There are some interesting statistics on influenza in the current issue of CDC’s Morbidity and Mortality Weekly Report.
With the exception of a few countries, the vast majority of confirmed cases have been in the northern hemisphere. I suspect this situation will change rapidly - flu season is winding down in the northern hemisphere, and is about to begin in the south. The fact that there are already confirmed cases in the southern hemisphere means the virus is already there, and likely to spread further.
Also shown in bar graph form are the number of confirmed (N = 822) and suspected (N = 11,356) cases of novel influenza A (H1N1) virus infection, by date of illness onset in Mexico, from March 11 - May 3, 2009
These data suggest several interesting possibilities. Infection with the new H1N1 strain might have begun as early as March 11, although the early cases are suspected, not laboratory confirmed. After a period of relatively few infections, the number of cases rapidly climbed to a peak and then quickly declined. Of course, new outbreaks are still possible. If we take the number of deaths in Mexico (42) and divide them by the number of laboratory confirmed cases (949), the mortality rate is 4.4% - higher than the 2.5% observed during the 1918-19 pandemic. However, if we divide the number of deaths by the number of suspected cases (11,932), the fatality rate is more in line with typical seasonal influenza - 0.4%. Which number is correct awaits determination of the actual number of cases.
It is also informative to examine CDC’s case definition for influenza - fever plus cough or sore throat. This definition is very broad and could easily include infections caused by other respiratory viruses, such as rhinoviruses, coronaviruses, adenoviruses, and paramyxoviruses. Indeed, I have heard that some of the suspected cases of H1N1 influenza in New York are in fact a consequence of rhinovirus infection. Analyzing an outbreak by using the suspected number of cases is dangerous - akin to a scientist ‘wasting clean thoughts on dirty data’.