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1.
EuropePMC; 2020.
Preprint in English | EuropePMC | ID: ppcovidwho-325372

ABSTRACT

The worldwide outbreak of COVID-19 has posed a dire threat to the public. Human mobility has changed in various ways over the course of the pandemic. Despite current studies on common mobility metrics, research specifically on state-to-state mobility is very limited. By leveraging the mobile phone location data from over 100 million anonymous devices, we estimate the population flow between all states in the United States. We first analyze the temporal pattern and spatial differences of between-state flow from January 1, 2020 to May 15, 2020. Then, with repeated measures ANOVA and post-hoc analysis, we discern different time-course patterns of between-state population flow by pandemic severity groups. A further analysis shows moderate to high correlation between the flow reduction and the pandemic severity, the strength of which varies with different policies. This paper is promising in predicting imported cases.

2.
EuropePMC; 2020.
Preprint in English | EuropePMC | ID: ppcovidwho-322255

ABSTRACT

Due to immature treatment and rapid transmission of COVID-19, mobility interventions play a crucial role in containing the outbreak. Among various non-pharmacological interventions, community infection control is considered to be a quite promising approach. However, there is a lack of research on improving community-level interventions based on a community's real conditions and characteristics using real-world observations. Our paper aims to investigate the different responses to mobility interventions between communities in the United States with a specific focus on different income levels. We produced six daily mobility metrics for all communities using the mobility location data from over 100 million anonymous devices on a monthly basis. Each metric is tabulated by three performance indicators: "best performance," "effort," and "consistency." We found that being high-income improves social distancing behavior after controlling multiple confounding variables in each of the eighteen scenarios. In addition to the reality that it is more difficult for low-income communities to comply with social distancing, the comparisons between scenarios raise concerns on the employment status, working condition, accessibility to life supplies, and exposure to the virus of low-income communities.

3.
EuropePMC; 2020.
Preprint in English | EuropePMC | ID: ppcovidwho-322253

ABSTRACT

As a highly infectious respiratory disease, COVID-19 has become a pandemic that threatens global health. Without an effective treatment, non-pharmaceutical interventions, such as travel restrictions, have been widely promoted to mitigate the outbreak. Current studies analyze mobility metrics such as travel distance;however, there is a lack of research on interzonal travel flow and its impact on the pandemic. Our study specifically focuses on the inter-county mobility pattern and its influence on the COVID-19 spread in the United States. To retrieve real-world mobility patterns, we utilize an integrated set of mobile device location data including over 100 million anonymous devices. We first investigate the nationwide temporal trend and spatial distribution of inter-county mobility. Then we zoom in on the epicenter of the U.S. outbreak, New York City, and evaluate the impacts of its outflow on other counties. Finally, we develop a "log-linear double-risk" model at the county level to quantify the influence of both "external risk" imported by inter-county mobility flows and the "internal risk" defined as the vulnerability of a county in terms of population with high-risk phenotypes. Our study enhances the situation awareness of inter-county mobility in the U.S. and can help improve non-pharmaceutical interventions for COVID-19.

4.
EuropePMC; 2020.
Preprint in English | EuropePMC | ID: ppcovidwho-315018

ABSTRACT

In March of this year, COVID-19 was declared a pandemic and it continues to threaten public health. This global health crisis imposes limitations on daily movements, which have deteriorated every sector in our society. Understanding public reactions to the virus and the non-pharmaceutical interventions should be of great help to fight COVID-19 in a strategic way. We aim to provide tangible evidence of the human mobility trends by comparing the day-by-day variations across the U.S. Large-scale public mobility at an aggregated level is observed by leveraging mobile device location data and the measures related to social distancing. Our study captures spatial and temporal heterogeneity as well as the sociodemographic variations regarding the pandemic propagation and the non-pharmaceutical interventions. All mobility metrics adapted capture decreased public movements after the national emergency declaration. The population staying home has increased in all states and becomes more stable after the stay-at-home order with a smaller range of fluctuation. There exists overall mobility heterogeneity between the income or population density groups. The public had been taking active responses, voluntarily staying home more, to the in-state confirmed cases while the stay-at-home orders stabilize the variations. The study suggests that the public mobility trends conform with the government message urging to stay home. We anticipate our data-driven analysis offers integrated perspectives and serves as evidence to raise public awareness and, consequently, reinforce the importance of social distancing while assisting policymakers.

5.
EuropePMC; 2020.
Preprint in English | EuropePMC | ID: ppcovidwho-313629

ABSTRACT

The rapid spread of COVID-19 has affected thousands of people from different socio-demographic groups all over the country. A decisive step in preventing or slowing the outbreak is the use of mobility interventions, such as government stay-at-home orders. However, different socio-demographic groups might have different responses to these orders and regulations. In this paper, we attempt to fill the current gap in the literature by examining how different communities with different age groups performed social distancing by following orders such as the national emergency declaration on March 13, as well as how fast they started changing their behavior after the regulations were imposed. For this purpose, we calculated the behavior changes of people in different mobility metrics, such as percentage of people staying home during the study period (March, April, and May 2020), in different age groups in comparison to the days before the pandemic (January and February 2020), by utilizing anonymized and privacy-protected mobile device data. Our study indicates that senior communities outperformed younger communities in terms of their behavior change. Senior communities not only had a faster response to the outbreak in comparison to young communities, they also had better performance consistency during the pandemic.

6.
Transportation Research Record ; : 03611981211043813, 2021.
Article in English | Sage | ID: covidwho-1430338

ABSTRACT

The research team has utilized privacy-protected mobile device location data, integrated with COVID-19 case data and census population data, to produce a COVID-19 impact analysis platform that can inform users about the effects of COVID-19 spread and government orders on mobility and social distancing. The platform is being updated daily, to continuously inform decision-makers about the impacts of COVID-19 on their communities, using an interactive analytical tool. The research team has processed anonymized mobile device location data to identify trips and produced a set of variables, including social distancing index, percentage of people staying at home, visits to work and non-work locations, out-of-town trips, and trip distance. The results are aggregated to county and state levels to protect privacy, and scaled to the entire population of each county and state. The research team is making their data and findings, which are updated daily and go back to January 1, 2020, for benchmarking, available to the public to help public officials make informed decisions. This paper presents a summary of the platform and describes the methodology used to process data and produce the platform metrics.

7.
Non-conventional in English | Transportation Research Board, Grey literature | ID: grc-747483

ABSTRACT

The research team has utilized privacy-protected mobile device location data, integrated with COVID-19 case data and census population data, to produce a COVID-19 impact analysis platform that can inform users about the effects of COVID-19 spread and government orders on mobility and social distancing. The platform is being updated daily, to continuously inform decision-makers about the impacts of COVID-19 on their communities using an interactive analytical tool. The research team has processed anonymized mobile device location data to identify trips and produced a set of variables including social distancing index, percentage of people staying at home, visits to work and non-work locations, out-of-town trips, and trip distance. The results are aggregated to county and state levels to protect privacy and scaled to the entire population of each county and state. The research team are making their data and findings, which are updated daily and go back to January 1, 2020, for benchmarking, available to the public in order to help public officials make informed decisions. This paper presents a summary of the platform and describes the methodology used to process data and produce the platform metrics.

8.
PLoS One ; 15(11): e0241468, 2020.
Article in English | MEDLINE | ID: covidwho-917994

ABSTRACT

In March of this year, COVID-19 was declared a pandemic, and it continues to threaten public health. This global health crisis imposes limitations on daily movements, which have deteriorated every sector in our society. Understanding public reactions to the virus and the non-pharmaceutical interventions should be of great help to fight COVID-19 in a strategic way. We aim to provide tangible evidence of the human mobility trends by comparing the day-by-day variations across the U.S. from January 2020 to early April 2020. Large-scale public mobility at an aggregated level is observed by leveraging mobile device location data and the measures related to social distancing. Our study captures spatial and temporal heterogeneity as well as the sociodemographic variations and teleworking trends regarding the pandemic propagation and the non-pharmaceutical mobility interventions. All metrics adapted capture decreased public movements after the national emergency declaration. The population staying home has increased in all states before the stay-at-home mandates implemented and becomes more stable after the order with a smaller range of fluctuation. The public had been taking active responses, voluntarily staying home more, to the in-state confirmed cases while the stay-at-home orders stabilize the variations. As the estimated teleworking rates also continue to incline throughout the study period, the teleworking trend can be another driving factor for the growing stay-at-home population. We confirm that there exists overall mobility heterogeneity between the income or population density groups. The study suggests that public mobility trends are in line with the government message urging to stay home. We anticipate our data-driven analysis offers integrated perspectives and serves as evidence to raise public awareness and, consequently, reinforce the importance of social distancing while assisting policymakers.


Subject(s)
Coronavirus Infections/pathology , Movement , Pneumonia, Viral/pathology , Betacoronavirus/isolation & purification , COVID-19 , Cell Phone Use/statistics & numerical data , Coronavirus Infections/epidemiology , Coronavirus Infections/virology , Electronic Data Processing , Humans , Pandemics , Pneumonia, Viral/epidemiology , Pneumonia, Viral/virology , SARS-CoV-2 , Spatio-Temporal Analysis , United States/epidemiology
9.
Cell ; 182(2): 417-428.e13, 2020 07 23.
Article in English | MEDLINE | ID: covidwho-342735

ABSTRACT

Nucleotide analog inhibitors, including broad-spectrum remdesivir and favipiravir, have shown promise in in vitro assays and some clinical studies for COVID-19 treatment, this despite an incomplete mechanistic understanding of the viral RNA-dependent RNA polymerase nsp12 drug interactions. Here, we examine the molecular basis of SARS-CoV-2 RNA replication by determining the cryo-EM structures of the stalled pre- and post- translocated polymerase complexes. Compared with the apo complex, the structures show notable structural rearrangements happening to nsp12 and its co-factors nsp7 and nsp8 to accommodate the nucleic acid, whereas there are highly conserved residues in nsp12, positioning the template and primer for an in-line attack on the incoming nucleotide. Furthermore, we investigate the inhibition mechanism of the triphosphate metabolite of remdesivir through structural and kinetic analyses. A transition model from the nsp7-nsp8 hexadecameric primase complex to the nsp12-nsp7-nsp8 polymerase complex is also proposed to provide clues for the understanding of the coronavirus transcription and replication machinery.


Subject(s)
Betacoronavirus/chemistry , Betacoronavirus/enzymology , RNA-Dependent RNA Polymerase/chemistry , Viral Nonstructural Proteins/chemistry , Adenosine Monophosphate/analogs & derivatives , Adenosine Monophosphate/chemistry , Adenosine Monophosphate/metabolism , Adenosine Monophosphate/pharmacology , Alanine/analogs & derivatives , Alanine/chemistry , Alanine/metabolism , Alanine/pharmacology , Antiviral Agents/chemistry , Antiviral Agents/metabolism , Antiviral Agents/pharmacology , Catalytic Domain , Coronavirus RNA-Dependent RNA Polymerase , Cryoelectron Microscopy , Models, Chemical , Models, Molecular , RNA, Viral/metabolism , SARS-CoV-2 , Transcription, Genetic , Virus Replication
10.
Science ; 368(6492): 779-782, 2020 05 15.
Article in English | MEDLINE | ID: covidwho-47347

ABSTRACT

A novel coronavirus [severe acute respiratory syndrome-coronavirus 2 (SARS-CoV-2)] outbreak has caused a global coronavirus disease 2019 (COVID-19) pandemic, resulting in tens of thousands of infections and thousands of deaths worldwide. The RNA-dependent RNA polymerase [(RdRp), also named nsp12] is the central component of coronaviral replication and transcription machinery, and it appears to be a primary target for the antiviral drug remdesivir. We report the cryo-electron microscopy structure of COVID-19 virus full-length nsp12 in complex with cofactors nsp7 and nsp8 at 2.9-angstrom resolution. In addition to the conserved architecture of the polymerase core of the viral polymerase family, nsp12 possesses a newly identified ß-hairpin domain at its N terminus. A comparative analysis model shows how remdesivir binds to this polymerase. The structure provides a basis for the design of new antiviral therapeutics that target viral RdRp.


Subject(s)
Betacoronavirus/enzymology , RNA-Dependent RNA Polymerase/chemistry , RNA-Dependent RNA Polymerase/ultrastructure , Viral Nonstructural Proteins/chemistry , Viral Nonstructural Proteins/ultrastructure , Adenosine Monophosphate/analogs & derivatives , Adenosine Monophosphate/metabolism , Adenosine Monophosphate/pharmacology , Alanine/analogs & derivatives , Alanine/metabolism , Alanine/pharmacology , Antiviral Agents/metabolism , Antiviral Agents/pharmacology , Catalytic Domain , Coronavirus RNA-Dependent RNA Polymerase , Cryoelectron Microscopy , Drug Design , Models, Molecular , Multiprotein Complexes/chemistry , Multiprotein Complexes/metabolism , Multiprotein Complexes/ultrastructure , Protein Conformation, beta-Strand , Protein Domains , RNA-Dependent RNA Polymerase/antagonists & inhibitors , RNA-Dependent RNA Polymerase/metabolism , SARS-CoV-2 , Viral Nonstructural Proteins/antagonists & inhibitors , Viral Nonstructural Proteins/metabolism
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