Browse
Submit Data
Databases
API
Help

Dataset Information

21 Views

0 Connections

0 Citations

0 Reanalyses

0 Downloads

Omics score: 0

Network percolation reveals adaptive bridges of the mobility network response to COVID-19

ABSTRACT: Human mobility is crucial to understand the transmission pattern of COVID-19 on spatially embedded geographic networks. This pattern seems unpredictable, and the propagation appears unstoppable, resulting in over 350,000 death tolls in the U.S. by the end of 2020. Here, we create the spatiotemporal inter-county mobility network using 10 TB (Terabytes) trajectory data of 30 million smart devices in the U.S. in the first six months of 2020. We investigate the bond percolation process by removing the weakly connected edges. As we increase the threshold, the mobility network nodes become less interconnected and thus experience surprisingly abrupt phase transitions. Despite the complex behaviors of the mobility network, we devised a novel approach to identify a small, manageable set of recurrent critical bridges, connecting the giant component and the second-largest component. These adaptive links, located across the United States, played a key role as valves connecting components in divisions and regions during the pandemic. Beyond, our numerical results unveil that network characteristics determine the critical thresholds and the bridge locations. The findings provide new insights into managing and controlling the connectivity of mobility networks during unprecedented disruptions. The work can also potentially offer practical future infectious diseases both globally and locally.

SUBMITTER: Deng H

PROVIDER: S-EPMC8577732 | biostudies-literature |

REPOSITORIES: biostudies-literature

ACCESS DATA

Json Xml

Similar Datasets

Early epidemic spread, percolation and Covid-19.

Project description:Human to human transmissible infectious diseases spread in a population using human interactions as its transmission vector. The early stages of such an outbreak can be modeled by a graph whose edges encode these interactions between individuals, the vertices. This article attempts to account for the case when each individual entails in different kinds of interactions which have therefore different probabilities of transmitting the disease. The majority of these results can be also stated in the language of percolation theory. The main contributions of the article are: (1) Extend to this setting some results which were previously known in the case when each individual has only one kind of interactions. (2) Find an explicit formula for the basic reproduction number [Formula: see text] which depends only on the probabilities of transmitting the disease along the different edges and the first two moments of the degree distributions of the associated graphs. (3) Motivated by the recent Covid-19 pandemic, we use the framework developed to compute the [Formula: see text] of a model disease spreading in populations whose trees and degree distributions are adjusted to several different countries. In this setting, we shall also compute the probability that the outbreak will not lead to an epidemic. In all cases we find such probability to be very low if no interventions are put in place.

| S-EPMC7499022 | biostudies-literature

Twitter reveals human mobility dynamics during the COVID-19 pandemic.

Project description:The current COVID-19 pandemic raises concerns worldwide, leading to serious health, economic, and social challenges. The rapid spread of the virus at a global scale highlights the need for a more harmonized, less privacy-concerning, easily accessible approach to monitoring the human mobility that has proven to be associated with viral transmission. In this study, we analyzed over 580 million tweets worldwide to see how global collaborative efforts in reducing human mobility are reflected from the user-generated information at the global, country, and U.S. state scale. Considering the multifaceted nature of mobility, we propose two types of distance: the single-day distance and the cross-day distance. To quantify the responsiveness in certain geographic regions, we further propose a mobility-based responsive index (MRI) that captures the overall degree of mobility changes within a time window. The results suggest that mobility patterns obtained from Twitter data are amenable to quantitatively reflect the mobility dynamics. Globally, the proposed two distances had greatly deviated from their baselines after March 11, 2020, when WHO declared COVID-19 as a pandemic. The considerably less periodicity after the declaration suggests that the protection measures have obviously affected people's travel routines. The country scale comparisons reveal the discrepancies in responsiveness, evidenced by the contrasting mobility patterns in different epidemic phases. We find that the triggers of mobility changes correspond well with the national announcements of mitigation measures, proving that Twitter-based mobility implies the effectiveness of those measures. In the U.S., the influence of the COVID-19 pandemic on mobility is distinct. However, the impacts vary substantially among states.

| S-EPMC7654838 | biostudies-literature

Dysregulated adaptive immune response contributes to severe COVID-19.

Project description: Not available

| S-EPMC7403569 | biostudies-literature

A single-cell atlas of lymphocyte adaptive immune repertoires and transcriptomes reveals age-related differences in convalescent COVID-19 patients

Project description:We utilize single-cell sequencing (scSeq) of lymphocyte immune repertoires and transcriptomes to quantitatively profile the adaptive immune response in COVID-19 patients of varying age. Our scSeq analysis defines the adaptive immune repertoire and transcriptome in convalescent COVID-19 patients and shows important age-related differences implicated in immunity against SARS-CoV-2.

2021-03-20 | E-MTAB-10169 | biostudies-arrayexpress

Single-Cell Omics Reveals Dyssynchrony of the Innate and Adaptive Immune System in Progressive COVID-19

Project description:This SuperSeries is composed of the SubSeries listed below.

2021-04-05 | GSE155224 | GEO

Human mobility in response to COVID-19 in France, Italy and UK.

Project description:The COVID-19 pandemic is one of the defining events of our time. National Governments responded to the global crisis by implementing mobility restrictions to slow down the spread of the virus. To assess the impact of those policies on human mobility, we perform a massive comparative analysis on geolocalized data from 13 M Facebook users in France, Italy, and the UK. We find that lockdown generally affects national mobility efficiency and smallworldness-i.e., a substantial reduction of long-range connections in favor of local paths. The impact, however, differs among nations according to their mobility infrastructure. We find that mobility is more concentrated in France and UK and more distributed in Italy. In this paper we provide a framework to quantify the substantial impact of the mobility restrictions. We introduce a percolation model mimicking mobility network disruption and find that node persistence in the percolation process is significantly correlated with the economic and demographic characteristics of countries: areas showing higher resilience to mobility disruptions are those where Value Added per Capita and Population Density are high. Our methods and findings provide important insights to enhance preparedness for global critical events and to incorporate resilience as a relevant dimension to estimate the socio-economic consequences of mobility restriction policies.

| S-EPMC8222274 | biostudies-literature

Mouse models in COVID-19 research: analyzing the adaptive immune response.

Project description:The emergence of SARS-CoV-2, the severe acute respiratory syndrome coronavirus type 2 causing the COVID-19 pandemic, resulted in a major necessity for scientific countermeasures. Investigations revealing the exact mechanisms of the SARS-CoV-2 pathogenesis provide the basis for the development of therapeutic measures and protective vaccines against COVID-19. Animal models are inevitable for infection and pre-clinical vaccination studies as well as therapeutic testing. A well-suited animal model, mimicking the pathology seen in human COVID-19 patients, is an important basis for these investigations. Several animal models were already used during SARS-CoV-2 studies with different clinical outcomes after SARS-CoV-2 infection. Here, we give an overview of different animal models used in SARS-CoV-2 infection studies with a focus on the mouse model. Mice provide a well-established animal model for laboratory use and several different mouse models have been generated and are being used in SARS-CoV-2 studies. Furthermore, the analysis of SARS-CoV-2-specific T cells during infection and in vaccination studies in mice is highlighted.

| S-EPMC9166226 | biostudies-literature

In severe COVID-19, SARS-CoV-2 induces a chronic, TGF-β-dominated adaptive immune response

Project description:This SuperSeries is composed of the SubSeries listed below.

2021-02-09 | GSE158038 | GEO

The effect of human mobility restrictions on the COVID-19 transmission network in China.

Project description:BackgroundCOVID-19 poses a severe threat worldwide. This study analyzes its propagation and evaluates statistically the effect of mobility restriction policies on the spread of the disease.MethodsWe apply a variation of the stochastic Susceptible-Infectious-Recovered model to describe the temporal-spatial evolution of the disease across 33 provincial regions in China, where the disease was first identified. We employ Bayesian Markov Chain Monte-Carlo methods to estimate the model and to characterize a dynamic transmission network, which enables us to evaluate the effectiveness of various local and national policies.ResultsThe spread of the disease in China was predominantly driven by community transmission within regions, which dropped substantially after local governments imposed various lockdown policies. Further, Hubei was only the epicenter of the early epidemic stage. Secondary epicenters, such as Beijing and Guangdong, had already become established by late January 2020. The transmission from these epicenters substantially declined following the introduction of mobility restrictions across regions.ConclusionsThe spatial transmission network is able to differentiate the effect of the local lockdown policies and the cross-region mobility restrictions. We conclude that both are important policy tools for curbing the disease transmission. The coordination between central and local governments is important in suppressing the spread of infectious diseases.

| S-EPMC8289089 | biostudies-literature

Increasing N2200 Charge Transport Mobility to Improve Performance of All Polymer Solar Cells by Forming a Percolation Network Structure.

Project description:The poor electron transport ability of the polymer acceptor is one of the factors restricting the performance of all-polymer solar cells. The percolation network of conjugated polymers can promote its charge transfer. Hence, we aim to find out the critical molecular weight (MW) of N2200 on the forming of the percolation network and to improve its charge mobility and thus photovoltaic performance of J51:N2200 blend. Detailed measurements demonstrate that when the MW of N2200 is larger than 96k, a percolation network structure is formed due to the chain tangled and multi-chain aggregations. Analysis of kinetic experiments reveals that it is the memory of the N2200 long chain conformation and the extent of aggregation in solution are carried into cast films for the formation of the percolation network. Thus, the electron mobility increases from 5.58 × 10-6 cm2V-1s-1 (N220017k) to 9.03 × 10-5 cm2V-1s-1 when the MW of N2200 is >96k. It led to a balance between hole and electron mobility. The μh/μe decrease from 16.9 to 1.53, causing a significant enhancement in the PCEs, from 5.87 to 8.28% without additives.

| S-EPMC7251163 | biostudies-literature

OmicsDI is part of the ELIXIR infrastructure

OmicsDI is an Elixir interoperability service. Learn more ›

Tweets

OmicsDI Databases

PRIDE
PeptideAtlas
MassIVE
JPOST Repository
Physiome Model Repository

EGA
EVA
ENA
LINCS
PAXDB
Cell Collective

MetaboLights
Metabolomics Workbench
MetabolomeExpress
GNPS
BioModels
FAIRDOMHub

ArrayExpress
dbGaP
ExpressionAtlas
GEO
NODE

Information

Databases
Help
API
Contact us
Code on GitHub
Terms of use
Submit Data