H1N1 in Households, or the Math of Spreading Swine Flu

By

Home / H1N1 in Households, or the Math of Spreading Swine Flu

Conclusions from the Statistical Study of the 2009 Spread of H1N1 Swine Flu in Birmingham

A sneezing girl could transmit H1N1 : Image by SCA Svenska Cellulosa Aktiebolaget

The statistics led to three major conclusions, each with several facets.

First, this statistical model does indeed make it possible to estimate how thoroughly an infectious disease will spread through a household. This should guide clinical decisions about preventative measures within households, and perhaps at school or in the workplace as well.

However, the true number of cases will likely be somewhere between those found by laboratory testing and what doctors merely suspect from clinical evaluation of symptoms. Moreover, the lab tests might provide false negative tests due to improper swabbing or other issues.

The second conclusion was that swine flu transmission rates are quite variable. In two-person households, more often both people were ill than just one. Larger households most often had only one person fall ill; fewer had every person sick; and fewer still had some intermediate number of patients, but this study was not constructed to determine why some households were more completely stricken than others.

The final and most significant result found that a mathematical model can complement time-consuming laboratory work. The parameters of a new pandemic should be determined “early in an epidemic of something new,” as Dr. House hopes. Then, clinical practice can adapt to the severity and transmissibility of the outbreak. The statistics can help make health care delivery more efficient and more effective during a future pandemic.

Decoded Science asked Dr. House how many households would be required to identify an incipient epidemic, and he replied, “Others in the literature have done simpler analyses on much smaller datasets, and got reliable estimates but with large uncertainty. The method should not be biased, so the same will hold: more data means less uncertainty, but the method does not require some minimum amount of data to work (technically, it does not rely on asymptotically large amounts of data)“.

Future studies could examine disease transmissibility by age, as well as the effectiveness of increased hand-washing versus anti-viral medications.

Leave a Comment