A resilience perspective on governance for construction project delivery

This chapter discusses project governance from a resilience perspective. It argues that resilience is a key aspect of project governance, and that project governance is a key tool for project resilience. The chapter describes the resilience of projects and implications on project governance seen from different project actor perspectives. Projects are intentional interventions to change the client organisation's position in society and business. At a programme or portfolio level, projects may be changed or cancelled if the project is revised for a range of reasons. Such adjustments may be triggered by political decisions, policy changes, economic shifts, or other influences, often rooted well outside the projects themselves. The chapter illustrates examples of resilience and survival and its relationship with governance in the context of the overall railway organisational structure in Norway. The Venjar-Langset project had established a governance structure that served the project well when it was challenged by the COVID-19 situation. © 2023 John Wiley & Sons, Inc.

Observing Human Mobility Internationally During COVID-19

This article analyzes visual data captured from five countries and three U.S. states to evaluate the effectiveness of lockdown policies for reducing the spread of COVID-19. The main challenge is the scale: nearly six million images are analyzed to observe how people respond to the policy changes. © 1970-2012 IEEE.

Impact of COVID-19 Pandemic on Treatment Seeking in Opioid Users: A Retrospective Comparative Study

Background: The planet has been hit by the novel coronavirus since December 2019, which has not only affected the day-to-day activities but has also affected the health-seeking approach of the general population. The reports from the National Institute on Drug Abuse survey hint toward a rise in opioid use in the states. Even in India, the utilization of health services, especially the outpatient department (OPD) and opioid substitution clinics have been affected due to the impact of COVID-19 and subsequent national and regional lockdowns. The Indian studies reflect upon the experience and measures to continue the treatment facilities in substance users, yet none are available to reflect upon the impact of COVID-19 on the pattern of opioid use or functionality of OPD services. Objective: The objective of this study is to study the impact of COVID-19 pandemic on treatment seeking among opioid users from OPD-based opioid treatment centers: a retrospective study. Methodology: A retrospective study was done in an OPD-based opioid treatment center of a tertiary care hospital to analyze the impact of COVID-19 on treatment-seeking opioid users by comparing the data of pre-COVID-19 (April 2019-March 2020) and during COVID-19 (April 2020-March 2021) OPD visits by opioid users. The OPD visits data were collected, including the number of visits, the total number of buprenorphine tablets dispensed, and the dose of buprenorphine consumed per month. The data collected were further analyzed for the descriptive and analytic statistics using the SPSS software version 23.0. Results: During the pre-COVID-19 (April 2019-March 2020) duration, there were a total of 1104 (average 92/month) OPD visits and 4818 (Average 401/month) buprenorphine tablets were dispensed;whereas during the COVID-19 year (April 2020-March 2021), it was 980 (average 81.66/month) visits and 5174 (431/month) tablets. The results were further compared using the paired t-test, which was found to be statistically significant for the number of tablets dispensed, whereas not statistically significant for the number of OPD visits and doses. Conclusion: Although the COVID-19 pandemic has affected the feasibility to seek treatment in opioid users, it has not significantly affected OPD visits for opioid treatment at our center. Although the policy changes such as Indian Psychiatry Society (IPS) interim guidelines for opioid substitution therapy have been beneficial to facilitate the harm reduction and treatment-seeking attitude and have not affected the treatment seeking as expected due to lockdown and transportation which have been a hurdle. Further regulations on opioid treatment OPD, Tele-consultation services and mobile mental health services may be helpful to ensure continuity of treatment and harm reduction among the opioid users.

A Feedback SAIR Model for the Spread of Infectious Disease with Application to COVID-19 Pandemic

The COVID-19 global pandemic has highlighted the importance of identifying effective ways to control the spread of an infectious disease in a population. A solid understanding of the dynamics and the underlying mechanisms that govern this spread is an important step toward such a goal. Susceptible-Asymptomatic-Infected-Recovered (SAIR) models and their variants have played an important role in providing such insight. However, these models have limited explanatory and predictive power due to policy and behavior changes over time. In this paper we introduce a feedback version of the SAIR model by introducing feedback in the disease transmission rate. We apply this model to publicly available COVID-19 infection data. We show this model better captures the dynamics of the disease spread and has much better explanatory and predictive power. Our analysis suggests that public health policies based on daily infection numbers can be more effective than policies based on estimations of infection levels. © 2022 American Automatic Control Council.

Ambient Fine Particulate Matter and COVID-19 in India

We examine the correlation between COVID-19 case activity and air pollution in two cities of Delhi and Mumbai in India. Data regarding air quality index (AQI) of PM2.5 and PM10 from Delhi and Mumbai were collected between July and November 2020. Within the same time period, confirmed cases and daily deaths due to COVID-19 in these two cities were also recorded. AQI levels in Delhi were worst in November (PM2.5: 446 ± 144.6 µg/m3;PM10: 318 ± 131.7 µg/m3) and were significantly higher as compared to Mumbai (PM2.5: 130 ± 41.2 µg/m3;PM10: 86 ± 21.2 µg/m3). This correlated with greater number of cases and higher mortality in Delhi (cases: 6243;deaths: 85) relative to Mumbai (cases: 1526;deaths: 35) during the same time period. This observational study shows that air pollution is associated with poor outcomes in patients with COVID-19. There is an urgent unmet need for appropriate public health measures to decrease air pollution along with strict policy change. © 2022, The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd.

Designing a Sustainable Data Structure for Exploring Student Well-Being Patterns in Albemarle County Public Schools Throughout COVID-19

Student well-being has been affected by the COVID-19 pandemic. Albemarle County Public Schools (ACPS) has collected a significant and varied amount of K-12 student data throughout COVID-19. Researchers seek to utilize the student data to drive evidence-based policy changes with regard to ACPS student well-being. A structured data system for performing school-related research associated with the well-being of students throughout the pandemic does not exist. We have designed a sustainable, relational data structure for data consolidation and to advance the ongoing research initiatives related to COVID-19 student well-being in collaboration with ACPS. The data structure aims to play an important role in promoting student well-being policies through simplifying data collection, enhancing analysis, and acting as an ongoing tool that can support future phases of research. The design architecture includes a relational database populated with de-identified student data to be hosted in the cloud. Design implementation includes data cleaning, data preprocessing, populating the database, and querying data for validation. Specialized queries are utilized to answer the early questions posed to the data. Validation testing is performed to confirm the database is working as expected. Details of the data pipeline, validation, best data practices, and database design are discussed in the paper. © 2022 IEEE.

A Dynamic Social Vulnerability Index to COVID-19 in Mexico

Several months have passed since the appearance of COVID-19, populations that were the most vulnerable at the beginning might not be anymore, and vice-versa. Government interventions, people behaviours and vaccination policies, change the social vulnerability. Our work proposes a complementary framework to the classic vulnerability indexes which aggregate structural variables into composite indexes. We define a Dynamic Vulnerability Index as an evolving relation between structural indicators and mortality ratio, we construct this index using a data-driven approach that updates the mortality ratio and uses Partial Least Squares to find a weighting of the structural variables at each municipality. Our index is able to distinguish at any given time between zones that are potentially vulnerable but do not exhibit a high exposure, and zones that are not as vulnerable in terms of their structural variables but present higher levels of exposure. The southwest part of the country, comprising the states of Chiapas, Guerrero and Oaxaca, exhibits low Dynamic Vulnerability for most of the study period despite being one of the poorest regions in the country. This happens because most of the region is relatively isolated and doesn’t have a great influx of people that could carry the virus. On the contrary, the Central Region where the capital (Mexico City) is located and has been the epicenter of the pandemic in Mexico, has remained with a high vulnerability for the whole period, even if it is not particularly poor. Our index represents a complement to the static view of vulnerability in the context of an evolving pandemic. While static vulnerability highlights regions that could experience a strong impact, the dynamic vulnerability highlights regions where there is a strong relationship between the fixed structural conditions and the evolving epidemic. This complementary picture allows decision makers to take more focused actions. © 2022, The Author(s), under exclusive license to Springer Nature Switzerland AG.

Three phases of COVID-19: theoretical thinking on epidemic prevention and control.

The response to the COVID-19 outbreak was evaluated by analyzing the literature published after the outbreak. By reviewing and summarizing the different stages, nodes and measures during the occurrence and development of COVID-19, other countries can draw on their experience and reference for the fight against COVID-19 in China. In December 2019, the COVID-19 was first reported in Wuhan, China, and then the whole world continued to pay close attention to the development of the COVID-19 in China. Until now, the outbreak and development of the COVID-19 has lasted for more than a year. Looking back on the process of the COVID-19, several key stages and events have had a profound impact on the development of the COVID-19. We divided the evolution of the outbreak since its origin into different phases. The policy changes of the Chinese government in different periods of the COVID-19 pandemic were evaluated, and the transformation nodes of the policies and differences in each stage were determined. We divided the evolution of the outbreak into three stages of the outbreak are initial outbreak stage, the carrying of the virus (people or goods) stage, and the non-direct contact transmission (goods transmission) stage. This paper reviews the three stages of the evolution of responses by the Chinese government to COVID-19 using a phase analysis method to examine different policy starting points, changes to policy, and reasons for policy change. The study provides a resource for COVID-19 policy review and includes image of the three-phase shift in policy as the epidemic progressed. By summarizing and describing the policy changes in the three phases of the response to COVID-19 in China, this paper provides a resource for others in formulating their response to the pandemic. The commentary is also designed to provoke scholarly dialog among the readers of the journal.

Estimating COVID-19 outbreak risk through air travel.

BACKGROUND: Substantial limitations have been imposed on passenger air travel to reduce transmission of severe acute respiratory syndrome coronavirus 2 between regions and countries. However, as case numbers decrease, air travel will gradually resume. We considered a future scenario in which case numbers are low and air travel returns to normal. Under that scenario, there will be a risk of outbreaks in locations worldwide due to imported cases. We estimated the risk of different locations acting as sources of future coronavirus disease 2019 outbreaks elsewhere. METHODS: We use modelled global air travel data and population density estimates from locations worldwide to analyse the risk that 1364 airports are sources of future coronavirus disease 2019 outbreaks. We use a probabilistic, branching-process-based approach that considers the volume of air travelers between airports and the reproduction number at each location, accounting for local population density. RESULTS: Under the scenario we model, we identify airports in East Asia as having the highest risk of acting as sources of future outbreaks. Moreover, we investigate the locations most likely to cause outbreaks due to air travel in regions that are large and potentially vulnerable to outbreaks: India, Brazil and Africa. We find that outbreaks in India and Brazil are most likely to be seeded by individuals travelling from within those regions. We find that this is also true for less vulnerable regions, such as the United States, Europe and China. However, outbreaks in Africa due to imported cases are instead most likely to be initiated by passengers travelling from outside the continent. CONCLUSIONS: Variation in flight volumes and destination population densities creates a non-uniform distribution of the risk that different airports pose of acting as the source of an outbreak. Accurate quantification of the spatial distribution of outbreak risk can therefore facilitate optimal allocation of resources for effective targeting of public health interventions.