Fetzer, Thiemo, Marc Witte, Lukas Hensel, Jon Jachimowicz, Johannes Haushofer, Andriy Ivchenko, Stefano Caria, et al. 2020. “Global Behaviors and Perceptions in the COVID-19 Pandemic.” PsyArXiv. April 16. doi:10.31234/osf.io/3kfmh

 

We conducted a large-scale survey covering 58 countries and over 100,000 respondents between late March and early April 2020 to study beliefs and attitudes towards citizens’ and governments’ responses to the COVID-19 pandemic. Most respondents reacted strongly to the crisis: they report engaging in social distancing and hygiene behaviors, and believe that strong policy measures, such as shop closures and curfews, are necessary. They also believe that their government and their country’s citizens are not doing enough and underestimate the degree to which others in their country support strong behavioral and policy responses to the pandemic. The perception of a weak government and public response is associated with higher levels of worries and depression. Using both cross-country panel data and an event-study, we additionally show that strong government reactions correct misperceptions, and reduce worries and depression. Our findings highlight that policy-makers not only need to consider how their decisions affect the spread of COVID-19, but also how such choices influence the mental health of their population.

Source: psyarxiv.com

Dataset available 

Anne-Marie Grisogono

Front. Robot. AI, 21 April 2020

 

How does AI need to evolve in order to better support more effective decision-making in managing the many complex problems we face at every scale, from global climate change, collapsing ecosystems, international conflicts and extremism, through to all the dimensions of public policy, economics, and governance that affect human well-being? Research in complex decision-making at an individual human level (understanding of what constitutes more, and less, effective decision-making behaviors, and in particular the many pathways to failures in dealing with complex problems), informs a discussion about the potential for AI to aid in mitigating those failures and enabling a more robust and adaptive (and therefore more effective) decision-making framework, calling for AI to move well-beyond the current envelope of competencies.

Source: www.frontiersin.org

Benjamin Rader, Samuel Scarpino, Anjalika Nande, Alison Hill, Benjamin Dalziel, Robert Reiner Jr., David Pigott, Bernardo Gutierrez, Munik Shrestha, John Brownstein, Marcia Castro, Huaiyu Tian, Bryan Grenfell, Oliver Pybus, Jessica Metcalf, Moritz U.G. Kraemer

 

The COVID-19 pandemic is straining public health systems worldwide and major non-pharmaceutical interventions have been implemented to slow its spread. During the initial phase of the outbreak the spread was primarily determined by human mobility. Yet empirical evidence on the effect of key geographic factors on local epidemic spread is lacking. We analyse highly-resolved spatial variables for cities in China together with case count data in order to investigate the role of climate, urbanization, and variation in interventions across China. Here we show that the epidemic intensity of COVID-19 is strongly shaped by crowding, such that epidemics in dense cities are more spread out through time, and denser cities have larger total incidence. Observed differences in epidemic intensity are well captured by a metapopulation model of COVID-19 that explicitly accounts for spatial hierarchies. Densely-populated cities worldwide may experience more prolonged epidemics. Whilst stringent interventions can shorten the time length of these local epidemics, although these may be difficult to implement in many affected settings.

Source: www.medrxiv.org

Deisy Morselli Gysi, Ítalo Do Valle, Marinka Zitnik, Asher Ameli, Xiao Gan, Onur Varol, Helia Sanchez, Rebecca Marlene Baron, Dina Ghiassian, Joseph Loscalzo, Albert-László Barabási

 

The COVID-19 pandemic demands the rapid identification of drug-repurpusing candidates. In the past decade, network medicine had developed a framework consisting of a series of quantitative approaches and predictive tools to study host-pathogen interactions, unveil the molecular mechanisms of the infection, identify comorbidities as well as rapidly detect drug repurpusing candidates. Here, we adapt the network-based toolset to COVID-19, recovering the primary pulmonary manifestations of the virus in the lung as well as observed comorbidities associated with cardiovascular diseases. We predict that the virus can manifest itself in other tissues, such as the reproductive system, and brain regions, moreover we predict neurological comorbidities. We build on these findings to deploy three network-based drug repurposing strategies, relying on network proximity, diffusion, and AI-based metrics, allowing to rank all approved drugs based on their likely efficacy for COVID-19 patients, aggregate all predictions, and, thereby to arrive at 81 promising repurposing candidates. We validate the accuracy of our predictions using drugs currently in clinical trials, and an expression-based validation of selected candidates suggests that these drugs, with known toxicities and side effects, could be moved to clinical trials rapidly.

Source: arxiv.org

Thomas Asikis, Evangelos Pournaras

Future Generation Computer Systems

 

•A generic, novel framework for measuring & optimizing privacy-utility trade-offs.

•An analytical proof & application to real-world data from a Smart-Grid pilot project.

•Privacy-utility tradeoffs are optimized under informational self-evaluation.

Source: www.sciencedirect.com