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The Role of Influenza in the Epidemiology of Pneumonia


The Role of Influenza in the Epidemiology of Pneumonia

Sourya Shrestha et al. Sci Rep.


Interactions arising from sequential viral and bacterial infections play important roles in the epidemiological outcome of many respiratory pathogens. Influenza virus has been implicated in the pathogenesis of several respiratory bacterial pathogens commonly associated with pneumonia. Though clinical evidence supporting this interaction is unambiguous, its population-level effects-magnitude, epidemiological impact and variation during pandemic and seasonal outbreaks-remain unclear. To address these unknowns, we used longitudinal influenza and pneumonia incidence data, at different spatial resolutions and across different epidemiological periods, to infer the nature, timing and the intensity of influenza-pneumonia interaction. We used a mechanistic transmission model within a likelihood-based inference framework to carry out formal hypothesis testing. Irrespective of the source of data examined, we found that influenza infection increases the risk of pneumonia by ~100-fold. We found no support for enhanced transmission or severity impact of the interaction. For model-validation, we challenged our fitted model to make out-of-sample pneumonia predictions during pandemic and non-pandemic periods. The consistency in our inference tests carried out on several distinct datasets, and the predictive skill of our model increase confidence in our overall conclusion that influenza infection substantially enhances the risk of pneumonia, though only for a short period.


Figure 1
Figure 1. Datasets 1, and 2.
(A) Weekly incidences of influenza and pneumonia in New York city (Dataset 2). (B,C) Weekly incidences of influenza and pneumonia in Illinois, before (dataset 1A) and after (dataset 1B) the introduction of pneumococcal conjugate vaccine (PCV), respectively. The variability in influenza in each of the datasets are presented as the ratios of largest to smallest peaks.
Figure 2
Figure 2. Dataset 3.
The figure shown monthly incidences of influenza (blue), pneumonia (red), and coinfections (purple) across 34 US Army camps spanning 8 months, from May of 2018 to Dec 1918. This covers the fall wave in 1918. The vertical colored lines indicate the scale of the graph where the length indicates incidence of 10%.
Figure 3
Figure 3. The nature and intensity of interactions between influenza and pneumonia.
The nature and intensity of interactions between influenza and pneumonia, inferred in (AC) New York City from 1920 to 1924 (dataset 2); and in the state of Illinois before the introduction of PCV from (DF) 1990 to 1997 (dataset 1A); and after the introduction of PCV from (GI) 2000 to 2009 (dataset 1B). Arranged column-wise are the tests for the three hypotheses, hypothesis 1 (transmission impact), hypothesis 2 (susceptibility impact), and hypothesis 3 (pathogenesis impact). Plotted in each graph are likelihood profiles for the respective parameters—the profiles are created by fitting a smooth line through the log of the arithmetic mean likelihoods (shown in large dots) in 10 repeated likelihood estimates (shown in small dots). The values within the two dashed black lines are within the estimated 95% confidence interval, and the value marked with dashed colored line represents the maximum likelihood estimate (MLE). For each of the three parameters, value of 1 represents the null hypothesis. For hypothesis 2, we show the profiles with θ = 1, and ξ = 1 (i.e. after rejecting hypotheses 1 and 3) in the inset graphs.
Figure 4
Figure 4. Susceptibility impact of influenza on pneumonia in four cities between 1920–1923.
We examine the susceptibility impact in four cities, Chicago, Philadelphia, Baltimore and Los Angeles in two different ways. (A) First, we analyze out-of-fit predictions in the four cities, using the null (ϕ = 1) and the MLE (ϕ = 80) model arising from the New York City data. Presented are R2 goodness of fits for both models for each of the cities. We do not present R2 goodness of fits for New York because New York data were used in constructing the MLE model. [See Fig. S-7 in SOM for comparisons of the data and the predictions.] (B) Second, we independently estimate the susceptibility impact, ϕ, from each of the 4 datasets, following the same procedure used for New York City data. Presented are the likelihood profiles, and the 95% confidence intervals.
Figure 5
Figure 5. Prediction of pneumonia and coinfections across 34 US Army Camps during the Fall wave of 1918 influenza pandemic.
We predict monthly pneumonia incidence (fraction of the camp population reported with pneumonia, shown in red) and monthly coinfection incidence (fraction of the camp population reported with both influenza and pneumonia, shown in purple) over a period of 8 months covering the fall wave of 1918 pandemic, across 34 camps. (A) Shown are comparisons of model predictions (which are forward simulations of the MLE+ model, averaged over 1000 simulations per camp) and the data for each of the 34 camps. Comparisons of the predictions of pneumonia (B) and coinfections (C) during the 1918 fall pandemic over 34 US Army camps, with the data during the data period. Presented are average monthly incidences (%) during the 3 months (S,O,N) spanning the fall pandemic. The R2 goodness of fit between the data and the prediction were 0.52 and 0.6 for pneumonia predictions and coinfection predictions, respectively.

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