Month: February 2020

Mining social media data for biomedical signals and health-related behavior

Rion Brattig Correia, Ian B. Wood, Johan Bollen, Luis M. Rocha

 

Social media data has been increasingly used to study biomedical and health-related phenomena. From cohort level discussions of a condition to planetary level analyses of sentiment, social media has provided scientists with unprecedented amounts of data to study human behavior and response associated with a variety of health conditions and medical treatments. Here we review recent work in mining social media for biomedical, epidemiological, and social phenomena information relevant to the multilevel complexity of human health. We pay particular attention to topics where social media data analysis has shown the most progress, including pharmacovigilance, sentiment analysis especially for mental health, and other areas. We also discuss a variety of innovative uses of social media data for health-related applications and important limitations in social media data access and use.

Source: arxiv.org

Computational Social Science and Complex Systems, edited by J. Kertész, R.N. Mantegna, S. Miccichè

For many years, the development of large-scale quantitative social science was hindered by a lack of data. Traditional methods of data collection like surveys were very useful, but were limited. The situation has of course changed with the development of computing and information communication technology, and we now live in a world of data deluge, where the question has become how to extract important information from the plethora of data that can be accessed. Big Data has made it possible to study societal questions which were once impossible to deal with, but new tools and new multidisciplinary approaches are required. Physicists, together with economists, sociologists, computer scientists, etc. have played an important role in their development.

 

This book presents the 9 lectures delivered at the CCIII Summer Course Computational Social Science and Complex Systems, held as part of the International School of Physics Enrico Fermi in Varenna, Italy, from 16-21 July 2018. The course had the aim of presenting some of the recent developments in the interdisciplinary fields of computational social science and econophysics to PhD students and young researchers, with lectures focused on recent problems investigated in computational social science.

 

Addressing some of the basic questions and many of the subtleties of the emerging field of computational social science, the book will be of interest to students, researchers and advanced research professionals alike.

Source: www.iospress.nl

Early epidemiological analysis of the 2019-nCoV outbreak based on a crowdsourced data

Kaiyuan Sun, Jenny Chen, Cécile Viboud.

 

Starting in December 2019, Chinese health authorities have been closely monitoring a cluster of pneumonia cases in the city of Wuhan, in Hubei Province. It has been determined that the causing agent of the viral pneumonia among affected individuals is a new coronavirus (2019-nCoV). As of January 29, 2020, a total of 6,088 cases have been detected and confirmed in Mainland China, with more than 70 additional cases detected and confirmed internationally in Japan, Thailand, South Korea, Taiwan, Singapore, Vietnam, United States, France, Australia, Nepal, Canada, Cambodia, Sri Lanka, United Arab Emirates, Finland, and Germany. By using the cases detected outside China we are providing estimates of size of the Wuhan outbreak as of January 29th, 2020.​ By using an estimate of 10 days from exposure to detection and an effective population of 20 million people in Wuhan catchment area the estimated median size of the Wuhan outbreak is 31,200 infections [95% CI: 23,400-40,400]. Technical details are in the full report available below. 

Source: www.mobs-lab.org