In an ongoing epidemic, the case fatality rate is not a reliable estimate of a disease's severity. This is particularly so when a large share of asymptomatic or pauci-symptomatic patients escape testing, or when overwhelmed healthcare systems are forced to limit testing further to severe cases only.By leveraging data on COVID-19, we propose a novel way to estimate a disease's infected fatality rate, the true lethality of the disease, in the presence of sparse and partial information. We show that this is feasible when the disease has turned into a pandemic and data comes from a large number of countries, or regions within countries, as long as testing strategies vary sufficiently.For Italy, our method estimates an IFR of 1.1% (95% CI: 0.2% -2.1%), which is strongly in line with other methods. At the global level, our method estimates an IFR of 1.6% (95% CI: 1.1% -2.1%).This method also allows us to show that the IFR varies according to each country's age structure and healthcare capacity.

At the outbreak of equine influenza (EI) we chose to close our horse-based business, as we did not want to risk our horses contracting the disease and the demand for our services ceased. We report our experiences of the outbreak.