Disease Burden of Influenza

Centers for Disease Control and Prevention (CDC.gov) ^ | Jan 10, 2020 | unknown

[dhuls]

Each year CDC estimates the burden of influenza in the U.S. CDC uses modeling to estimate the number of influenza illnesses, medical visits, flu-associated hospitalizations, and flu-associated deaths that occur in the U.S. in a given season. The methods used to calculate these estimates are described on CDC’s webpage, How CDC Estimates the Burden of Seasonal Influenza in the U.S.

CDC uses the estimates of the burden of influenza in the population and the impact of influenza vaccination to inform policy and communications related to influenza.

Figure 1: Estimated Range of Annual Burden of Flu in the U.S. since 2010

The burden of influenza disease in the United States can vary widely and is determined by a number of factors including the characteristics of circulating viruses, the timing of the season, how well the vaccine is working to protect against illness, and how many people got vaccinated. While the impact of flu varies, it places a substantial burden on the health of people in the United States each year.

CDC estimates that influenza has resulted in between 9 million – 45 million illnesses, between 140,000 – 810,000 hospitalizations and between 12,000 – 61,000 deaths annually since 2010.

Figure 2: Estimated U.S. Influenza Burden, By Season

[dhuls]

2017-2018 looks very similar to current COVID-19 projections.

[spintreebob]

All models are wrong. Some models are useful - George Box famous modeler.

Everyone, especiall media, need to understand that all models are a range of probabilities. There was probability X that Hilary would get 51% of the Electoral college. There was probability Y that she would get 55% of the Electoral College. There was probability Z that she would get 45% of the Electoral College.

The same applies to the spread of a virus, and the deaths from that virus.

A model has variables: If people follow social distancing, the model predicts probability A. If people don’t social distance, the model predicts probabillity B. If we develop a vaccine quickly the model predicts probability C. etc.

Not only do the modelers not know exactly what the virus will do (as it is new and Chinese data is crap). The modelers do not know what the variables will do. The modelers do not know if we will social distance or not. The modelers do not know what orders politicians will give, nor whether those orders will be obeyed, nor if they will be timely. For most variables, the modelers know the probability no better than the role of the dice.

Then comes the political decisions. What level of probability is acceptable? What level of hospitalization and death is acceptable?

If a certain virus hits the GAY community hard due to the weakened immune systems of the GAY lifestyle, should we err more on the side of caution than if it hits some other demographic group harder? If the virus hits city dwellers who live close to each other more than rural voters who are inherently social distanced, then should the be a factor in whether to err on the side of caution?

A key task in analysis is to separate out (to the extent possible) the mathematical from the political. But of course, the sheer fact that we collect statistics on how it affects demographic group A but not demographic group B is political. Politics drives which facts are the convenient facts.