id	author	title	date	pages	extension	mime	words	sentences	flesch	summary	cache	txt
cord-300930-47a4pu27	Beigel, R.	Rate Estimation and Identification of COVID-19 Infections: Towards Rational Policy Making During Early and Late Stages of Epidemics	2020-05-24		.txt	text/plain	4535	366	64	Mathematically, the problems of identifying infected individuals ( identification ) and estimating the total number of infected individuals in a given population ( infection rate ) are related but in fact can be addressed by subtly different algorithms to reduce the number of tests needed and thereby the total cost of doing testing. However, as we will demonstrate in this brief communication, estimating the number of infected individuals can be solved by novel adaptation of methods developed in theoretical computer science aimed at approximate counting. In addition to rate estimation we provide a review and analysis of several identification algorithms that can be deployed in communities with low infection rates that achieve reasonable improvement over the standard algorithms for group testing that have been previously explored. • Estimate the rate of the infection in the population or approximately count how many people test positive in a population of a given size with as few partially pooled tests as possible. We now describe approximate counting algorithms that use pools of samples to estimate accurate infection rates.	./cache/cord-300930-47a4pu27.txt	./txt/cord-300930-47a4pu27.txt