residents + transients, and two intervention strategies, closing museums (four most-visited locations) and practice of healthy behavior (at these museums with the compliance rate of 50%), with 50 simulations for each case) in terms of the day of peak prevalence as shown in the box plot.The presence of a transient population in the city makes the outbreak peak earlier as compared to a scenario with residents only. Closing major museums does not delay the peak (both in the presence and absence of transients). However, using promoting healthy behavior at these museums could delay the outbreak considerably (2 to 5 weeks in the presence of transients and about 10 days to at least a couple of months (disease does not reach peak during the simulation period of 12 days when efficacy is assumed to be 40% and 20%) in the absence of transients).
Mentions: As we are interested in the effect of transients on the number of residents being infected, Figures 1, 2, and 3 show scatter plots for the fraction of residents infected at peak, the fraction of residents infected cumulatively over the simulation period (120 days), and the day when the the disease peaks, respectively. This helps to show the differences in the variances of different scenarios and motivates our choice of statistical tests (see Supplementary information for details on statistical tests). We also create box plots for the fraction of the resident population currently infected at peak (Figure 4), the fraction of the resident population infected cumulatively over 120 days (Figure 5), and the day of peak (Figure 6). The simulations show that the disease peaks about 10 days earlier and there are about 23% more resident infections on average at peak when the transients are considered. Over the period of 120 days, 9% more residents are infected. All these differences are statistically significant (t-test, α = 0.05, Supplementary Information). Specifically the difference in the number of the number of infections at peak is very important from a public health perspective because it determines elements of response such as the number of beds required in hospitals.

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