10,386 research outputs found
INDEMICS: An Interactive High-Performance Computing Framework for Data Intensive Epidemic Modeling
We describe the design and prototype implementation of Indemics (_Interactive; Epi_demic; _Simulation;)—a modeling environment utilizing high-performance computing technologies for supporting complex epidemic simulations. Indemics can support policy analysts and epidemiologists interested in planning and control of pandemics. Indemics goes beyond traditional epidemic simulations by providing a simple and powerful way to represent and analyze policy-based as well as individual-based adaptive interventions. Users can also stop the simulation at any point, assess the state of the simulated system, and add additional interventions. Indemics is available to end-users via a web-based interface.
Detailed performance analysis shows that Indemics greatly enhances the capability and productivity of simulating complex intervention strategies with a marginal decrease in performance. We also demonstrate how Indemics was applied in some real case studies where complex interventions were implemented
A multi-group SEIRA model for the spread of COVID-19 among heterogeneous populations
The outbreak and propagation of COVID-19 have posed a considerable challenge
to modern society. In particular, the different restrictive actions taken by
governments to prevent the spread of the virus have changed the way humans
interact and conceive interaction. Due to geographical, behavioral, or economic
factors, different sub-groups among a population are more (or less) likely to
interact, and thus to spread/acquire the virus. In this work, we present a
general multi-group SEIRA model for representing the spread of COVID-19 among a
heterogeneous population and test it in a numerical case of study. By
highlighting its applicability and the ease with which its general formulation
can be adapted to particular studies, we expect our model to lead us to a
better understanding of the evolution of this pandemic and to better
public-health policies to control it
Dashboard COMPRIME_COMPRI_MOv: Multiscalar Spatio-Temporal Monitoring of the COVID-19 Pandemic in Portugal
Due to its novelty, the recent pandemic of the coronavirus disease (COVID-19), which is associated with the spread of the new severe acute respiratory syndrome coronavirus (SARS-CoV-2), triggered the public’s interest in accessing information, demonstrating the importance of obtaining and analyzing credible and updated information from an epidemiological surveillance context. For this purpose, health authorities, international organizations, and university institutions have published online various graphic and cartographic representations of the evolution of the pandemic with daily updates that allow the almost real-time monitoring of the evolutionary behavior of the spread, lethality, and territorial distribution of the disease. The purpose of this article is to describe the technical solution and the main results associated with the publication of the COMPRIME_COMPRI_MOv dashboard for the dissemination of information and multi-scale knowledge of COVID-19. Under two rapidly implementing research projects for innovative solutions to respond to the COVID-19 pandemic, promoted in Portugal by the FCT (Foundation for Science and Technology), a website was created. That website brings together a diverse set of variables and indicators in a dynamic and interactive way that reflects the evolutionary behavior of the pandemic from a multi-scale perspective, in Portugal, constituting itself as a system for monitoring the evolution of the pandemic. In the current situation, this type of exploratory solutions proves to be crucial to guarantee everyone’s access to information while simultaneously emerging as an epidemiological surveillance tool that is capable of assisting decision-making by public authorities with competence in defining control policies and fight the spread of the new coronavirusinfo:eu-repo/semantics/publishedVersio
Modeling the Worldwide Spread of Pandemic Influenza: Baseline Case and Containment Interventions
We present a study of the worldwide spread of a pandemic influenza and its
possible containment at a global level taking into account all available
information on air travel. We studied a metapopulation stochastic epidemic
model on a global scale that considers airline travel flow data among urban
areas. We provided a temporal and spatial evolution of the pandemic with a
sensitivity analysis of different levels of infectiousness of the virus and
initial outbreak conditions (both geographical and seasonal). For each
spreading scenario we provided the timeline and the geographical impact of the
pandemic in 3,100 urban areas, located in 220 different countries. We compared
the baseline cases with different containment strategies, including travel
restrictions and the therapeutic use of antiviral (AV) drugs. We show that the
inclusion of air transportation is crucial in the assessment of the occurrence
probability of global outbreaks. The large-scale therapeutic usage of AV drugs
in all hit countries would be able to mitigate a pandemic effect with a
reproductive rate as high as 1.9 during the first year; with AV supply use
sufficient to treat approximately 2% to 6% of the population, in conjunction
with efficient case detection and timely drug distribution. For highly
contagious viruses (i.e., a reproductive rate as high as 2.3), even the
unrealistic use of supplies corresponding to the treatment of approximately 20%
of the population leaves 30%-50% of the population infected. In the case of
limited AV supplies and pandemics with a reproductive rate as high as 1.9, we
demonstrate that the more cooperative the strategy, the more effective are the
containment results in all regions of the world, including those countries that
made part of their resources available for global use.Comment: 16 page
A Comparative Analysis of Influenza Vaccination Programs
The threat of avian influenza and the 2004-2005 influenza vaccine supply
shortage in the United States has sparked a debate about optimal vaccination
strategies to reduce the burden of morbidity and mortality caused by the
influenza virus. We present a comparative analysis of two classes of suggested
vaccination strategies: mortality-based strategies that target high risk
populations and morbidity-based that target high prevalence populations.
Applying the methods of contact network epidemiology to a model of disease
transmission in a large urban population, we evaluate the efficacy of these
strategies across a wide range of viral transmission rates and for two
different age-specific mortality distributions. We find that the optimal
strategy depends critically on the viral transmission level (reproductive rate)
of the virus: morbidity-based strategies outperform mortality-based strategies
for moderately transmissible strains, while the reverse is true for highly
transmissible strains. These results hold for a range of mortality rates
reported for prior influenza epidemics and pandemics. Furthermore, we show that
vaccination delays and multiple introductions of disease into the community
have a more detrimental impact on morbidity-based strategies than
mortality-based strategies. If public health officials have reasonable
estimates of the viral transmission rate and the frequency of new introductions
into the community prior to an outbreak, then these methods can guide the
design of optimal vaccination priorities. When such information is unreliable
or not available, as is often the case, this study recommends mortality-based
vaccination priorities
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