Artificial intelligence and prevention of panic buying during disasters

Panic buying occurs when unusually excess amounts of goods are bought in an anticipation of a crisis, perceived crisis, or in the aftermath of a crisis. Especially during the ongoing COVID-19 crisis, it was influenced by individuals' threat perception, fear of uncertainty, maladaptive coping, and social modeling. Artificial intelligence (AI) is an ever-evolving field, and its role in mental health has been widely studied. The traditional aspects of AI, namely, probability, linguistics, learning, reasoning, knowledge representation, and perception, may all be helpful in targeting various correlates of panic buying. Even though literature on the use of AI and machine learning to prevent panic buying is very limited, the existing models in healthcare can be extrapolated to that effect. Predicting buying patterns during crisis, personalizing supplies, warning signals for optimal threshold of buying, surveillance in markets, and ensuring enough resources of essential items are some of the areas that can be helped by AI. However, specific research, understanding, funding, standardization, and technical optimization are needed in this area before the promising field of AI helps prevent panic buying. This chapter provides a bird's-eye view related to the intersections of AI and panic buying as well as the directions ahead. (PsycInfo Database Record (c) 2022 APA, all rights reserved)

Artificial intelligence and prevention of panic buying during disasters

Panic buying occurs when unusually excess amounts of goods are bought in an anticipation of a crisis, perceived crisis, or in the aftermath of a crisis. Especially during the ongoing COVID-19 crisis, it was influenced by individuals' threat perception, fear of uncertainty, maladaptive coping, and social modeling. Artificial intelligence (AI) is an ever-evolving field, and its role in mental health has been widely studied. The traditional aspects of AI, namely, probability, linguistics, learning, reasoning, knowledge representation, and perception, may all be helpful in targeting various correlates of panic buying. Even though literature on the use of AI and machine learning to prevent panic buying is very limited, the existing models in healthcare can be extrapolated to that effect. Predicting buying patterns during crisis, personalizing supplies, warning signals for optimal threshold of buying, surveillance in markets, and ensuring enough resources of essential items are some of the areas that can be helped by AI. However, specific research, understanding, funding, standardization, and technical optimization are needed in this area before the promising field of AI helps prevent panic buying. This chapter provides a bird's-eye view related to the intersections of AI and panic buying as well as the directions ahead. (PsycInfo Database Record (c) 2022 APA, all rights reserved)

A Comprehensive Literature Review on Children's Databases for Machine Learning Applications

The COVID-19 pandemic can be attributed as a main factor to accelerate the current digital transformation and to encourage innovation and technological adoption. Consequently, the care provided to our children, one of the significant aspects of life, needs to be adapted with the life’s changes. Children are our future and our most precious resources. They need our attention in all life domains including health, education, safety and social interaction. Nowadays, technologies have been incorporated with machine learning and it has been proven that they are more powerful, reliable and profitable. Machine learning methods have been applied by many children-related studies to generate predictive models for different applications. The efficacy of the generated models mainly rely on the constructed databases. This article carries out a comprehensive survey on available children’s databases constructed for machine-learning-based solutions with their methodologies, characteristics, challenges, and applications. First, it provides an overview of the available studies and classifies them based on their applications. Next, it defines a set of attributes and evaluates them while also shedding light on their pros and cons. The primary concerns related to collection, development and distribution of children’s databases are also discussed. This study can be considered as a guideline for researchers in multidisciplinary fields to construct reliable databases and to develop more advanced techniques. Author

Epistatic models predict mutable sites in SARS-CoV-2 proteins and epitopes.

The emergence of new variants of severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) is a major concern given their potential impact on the transmissibility and pathogenicity of the virus as well as the efficacy of therapeutic interventions. Here, we predict the mutability of all positions in SARS-CoV-2 protein domains to forecast the appearance of unseen variants. Using sequence data from other coronaviruses, preexisting to SARS-CoV-2, we build statistical models that not only capture amino acid conservation but also more complex patterns resulting from epistasis. We show that these models are notably superior to conservation profiles in estimating the already observable SARS-CoV-2 variability. In the receptor binding domain of the spike protein, we observe that the predicted mutability correlates well with experimental measures of protein stability and that both are reliable mutability predictors (receiver operating characteristic areas under the curve ∼0.8). Most interestingly, we observe an increasing agreement between our model and the observed variability as more data become available over time, proving the anticipatory capacity of our model. When combined with data concerning the immune response, our approach identifies positions where current variants of concern are highly overrepresented. These results could assist studies on viral evolution and future viral outbreaks and, in particular, guide the exploration and anticipation of potentially harmful future SARS-CoV-2 variants.

Data-driven estimation of COVID-19 community prevalence through wastewater-based epidemiology.

Wastewater-based epidemiology (WBE) has been regarded as a potential tool for the prevalence estimation of coronavirus disease 2019 (COVID-19) in the community. However, the application of the conventional back-estimation approach is currently limited due to the methodological challenges and various uncertainties. This study systematically performed meta-analysis for WBE datasets and investigated the use of data-driven models for the COVID-19 community prevalence in lieu of the conventional WBE back-estimation approach. Three different data-driven models, i.e. multiple linear regression (MLR), artificial neural network (ANN), and adaptive neuro fuzzy inference system (ANFIS) were applied to the multi-national WBE dataset. To evaluate the robustness of these models, predictions for sixteen scenarios with partial inputs were compared against the actual prevalence reports from clinical testing. The performance of models was further validated using unseen data (data sets not included for establishing the model) from different stages of the COVID-19 outbreak. Generally, ANN and ANFIS models showed better accuracy and robustness over MLR models. Air and wastewater temperature played a critical role in the prevalence estimation by data-driven models, especially MLR models. With unseen datasets, ANN model reasonably estimated the prevalence of COVID-19 (cumulative cases) at the initial phase and forecasted the upcoming new cases in 2-4 days at the post-peak phase of the COVID-19 outbreak. This study provided essential information about the feasibility and accuracy of data-driven estimation of COVID-19 prevalence through the WBE approach.

Towards Providing Effective Data-Driven Responses to Predict the Covid-19 in São Paulo and Brazil.

São Paulo is the most populous state in Brazil, home to around 22% of the country's population. The total number of Covid-19-infected people in São Paulo has reached more than 1 million, while its total death toll stands at 25% of all the country's fatalities. Joining the Brazilian academia efforts in the fight against Covid-19, in this paper we describe a unified framework for monitoring and forecasting the Covid-19 progress in the state of São Paulo. More specifically, a freely available, online platform to collect and exploit Covid-19 time-series data is presented, supporting decision-makers while still allowing the general public to interact with data from different regions of the state. Moreover, a novel forecasting data-driven method has also been proposed, by combining the so-called Susceptible-Infectious-Recovered-Deceased model with machine learning strategies to better fit the mathematical model's coefficients for predicting Infections, Recoveries, Deaths, and Viral Reproduction Numbers. We show that the obtained predictor is capable of dealing with badly conditioned data samples while still delivering accurate 10-day predictions. Our integrated computational system can be used for guiding government actions mainly in two basic aspects: real-time data assessment and dynamic predictions of Covid-19 curves for different regions of the state. We extend our analysis and investigation to inspect the virus spreading in Brazil in its regions. Finally, experiments involving the Covid-19 advance in other countries are also given.