Flu Planning Can Go Awry When Math Misleads

Flu planning

Math is suppose to be simple and truthful, but when the data put into the calculations are not accurate the results can be misleading. When it involves flu, the misleading calculations can lead to poor planning for the flu season or pandemics such as the H1N1 pandemic.

Dr Tini Garske and colleagues fell that the flu mortality formula may be an example of math misleading us. In a study published today in the British Medical Journal, the scientists looked at the standard calculations forecasting potential numbers of death during the swine flu pandemic. They found that the numbers may mislead healthcare planners in both directions: over- and undr-estimation.

Currently the case fatality ratio (the proportion of people who die due to infection during an influenza outbreak) is calculated by dividing the number of deaths by the total number of cases in the same time period. For the math calculations to be correct and not mislead healthcare planners, the data entered must be correct.

Using the above calculations, early data from the current swine flu pandemic suggested that the new influenza A (H1N1) virus causes mild disease. The case fatality ratios was estimated to be 0.5%, or 5 deaths per 1000 people infected.


Garske and colleagues fell this ratio is potentially inaccurate. They give three main reasons why they feel this is so.

> 1. They feel the total number of deaths during this H1N1 pandemic is being underestimated due to “cause of death” not always being correctly attributed to swine flu (e.g. influenza can temporarily increase the risk of vascular events, such as heart attacks)

> 2. They feel that as a pandemic progresses, the total number of cases tends towards underestimation. This is most likely due to people with milder symptoms may not be tested or visit a doctor at all. The total number ends up representing only the most severe cases and not all the cases.

> 3. They feel that the 'snapshot' calculation does not take account of the time delay between infection and death. The researches feel this may lead to the false impression that the infection is actually becoming more severe as the pandemic progresses.

Dr Tini Garske is quote, "Accurately predicting the severity of this swine flu pandemic is a very tricky business, and our research shows that this can only be achieved if data is collected according to well designed study protocols and analysed in a more sophisticated way than is frequently being performed at present. If we fail to get an accurate prediction of severity, we will not be providing healthcare planners, doctors and nurses, with the information that they need to ensure they are best prepared to fight the pandemic as we head into the flu season this autumn.”

“Assessing the severity of the novel influenza A(H1N1) pandemic" British Medical Journal, 15 July 2009; Lead author: Dr Tini Garske, Imperial College London
Imperial College London