When a disease spreads globally (a pandemic), tying global data together in a comprehensive model (or suite of models) is important to develop an accurate picture of the problem and to make effective decisions for public health. This makes sense, but is difficult to accomplish across many countries, as the last pandemic (the H1N1 virus) demonstrated. This is the conclusion drawn by an international study (18 institutions participating) in part under the auspices of the World Health Organization (U.N.) and led by Dr. Maria Van Kerkhove, Imperial College, London (UK). Published in the Public Library of Science (PLoS) as PLoS Medicine: Studies Needed to Address Public Health Challenges of the 2009 H1N1 Influenza Pandemic: Insights from Modeling, the study points to six public health challenges and the non-routine data (information not routinely collected by all countries) required to meet them:
1. Measuring age-specific immunity to infection. Pandemics typically affect certain age groups more virulently. The earlier this can be identified, the better.
2. Accurately quantifying severity. This was a crucial issue with the H1N1 virus, as early reports of severity were inaccurate and the virus was less virulent than thought.
3. Improving treatment outcomes for severe cases. The ability to identify pockets of severe cases and concentrate the best known solutions requires more organized data both locally and globally.
4. Quantifying the effectiveness of interventions. Over the course of the pandemic statistics on treatments used, policy and public health measures taken, and the resulting outcomes need to be measured as accurately as possible.
5. Capturing the full impact of the pandemic on mortality. While all pandemics have fatalities, it is sometimes difficult to tease out the cause and effect to arrive at a true picture of the pandemic’s mortality rate.
6. Rapidly identifying and responding to antigenic variants. Very often pandemic causes, typically viral in origin, mutate and produce variants even during the course of the pandemic. It is extremely important to identify and analyze all variants and put them into the overall picture of the pandemic.
The study singled out serology tests (blood testing), especially those covering entire communities, as both critical and lacking in much of the data currently collected. Blood tests provide the most accurate picture of the effects of a pandemic infection, including the level of immunity – both natural and inoculated.
The hope of the participants is that lessons learned from the data of the H1N1 pandemic (or the lack of data) will help raise the level of preparedness for the next pandemic. Many countries are already responding, and this study is one of the principal documents of groups working to get more countries involved.
Preparedness plans will be revised by many nations in the medium term to incorporate lessons learned from the 2009 pandemic. A thorough assessment of the value of data from all sources will be crucial if the quality of information available for decision-makers during future pandemics is to be improved. We suggest that some of the most valuable data, such as estimates of age-specific serological attack rates, have not become available until far after the time when it would have been needed to support decision making. The establishment of a preapproved ethical review status for key field studies is a priority. Also, if such studies are to be initiated in a short time, investigators may choose to design and pilot them in association with nonacademic partners.
[Source: PLoS Medicine]