text: 184,1,1367648813364649989,2492016278,Adrian Raftery,"New paper on arXiv: ""Estimating SARS-CoV-2 Infections from Deaths, Confirmed Cases, Tests, and Random Surveys"" w Nick Irons: <LINK> 1/4 <LINK> Most data sources for estimating Covid incidence & prevalence are biased or delayed: cases underestimate, positivity rate overestimates, deaths data are delayed, hospitalizations aren't comparable between states. Random testing surveys are the least biased, but rare & delayed 2/4 We propose a Bayesian estimation method for all states that bias-corrects and combines number of cases, test positivity rates & deaths, and anchors them with the few random testing surveys that have been done, in Indiana & Ohio. 3/4 Results for USA to Feb 25: - Cumulative undercount factor: 2.2. - Initial undercount (to April 15, 2020): 11.1. - Cumulative incidence 18.4% of population (61M) - current reproductive rate R: 0.87 - decline of infections since peak of cases on Jan 8: 70% 4/4",https://arxiv.org/abs/2102.10741,"There are many sources of data giving information about the number of SARS-CoV-2 infections in the population, but all have major drawbacks, including biases and delayed reporting. For example, the number of confirmed cases largely underestimates the number of infections, deaths lag infections substantially, while test positivity rates tend to greatly overestimate prevalence. Representative random prevalence surveys, the only putatively unbiased source, are sparse in time and space, and the results come with a big delay. Reliable estimates of population prevalence are necessary for understanding the spread of the virus and the effects of mitigation strategies. We develop a simple Bayesian framework to estimate viral prevalence by combining the main available data sources. It is based on a discrete-time SIR model with time-varying reproductive parameter. Our model includes likelihood components that incorporate data of deaths due to the virus, confirmed cases, and the number of tests administered on each day. We anchor our inference with data from random sample testing surveys in Indiana and Ohio. We use the results from these two states to calibrate the model on positive test counts and proceed to estimate the infection fatality rate and the number of new infections on each day in each state in the USA. We estimate the extent to which reported COVID cases have underestimated true infection counts, which was large, especially in the first months of the pandemic. We explore the implications of our results for progress towards herd immunity. ","Estimating SARS-CoV-2 Infections from Deaths, Confirmed Cases, Tests,