Modeling mobility, risk, and pandemic severity during the first year of COVID.

During the COVID-19 pandemic, most US states have taken measures of varying strength, enforcing social and physical distancing in the interest of public safety. These measures have enabled counties and states, with varying success, to slow down the propagation and mortality of the disease by matching the propagation rate to the capacity of medical facilities. However, each state's government was making its decisions based on limited information and without the benefit of being able to look retrospectively at the problem at large and to analyze the commonalities and the differences among the states and the counties across the country.

Content-based user classifier to uncover information exchange in disaster-motivated networks.

Disasters strike communities around the world, with a reduced time-frame for warning and action leaving behind high rates of damage, mortality, and years in rebuilding efforts. For the past decade, social media has indicated a positive role in communicating before, during, and after disasters. One important question that remained un-investigated is that whether social media efficiently connect affected individuals to disaster relief agencies, and if not, how AI models can use historical data from previous disasters to facilitate information exchange between the two groups.

Social Cohesion: Mitigating Societal Risk in Case Studies of Digital Media in Hurricanes Harvey, Irma, and Maria.

Natural disasters affect thousands of communities every year, leaving behind human losses, billions of dollars in rebuilding efforts, and psychological affectation in survivors. How fast a community recovers from a disaster or even how well a community can mitigate risk from disasters depends on how resilient that community is. One main factor that influences communities' resilience is how a community comes together in times of need.

Risk factors for mortality of adult patients with COVID-19 hospitalised in an emerging country: a cohort study.

Objective: To describe mortality of in-hospital patients with COVID-19 and compare risk factors between survivors and non-survivors.

Design: Prospective cohort of adult inpatients.

Setting: Tertiary healthcare teaching hospital in Guadalajara, Mexico.

Using social media data for assessing children's exposure to violence during the COVID-19 pandemic.

Background: The COVID-19 pandemic brought unforeseen challenges that could forever change the way societies prioritize and deal with public health issues. The approaches to contain the spread of the virus have entailed governments issuing recommendations on social distancing, lockdowns to restrict movements, and suspension of services.

Objective: There are concerns that the COVID-19 crisis and the measures adopted by countries in response to the pandemic may have led to an upsurge in violence against children.

Evaluating and Visualizing the Economic Impact of Commercial Districts Due to an Electric Power Network Disruption.

Critical infrastructure networks enable social behavior, economic productivity, and the way of life of communities. Disruptions to these cyber-physical-social networks highlight their importance. Recent disruptions caused by natural phenomena, including Hurricanes Harvey and Irma in 2017, have particularly demonstrated the importance of functioning electric power networks.

A Community Perspective on Resilience Analytics: A Visual Analysis of Community Mood.

Social networks are ubiquitous in everyday life. Although commonly analyzed from a perspective of individual interactions, social networks can provide insights about the collective behavior of a community. It has been shown that changes in the mood of social networks can be correlated to economic trends, public demonstrations, and political reactions, among others.

[Prevalence of cardiovascular risk factors in a population of health-care workers].

Background: To determine the prevalence of cardiovascular risk factors (CVRF) in healthcare workers from two tertiary-care hospitals of the Mexican Institute of Social Security, as well as their association with professional activities (PA).

Methods: Descriptive study. One-thousand eighty-nine health-care workers ≥ 18 years were included.

Inherent costs and interdependent impacts of infrastructure network resilience.

Recent studies in system resilience have proposed metrics to understand the ability of systems to recover from a disruptive event, often offering a qualitative treatment of resilience. This work provides a quantitative treatment of resilience and focuses specifically on measuring resilience in infrastructure networks. Inherent cost metrics are introduced: loss of service cost and total network restoration cost.

Stochastic measures of network resilience: applications to waterway commodity flows.

Given the ubiquitous nature of infrastructure networks in today's society, there is a global need to understand, quantify, and plan for the resilience of these networks to disruptions. This work defines network resilience along dimensions of reliability, vulnerability, survivability, and recoverability, and quantifies network resilience as a function of component and network performance. The treatment of vulnerability and recoverability as random variables leads to stochastic measures of resilience, including time to total system restoration, time to full system service resilience, and time to a specific α% resilience.