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The impact of the unstructured contacts component in influenza pandemic modeling.

Ajelli M, Merler S - PLoS ONE (2008)

Bottom Line: Here we show how diverse choices can lead to different model outputs and thus to a different evaluation of the effectiveness of the containment/mitigation strategies.To reduce uncertainty in the models it is thus important to employ data, which start being available, on contacts on neglected but important activities (leisure time, sport mall, restaurants, etc.) and time-use data for improving the characterization of the unstructured contacts.Moreover, all the possible effects of different assumptions should be considered for taking public health decisions: not only sensitivity analysis to various model parameters should be performed, but intervention options should be based on the analysis and comparison of different modeling choices.

View Article: PubMed Central - PubMed

Affiliation: Fondazione Bruno Kessler, Trento, Italy. ajelli@fbk.eu

ABSTRACT

Background: Individual based models have become a valuable tool for modeling the spatiotemporal dynamics of epidemics, e.g. influenza pandemic, and for evaluating the effectiveness of intervention strategies. While specific contacts among individuals into diverse environments (family, school/workplace) can be modeled in a standard way by employing available socio-demographic data, all the other (unstructured) contacts can be dealt with by adopting very different approaches. This can be achieved for instance by employing distance-based models or by choosing unstructured contacts in the local communities or by employing commuting data.

Methods/results: Here we show how diverse choices can lead to different model outputs and thus to a different evaluation of the effectiveness of the containment/mitigation strategies. Sensitivity analysis has been conducted for different values of the first generation index G(0), which is the average number of secondary infections generated by the first infectious individual in a completely susceptible population and by varying the seeding municipality. Among the different considered models, attack rate ranges from 19.1% to 25.7% for G(0) = 1.1, from 47.8% to 50.7% for G(0) = 1.4 and from 62.4% to 67.8% for G(0) = 1.7. Differences of about 15 to 20 days in the peak day have been observed. As regards spatial diffusion, a difference of about 100 days to cover 200 km for different values of G(0) has been observed.

Conclusion: To reduce uncertainty in the models it is thus important to employ data, which start being available, on contacts on neglected but important activities (leisure time, sport mall, restaurants, etc.) and time-use data for improving the characterization of the unstructured contacts. Moreover, all the possible effects of different assumptions should be considered for taking public health decisions: not only sensitivity analysis to various model parameters should be performed, but intervention options should be based on the analysis and comparison of different modeling choices.

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Spatiotemporal dynamics at 40 (on the left) and 60 (on the right) days, roughly corresponding to the begin and the end of the exponential growth phase. Infection is seeded in Rome and G0 = 1.7.Colored areas (model M in orange, M+T in red, L in cyan, L+T in blue, and S in green) indicate presence of at least one infected, infectious or removed individual.
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pone-0001519-g003: Spatiotemporal dynamics at 40 (on the left) and 60 (on the right) days, roughly corresponding to the begin and the end of the exponential growth phase. Infection is seeded in Rome and G0 = 1.7.Colored areas (model M in orange, M+T in red, L in cyan, L+T in blue, and S in green) indicate presence of at least one infected, infectious or removed individual.

Mentions: Significant differences are observed in the peak day (see Table 3). In particular, for large values of G0 (G0≥1.4) the epidemic peak of S models occurs systematically earlier than M and L models (with differences of about 15 to 20 days for different values of the first generation index). Since in S models the epidemic is spread much more quickly, new infection foci occur simultaneously in many different regions, thus inducing a spatial synchronization of the epidemic (see Figure 3 and Movie S2). No substantial differences are observed by varying the seeding region (see Table 4). For G0 = 1.1, no significant differences are observed between M and S models, while, on average, the peak day of L models occurs later than M and S models. This is due to the several simulations behaving very differently from all the others (and independently from the seeding region), characterized by a very long initial phase and giving rise to a high standard deviation. In fact, for low values of the first generation index, L models are less likely to spread out the epidemic because of the reduced set of unstructured contacts. Not surprisingly, the introduction of occasional long-distance trips significantly anticipates the epidemic peak in both the M and L models (5 to 10 days earlier than the respective M and L models). See also Figure 4 where the number of cases in time of the different models are reported for different seeding municipalities and different first generation indices.


The impact of the unstructured contacts component in influenza pandemic modeling.

Ajelli M, Merler S - PLoS ONE (2008)

Spatiotemporal dynamics at 40 (on the left) and 60 (on the right) days, roughly corresponding to the begin and the end of the exponential growth phase. Infection is seeded in Rome and G0 = 1.7.Colored areas (model M in orange, M+T in red, L in cyan, L+T in blue, and S in green) indicate presence of at least one infected, infectious or removed individual.
© Copyright Policy
Related In: Results  -  Collection

Show All Figures
getmorefigures.php?uid=PMC3278282&req=5

pone-0001519-g003: Spatiotemporal dynamics at 40 (on the left) and 60 (on the right) days, roughly corresponding to the begin and the end of the exponential growth phase. Infection is seeded in Rome and G0 = 1.7.Colored areas (model M in orange, M+T in red, L in cyan, L+T in blue, and S in green) indicate presence of at least one infected, infectious or removed individual.
Mentions: Significant differences are observed in the peak day (see Table 3). In particular, for large values of G0 (G0≥1.4) the epidemic peak of S models occurs systematically earlier than M and L models (with differences of about 15 to 20 days for different values of the first generation index). Since in S models the epidemic is spread much more quickly, new infection foci occur simultaneously in many different regions, thus inducing a spatial synchronization of the epidemic (see Figure 3 and Movie S2). No substantial differences are observed by varying the seeding region (see Table 4). For G0 = 1.1, no significant differences are observed between M and S models, while, on average, the peak day of L models occurs later than M and S models. This is due to the several simulations behaving very differently from all the others (and independently from the seeding region), characterized by a very long initial phase and giving rise to a high standard deviation. In fact, for low values of the first generation index, L models are less likely to spread out the epidemic because of the reduced set of unstructured contacts. Not surprisingly, the introduction of occasional long-distance trips significantly anticipates the epidemic peak in both the M and L models (5 to 10 days earlier than the respective M and L models). See also Figure 4 where the number of cases in time of the different models are reported for different seeding municipalities and different first generation indices.

Bottom Line: Here we show how diverse choices can lead to different model outputs and thus to a different evaluation of the effectiveness of the containment/mitigation strategies.To reduce uncertainty in the models it is thus important to employ data, which start being available, on contacts on neglected but important activities (leisure time, sport mall, restaurants, etc.) and time-use data for improving the characterization of the unstructured contacts.Moreover, all the possible effects of different assumptions should be considered for taking public health decisions: not only sensitivity analysis to various model parameters should be performed, but intervention options should be based on the analysis and comparison of different modeling choices.

View Article: PubMed Central - PubMed

Affiliation: Fondazione Bruno Kessler, Trento, Italy. ajelli@fbk.eu

ABSTRACT

Background: Individual based models have become a valuable tool for modeling the spatiotemporal dynamics of epidemics, e.g. influenza pandemic, and for evaluating the effectiveness of intervention strategies. While specific contacts among individuals into diverse environments (family, school/workplace) can be modeled in a standard way by employing available socio-demographic data, all the other (unstructured) contacts can be dealt with by adopting very different approaches. This can be achieved for instance by employing distance-based models or by choosing unstructured contacts in the local communities or by employing commuting data.

Methods/results: Here we show how diverse choices can lead to different model outputs and thus to a different evaluation of the effectiveness of the containment/mitigation strategies. Sensitivity analysis has been conducted for different values of the first generation index G(0), which is the average number of secondary infections generated by the first infectious individual in a completely susceptible population and by varying the seeding municipality. Among the different considered models, attack rate ranges from 19.1% to 25.7% for G(0) = 1.1, from 47.8% to 50.7% for G(0) = 1.4 and from 62.4% to 67.8% for G(0) = 1.7. Differences of about 15 to 20 days in the peak day have been observed. As regards spatial diffusion, a difference of about 100 days to cover 200 km for different values of G(0) has been observed.

Conclusion: To reduce uncertainty in the models it is thus important to employ data, which start being available, on contacts on neglected but important activities (leisure time, sport mall, restaurants, etc.) and time-use data for improving the characterization of the unstructured contacts. Moreover, all the possible effects of different assumptions should be considered for taking public health decisions: not only sensitivity analysis to various model parameters should be performed, but intervention options should be based on the analysis and comparison of different modeling choices.

Show MeSH
Related in: MedlinePlus