28 research outputs found
Jamming in complex networks with degree correlation
We study the effects of the degree-degree correlations on the pressure
congestion J when we apply a dynamical process on scale free complex networks
using the gradient network approach. We find that the pressure congestion for
disassortative (assortative) networks is lower (bigger) than the one for
uncorrelated networks which allow us to affirm that disassortative networks
enhance transport through them. This result agree with the fact that many real
world transportation networks naturally evolve to this kind of correlation. We
explain our results showing that for the disassortative case the clusters in
the gradient network turn out to be as much elongated as possible, reducing the
pressure congestion J and observing the opposite behavior for the assortative
case. Finally we apply our model to real world networks, and the results agree
with our theoretical model
Using relaxational dynamics to reduce network congestion
We study the effects of relaxational dynamics on congestion pressure in scale
free networks by analyzing the properties of the corresponding gradient
networks (Z. Toroczkai, K. E. Bassler, Nature {\bf 428}, 716 (2004)). Using the
Family model (F. Family, J. Phys. A, {\bf 19}, L441 (1986)) from surface-growth
physics as single-step load-balancing dynamics, we show that the congestion
pressure considerably drops on scale-free networks when compared with the same
dynamics on random graphs. This is due to a structural transition of the
corresponding gradient network clusters, which self-organize such as to reduce
the congestion pressure. This reduction is enhanced when lowering the value of
the connectivity exponent towards 2.Comment: 10 pages, 6 figure
Anatomy of the first six months of COVID-19 vaccination campaign in Italy.
We analyze the effectiveness of the first six months of vaccination campaign against SARS-CoV-2 in Italy by using a computational epidemic model which takes into account demographic, mobility, vaccines data, as well as estimates of the introduction and spreading of the more transmissible Alpha variant. We consider six sub-national regions and study the effect of vaccines in terms of number of averted deaths, infections, and reduction in the Infection Fatality Rate (IFR) with respect to counterfactual scenarios with the actual non-pharmaceuticals interventions but no vaccine administration. Furthermore, we compare the effectiveness in counterfactual scenarios with different vaccines allocation strategies and vaccination rates. Our results show that, as of 2021/07/05, vaccines averted 29, 350 (IQR: [16, 454-42, 826]) deaths and 4, 256, 332 (IQR: [1, 675, 564-6, 980, 070]) infections and a new pandemic wave in the country. During the same period, they achieved a -22.2% (IQR: [-31.4%; -13.9%]) IFR reduction. We show that a campaign that would have strictly prioritized age groups at higher risk of dying from COVID-19, besides frontline workers and the fragile population, would have implied additional benefits both in terms of avoided fatalities and reduction in the IFR. Strategies targeting the most active age groups would have prevented a higher number of infections but would have been associated with more deaths. Finally, we study the effects of different vaccination intake scenarios by rescaling the number of available doses in the time period under study to those administered in other countries of reference. The modeling framework can be applied to other countries to provide a mechanistic characterization of vaccination campaigns worldwide
Discrete surface growth process as a synchronization mechanism for scale free complex networks
We consider the discrete surface growth process with relaxation to the
minimum [F. Family, J. Phys. A {\bf 19} L441, (1986).] as a possible
synchronization mechanism on scale-free networks, characterized by a degree
distribution , where is the degree of a node and
his broadness, and compare it with the usually applied
Edward-Wilkinson process [S. F. Edwards and D. R. Wilkinson, Proc. R. Soc.
London Ser. A {\bf 381},17 (1982) ]. In spite of both processes belong to the
same universality class for Euclidean lattices, in this work we demonstrate
that for scale-free networks with exponents this is not true.
Moreover, we show that for these ubiquitous cases the Edward-Wilkinson process
enhances spontaneously the synchronization when the system size is increased,
which is a non-physical result. Contrarily, the discrete surface growth process
do not present this flaw and is applicable for every .Comment: 8 pages, 4 figure
Inferring high-resolution human mixing patterns for disease modeling
Mathematical and computational modeling approaches are increasingly used as
quantitative tools in the analysis and forecasting of infectious disease
epidemics. The growing need for realism in addressing complex public health
questions is however calling for accurate models of the human contact patterns
that govern the disease transmission processes. Here we present a data-driven
approach to generate effective descriptions of population-level contact
patterns by using highly detailed macro (census) and micro (survey) data on key
socio-demographic features. We produce age-stratified contact matrices for 277
sub-national administrative regions of countries covering approximately 3.5
billion people and reflecting the high degree of cultural and societal
diversity of the focus countries. We use the derived contact matrices to model
the spread of airborne infectious diseases and show that sub-national
heterogeneities in human mixing patterns have a marked impact on epidemic
indicators such as the reproduction number and overall attack rate of epidemics
of the same etiology. The contact patterns derived here are made publicly
available as a modeling tool to study the impact of socio-economic differences
and demographic heterogeneities across populations on the epidemiology of
infectious diseases.Comment: 18 pages, 7 figure
Early insights from statistical and mathematical modeling of key epidemiologic parameters of COVID-19
We report key epidemiologic parameter estimates for coronavirus disease identified in peer-reviewed publications, preprint articles, and online reports. Range estimates for incubation period were 1.8–6.9 days, serial interval 4.0–7.5 days, and doubling time 2.3–7.4 days. The effective reproductive number varied widely, with reductions attributable to interventions. Case burden and infection fatality ratios increased with patient age. Implementation of combined interventions could reduce cases and delay epidemic peak up to 1 month. These parameters for transmission, disease severity, and intervention effectiveness are critical for guiding policy decisions. Estimates will likely change as new information becomes available
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COVID-19 reopening strategies at the county level in the face of uncertainty: Multiple Models for Outbreak Decision Support
Policymakers make decisions about COVID-19 management in the face of considerable uncertainty. We convened multiple modeling teams to evaluate reopening strategies for a mid- sized county in the United States, in a novel process designed to fully express scientific uncertainty while reducing linguistic uncertainty and cognitive biases. For the scenarios considered, the consensus from 17 distinct models was that a second outbreak will occur within 6 months of reopening, unless schools and non-essential workplaces remain closed. Up to half the population could be infected with full workplace reopening; non-essential business closures reduced median cumulative infections by 82%. Intermediate reopening interventions identified no win-win situations; there was a trade-off between public health outcomes and duration of workplace closures. Aggregate results captured twice the uncertainty of individual models, providing a more complete expression of risk for decision-making purposes.Integrative Biolog
Impact of SARS-CoV-2 vaccination of children ages 5–11 years on COVID-19 disease burden and resilience to new variants in the United States, November 2021–March 2022: A multi-model study
Background: The COVID-19 Scenario Modeling Hub convened nine modeling teams to project the impact of expanding SARS-CoV-2 vaccination to children aged 5–11 years on COVID-19 burden and resilience against variant strains. Methods: Teams contributed state- and national-level weekly projections of cases, hospitalizations, and deaths in the United States from September 12, 2021 to March 12, 2022. Four scenarios covered all combinations of 1) vaccination (or not) of children aged 5–11 years (starting November 1, 2021), and 2) emergence (or not) of a variant more transmissible than the Delta variant (emerging November 15, 2021). Individual team projections were linearly pooled. The effect of childhood vaccination on overall and age-specific outcomes was estimated using meta-analyses. Findings: Assuming that a new variant would not emerge, all-age COVID-19 outcomes were projected to decrease nationally through mid-March 2022. In this setting, vaccination of children 5–11 years old was associated with reductions in projections for all-age cumulative cases (7.2%, mean incidence ratio [IR] 0.928, 95% confidence interval [CI] 0.880–0.977), hospitalizations (8.7%, mean IR 0.913, 95% CI 0.834–0.992), and deaths (9.2%, mean IR 0.908, 95% CI 0.797–1.020) compared with scenarios without childhood vaccination. Vaccine benefits increased for scenarios including a hypothesized more transmissible variant, assuming similar vaccine effectiveness. Projected relative reductions in cumulative outcomes were larger for children than for the entire population. State-level variation was observed. Interpretation: Given the scenario assumptions (defined before the emergence of Omicron), expanding vaccination to children 5–11 years old would provide measurable direct benefits, as well as indirect benefits to the all-age U.S. population, including resilience to more transmissible variants. Funding: Various (see acknowledgments)
Evaluation of the US COVID-19 Scenario Modeling Hub for informing pandemic response under uncertainty
Our ability to forecast epidemics far into the future is constrained by the many complexities of disease systems. Realistic longer-term projections may, however, be possible under well-defined scenarios that specify the future state of critical epidemic drivers. Since December 2020, the U.S. COVID-19 Scenario Modeling Hub (SMH) has convened multiple modeling teams to make months ahead projections of SARS-CoV-2 burden, totaling nearly 1.8 million national and state-level projections. Here, we find SMH performance varied widely as a function of both scenario validity and model calibration. We show scenarios remained close to reality for 22 weeks on average before the arrival of unanticipated SARS-CoV-2 variants invalidated key assumptions. An ensemble of participating models that preserved variation between models (using the linear opinion pool method) was consistently more reliable than any single model in periods of valid scenario assumptions, while projection interval coverage was near target levels. SMH projections were used to guide pandemic response, illustrating the value of collaborative hubs for longer-term scenario projections