Applicability of Artificial Societies to Evaluate Health Care Policies.

We propose the use of artificial societies to support health care policymakers in understanding and forecasting the impact and adverse effects of policies. Artificial societies extend the agent-based modeling paradigm using social science research to allow integrating the human component. We simulate individuals as socially capable software agents with their individual parameters in their situated environment including social networks.

Internet-of-Things Devices in Support of the Development of Echoic Skills among Children with Autism Spectrum Disorder.

A significant therapeutic challenge for people with disabilities is the development of verbal and echoic skills. Digital voice assistants (DVAs), such as Amazon's Alexa, provide networked intelligence to billions of Internet-of-Things devices and have the potential to offer opportunities to people, such as those diagnosed with autism spectrum disorder (ASD), to advance these necessary skills. Voice interfaces can enable children with ASD to practice such skills at home; however, it remains unclear whether DVAs can be as proficient as therapists in recognizing utterances by a developing speaker.

Computational Simulation Is a Vital Resource for Navigating the COVID-19 Pandemic.

Introduction: COVID-19 has prompted the extensive use of computational models to understand the trajectory of the pandemic. This article surveys the kinds of dynamic simulation models that have been used as decision support tools and to forecast the potential impacts of nonpharmaceutical interventions (NPIs). We developed the Values in Viral Dispersion model, which emphasizes the role of human factors and social networks in viral spread and presents scenarios to guide policy responses.

Minding morality: ethical artificial societies for public policy modeling.

Public policies are designed to have an impact on particular societies, yet policy-oriented computer models and simulations often focus more on articulating the policies to be applied than on realistically rendering the cultural dynamics of the target society. This approach can lead to policy assessments that ignore crucial social contextual factors. For example, by leaving out distinctive moral and normative dimensions of cultural contexts in artificial societies, estimations of downstream policy effectiveness fail to account for dynamics that are fundamental in human life and central to many public policy challenges.

A content analysis-based approach to explore simulation verification and identify its current challenges.

Verification is a crucial process to facilitate the identification and removal of errors within simulations. This study explores semantic changes to the concept of simulation verification over the past six decades using a data-supported, automated content analysis approach. We collect and utilize a corpus of 4,047 peer-reviewed Modeling and Simulation (M&S) publications dealing with a wide range of studies of simulation verification from 1963 to 2015.

Prevalence and risk factors for low back pain among university teaching staff in Nairobi, Kenya: a cross-sectional study.

: To date, there are few studies carried out on low back pain (LBP) among university teaching staff in developing countries despite academics being a high-risk group for LBP. In Kenya, to the best of our knowledge, there are no published studies that have investigated risk factors for LBP among teaching staff. The objectives of this study were to estimate the prevalence of LBP among teaching staff of the University of Nairobi (UoN), during the period June 2016 - May 2017, and to identify its socio-demographic and work-related risk factors.

Toward Live Virtual Constructive Simulations in Healthcare Learning.

This article explores the combination of live, virtual, and constructive (LVC) simulations in healthcare. Live, virtual, and constructive simulations have long existed in the military, but their consideration (and deployment) in medical and healthcare domains is relatively new. We conducted a review on LVC- its current application in the military domain -and highlight an approach, challenges, and present suggestions for its implementation in healthcare learning.

Identifying key papers within a journal via network centrality measures.

This article examines the extent to which existing network centrality measures can be used (1) as filters to identify a set of papers to start reading within a journal and (2) as article-level metrics to identify the relative importance of a paper within a journal. We represent a dataset of published papers in the Public Library of Science (PLOS) via a co-citation network and compute three established centrality metrics for each paper in the network: closeness, betweenness, and eigenvector. Our results show that the network of papers in a journal is scale-free and that eigenvector centrality (1) is an effective filter and article-level metric and (2) correlates well with citation counts within a given journal.

You Are What You Tweet: Connecting the Geographic Variation in America's Obesity Rate to Twitter Content.

We conduct a detailed investigation of the relationship among the obesity rate of urban areas and expressions of happiness, diet and physical activity on social media. We do so by analyzing a massive, geo-tagged data set comprising over 200 million words generated over the course of 2012 and 2013 on the social network service Twitter. Among many results, we show that areas with lower obesity rates: (1) have happier tweets and frequently discuss (2) food, particularly fruits and vegetables, and (3) physical activities of any intensity.