scholarly journals Unequal impact and spatial aggregation distort COVID-19 growth rates

2021 ◽  
Vol 380 (2214) ◽  
Author(s):  
Keith Burghardt ◽  
Siyi Guo ◽  
Kristina Lerman
Keyword(s):  
Growth Rates ◽  
Hot Spots ◽  
Disease Surveillance ◽  
Data Science ◽  
Spatial Scales ◽  
Spatial Aggregation ◽  
Mitigation Strategies ◽  
Policy Makers ◽  
Infectious Disease Surveillance ◽  
Know How

The COVID-19 pandemic has posed unprecedented challenges to public health world-wide. To make decisions about mitigation strategies and to understand the disease dynamics, policy makers and epidemiologists must know how the disease is spreading in their communities. Here we analyse confirmed infections and deaths over multiple geographic scales to show that COVID-19’s impact is highly unequal: many regions have nearly zero infections, while others are hot spots. We attribute the effect to a Reed–Hughes-like mechanism in which the disease arrives to regions at different times and grows exponentially at different rates. Faster growing regions correspond to hot spots that dominate spatially aggregated statistics, thereby skewing growth rates at larger spatial scales. Finally, we use these analyses to show that, across multiple spatial scales, the growth rate of COVID-19 has slowed down with each surge. These results demonstrate a trade-off when estimating growth rates: while spatial aggregation lowers noise, it can increase bias. Public policy and epidemic modelling should be aware of, and aim to address, this distortion. This article is part of the theme issue ‘Data science approaches to infectious disease surveillance’.

scholarly journals Calibrating COVID-19 susceptible-exposed-infected-removed models with time-varying effectivecontact rates

2021 ◽  
Vol 380 (2214) ◽  
Author(s):  
James P. Gleeson ◽  
Thomas Brendan Murphy ◽  
Joseph D. O’Brien ◽  
Nial Friel ◽  
Norma Bargary ◽  
...  
Keyword(s):  
Disease Surveillance ◽  
Data Science ◽  
Statistical Modelling ◽  
Population Based ◽  
Advisory Group ◽  
Time Varying ◽  
Infectious Disease Surveillance ◽  
Spline Fitting ◽  
Effective Contact ◽  
History Of

We describe the population-based susceptible-exposed-infected-removed (SEIR) model developed by the Irish Epidemiological Modelling Advisory Group (IEMAG), which advises the Irish government on COVID-19 responses. The model assumes a time-varying effective contact rate (equivalently, a time-varying reproduction number) to model the effect of non-pharmaceutical interventions. A crucial technical challenge in applying such models is their accurate calibration to observed data, e.g. to the daily number of confirmed new cases, as the history of the disease strongly affects predictions of future scenarios. We demonstrate an approach based on inversion of the SEIR equations in conjunction with statistical modelling and spline-fitting of the data to produce a robust methodology for calibration of a wide class of models of this type. This article is part of the theme issue ‘Data science approaches to infectious disease surveillance’.

scholarly journals Understanding components of mobility during the COVID-19 pandemic

2021 ◽  
Vol 380 (2214) ◽  
Author(s):  
Peter Edsberg Møllgaard ◽  
Sune Lehmann ◽  
Laura Alessandretti
Keyword(s):  
Disease Surveillance ◽  
Data Science ◽  
Mobile Network ◽  
Travel Behaviour ◽  
Mobility Patterns ◽  
Infectious Disease Surveillance ◽  
Disease Propagation ◽  
Different Types ◽  
Travel Restrictions ◽  
Mobile Network Operators

Travel restrictions have proven to be an effective strategy to control the spread of the COVID-19 epidemics, in part because they help delay disease propagation across territories. The question, however, as to how different types of travel behaviour, from commuting to holiday-related travel, contribute to the spread of infectious diseases remains open. Here, we address this issue by using factorization techniques to decompose the temporal network describing mobility flows throughout 2020 into interpretable components. Our results are based on two mobility datasets: the first is gathered from Danish mobile network operators; the second originates from the Facebook Data-For-Good project. We find that mobility patterns can be described as the aggregation of three mobility network components roughly corresponding to travel during workdays, weekends and holidays, respectively. We show that, across datasets, in periods of strict travel restrictions the component corresponding to workday travel decreases dramatically. Instead, the weekend component, increases. Finally, we study how each type of mobility (workday, weekend and holiday) contributes to epidemics spreading, by measuring how the effective distance, which quantifies how quickly a disease can travel between any two municipalities, changes across network components. This article is part of the theme issue ‘Data science approaches to infectious disease surveillance’.

scholarly journals Data science approaches to infectious disease surveillance

2021 ◽  
Vol 380 (2214) ◽  
Author(s):  
Qingpeng Zhang
Keyword(s):  
Public Health ◽  
Infectious Disease ◽  
Infectious Diseases ◽  
Disease Surveillance ◽  
Data Science ◽  
Swine Flu ◽  
Human Beings ◽  
Theme Issue ◽  
Infectious Disease Surveillance ◽  
Introductory Article

Novel data science approaches are needed to confront large-scale infectious disease epidemics such as COVID-19, human immunodeficiency viruses, African swine flu and Ebola. Human beings are now equipped with richer data and more advanced data analytics methodologies, many of which have become available only in the last decade. The theme issue Data Science Approaches to Infectious Diseases Surveillance reports the latest interdisciplinary research on developing novel data science methodologies to capitalize on the rich ‘big data’ of human behaviours to confront infectious diseases, with a particular focus on combating the ongoing COVID-19 pandemic. Compared to conventional public health research, articles in this issue present innovative data science approaches that were not possible without the growing human behaviour data and the recent advances in information and communications technology. This issue has 12 research papers and one review paper from a strong lineup of contributors from multiple disciplines, including data science, computer science, computational social sciences, applied maths, statistics, physics and public health. This introductory article provides a brief overview of the issue and discusses the future of this emerging field. This article is part of the theme issue ‘Data science approaches to infectious disease surveillance’.

scholarly journals A global assessment of the impact of school closure in reducing COVID-19 spread

2021 ◽  
Vol 380 (2214) ◽  
Author(s):  
Joseph T. Wu ◽  
Shujiang Mei ◽  
Sihui Luo ◽  
Kathy Leung ◽  
Di Liu ◽  
...  
Keyword(s):  
School Children ◽  
Disease Surveillance ◽  
Data Science ◽  
The Elderly ◽  
School Closure ◽  
Clinical Severity ◽  
School Closures ◽  
Infectious Disease Surveillance ◽  
The Impact ◽  
Academic Year

Prolonged school closure has been adopted worldwide to control COVID-19. Indeed, UN Educational, Scientific and Cultural Organization figures show that two-thirds of an academic year was lost on average worldwide due to COVID-19 school closures. Such pre-emptive implementation was predicated on the premise that school children are a core group for COVID-19 transmission. Using surveillance data from the Chinese cities of Shenzhen and Anqing together, we inferred that compared with the elderly aged 60 and over, children aged 18 and under and adults aged 19–59 were 75% and 32% less susceptible to infection, respectively. Using transmission models parametrized with synthetic contact matrices for 177 jurisdictions around the world, we showed that the lower susceptibility of school children substantially limited the effectiveness of school closure in reducing COVID-19 transmissibility. Our results, together with recent findings that clinical severity of COVID-19 in children is lower, suggest that school closure may not be ideal as a sustained, primary intervention for controlling COVID-19. This article is part of the theme issue ‘Data science approach to infectious disease surveillance’.

scholarly journals Promoting Renewable Energy to Cope with Climate Change—Policy Discourse in Israel

2021 ◽  
Vol 13 (6) ◽  
pp. 3170
Author(s):  
Avri Eitan
Keyword(s):  
Climate Change ◽  
Renewable Energy ◽  
Greenhouse Gas Emissions ◽  
Greenhouse Gas ◽  
Climate Change Mitigation ◽  
Renewable Energies ◽  
Mitigation Strategies ◽  
Policy Discourse ◽  
Policy Makers ◽  
Change Mitigation

Evidence shows that global climate change is increasing over time, and requires the adoption of a variety of coping methods. As an alternative for conventional electricity systems, renewable energies are considered to be an important policy tool for reducing greenhouse gas emissions, and therefore, they play an important role in climate change mitigation strategies. Renewable energies, however, may also play a crucial role in climate change adaptation strategies because they can reduce the vulnerability of energy systems to extreme events. The paper examines whether policy-makers in Israel tend to focus on mitigation strategies or on adaptation strategies in renewable energy policy discourse. The results indicate that despite Israel’s minor impact on global greenhouse gas emissions, policy-makers focus more on promoting renewable energies as a climate change mitigation strategy rather than an adaptation strategy. These findings shed light on the important role of international influence—which tends to emphasize mitigation over adaptation—in motivating the domestic policy discourse on renewable energy as a coping method with climate change.

scholarly journals Phylogenomics reveals viral sources, transmission, and potential superinfection in early-stage COVID-19 patients in Ontario, Canada

2021 ◽  
Vol 11 (1) ◽  
Author(s):  
Calvin P. Sjaarda ◽  
Nazneen Rustom ◽  
Gerald A. Evans ◽  
David Huang ◽  
Santiago Perez-Patrigeon ◽  
...  
Keyword(s):  
Phylogenetic Analysis ◽  
Disease Surveillance ◽  
Early Stage ◽  
Emerging Diseases ◽  
Contact Tracing ◽  
Travel History ◽  
Infectious Disease Surveillance ◽  
Viral Genomes ◽  
Heterozygous Variant ◽  
Sequencing Platforms

AbstractThe emergence and rapid global spread of SARS-CoV-2 demonstrates the importance of infectious disease surveillance, particularly during the early stages. Viral genomes can provide key insights into transmission chains and pathogenicity. Nasopharyngeal swabs were obtained from thirty-two of the first SARS-CoV-2 positive cases (March 18–30) in Kingston Ontario, Canada. Viral genomes were sequenced using Ion Torrent (n = 24) and MinION (n = 27) sequencing platforms. SARS-CoV-2 genomes carried forty-six polymorphic sites including two missense and three synonymous variants in the spike protein gene. The D614G point mutation was the predominate viral strain in our cohort (92.6%). A heterozygous variant (C9994A) was detected by both sequencing platforms but filtered by the ARTIC network bioinformatic pipeline suggesting that heterozygous variants may be underreported in the SARS-CoV-2 literature. Phylogenetic analysis with 87,738 genomes in the GISAID database identified global origins and transmission events including multiple, international introductions as well as community spread. Reported travel history validated viral introduction and transmission inferred by phylogenetic analysis. Molecular epidemiology and evolutionary phylogenetics may complement contact tracing and help reconstruct transmission chains of emerging diseases. Earlier detection and screening in this way could improve the effectiveness of regional public health interventions to limit future pandemics.

scholarly journals COVID-TRACK: world and USA SARS-COV-2 testing and COVID-19 tracking

2021 ◽  
Vol 14 (1) ◽  
Author(s):  
Ye Emma Zohner ◽  
Jeffrey S. Morris
Keyword(s):  
Web Application ◽  
The United States ◽  
Mitigation Strategies ◽  
Time Lags ◽  
Policy Makers ◽  
Major Health ◽  
Dynamic Representation ◽  
Graphical Displays ◽  
The Usa ◽  
User Friendly

Abstract Background The COVID-19 pandemic has caused major health and socio-economic disruptions worldwide. Accurate investigation of emerging data is crucial to inform policy makers as they construct viral mitigation strategies. Complications such as variable testing rates and time lags in counting cases, hospitalizations and deaths make it challenging to accurately track and identify true infectious surges from available data, and requires a multi-modal approach that simultaneously considers testing, incidence, hospitalizations, and deaths. Although many websites and applications report a subset of these data, none of them provide graphical displays capable of comparing different states or countries on all these measures as well as various useful quantities derived from them. Here we introduce a freely available dynamic representation tool, COVID-TRACK, that allows the user to simultaneously assess time trends in these measures and compare various states or countries, equipping them with a tool to investigate the potential effects of the different mitigation strategies and timelines used by various jurisdictions. Findings COVID-TRACK is a Python based web-application that provides a platform for tracking testing, incidence, hospitalizations, and deaths related to COVID-19 along with various derived quantities. Our application makes the comparison across states in the USA and countries in the world easy to explore, with useful transformation options including per capita, log scale, and/or moving averages. We illustrate its use by assessing various viral trends in the USA and Europe. Conclusion The COVID-TRACK web-application is a user-friendly analytical tool to compare data and trends related to the COVID-19 pandemic across areas in the United States and worldwide. Our tracking tool provides a unique platform where trends can be monitored across geographical areas in the coming months to watch how the pandemic waxes and wanes over time at different locations around the USA and the globe.

scholarly journals Corruption in Construction Projects: Bibliometric Analysis of Global Research

2021 ◽  
Vol 13 (8) ◽  
pp. 4400
Author(s):  
Zhao Zhai ◽  
Ming Shan ◽  
Amos Darko ◽  
Albert P. C. Chan
Keyword(s):  
Construction Projects ◽  
Research Policy ◽  
Mitigation Strategies ◽  
Future Research ◽  
Policy And Practice ◽  
Policy Makers ◽  
Research Areas ◽  
Body Of Knowledge ◽  
Emerging Trends ◽  
Global Research

Corruption has been identified as a major problem in construction projects. It can jeopardize the success of these projects. Consequently, corruption has garnered significant attention in the construction industry over the past two decades, and several studies on corruption in construction projects (CICP) have been conducted. Previous efforts to analyze and review this body of knowledge have been manual, qualitative and subjective, thus prone to bias and limited in the number of reviewed studies. There remains a lack of inclusive, quantitative, objective and computational analysis of global CICP research to inform future research, policy and practice. This study aims to address this lack by providing the first inclusive bibliometric study exploring the state-of-the-art of global CICP research. To this end, a quantitative and objective technique aided by CiteSpace was used to systematically and computationally analyze a large corpus of 542 studies retrieved from the Web of Science and published from 2000 to 2020. The findings revealed major and influential CICP research journals, persons, institutions, countries, references and areas of focus, as well as revealing how these interact with each other in research networks. This study contributes to the in-depth understanding of global research on CICP. By highlighting the principal research areas, gaps, emerging trends and directions, as well as patterns in CICP research, the findings could help researchers, practitioners and policy makers position their future CICP research and/or mitigation strategies.

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