By applying AI techniques to a variety of pandemic-relevant data, artificial intelligence (AI) has substantially supported the control of the spread of the SARS-CoV-2 virus. Along with this, epidemiological machine learning studies of SARS-CoV-2 have been frequently published. While these models can be perceived as precise and policy-relevant to guide governments towards optimal containment policies, their black box nature can hamper building trust and relying confidently on the prescriptions proposed. This paper focuses on interpretable AI-based epidemiological models in the context of the recent SARS-CoV-2 pandemic. We systematically review existing studies, which jointly incorporate AI, SARS-CoV-2 epidemiology, and explainable AI approaches (XAI). First, we propose a conceptual framework by synthesizing the main methodological features of the existing AI pipelines of SARS-CoV-2. Upon the proposed conceptual framework and by analyzing the selected epidemiological studies, we reflect on current research gaps in epidemiological AI toolboxes and how to fill these gaps to generate enhanced policy support in the next potential pandemic.

For efficient radio network planning, empirical path loss (PL) prediction models are utilized to predict signal attenuation in different environments. Alternatively, machine learning (ML) models are proposed to predict path loss. While empirical models are transparent and require less computational capacity, their predictions are not able to generate accurate forecasting in complex environments. While ML models are precise and can cope with complex terrains, their opaque nature hampers building trust and relying assertively on their predictions. To fill the gap between transparency and accuracy, in this paper, we utilize glass box ML using Microsoft research’s explainable boosting machines (EBM) together with the PL data measured for a university campus environment. Moreover, polar coordinate transformation is applied in our paper, which unravels the superior explanation capacity of the feature transmitting angle beyond the feature distance. PL predictions of glass box ML are compared with predictions of black box ML models as well as those generated by empirical models. The glass box EBM exhibits the highest performance. The glass box ML, furthermore, sheds light on the important explanatory features and the magnitude of their effects on signal attenuation in the underlying propagation environment.

Appeals to governments for implementing basic income are contemporary. The theoretical backgrounds of the basic income notion only prescribe transferring equal amounts to individuals irrespective of their specific attributes. However, the most recent basic income initiatives all around the world are attached to certain rules with regard to the attributes of the households. This approach is facing significant challenges to appropriately recognize vulnerable groups. A possible alternative for setting rules with regard to the welfare attributes of the households is to employ artificial intelligence algorithms that can process unprecedented amounts of data. Can integrating machine learning change the future of basic income by predicting households vulnerable to future poverty? In this paper, we utilize multidimensional and longitudinal welfare data comprising one and a half million individuals’ data and a Bayesian beliefs network approach to examine the feasibility of predicting households’ vulnerability to future poverty based on the existing households’ welfare attributes.

Even when the SARS-CoV-2 pandemic recedes, evidence-based researches regarding the effectiveness of pharmaceutical and non-pharmaceutical government interventions (NPIs) remain important. In this study, SARS-CoV-2 data of 30 European countries from early 2020 up to mid-2022 are analyzed using Bayesian deep learning and statistical analysis. Four data sources containing each country’s daily NPIs (consisting of 66 government measures, virus variant distributions of 31 virus types, the vaccinated population percentages by the first five doses as well as the reported daily infections in each country) are concatenated to undertake a comprehensive assessment of the impact of SARS-CoV-2 influential factors on the spread of the virus. First, a Bayesian deep learning model is constructed with a set of input factors to predict the growth rate of the virus one month ahead of the time from each day. Based on the trained model, the importance and the marginal effect of each relevant influencing input factor on the predicted outcome of the neural network model is computed by applying the relevant explainable machine learning algorithms. Subsequently, in order to look at the problem from a different perspective and re-examine the influencing input factors inferred from the deep learning model, a Bayesian statistical inference analysis is performed within each country’s data. In the statistical analysis, for each influencing input factor, the distribution of pandemic growth rates, in the days where the selected explanatory factor has been active, is compared with the distribution of the pandemic growth rates, in the days where the selected explanatory variable has not been active. The results of the statistical inference approve the predictions of the deep learning model to a significant extent. Similar conclusions from the SARS-CoV-2 experiences of the thirty studied European countries have been drawn.

The pricing behavior of agricultural processing firms in input markets has large impacts on farmers’ and processors’ prosperity as well as the overall market structure. Despite analytical approaches to food processors’ pricing in agricultural input markets, the need for models to represent complex market features is urgent. Agent-based models (ABMs) serve as computational laboratories to understand complex markets emerging from autonomously interacting agents. Yet, individual agents within ABMs must be equipped with intelligent learning algorithms. In this paper, we propose supervised and unsupervised learning agents to simulate the pricing behavior of firms in agricultural markets’ ABMs. Supervised learning firms are pre-trained to accurately best respond to their competitors and are deemed to result in the market Nash equilibria. Unsupervised learning firms play a course of pricing interaction with their competitors without any pre-knowledge but based on deep reinforcement learning. The simulation results show that unsupervised deep learning firms are capable of approximating the pricing equilibria obtained by the supervised firms in different spatial market settings. Optimal discriminatory and uniform delivery pricing emerges in agricultural input markets with the high and intermediary importance placed on space. Free on board pricing emerges in agricultural input markets with small importance placed on space.

Even when the SARS-CoV-2 pandemic recedes, evidence-based researches regarding the effectiveness of pharmaceutical and non-pharmaceutical government interventions (NPIs) remain important. In this study, SARS-CoV-2 data of 30 European countries from early 2020 up to mid 2022 are analyzed using Bayesian machine learning. Four data sources containing each country’s daily NPIs (consisting of 66 government measures, virus variant distributions of 31 virus types, the vaccinated population percentages by the first five doses as well as the reported daily infections in each country) are brought together to undertake a comprehensive assessment of the impact of SARS-CoV-2 influential factors on the spread of the virus. First, a Bayesian deep learning model is constructed with a set of input factors to predict the growth rate of the virus one month ahead of the time from each day. Based on this, the importance and the marginal effect of each relevant influencing input factor on the predicted outcome of the neural network model is computed by applying the relevant algorithms. Subsequently, in order to examine the performed deep learning analysis, a Bayesian statistical inference analysis is performed within each country’s data. For each influencing input factor, the distribution of pandemic growth rates, in the days where the selected explanatory factor has been active, is compared with the distribution of the pandemic growth rates, in the days where the selected explanatory variable has not been active. The results of the statistical inference confirm the predictions of the deep learning model to a significant extent. Similar conclusions from the SARS-CoV-2 experiences of the thirty studied European countries have been drawn.