In the most studied models in the literature, it is assumed that the target of the search is either stationary with its hidden position being chosen according to someknown distribution, or it is moving and its movements follow some known rules. In such cases, we talk about one-sided search, meaning that the target’s behaviour is somehow independent of the searcher’s attempt to catch it. Conversely, if the target can attempt to contrast the searcher’s activity and react in some intelligent way in order not to be found, the model is referred to as two-sided search. Two-sided search was introduced by Koopman. The goal is to implement a two-sided search algorithm.
Short Description: Use ideas of Information Theory to get new group testing strategies.
The concept of group testing (also called pooled testing or pooling) dates back to mathematical ideas for improving the efficiency of syphilis tests developed by Dorfman in 1943 [Dor43].The idea of Dorfman’s method is to combine portions of k different individual blood samples into onesample in a first stage. If it tested negative then that entire group could be dismissed without further testing. Then, separately retesting the samples of individuals from positive pools in a second stage. Forlow to moderate infection rates, this strategy has a high throughput since most of the group, when chosen wisely, will be declared negative. Naturally, the efficiency of pooling strategies for the current pandemichas been shown to depend on the prevalence of SARS-CoV-2, patient-pool size and test sensitivity. While the sensitivity needs to be understood on an experimental level, the prevalence needs to be estimated from available test results and the pool size should be optimized with Information Theory models before widespread implementation.