1/12 How does #Demography impact #COVID19 deaths? In new pre-print, we illustrate how older population age structure can interact with high mortality rates at older ages to produce a large # of fatalities, as in Italy. osf.io/se6wy/?view_on… #poptwitter #epitwitter
2/12 #COVID19 fatalities are hitting older age groups hard. Case fatality rates for 80-90 currently 17.5% in Italy. While these numbers will hopefully be overestimates, the burden on older ages groups is frighteningly high.
3/12 With current concentration of deaths at older ages, COVID-19 deaths will hit older countries hard (Italy has 23% of population >65). Figure 1 compares Italy to South Korea (top) and Nigeria to Brazil (bottom) – two countries similar in size but different age distributions. 
4/12 A different visualization incl. US & UK, but same message: Lots more deaths expected in countries with higher %s of older populations. Figures assume 40% of the population becomes infected and the current age-specific mortality of COVID-19 in Italy, as an illustration.
5/12 Assumptions can be adjusted, but tight relationship between pop. age structure & total deaths remains. This should affect the intensity of measures in a population required to reduce the number of most critical cases and overload on the health system-aka #flatteningthecurve
6/12 Our illustrations suggest that countries with older populations will need to take more aggressive protective measures to stay below the threshold of critical cases that outstrip health system capacity. #flatteningthecurve
7/12 High levels of intergenerational contact may also have put Italy more at risk, so the interaction of age structure and social contacts will also be important to predict local and national risk going forward. #soctwitter #poptwitter
8/12 We find some real-world evidence of “flattening the curve” in the province of Lodi where harsh movement restrictions were enacted quickly (Feb 23rd) vs later in Bergamo (March 8th). #flatteningthecurve
9/12 Few countries are routinely releasing their #COVID19 data with key demographic information such age, sex, or comorbidities. Timely release of disaggregated data to allow researchers and governments to better forecast risk and more focused prevention and preparedness.
10/12 For now the concentration of mortality risk in the oldest old ages remains one of the best tools we have to predict the burden of critical cases and thus more precise planning of availability of hospital beds, staff and other resources.
11/12 We thank others also making this important demographic point: @AndrewNoymer () @AndreasShrugged () and many others.
We hope our illustrations help to drive home.
We hope our illustrations help to drive home.
12/12 Finally thanks to the @OxfordDemSci team working tirelessly, esp @rotondivale and Liliana Andriano from lockdown in Italy, @melindamills @block_per @XuejieDing @YanLiu49911516 and David Brazel. @SociologyOxford
