Quantifying the annual incidence and underestimation of seasonal influenza: A modelling approach
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Date
2020-07-10
Authors
McCarthy, Zachary
Athar, Safia
Alavinejad, Mahnaz
Chow, Christopher
Moyles, Iain
Nah, Kyeongah
Kong, Jude D
Agrawal, Nishant
Jaber, Ahmed
Keane, Laura
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Abstract
Abstract
Background
Seasonal influenza poses a significant public health and economic burden, associated with the outcome of infection and resulting complications. The true burden of the disease is difficult to capture due to the wide range of presentation, from asymptomatic cases to non-respiratory complications such as cardiovascular events, and its seasonal variability. An understanding of the magnitude of the true annual incidence of influenza is important to support prevention and control policy development and to evaluate the impact of preventative measures such as vaccination.
Methods
We use a dynamic disease transmission model, laboratory-confirmed influenza surveillance data, and randomized-controlled trial (RCT) data to quantify the underestimation factor, expansion factor, and symptomatic influenza illnesses in the US and Canada during the 2011-2012 and 2012-2013 influenza seasons.
Results
Based on 2 case definitions, we estimate between 0.42−3.2% and 0.33−1.2% of symptomatic influenza illnesses were laboratory-confirmed in Canada during the 2011-2012 and 2012-2013 seasons, respectively. In the US, we estimate between 0.08−0.61% and 0.07−0.33% of symptomatic influenza illnesses were laboratory-confirmed in the 2011-2012 and 2012-2013 seasons, respectively. We estimated the symptomatic influenza illnesses in Canada to be 0.32−2.4 million in 2011-2012 and 1.8−8.2 million in 2012-2013. In the US, we estimate the number of symptomatic influenza illnesses to be 4.4−34 million in 2011-2012 and 23−102 million in 2012-2013.
Conclusions
We illustrate that monitoring a representative group within a population may aid in effectively modelling the transmission of infectious diseases such as influenza. In particular, the utilization of RCTs in models may enhance the accuracy of epidemiological parameter estimation.
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Citation
Theoretical Biology and Medical Modelling. 2020 Jul 10;17(1):11