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. We developed models connecting people's mobility, socioeconomic, and demographic factors with severity of the COVID pandemic in the US at the County level. These models can be used to inform policymakers and other stakeholders on measures to be taken during a pandemic. They also enable in-depth analysis of factors affecting the relationship between mobility and the severity of the disease. With the exception of one model, that of COVID recovery time, the resulting models accurately predict the vulnerability and severity metrics and rank the explanatory variables in the order of statistical importance. We also analyze and explain why recovery time did not allow for a good model.

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. Social cohesion is considered to be"the glue that holds society together, which can be better examined in a critical situation. There is no consensus on measuring social cohesion, but recent literature indicates that social media communications and communities play an essential role in today's disaster mitigation strategies.This research explores how to quantify social cohesion through social media outlets during disasters. The approach involves combining and implementing text processing techniques and graph network analysis to understand the relationships between nine different types of participants during hurricanes Harvey, Irma, and Maria. Visualizations are employed to illustrate these connections, their evolution before, during, and after disasters, and the degree of social cohesion throughout their timeline. The proposed measurement of social cohesion through social media networks presented in this work can provide future risk management and disaster mitigation policies. This social cohesion measure identifies the types of actors in a social network and how this network varies daily. Therefore, decisionmakers could use this measure to release strategic communication before, during, and after a disaster strikes, thus providing relevant information to people in need.

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. In this study, the BERT model is first fine-tuned using historical data and then it is used to classify the tweets associated with hurricanes Dorian and Harvey based on the type of information provided; and alongside, the network between users is constructed based on the retweets and replies on Twitter. Afterwards, some network metrics are used to measure the diffusion rate of each type of disaster-motivated information. The results show that the messages by disaster eyewitnesses get the least spread while the posts by governments and media have the highest diffusion rates through the network. Additionally, the "cautions and advice" messages get the most spread among other information types while "infrastructure and utilities" and "affected individuals" messages get the least diffusion even compared with "sympathy and support". The analysis suggests that facilitating the propagation of information provided by affected individuals, using AI models, will be a valuable strategy to pursue in order to accelerate communication between affected individuals and survival groups during the disaster and aftermath.

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. PARTICIPANTS: All patients with confirmed COVID-19 hospitalised from 25 March to 7 September 2020 were included. End of study: 7 November 2020. PRIMARY OUTCOME MEASURES: Patient survival analysed by the Kaplan-Meier method and comparison of factors by the log-rank test. Mortality risk factors analysed by multivariate Cox's proportional-hazard model. RESULTS: One thousand ten patients included: 386 (38%) died, 618 (61%) alive at discharge and six (0.6%) remained hospitalised. There was predominance of men (63%) and high frequency of overweight-obesity (71%); hypertension (54%); diabetes (40%); and lung (9%), cardiovascular (8%) and kidney diseases (11%); all of them significantly more frequent in non-survivors. Overweight-obesity was not different between groups, but severity of disease (Manchester Triage System and quick Sequential Organ Failure Assessment) was significantly worse in non-survivors, who were also significantly older (65 vs 45 years, respectively) and had haematological, biochemical, coagulation and inflammatory biomarkers more altered than survivors. Mortality predictors were invasive mechanical ventilation (IMV; OR 3.31, p<0.0001), admission to intensive care unit (ICU; OR 2.18, p<0.0001), age (OR 1.02, p<0.0001), Manchester Triage System (urgent OR 1.44, p=0.02; immediate/very urgent OR 2.02, p=0.004), baseline C reactive protein (CRP; OR 1.002, p=0.009) and antecedent of kidney disease (OR 1.58, p=0.04) CONCLUSIONS: Mortality in hospitalised patients with COVID-19 in this emerging country centre seemed to be higher than in developed countries. Patients displayed a high frequency of risk factors for poor outcome, but the need for IMV, ICU admission, older age, more severe disease at admission, antecedent of kidney disease and higher CRP levels significantly predicted mortality.

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. Added stressors placed on caregivers, economic uncertainty, job loss or disruption to livelihoods and social isolation may have led to a rise in children's experience of violence in the home. Extended online presence by children may have resulted in increased exposure to abusive content and cyberbullying. PARTICIPANTS AND SETTING: This study uses testimonial-based and conversational-based data collected from social media users. METHODS: Conversations on Twitter were reviewed to measure increases in abusive or hateful content, and cyberbullying, while testimonials from Reddit forums were examined to monitor changes in references to family violence before and after the start of the stay-at-home restrictions. RESULTS: Violence-related subreddits were among the topics with the highest growth after the COVID-19 outbreak. The analysis of Twitter data shows a significant increase in abusive content generated during the stay-at-home restrictions. CONCLUSIONS: The collective experience of the COVID-19 pandemic and related containment measures offers insights into the wide-ranging risks that children are exposed to in times of crisis. As societies shift towards a new normal, which places emerging technology, remote working and online learning at its center, and in anticipation of similar future threats, governments and other stakeholders need to put in place measures to protect children from violence.

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. Assessing the economic impact (EI) of electricity outages after a service disruption is a challenging task, particularly when interruption costs vary by the type of electric power use (e.g., residential, commercial, industrial). In contrast with most of the literature, this work proposes an approach to spatially evaluate EIs of disruptions to particular components of the electric power network, thus enabling resilience-based preparedness planning from economic and community perspectives. Our contribution is a mix-method approach that combines EI evaluation, component importance analysis, and GIS visualization for decision making. We integrate geographic information systems and an economic evaluation of sporadic electric power outages to provide a tool to assist with prioritizing restoration of power in commercial areas that have the largest impact. By making use of public data describing commercial market value, gross domestic product, and electric area distribution, this article proposes a method to evaluate the EI experienced by commercial districts. A geospatial visualization is presented to observe and compare the areas that are more vulnerable in terms of EI based on the areas covered by each distribution substation. Additionally, a heat map is developed to observe the behavior of disrupted substations to determine the important component exhibiting the highest EI. The proposed resilience analytics approach is applied to analyze outages of substations in the boroughs of New York City.

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. In this work, we study community resilience in terms of the mood variations of the community. We have developed a method to characterize the mood steady-state of online social networks and to analyze how this steady-state is affected under certain perturbations or events that affect a community. We applied this method to study community behavior for three real social network situations, with promising results.

[Prevalence of cardiovascular risk factors in a population of health-care workers]. / Prevalencia de factores de riesgo cardiovascular en trabajadores de la salud.

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. Clinical history, physical exam, and blood tests were performed. RESULTS: Mean age 41 ± 9 years, 76% women. Hypertension prevalence was 19%, diabetes mellitus 9.6%, dyslipidemia 78%, overweight and obesity 73%, metabolic syndrome (MS) 32.5%, and smoking 19%. The following significant associations (p < 0.05) were found: MS with medical asisstants (OR: 2.73, CI 95%: 1.31-5.69) and nutritionist (OR: 2.6, CI 95%: 1.31-5.24); obesity with administrative personnel (OR: 3.64, CI 95%: 1.40-7.46); dyslipidemia with medical asisstants (OR: 2.58, CI 95%: 1.15-6.34). In the whole sample, the probability to have a vascular event in the following 10 years was 10%. CONCLUSION: Prevalence of CVRF was high in this sample of health-care workers and did not seem to be different from those in general population. Medical assistants, nutritionist, and administrative personnel displayed a higher risk. It is necessary to create programs to promote healthy lifestyle and to improve the epidemiological profile of health-care workers.

Introducción: el objetivo de este trabajo es determinar la prevalencia de los factores de riesgo cardiovascular (FRCV) y su asociación con actividad laboral (AL) en trabajadores de dos hospitales de enseñanza de tercer nivel de atención del IMSS. Métodos: estudio descriptivo que incluyó a trabajadores ≥ 18 años. Se realizó historia clínica, examen físico y pruebas de laboratorio para identificar FRCV y asociarlos con AL. Resultados: se estudió un total de 1089 trabajadores, con edad de 41 ± 9 años, el 76% fueron mujeres. La prevalencia de hipertensión fue de 19%, diabetes mellitus 9.6%, dislipidemia 78%, sobrepeso y obesidad 73%; síndrome metabólico (SM) 32.5%, tabaquismo 19%. El SM se asoció con el área de asistentes médicas (OR: 2.73, IC 95%: 1.31-5.69) y nutrición/dietética (OR: 2.6, IC 95%: 1.31-5.24). La obesidad con el área administrativa (OR 3.64 IC 95%: 1.40-7.46). La dislipidemia con el área de asistentes médicas (OR 2.58, IC 95%: 1.15-6.34). La probabilidad de sufrir evento vascular en 10 años fue de 10%. Conclusiones: la prevalencia de FRCV fue alta y no es diferente a la de la población general. Las actividades laborales en riesgo fueron: asistentes médicas, nutricionistas y personal administrativo. Es necesario reorientar programas de promoción de la salud en unidades médicas para mejorar el perfil epidemiológico de los trabajadores.

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. Further, "costs" of network resilience are often shared across multiple infrastructures and industries that rely upon those networks, particularly when such networks become inoperable in the face of disruptive events. As such, this work integrates the quantitative resilience approach with a model describing the regional, multi-industry impacts of a disruptive event to measure the interdependent impacts of network resilience. The approaches discussed in this article are deployed in a case study of an inland waterway transportation network, the Mississippi River Navigation System.

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. Ultimately, a means to optimize network resilience strategies is discussed, primarily through an adaption of the Copeland Score for nonparametric stochastic ranking. The measures of resilience and optimization techniques are applied to inland waterway networks, an important mode in the larger multimodal transportation network upon which we rely for the flow of commodities. We provide a case study analyzing and planning for the resilience of commodity flows along the Mississippi River Navigation System to illustrate the usefulness of the proposed metrics.