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Register a new userPopulation-Scale Study of Human Needs During the COVID-19 Pandemic: Analysis and Implications
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Added by
Jina Suh
Publication date
2020
fields
Informatics Engineering
and research's language is
English
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Most work to date on mitigating the COVID-19 pandemic is focused urgently on biomedicine and epidemiology. Yet, pandemic-related policy decisions cannot be made on health information alone. Decisions need to consider the broader impacts on people and their needs. Quantifying human needs across the population is challenging as it requires high geo-temporal granularity, high coverage across the population, and appropriate adjustment for seasonal and other external effects. Here, we propose a computational methodology, building on Maslows hierarchy of needs, that can capture a holistic view of relative changes in needs following the pandemic through a difference-in-differences approach that corrects for seasonality and volume variations. We apply this approach to characterize changes in human needs across physiological, socioeconomic, and psychological realms in the US, based on more than 35 billion search interactions spanning over 36,000 ZIP codes over a period of 14 months. The analyses reveal that the expression of basic human needs has increased exponentially while higher-level aspirations declined during the pandemic in comparison to the pre-pandemic period. In exploring the timing and variations in statewide policies, we find that the durations of shelter-in-place mandates have influenced social and emotional needs significantly. We demonstrate that potential barriers to addressing critical needs, such as support for unemployment and domestic violence, can be identified through web search interactions. Our approach and results suggest that population-scale monitoring of shifts in human needs can inform policies and recovery efforts for current and anticipated needs.
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Due to the COVID-19 pandemic, there was an urgent need to move to online teaching and develop innovations to guarantee the Student Learning Outcomes (SLOs) are being fulfilled. The contributions of this paper are two-fold: the effects of an experimented teaching strategy, i.e. multi-course project-based learning (MPL) approach, are presented followed with online assessment techniques investigation for senior level electrical engineering (EE) courses at Qatar University. The course project of the senior course was designed in such a way that it helps in simultaneously attaining the objectives of the senior and capstone courses, that the students were taking at the same time. It is known that the MPL approach enhances the critical thinking capacity of students which is also a major outcome of Education for Sustainable Development (ESD). The developed project ensures the fulfillment of a series of SLOs, that are concentrated on soft engineering and project management skills. The difficulties of adopting the MPL method for the senior level courses are in aligning the project towards fulfilling the learning outcomes of every individual course. The study also provides the students feedback on online assessment techniques incorporated with the MPL, due to online teaching during COVID-19 pandemic. In order to provide a benchmark and to highlight the obtained results, the innovative teaching approaches were compared to conventional methods taught on the same senior course in a previous semester. Based on the feedback from teachers and students from previously conducted case study it was believed that the MPL approach would support the students. With the statistical analysis (Chi-square, two-tailed T statistics and hypothesis testing using z-test) it can be concluded that the MPL and online assessment actually help to achieve better attainment of the SLOs, even during a pandemic situation.
The ongoing COVID-19 global pandemic is affecting every facet of human lives (e.g., public health, education, economy, transportation, and the environment). This novel pandemic and citywide implemented lockdown measures are affecting virus transmission, peoples travel patterns, and air quality. Many studies have been conducted to predict the COVID-19 diffusion, assess the impacts of the pandemic on human mobility and air quality, and assess the impacts of lockdown measures on viral spread with a range of Machine Learning (ML) techniques. This review study aims to analyze results from past research to understand the interactions among the COVID-19 pandemic, lockdown measures, human mobility, and air quality. The critical review of prior studies indicates that urban form, peoples socioeconomic and physical conditions, social cohesion, and social distancing measures significantly affect human mobility and COVID-19 transmission. during the COVID-19 pandemic, many people are inclined to use private transportation for necessary travel purposes to mitigate coronavirus-related health problems. This review study also noticed that COVID-19 related lockdown measures significantly improve air quality by reducing the concentration of air pollutants, which in turn improves the COVID-19 situation by reducing respiratory-related sickness and deaths of people. It is argued that ML is a powerful, effective, and robust analytic paradigm to handle complex and wicked problems such as a global pandemic. This study also discusses policy implications, which will be helpful for policymakers to take prompt actions to moderate the severity of the pandemic and improve urban environments by adopting data-driven analytic methods.
The impact of the ongoing COVID-19 pandemic is being felt in all spheres of our lives -- cutting across the boundaries of nation, wealth, religions or race. From the time of the first detection of infection among the public, the virus spread though almost all the countries in the world in a short period of time. With humans as the carrier of the virus, the spreading process necessarily depends on the their mobility after being infected. Not only in the primary spreading process, but also in the subsequent spreading of the mutant variants, human mobility plays a central role in the dynamics. Therefore, on one hand travel restrictions of varying degree were imposed and are still being imposed, by various countries both nationally and internationally. On the other hand, these restrictions have severe fall outs in businesses and livelihood in general. Therefore, it is an optimization process, exercised on a global scale, with multiple changing variables. Here we review the techniques and their effects on optimization or proposed optimizations of human mobility in different scales, carried out by data driven, machine learning and model approaches.
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.
Without proper medication and vaccination for the COVID-19, many governments are using automated digital healthcare surveillance system to prevent and control the spread. There is not enough literature explaining the concerns and privacy issues; hence, we have briefly explained the topics in this paper. We focused on digital healthcare surveillance systems privacy concerns and different segments. Further research studies should be conducted in different sectors. This paper provides an overview based on the published articles, which are not focusing on the privacy issues that much. Artificial intelligence and 5G networks combine the advanced digital healthcare surveillance system; whereas Bluetooth-based contact tracing systems have fewer privacy concerns. More studies are required to find the appropriate digital healthcare surveillance system, which would be ideal for monitoring, controlling, and predicting the COVID-19 trajectory.
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