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Early Characterization of the Severity and Transmissibility of Pandemic Influenza Using Clinical Episode Data from Multiple Populations.

Riley P, Ben-Nun M, Linker JA, Cost AA, Sanchez JL, George D, Bacon DP, Riley S - PLoS Comput. Biol. (2015)

Bottom Line: The 2009 pandemic illustrated that estimating individual-level severity, as described by the proportion pC of infections that result in clinical cases, can remain uncertain for a prolonged period of time.These fits were obtained despite widely varying incidence profiles: some with spring waves, some with fall waves and some with both.This study provides a clear reference in this two-dimensional space against which future novel respiratory pathogens can be rapidly assessed and compared with previous pandemics.

View Article: PubMed Central - PubMed

Affiliation: Predictive Science Inc., San Diego, California, United States of America.

ABSTRACT
The potential rapid availability of large-scale clinical episode data during the next influenza pandemic suggests an opportunity for increasing the speed with which novel respiratory pathogens can be characterized. Key intervention decisions will be determined by both the transmissibility of the novel strain (measured by the basic reproductive number R0) and its individual-level severity. The 2009 pandemic illustrated that estimating individual-level severity, as described by the proportion pC of infections that result in clinical cases, can remain uncertain for a prolonged period of time. Here, we use 50 distinct US military populations during 2009 as a retrospective cohort to test the hypothesis that real-time encounter data combined with disease dynamic models can be used to bridge this uncertainty gap. Effectively, we estimated the total number of infections in multiple early-affected communities using the model and divided that number by the known number of clinical cases. Joint estimates of severity and transmissibility clustered within a relatively small region of parameter space, with 40 of the 50 populations bounded by: pC, 0.0133-0.150 and R0, 1.09-2.16. These fits were obtained despite widely varying incidence profiles: some with spring waves, some with fall waves and some with both. To illustrate the benefit of specific pairing of rapidly available data and infectious disease models, we simulated a future moderate pandemic strain with pC approximately ×10 that of 2009; the results demonstrating that even before the peak had passed in the first affected population, R0 and pC could be well estimated. This study provides a clear reference in this two-dimensional space against which future novel respiratory pathogens can be rapidly assessed and compared with previous pandemics.

No MeSH data available.


Related in: MedlinePlus

Characterizing a model strain of pandemic influenza in real time for a single population.a-c Three frames illustrating stochastic predictions for an outbreak based on parameters that give a typical two-peak profile (such as that of MPZ-92055), but increasing pC by about an order of magnitude. The grey lines give individual simulation realizations while the green line shows the ensemble average. The data contributing to the realizations is marked in red. The insets show the evolution of computed pC, RA, and RB as more simulated data are added to the predictions. The red line is our median estimate and the blue lines are the 95% confidence intervals. d-f Analogous frames for a typical single-peaked outbreak such as that of MPZ-23708, but again increasing pc by about an order of magnitude.
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pcbi.1004392.g003: Characterizing a model strain of pandemic influenza in real time for a single population.a-c Three frames illustrating stochastic predictions for an outbreak based on parameters that give a typical two-peak profile (such as that of MPZ-92055), but increasing pC by about an order of magnitude. The grey lines give individual simulation realizations while the green line shows the ensemble average. The data contributing to the realizations is marked in red. The insets show the evolution of computed pC, RA, and RB as more simulated data are added to the predictions. The red line is our median estimate and the blue lines are the 95% confidence intervals. d-f Analogous frames for a typical single-peaked outbreak such as that of MPZ-23708, but again increasing pc by about an order of magnitude.

Mentions: To assess the likely utility of this type of data stream to improve the speed of characterization of a new strain in real time, we simulated infections and clinical attendance in two illustrative populations and then estimated key parameters at different time points (Fig 3). Parameter values for the simulated incidence were chosen to simulate a double and single peak profiles similar to the 2009 ILI profiles of MPZs 92055 (Camp Pendleton, Fig 3A–3C) and 23708 (Portsmouth VA, Fig 3D–3F), other than that we used a of pC about ten times greater, to increase the overall incidence. We considered first the simpler single peak profile (Fig 3D–3F and S2 Movie in the Supplementary Materials). Initially, during the exponential phase of the epidemic, unbiased point estimates of R0 were possible but only with considerable uncertainty (Fig 3D). However, with this limited data, it was not yet possible to estimate pC or to make predictions for the peak number of ILI cases. Once the growth in incidence had slowed and was clearly sub-exponential, uncertainty in parameter estimates and model predictions was greatly reduced (Fig 3E). In relative terms, little additional information was contained in the additional data gathered between the second and the third time points (Fig 3F).


Early Characterization of the Severity and Transmissibility of Pandemic Influenza Using Clinical Episode Data from Multiple Populations.

Riley P, Ben-Nun M, Linker JA, Cost AA, Sanchez JL, George D, Bacon DP, Riley S - PLoS Comput. Biol. (2015)

Characterizing a model strain of pandemic influenza in real time for a single population.a-c Three frames illustrating stochastic predictions for an outbreak based on parameters that give a typical two-peak profile (such as that of MPZ-92055), but increasing pC by about an order of magnitude. The grey lines give individual simulation realizations while the green line shows the ensemble average. The data contributing to the realizations is marked in red. The insets show the evolution of computed pC, RA, and RB as more simulated data are added to the predictions. The red line is our median estimate and the blue lines are the 95% confidence intervals. d-f Analogous frames for a typical single-peaked outbreak such as that of MPZ-23708, but again increasing pc by about an order of magnitude.
© Copyright Policy
Related In: Results  -  Collection

License
Show All Figures
getmorefigures.php?uid=PMC4581836&req=5

pcbi.1004392.g003: Characterizing a model strain of pandemic influenza in real time for a single population.a-c Three frames illustrating stochastic predictions for an outbreak based on parameters that give a typical two-peak profile (such as that of MPZ-92055), but increasing pC by about an order of magnitude. The grey lines give individual simulation realizations while the green line shows the ensemble average. The data contributing to the realizations is marked in red. The insets show the evolution of computed pC, RA, and RB as more simulated data are added to the predictions. The red line is our median estimate and the blue lines are the 95% confidence intervals. d-f Analogous frames for a typical single-peaked outbreak such as that of MPZ-23708, but again increasing pc by about an order of magnitude.
Mentions: To assess the likely utility of this type of data stream to improve the speed of characterization of a new strain in real time, we simulated infections and clinical attendance in two illustrative populations and then estimated key parameters at different time points (Fig 3). Parameter values for the simulated incidence were chosen to simulate a double and single peak profiles similar to the 2009 ILI profiles of MPZs 92055 (Camp Pendleton, Fig 3A–3C) and 23708 (Portsmouth VA, Fig 3D–3F), other than that we used a of pC about ten times greater, to increase the overall incidence. We considered first the simpler single peak profile (Fig 3D–3F and S2 Movie in the Supplementary Materials). Initially, during the exponential phase of the epidemic, unbiased point estimates of R0 were possible but only with considerable uncertainty (Fig 3D). However, with this limited data, it was not yet possible to estimate pC or to make predictions for the peak number of ILI cases. Once the growth in incidence had slowed and was clearly sub-exponential, uncertainty in parameter estimates and model predictions was greatly reduced (Fig 3E). In relative terms, little additional information was contained in the additional data gathered between the second and the third time points (Fig 3F).

Bottom Line: The 2009 pandemic illustrated that estimating individual-level severity, as described by the proportion pC of infections that result in clinical cases, can remain uncertain for a prolonged period of time.These fits were obtained despite widely varying incidence profiles: some with spring waves, some with fall waves and some with both.This study provides a clear reference in this two-dimensional space against which future novel respiratory pathogens can be rapidly assessed and compared with previous pandemics.

View Article: PubMed Central - PubMed

Affiliation: Predictive Science Inc., San Diego, California, United States of America.

ABSTRACT
The potential rapid availability of large-scale clinical episode data during the next influenza pandemic suggests an opportunity for increasing the speed with which novel respiratory pathogens can be characterized. Key intervention decisions will be determined by both the transmissibility of the novel strain (measured by the basic reproductive number R0) and its individual-level severity. The 2009 pandemic illustrated that estimating individual-level severity, as described by the proportion pC of infections that result in clinical cases, can remain uncertain for a prolonged period of time. Here, we use 50 distinct US military populations during 2009 as a retrospective cohort to test the hypothesis that real-time encounter data combined with disease dynamic models can be used to bridge this uncertainty gap. Effectively, we estimated the total number of infections in multiple early-affected communities using the model and divided that number by the known number of clinical cases. Joint estimates of severity and transmissibility clustered within a relatively small region of parameter space, with 40 of the 50 populations bounded by: pC, 0.0133-0.150 and R0, 1.09-2.16. These fits were obtained despite widely varying incidence profiles: some with spring waves, some with fall waves and some with both. To illustrate the benefit of specific pairing of rapidly available data and infectious disease models, we simulated a future moderate pandemic strain with pC approximately ×10 that of 2009; the results demonstrating that even before the peak had passed in the first affected population, R0 and pC could be well estimated. This study provides a clear reference in this two-dimensional space against which future novel respiratory pathogens can be rapidly assessed and compared with previous pandemics.

No MeSH data available.


Related in: MedlinePlus