scholarly journals Evaluation of Work Resumption Strategies after COVID-19 Reopening in the Chinese City of Shenzhen: A Mathematical Modeling Study

2020 ◽  
Author(s):  
Lu Bai ◽  
Haonan Lu ◽  
Hailin Hu ◽  
M. Kumi Smith ◽  
Katherine Harripersaud ◽  
...  
Keyword(s):  
Population Control ◽  
Epidemiological Data ◽  
Population Mobility ◽  
Mobility Data ◽  
Intervention Measures ◽  
Imported Cases ◽  
High Level ◽  
The Impact ◽  
Second Wave ◽  
Work Resumption

Abstract BackgroundAs China is facing a potential second wave of the epidemic, we reviewed and evaluated the intervention measures implemented in a major metropolitan city, Shenzhen, during the early phase of Wuhan lockdown. MethodsBased on published epidemiological data on COVID-19 and population mobility data from Baidu Qianxi, we constructed a compartmental model to evaluate the impact of work and traffic resumption on the epidemic in Shenzhen in various scenarios.ResultsImported cases account for the majority (58.6%) of the early reported cases in Shenzhen. We demonstrated that with strict inflow population control and a high level of mask usage following work resumption, various resumption schemes resulted in only an insignificant difference in the number of cumulative infections. Shenzhen may experience this second wave of infections approximately two weeks after the traffic resumption if the incidence risk in Hubei is high at the moment of resumption.ConclusionControl of imported cases and extensive use of facial masks were the key for the prevention of the COVID-19 epidemic in Shenzhen during its reopening and work resumption.

scholarly journals SARS-CoV-2 European resurgence foretold: interplay of introductions and persistence by leveraging genomic and mobility data

2021 ◽  
Author(s):  
Philippe Lemey ◽  
Nick Ruktanonchai ◽  
Samuel Hong ◽  
Vittoria Colizza ◽  
Chiara Poletto ◽  
...  
Keyword(s):  
United Kingdom ◽  
Late Summer ◽  
Epidemiological Data ◽  
European Countries ◽  
Cross Border ◽  
Mobility Data ◽  
The United Kingdom ◽  
Intervention Measures ◽  
Novel Variants ◽  
Second Wave

Abstract Following the first wave of SARS-CoV-2 infections in spring 2020, Europe experienced a resurgence of the virus starting late summer that was deadlier and more difficult to contain. Relaxed intervention measures and summer travel have been implicated as drivers of the second wave. Here, we build a phylogeographic model to evaluate how newly introduced lineages, as opposed to the rekindling of persistent lineages, contributed to the COVID-19 resurgence in Europe. We inform this model using genomic, mobility and epidemiological data from 10 West European countries and estimate that in many countries more than 50% of the lineages circulating in late summer resulted from new introductions since June 15th. The success in onwards transmission of these lineages is predicted by SARS-CoV-2 incidence during this period. Relatively early introductions from Spain into the United Kingdom contributed to the successful spread of the 20A.EU1/B.1.177 variant. The pervasive spread of variants that have not been associated with an advantage in transmissibility highlights the threat of novel variants of concern that emerged more recently and have been disseminated by holiday travel. Our findings indicate that more effective and coordinated measures are required to contain spread through cross-border travel.

scholarly journals Bayesian small-area modelling of COVID-19 cases and deaths in England and association with key risk factors: a combined space–time Susceptible-Exposed-Infected-Removed (SEIR) model

2020 ◽  
Author(s):  
Benn Sartorius ◽  
Andrew Lawson ◽  
Rachel L. Pullan
Keyword(s):  
Risk Factors ◽  
Small Area ◽  
Mortality Rates ◽  
Space Time ◽  
Spatiotemporal Variability ◽  
Population Mobility ◽  
Seir Model ◽  
Social Distancing ◽  
The Impact ◽  
Second Wave

Abstract Background: COVID-19 caseloads in England appear have passed through a first peak, with evidence of an emerging second wave. To ensure continued response to the epidemic is most effective, it is imperative to better understand both retrospectively and prospectively the geographical evolution of COVID-19 caseloads and deaths, identify localised areas in space-time at significantly higher risk, quantify the impact of changes in localised population mobility (or movement) on caseloads, identify localised risk factors for increased mortality and project the likely course of the epidemic at small-area resolution in coming weeks.Methods: We applied a Bayesian space–time SEIR model to assess the spatiotemporal variability of COVID-19 caseloads (transmission) and deaths at small-area scale in England (Middle Layer Super Output Area [MSOA], 6791 units) and by week (using observed data from week 5 to 34), including key determinants, the modelled transmission dynamics and spatial-temporal random effects. We also estimate the number of cases and deaths at small-area resolution with uncertainty projected forward in time by MSOA (up to week 51 of 2020), the impact mobility reductions (and subsequent easing) have had on COVID-19 caseloads and quantify the impact of key socio-demographic risk factors on COVID-19 related mortality risk by MSOA.Results: Reductions in population mobility due the course of the first lockdown had a significant impact on the reduction of COVID-19 caseloads across England, however local authorities have had a varied rate of reduction in population movement which our model suggest has substantially impacted the geographic heterogeneity in caseloads at small-area scale. The steady gain in population mobility, observed from late April, appears to have contributed to a slowdown in caseload reductions towards late June and subsequent steady increase signalling the start of the second wave. MSOA with higher proportions of elderly (70+ years of age) and elderly living in deprivation, both with very distinct geographic distributions, have a significantly elevated COVID-19 mortality rates.Conclusions: While non-pharmaceutical interventions (that is, reductions in population mobility and social distancing) had a profound impact on the trajectory of the first wave of the COVID-19 outbreak in England, increased population mobility appears to have contributed to the current increase signalling the start of the second wave. A number of contiguous small-areas appear to be at a significant elevated risk of high COVID-19 transmission, many of which are also at increased risk for higher mortality rates. A geographically staggered re-introduction of intensified social distancing measures is advised and limited cross MSOA movement if the magnitude and geographic extent of the second wave is to be reduced.

scholarly journals How Schools affected COVID-19 Pandemic in Italy: Data Analysis for Lombardy Region, Campania Region, and Emilia Region

2021 ◽  
Author(s):  
Davide Tosi ◽  
Alessandro Siro Campi
Keyword(s):  
Geographical Area ◽  
Clear Correlation ◽  
Campania Region ◽  
Lombardy Region ◽  
Epidemic Curve ◽  
Mobility Data ◽  
Emilia Romagna Region ◽  
Opening And Closing ◽  
The Impact ◽  
Second Wave

Background: CoronaVirus Disease 2019 (COVID-19) is the main discussed topic world-wide in 2020 and at the beginning of the Italian epidemic, scientists tried to understand the virus diffusion and the epidemic curve of positive cases with controversial findings and numbers. Objectives: In this paper, a data analytics study on the diffusion of COVID-19 in Lombardy Region and Campania Region is developed in order to identify the driver that sparked the second wave in Italy Methods: Starting from all the available official data collected about the diffusion of COVID-19, we analyzed google mobility data, school data and infection data for two big regions in Italy: Lombardy Region and Campania Region, which adopted two different approaches in opening and closing schools. To reinforce our findings, we also extended the analysis to the Emilia Romagna Region. Results: The paper aims at showing how different policies adopted in school opening / closing may have on the impact on the COVID-19 spread. Conclusions: The paper shows that a clear correlation exists between the school contagion and the subsequent temporal overall contagion in a geographical area.

scholarly journals A data-driven model of the COVID-19 spread among interconnected populations: epidemiological and mobility aspects following the lockdown in Italy

2021 ◽  
Author(s):  
Paolo Di Giamberardino ◽  
Daniela Iacoviello ◽  
Federico Papa ◽  
Carmela Sinisgalli
Keyword(s):  
Group Structure ◽  
Human Mobility ◽  
Epidemiological Data ◽  
Group Model ◽  
Disease Evolution ◽  
Analytical Stability ◽  
Containment Measures ◽  
Disease Diffusion ◽  
The Impact ◽  
Second Wave

AbstractAn epidemic multi-group model formed by interconnected SEIR-like structures is formulated and used for data fitting to gain insight into the COVID-19 dynamics and into the role of non-pharmaceutical control actions implemented to limit the infection spread since its outbreak in Italy. The single submodels provide a rather accurate description of the COVID-19 evolution in each subpopulation by an extended SEIR model including the class of asymptomatic infectives, which is recognized as a determinant for disease diffusion. The multi-group structure is specifically designed to investigate the effects of the inter-regional mobility restored at the end of the first strong lockdown in Italy (June 3, 2020). In its time-invariant version, the model is shown to enjoy some analytical stability properties which provide significant insights on the efficacy of the implemented control measurements. In order to highlight the impact of human mobility on the disease evolution in Italy between the first and second wave onset, the model is applied to fit real epidemiological data of three geographical macro-areas in the period March–October 2020, including the mass departure for summer holidays. The simulation results are in good agreement with the data, so that the model can represent a useful tool for predicting the effects of the combination of containment measures in triggering future pandemic scenarios. Particularly, the simulation shows that, although the unrestricted mobility alone appears to be insufficient to trigger the second wave, the human transfers were crucial to make uniform the spatial distribution of the infection throughout the country and, combined with the restart of the production, trade, and education activities, determined a time advance of the contagion increase since September 2020.

scholarly journals Modeling the Impact of Unreported Cases of the COVID-19 in the North African Countries

Biology ◽  
2020 ◽  
Vol 9 (11) ◽  
pp. 373
Author(s):  
Salih Djilali ◽  
Lahbib Benahmadi ◽  
Abdessamad Tridane ◽  
Khadija Niri
Keyword(s):  
Population Movement ◽  
Population Mobility ◽  
North African ◽  
Active Infection ◽  
African Countries ◽  
The North ◽  
North African Countries ◽  
The Impact ◽  
Second Wave ◽  
Epidemic Wave

In this paper, we study a mathematical model investigating the impact of unreported cases of the COVID-19 in three North African countries: Algeria, Egypt, and Morocco. To understand how the population respects the restriction of population mobility implemented in each country, we use Google and Apple’s mobility reports. These mobility reports help to quantify the effect of the population movement restrictions on the evolution of the active infection cases. We also approximate the number of the population infected unreported, the proportion of those that need hospitalization, and estimate the end of the epidemic wave. Moreover, we use our model to estimate the second wave of the COVID-19 Algeria and Morocco and to project the end of the second wave. Finally, we suggest some additional measures that can be considered to reduce the burden of the COVID-19 and would lead to a second wave of the spread of the virus in these countries.

scholarly journals An Empirical Study of the Relationship among Population Mobility, Industrial Structure Upgrading, and Economic Growth – Based on the SPVAR Model

2021 ◽  
Vol 4 (4) ◽  
pp. 14-25
Author(s):  
Bowen Xu ◽  
Yang Lu
Keyword(s):  
Economic Growth ◽  
Industrial Development ◽  
Industrial Structure ◽  
Impulse Response Function ◽  
Space Time ◽  
Population Mobility ◽  
Vector Autoregressive ◽  
High Level ◽  
The Impact ◽  
Industrial Structure Upgrading

Based on the inter-provincial panel data for 31 provinces in China from 2000 to 2019, and incorporating geospatial factors, a spatial panel vector autoregressive (SPVAR) model consisting of population mobility, industrial structure upgrading, and economic growth is constructed. The space-time impulse response function is used to analyze the space-time conduction of exogenous variables on the impact of three endogenous variables. The study found that first, the population influx barely benefited the industrial structure upgrading and economic growth. Second, the upgrading of the industrial structure would aggravate the population mobility in the province, causing low-level laborers to leave the province in short-term, but in long-term, there would be influx of talents. Third, the economic growth in developed regions plays a significant role in promoting the industrial development of their province and population-rich provinces, but it has less impact on provinces with high-level industrial structure. Finally, policy recommendations are provided in regard to the benign interaction among population mobility, industrial structure upgrading, and economic growth in addition to clarifying the idea of economic development, implementing correct population policies, and promoting the coordinated regional development.

scholarly journals Bali in the Covid-19 Pandemic: Population and Employment Dilemmas

2020 ◽  
Vol 1 (3) ◽  
pp. 143-154
Author(s):  
I Gusti Wayan Murjana Yasa
Keyword(s):  
Case Fatality ◽  
High Potential ◽  
Population Mobility ◽  
Imported Cases ◽  
Huge Impact ◽  
Potential Case ◽  
Rapid Tests ◽  
The Right ◽  
High Level ◽  
Local Transmission

Purpose: The COVID-19 pandemic has a huge impact on the lives of Balinese citizens. The aim of this study is to find the right steps and frameworks that enable to reduce the spread and death caused by COVID-19 as quickly and as minimally as possible, so that the sustainable impact on the socioeconomic can be reduce. Research methods: Preventive measures are needed, especially for the Field Facilitator Staff as the front guard, so that the BSPS program channelled adequately. The key to smooth distribution, in addition to the precise mechanism, must also follow the government's recommendations and implement health protocols. Findings: The results show that the population and employment structure of Bali has high potential for the spread of the COVID-19 pandemic. This is due to the high level of population mobility, both population mobility between regions within the country and population mobility between countries. The second cause is the Balinese population structure consists of many elderly people, thus causing a high potential case fatality rate from a pandemic. Implications: Based on the results of the study, it is recommend minimize the possibility of the spread of covid-19 through the first and second rapid tests involving as many residents that potential to be covid-19 deployment carrier, both through local transmission and imported cases.

scholarly journals Early social distancing policies in Europe, changes in mobility & COVID-19 case trajectories: Insights from Spring 2020

PLoS ONE ◽  
2021 ◽  
Vol 16 (6) ◽  
pp. e0253071
Author(s):  
Liana R. Woskie ◽  
Jonathan Hennessy ◽  
Valeria Espinosa ◽  
Thomas C. Tsai ◽  
Swapnil Vispute ◽  
...  
Keyword(s):  
Comprehensive Evaluation ◽  
Population Mobility ◽  
School Closures ◽  
Social Distancing ◽  
Government Response ◽  
Mobility Data ◽  
Aggregate Population ◽  
The Impact ◽  
The Relationship ◽  
At Home

Background Social distancing have been widely used to mitigate community spread of SARS-CoV-2. We sought to quantify the impact of COVID-19 social distancing policies across 27 European counties in spring 2020 on population mobility and the subsequent trajectory of disease. Methods We obtained data on national social distancing policies from the Oxford COVID-19 Government Response Tracker and aggregated and anonymized mobility data from Google. We used a pre-post comparison and two linear mixed-effects models to first assess the relationship between implementation of national policies and observed changes in mobility, and then to assess the relationship between changes in mobility and rates of COVID-19 infections in subsequent weeks. Results Compared to a pre-COVID baseline, Spain saw the largest decrease in aggregate population mobility (~70%), as measured by the time spent away from residence, while Sweden saw the smallest decrease (~20%). The largest declines in mobility were associated with mandatory stay-at-home orders, followed by mandatory workplace closures, school closures, and non-mandatory workplace closures. While mandatory shelter-in-place orders were associated with 16.7% less mobility (95% CI: -23.7% to -9.7%), non-mandatory orders were only associated with an 8.4% decrease (95% CI: -14.9% to -1.8%). Large-gathering bans were associated with the smallest change in mobility compared with other policy types. Changes in mobility were in turn associated with changes in COVID-19 case growth. For example, a 10% decrease in time spent away from places of residence was associated with 11.8% (95% CI: 3.8%, 19.1%) fewer new COVID-19 cases. Discussion This comprehensive evaluation across Europe suggests that mandatory stay-at-home orders and workplace closures had the largest impacts on population mobility and subsequent COVID-19 cases at the onset of the pandemic. With a better understanding of policies’ relative performance, countries can more effectively invest in, and target, early nonpharmacological interventions.

scholarly journals Socio-economic indicators correlate with daily mobility during the second-wave of the Covid-19 pandemic

2021 ◽  
Author(s):  
Jed Long ◽  
Chang Ren
Keyword(s):  
Positive Association ◽  
Economic Indicators ◽  
Mobility Patterns ◽  
Mobility Data ◽  
Individual Mobility ◽  
Mobility Behaviour ◽  
The Impact ◽  
Second Wave ◽  
Pharmaceutical Interventions ◽  
Over Time

Non-pharmaceutical interventions are being used globally to limit the spread of Covid-19, which are in turn affecting individual mobility patterns. Mobility measures were found to be strongly associated with regional socio-economic indicators during the first wave of the pandemic. Here, we use network mobility data from an ~3.5 million person sample of individuals in Ontario, Canada to study the association between three different individual-mobility measures and four socio-economic indicators throughout the first and second wave of Covid-19 (January to December 2020). We demonstrate that understanding how mobility behaviours have changed in response to Covid-19 varies considerably depending on how mobility is measured. We find a strong positive association between different mobility levels and the economic deprivation index, which demonstrates that inequities in the changes to mobility across economic gradients observed during the initial lockdown have persisted into the later stages of the pandemic. However, the associations between mobility and other socio-economic indicators vary over time. We capture a strong day-of-week pattern of association between socio-economic indicators and mobility levels. Our findings have important implications for understanding if and how mobility data should be used to study the impact of non-pharmaceutical interventions on the socio-economic conditions across geographical space, and over time. Our results support that Covid-19 non-pharmaceutical interventions have resulted in geographically disparate responses to mobility behaviour, and quantifying mobility changes at fine geographical scales is crucial to understanding the impacts of Covid-19 on local populations.

scholarly journals Modelling and predicting the spatio-temporal spread of COVID-19, associated deaths and impact of key risk factors in England

2021 ◽  
Vol 11 (1) ◽  
Author(s):  
B. Sartorius ◽  
A. B. Lawson ◽  
R. L. Pullan
Keyword(s):  
Risk Factors ◽  
Small Area ◽  
Mortality Rates ◽  
Space Time ◽  
Spatiotemporal Variability ◽  
Population Mobility ◽  
Social Distancing ◽  
Increased Risk ◽  
The Impact ◽  
Second Wave

AbstractCOVID-19 caseloads in England have passed through a first peak, and at the time of this analysis appeared to be gradually increasing, potentially signalling the emergence of a second wave. To ensure continued response to the epidemic is most effective, it is imperative to better understand both retrospectively and prospectively the geographical evolution of COVID-19 caseloads and deaths at small-area resolution, identify localised areas in space–time at significantly higher risk, quantify the impact of changes in localised population mobility (or movement) on caseloads, identify localised risk factors for increased mortality and project the likely course of the epidemic at high spatial resolution in coming weeks. We applied a Bayesian hierarchical space–time SEIR model to assess the spatiotemporal variability of COVID-19 caseloads (transmission) and deaths at small-area scale in England [Middle Layer Super Output Area (MSOA), 6791 units] and by week (using observed data from week 5 to 34 of 2020), including key determinants, the modelled transmission dynamics and spatial–temporal random effects. We also estimate the number of cases and deaths at small-area resolution with uncertainty projected forward in time by MSOA (up to week 51 of 2020), the impact mobility reductions (and subsequent easing) have had on COVID-19 caseloads and quantify the impact of key socio-demographic risk factors on COVID-19 related mortality risk by MSOA. Reductions in population mobility during the course of the first lockdown had a significant impact on the reduction of COVID-19 caseloads across England, however local authorities have had a varied rate of reduction in population movement which our model suggest has substantially impacted the geographic heterogeneity in caseloads at small-area scale. The steady gain in population mobility, observed from late April, appears to have contributed to a slowdown in caseload reductions towards late June and subsequent start of the second wave. MSOA with higher proportions of elderly (70+ years of age) and elderly living in deprivation, both with very distinct geographic distributions, have a significantly elevated COVID-19 mortality rates. While non-pharmaceutical interventions (that is, reductions in population mobility and social distancing) had a profound impact on the trajectory of the first wave of the COVID-19 outbreak in England, increased population mobility appears to have significantly contributed to the second wave. A number of contiguous small-areas appear to be at a significant elevated risk of high COVID-19 transmission, many of which are also at increased risk for higher mortality rates. A geographically staggered re-introduction of intensified social distancing measures is advised and limited cross MSOA movement if the magnitude and geographic extent of the second wave is to be reduced.

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