The beginning of a pandemic is a crucial stage for policymakers. Proper management at this stage can reduce overall health and economical damage. However, knowledge about the pandemic is insufficient. Thus, the use of complex and sophisticated models is challenging. In this study, we propose analytical and stochastic heat spread-based boundaries for the pandemic spread. These are compared with the stochastic Susceptible-Infected-Recovered (SIR) model taking place on an interaction graph. The proposed boundaries are not requiring accurate biological knowledge such as the SIR model does.

In a world where pandemics are a matter of time and increasing urbanization of the world's population, governments should be prepared with pandemic intervention policies to minimize the crisis direct and indirect adverse effects while keeping normal life as much as possible. Successful pandemic intervention policies have to take into consideration the unique behavior of individuals in different types of buildings and social contexts. In this study, we propose a Spatio-temporal model and in silico simulation to evaluate pandemic intervention policies in four types of buildings - home, office, school, and mall. We show that indeed each building type has a unique pandemic spread and therefore a different optimal intervention policy.

Decision tree (DT) is one of the most popular and efficient techniques in data mining. Specifically, in the clinical domain, DTs have been wildly used thanks to relatively easy interpretation and efficient computation time. However, some DT models may produce a large tree size structure which is difficult to understand and often leads to misclassification of data in the testing process. Therefore, a DT model which is a simple tree with high accuracy is the desired goal. Post pruning (PP) algorithms have been introduced to reduce the complexity of the tree structure with a minor decrease in the accuracy of classification. We propose a new Boolean satisfiability (SAT) based PP algorithm (Namely, SAT-PP algorithm) which reduces the tree size while preserving the accuracy of the unpruned tree, since in medical-related tasks, decreasing the model’s performance is something we emphatically try to avoid. Namely, in the case of medical-related tasks, one may prefer an unpruned DT model to a pruned DT model with worse performance. To evaluate the proposed algorithm and other PP algorithms, we compare the performance in terms of the model query response time and accuracy of classification using three oncology data sets. The SAT-PP DT model obtained the same accuracy and F1 score as the DT model without PP while significantly reducing computation time (6.8%).