Multi-strain pandemics have emerged as a major concern. We introduce a new model for assessing the connection between multi-strain pandemics and mortality rate, basic reproduction number, and maximum infected individuals. The proposed model provides a general mathematical approach for representing multi-strain pandemics, generalizing for an arbitrary number of strains. We show that the proposed model fits well with epidemiological historical world health data over a long time period. From a theoretical point of view, we show that the increasing number of strains increases logarithmically the maximum number of infected individuals and the mean mortality rate. Moreover, the mean basic reproduction number is statistically identical to the single, most aggressive pandemic strain for multi-strain pandemics.

Background: One of the most prevalent complications of Partial Nephrectomy (PN) is Acute Kidney Injury (AKI), which could have a negative impact on subsequent renal function and occurs in up to 24.3% of patients undergoing PN. The aim of this study was to predict the occurrence of AKI following PN using preoperative parameters by applying machine learning algorithms. Methods: We included all adult patients (n = 723) who underwent open PN in our department since 1995 and on whom we have data on the pre-operative renal function. We developed a random forest (RF) model with Boolean satisfaction-based pruned decision trees for binary classification (AKI or non-AKI). Hyper-parameter grid search was performed to optimize the model's performance. Fivefold cross-validation was applied to evaluate the model. We implemented a RF model with greedy feature selection to binary classify AKI and non-AKI cases based on pre-operative data. Results: The best model obtained a 0.69 precision and 0.69 recall in classifying the AKI and non-AKI groups on average (k = 5). In addition, the model's probability to correctly classify a new prediction is 0.75. The proposed model is available as an online calculator. Conclusions: Our model predicts the occurrence of AKI following open PN with (75%) accuracy. We plan to externally validate this model and modify it to minimally-invasive PN.

Data-driven economic tasks have gained significant attention in economics, allowing researchers and policymakers to make better decisions and design efficient policies. Recently, with the advancement of machine learning (ML) and other artificial intelligence (AI) methods, researchers can now solve complex economic tasks with previously unseen performance and ease. However, to use such methods, one is required to have a non-trivial level of expertise in ML or AI, which currently is not standard knowledge in economics. In order to bridge this gap, automatic machine learning (AutoML) models have been developed, allowing non-experts to efficiently use advanced ML models with their data. Nonetheless, not all AutoML models are created equal in general, particularly for the unique properties associated with economic data. In this paper, we present a benchmarking study of biologically inspired and other AutoML techniques for economic tasks. We evaluate four different AutoML models alongside two baseline methods using a set of 50 diverse economic tasks. Our results show that biologically inspired AutoML models (slightly) outperformed non-biological AutoML in economic tasks, while all AutoML models outperformed the traditional methods. Based on our results, we conclude that biologically inspired AutoML has the potential to improve our economic understanding while shifting a large portion of the analysis burden from the economist to a computer.