Our data analysis shows that across three prominent models, projections made on April 12th for the death toll two weeks ahead had an average absolute error of 17%
Two other long-run models have also entered the forecasting fray. The first, from the Los Alamos National Laboratory , is a-like model. It has tended to be more pessimistic than other forecasters. Youyang Gu, a young graduate from the Massachusetts Institute of Technology, built another after seeing themodel that has recently been more accurate than forecasts from many established outfits.
Although long-run forecasts will always be subject to larger errors than short-run projections, they have been getting smaller. An analysis byfinds that all models succumbed to large forecast errors in their early stages. Across the three models, projections made on April 12th for the death toll two weeks ahead had an average absolute error of 17%. Those made at the beginning of May for the next two weeks had an average error of just 4%.
There are several reasons why forecasts got off to a bad start. First, the early models suffered from an absence of good historical numbers. The first models extrapolated patterns from the virus’s path through Wuhan and Italy, where the transmission rate was lower than in America. Second, the stabilisation of the death rate, which is now falling gradually, has made forecasting easier.
With better data and improved models, the forecasts made today should prove more accurate—either by luck or design—than earlier ones. Yet future estimates remain widely divergent. Theexpects total deaths to stabilise in July at 140,000, whereas Mr Gu expects the virus to persist into August, at which time there will have been nearly 200,000 deaths. Either total would mean that covid-19 would have exacted a truly terrible toll.Sources: Covid19-Projections.
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