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

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 R 0) and its individual-level severity. The 2009 pandemic illustrated that estimating individual-level severity, as described by the proportion p C 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: p C, 0.0133–0.150 and R 0, 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 p C approximately ×10 that of 2009; the results demonstrating that even before the peak had passed in the first affected population, R 0 and p C 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.

Identifying factors that may improve mechanistic forecasting models for influenza

“…We note that even though we did not find our 274 simple ensemble approach to be an improvement over our flexible single model [20,21], 275 the adoption and development of more sophisticated ensemble methods [8,9] may 276 achieve exactly that goal. Given the relatively high variations in both school vacations 277 and specific humidity across small spatial scales, future ensemble approaches may need 278 to allow different model weights for different small units of geographical space.…”

“…17 Models of influenza incidence, whether used for forecasting or as retrospective 18 epidemiological tools, have been applied at different spatial scales and have included a 19 large variety of mechanisms and methodologies. For example, the impact of school 20 closures on transmission has been modeled for cities, such as Hong Kong [12], and 21 Countries, such as France [13], while the contribution of climatic drivers has been 22 assessed for U.S. states [14] as well as individual cities [8]. 23 Meteorological conditions have long been thought to play a role in the transmission 24 dynamics of influenza [15].…”

“…The first term F 1 (t), captures the dependence of the transmission rate on the specific 90 humidity, the second on school vacation, and the third a simple two-value model [21].…”

“…We have found this model to be useful when modeling both military and civilian 116 datasets [20,21]. By allowing the parameter ∆ to vary between -1 and +1, we can 117 model both an increase and decrease in transmission due to behavior modification.…”

National and Regional Influenza-Like-Illness Forecasts for the USA

“…The integral runs over one 135 week determining the number of model cases for week t i . ∆ t approximates the time 136 delay from when an individual becomes infectious to when they visit a sentinel provider 137 for ILI symptoms and is set to 0.5 weeks based on prior calibration [25,26]. Eq.…”