19,971 research outputs found
Estimating Potential Infection Transmission Routes in Hospital Wards Using Wearable Proximity Sensors
Contacts between patients, patients and health care workers (HCWs) and among
HCWs represent one of the important routes of transmission of hospital-acquired
infections (HAI). A detailed description and quantification of contacts in
hospitals provides key information for HAIs epidemiology and for the design and
validation of control measures. We used wearable sensors to detect close-range
interactions ("contacts") between individuals in the geriatric unit of a
university hospital. Contact events were measured with a spatial resolution of
about 1.5 meters and a temporal resolution of 20 seconds. The study included 46
HCWs and 29 patients and lasted for 4 days and 4 nights. 14037 contacts were
recorded. The number and duration of contacts varied between mornings,
afternoons and nights, and contact matrices describing the mixing patterns
between HCW and patients were built for each time period. Contact patterns were
qualitatively similar from one day to the next. 38% of the contacts occurred
between pairs of HCWs and 6 HCWs accounted for 42% of all the contacts
including at least one patient, suggesting a population of individuals who
could potentially act as super-spreaders. Wearable sensors represent a novel
tool for the measurement of contact patterns in hospitals. The collected data
provides information on important aspects that impact the spreading patterns of
infectious diseases, such as the strong heterogeneity of contact numbers and
durations across individuals, the variability in the number of contacts during
a day, and the fraction of repeated contacts across days. This variability is
associated with a marked statistical stability of contact and mixing patterns
across days. Our results highlight the need for such measurement efforts in
order to correctly inform mathematical models of HAIs and use them to inform
the design and evaluation of prevention strategies
Data Science and Ebola
Data Science---Today, everybody and everything produces data. People produce
large amounts of data in social networks and in commercial transactions.
Medical, corporate, and government databases continue to grow. Sensors continue
to get cheaper and are increasingly connected, creating an Internet of Things,
and generating even more data. In every discipline, large, diverse, and rich
data sets are emerging, from astrophysics, to the life sciences, to the
behavioral sciences, to finance and commerce, to the humanities and to the
arts. In every discipline people want to organize, analyze, optimize and
understand their data to answer questions and to deepen insights. The science
that is transforming this ocean of data into a sea of knowledge is called data
science. This lecture will discuss how data science has changed the way in
which one of the most visible challenges to public health is handled, the 2014
Ebola outbreak in West Africa.Comment: Inaugural lecture Leiden Universit
From Social Simulation to Integrative System Design
As the recent financial crisis showed, today there is a strong need to gain
"ecological perspective" of all relevant interactions in
socio-economic-techno-environmental systems. For this, we suggested to set-up a
network of Centers for integrative systems design, which shall be able to run
all potentially relevant scenarios, identify causality chains, explore feedback
and cascading effects for a number of model variants, and determine the
reliability of their implications (given the validity of the underlying
models). They will be able to detect possible negative side effect of policy
decisions, before they occur. The Centers belonging to this network of
Integrative Systems Design Centers would be focused on a particular field, but
they would be part of an attempt to eventually cover all relevant areas of
society and economy and integrate them within a "Living Earth Simulator". The
results of all research activities of such Centers would be turned into
informative input for political Decision Arenas. For example, Crisis
Observatories (for financial instabilities, shortages of resources,
environmental change, conflict, spreading of diseases, etc.) would be connected
with such Decision Arenas for the purpose of visualization, in order to make
complex interdependencies understandable to scientists, decision-makers, and
the general public.Comment: 34 pages, Visioneer White Paper, see http://www.visioneer.ethz.c
Evolution of the digital society reveals balance between viral and mass media influence
Online social networks (OSNs) enable researchers to study the social universe
at a previously unattainable scale. The worldwide impact and the necessity to
sustain their rapid growth emphasize the importance to unravel the laws
governing their evolution. We present a quantitative two-parameter model which
reproduces the entire topological evolution of a quasi-isolated OSN with
unprecedented precision from the birth of the network. This allows us to
precisely gauge the fundamental macroscopic and microscopic mechanisms
involved. Our findings suggest that the coupling between the real pre-existing
underlying social structure, a viral spreading mechanism, and mass media
influence govern the evolution of OSNs. The empirical validation of our model,
on a macroscopic scale, reveals that virality is four to five times stronger
than mass media influence and, on a microscopic scale, individuals have a
higher subscription probability if invited by weaker social contacts, in
agreement with the "strength of weak ties" paradigm
"Go eat a bat, {Chang!}": {A}n Early Look on the Emergence of Sinophobic Behavior on {Web} Communities in the Face of {COVID}-19
The outbreak of the COVID-19 pandemic has changed our lives in unprecedented ways. In the face of the projected catastrophic consequences, many countries have enacted social distancing measures in an attempt to limit the spread of the virus. Under these conditions, the Web has become an indispensable medium for information acquisition, communication, and entertainment. At the same time, unfortunately, the Web is being exploited for the dissemination of potentially harmful and disturbing content, such as the spread of conspiracy theories and hateful speech towards specific ethnic groups, in particular towards Chinese people since COVID-19 is believed to have originated from China. In this paper, we make a first attempt to study the emergence of Sinophobic behavior on the Web during the outbreak of the COVID-19 pandemic. We collect two large-scale datasets from Twitter and 4chan's Politically Incorrect board (/pol/) over a time period of approximately five months and analyze them to investigate whether there is a rise or important differences with regard to the dissemination of Sinophobic content. We find that COVID-19 indeed drives the rise of Sinophobia on the Web and that the dissemination of Sinophobic content is a cross-platform phenomenon: it exists on fringe Web communities like \dspol, and to a lesser extent on mainstream ones like Twitter. Also, using word embeddings over time, we characterize the evolution and emergence of new Sinophobic slurs on both Twitter and /pol/. Finally, we find interesting differences in the context in which words related to Chinese people are used on the Web before and after the COVID-19 outbreak: on Twitter we observe a shift towards blaming China for the situation, while on /pol/ we find a shift towards using more (and new) Sinophobic slurs
Epidemic processes in complex networks
In recent years the research community has accumulated overwhelming evidence
for the emergence of complex and heterogeneous connectivity patterns in a wide
range of biological and sociotechnical systems. The complex properties of
real-world networks have a profound impact on the behavior of equilibrium and
nonequilibrium phenomena occurring in various systems, and the study of
epidemic spreading is central to our understanding of the unfolding of
dynamical processes in complex networks. The theoretical analysis of epidemic
spreading in heterogeneous networks requires the development of novel
analytical frameworks, and it has produced results of conceptual and practical
relevance. A coherent and comprehensive review of the vast research activity
concerning epidemic processes is presented, detailing the successful
theoretical approaches as well as making their limits and assumptions clear.
Physicists, mathematicians, epidemiologists, computer, and social scientists
share a common interest in studying epidemic spreading and rely on similar
models for the description of the diffusion of pathogens, knowledge, and
innovation. For this reason, while focusing on the main results and the
paradigmatic models in infectious disease modeling, the major results
concerning generalized social contagion processes are also presented. Finally,
the research activity at the forefront in the study of epidemic spreading in
coevolving, coupled, and time-varying networks is reported.Comment: 62 pages, 15 figures, final versio
ECONOMICS, HYSTERESIS AND AGROTERRORISM
Environmental Economics and Policy,
Yellow fever disease : density equalizing mapping and gender analysis of international research output
Background: A number of scientific papers on yellow fever have been published but no broad scientometric analysis on the published research of yellow fever has been reported. The aim of the article based study was to provide an in-depth evaluation of the yellow fever field using large-scale data analysis and employment of bibliometric indicators of production and quantity.
Methods: Data were retrieved from the Web of Science database (WoS) and analyzed as part of the NewQis platform. Then data were extracted from each file, transferred to databases and visualized as diagrams. Partially by means of density-equalizing mapping makes the findings clear and emphasizes the output of the analysis.
Results: In the study period from 1900 to 2012 a total of 5,053 yellow fever-associated items were published by 79 countries. The United States (USA) having the highest publication rate at 42% (n = 751) followed by far from Brazil (n = 203), France (n = 149) and the United Kingdom (n = 113). The most productive journals are the "Public Health Reports", the "American Journal of Tropical Medicine and Hygiene" and the "Journal of Virology". The gender analysis showed an overall steady increase of female authorship from 1950 to 2011. Brazil is the only country of the five most productive countries with a higher proportion of female scientists.
Conclusions: The present data shows an increase in research productivity over the entire study period, in particular an increase of female scientists. Brazil shows a majority of female authors, a fact that is confirmed by other studies
In-situ and Remote Sensing Networks for Environmental Monitoring and Global Assessment of Leptospirosis Outbreaks
AbstractLeptospirosis is a disease that affects human population and can claim many victims with large outbreaks associated with natural disasters. This work focuses on the technological aspects for inexpensive climate monitoring techniques based on ground and satellite sensors for obtaining information prior to disease outbreaks in under-developed regions and on water-quality sensors that can lead to radical changes in our ability to detect and abate this disease. The remote deployment of such sensors in areas where outbreaks can occur can help in enhancingin real-time the spatial and temporal resolution of information and allows unattended operation that will be particularly useful for monitoring under extreme climate events. Such types of monitoring advancements, when coupled with regular geographical, population and habitat monitoring can assess the hazards and risks to local population prior to a disease outbreak. Then in the eventual aftermath, it can assist in identification of affected geographical locations where abatement solutions will be required, and eventually in the assessment of the effectiveness of control measures. This work explores recent releases of open global observation data and a range of in-situ environmental monitoring tools of increasing complexity for measuring several parameters andfor detecting contaminants and pathogens that were previously irresolvable due to the high degree of complexityinthe diagnosis of this disease
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