Tag: epidemiology

Postdoc Position(s) at the Max Planck – University of Helsinki Centre for Social Inequalities in Population Health

The Max Planck – University of Helsinki Center for Social Inequalities in Population Health is currently seeking to appoint one or more full-time post-doctoral researchers. We welcome applications from researchers with a PhD in demography, sociology, statistics, epidemiology, public health, biology, anthropology, economics, computer science, and allied fields. The successful candidate(s) will work on the role of genetic factors in shaping health inequalities, and/or they will develop novel techniques for leveraging genetic data. We are also open to applicants interested in the other research themes of the Center (family and health, health inequalities in an international perspective), and in other topics covered in the Department Social Demography at the Max Planck Institute for Demographic Research (MPIDR), including fertility, mortality and morbidity, and labor markets. The successful candidate(s) will develop their own agenda within the Center, and they will contribute their skills and knowledge to other projects in the Center and to the MPIDR. We are seeking creative, self-driven, and collaborative scholars. Advanced knowledge of quantitative methods and statistical software such as R, Python, or Stata is required.

Read the full call at: www.demogr.mpg.de

A Generic Encapsulation to Unravel Social Spreading of a Pandemic: An Underlying Architecture

Saad Alqithami
Computers 2021, 10(1), 12

Cases of a new emergent infectious disease caused by mutations in the coronavirus family, called “COVID-19,” have spiked recently, affecting millions of people, and this has been classified as a global pandemic due to the wide spread of the virus. Epidemiologically, humans are the targeted hosts of COVID-19, whereby indirect/direct transmission pathways are mitigated by social/spatial distancing. People naturally exist in dynamically cascading networks of social/spatial interactions. Their rational actions and interactions have huge uncertainties in regard to common social contagions with rapid network proliferations on a daily basis. Different parameters play big roles in minimizing such uncertainties by shaping the understanding of such contagions to include cultures, beliefs, norms, values, ethics, etc. Thus, this work is directed toward investigating and predicting the viral spread of the current wave of COVID-19 based on human socio-behavioral analyses in various community settings with unknown structural patterns. We examine the spreading and social contagions in unstructured networks by proposing a model that should be able to (1) reorganize and synthesize infected clusters of any networked agents, (2) clarify any noteworthy members of the population through a series of analyses of their behavioral and cognitive capabilities, (3) predict where the direction is heading with any possible outcomes, and (4) propose applicable intervention tactics that can be helpful in creating strategies to mitigate the spread. Such properties are essential in managing the rate of spread of viral infections. Furthermore, a novel spectra-based methodology that leverages configuration models as a reference network is proposed to quantify spreading in a given candidate network. We derive mathematical formulations to demonstrate the viral spread in the network structures.

Read the full article at: www.mdpi.com