by Gary Finnegan: Researchers in Finland have used computer modelling to estimate the true impact of infectious diseases, such as H1N1 (swine flu). Underreporting of cases means traditional surveillance systems miss the vast majority of infections that occur in the population.
The research, published in PLOS Computational Biology by Dr Mikhail Shubin from the National Institute for Health and Welfare, uses simulations to estimate the effect of the H1N1 pandemic in Finland during the 2009 – 2011 flu outbreaks.
This research also offers a platform to assess the severity of flu seasons at various levels of the healthcare system, where previously the number of infected individuals has been uncertain.
The researchers built a low-scale model of Finland that simulates the spread of influenza in the population. The model accounts for the transmission of influenza in the population, the impact of vaccination, outcomes of varying severity and imperfect detection of flu.
The study shows less than 10% of the population was infected with swine flu during the first two seasons in 2009/2010 and 2010/2011, with the highest incidences of the disease initially occurring in younger people.
The research team also measured the impact of the vaccination campaign in which approximately half of the Finnish population were vaccinated by February 2010. They showed that vaccinations significantly reduced the transmissibility of the virus as the proportion of the population infected during the second season was only 3%.
According to the study, the second season could have started earlier and caused a larger outbreak, leading to 4-8 times more infections overall, had vaccination uptake been lower.
The study emphasises that statistical modelling and simulation can be used to evaluate incomplete infectious disease surveillance data in emerging infections.