Modeling and analytics for the spread of influenza

Abstract

Influenza is a serious public health threat and causes recurrent outbreaks of respiratory infections every year in the world. The spread of influenza relies greatly on the close contacts between infectious individuals and susceptible individuals in the population, but no data have been published in Hong Kong to quantify the social contact pattern relevant to the transmission of influenza. To investigate the social contact pattern in Hong Kong, a social contact survey was conducted in 2015-2016 using a commonly used diary-based design. The high age-assortative within-group contact intensity was observed in all age groups, and measures about contact intensity were highly correlated with each other, including contact location, duration and frequency. The findings can help to improve the design of future social contact surveys, parameterize transmission models of respiratory infectious diseases, and inform intervention strategies based on model output. Although vaccination is the most effective intervention for the control influenza epidemics and pandemics, antiviral drugs are an important component to mitigate influenza morbidity and mortality. However, antigenic drift and antigenic shift of influenza viruses promote escape from population immunity and facilitates emergence of antiviral resistance, which reduces the effectiveness of antiviral interventions. In light of this, a statistical method was developed to estimate the fitness of antiviral resistant (AVR) influenza virus from only minimal surveillance data that are already part of the contemporary influenza surveillance system. It is showed that accurate estimates of the fitness of oseltamivir resistant viruses could be obtained retrospectively for the seasonal influenza A(H1N1) in 2007-2008 and the pandemic influenza A(H1N1)pdm09 in 2013-2014. Robust real-time interpretation of AVR surveillance data for estimating AVR fitness is an essential but currently missing function of AVR surveillance. This method has the potential to fill this knowledge gap and can be easily integrated into contemporary surveillance systems.