A Systematic Review of COVID-19 Geographical Research: Machine Learning and Bibliometric Approach

The rampant COVID-19 pandemic swept the globe rapidly in 2020, causing a tremendous impact on human health and the global economy. This pandemic has stimulated an explosive increase of related studies in various disciplines, including geography, which has contributed to pandemic mitigation with a unique spatiotemporal perspective. Reviewing relevant research has implications for understanding the contribution of geography to COVID-19 research. The sheer volume of publications, however, makes the review work more challenging. Here we use the support vector machine and term frequency-inverse document frequency algorithm to identify geographical studies and bibliometrics to discover primary research themes, accelerating the systematic review of COVID-19 geographical research. We confirmed 1,171 geographical papers about COVID-19 published from 1 January 2020 to 31 December 2021, of which a large proportion are in the areas of geographic information systems (GIS) and human geography. We identified four main research themes—the spread of the pandemic, social management, public behavior, and impacts of the pandemic—embodying the contribution of geography. Our findings show the feasibility of machine learning methods in reviewing large-scale literature and highlight the value of geography in the fight against COVID-19. This review could provide references for decision makers to formulate policies combined with spatial thinking and for scholars to find future research directions in which they can strengthen collaboration with geographers.Alternate :2020年, 新冠肺炎流行病迅速席卷全球, 对人类健康和全球经济造成了巨大影响。这次流行病激发了各个学科研究的爆炸性增长。其中, 地理学研究以独特的时空角度, 为流行病治理做出了贡献。对有关研究进行综述, 有助于理解地理学对新冠肺炎研究的贡献。然而, 海量的文献使得这个综述更具挑战性。为了加快对新冠肺炎地理研究的系统性综述, 我们利用支持向量机和词频-反文档频率算法寻找文献中的地理学研究, 利用文献计量学发掘主要研究题目。本文确认了2020年1月1日至2021年12月31日发表的1,171篇新冠肺炎地理学论文, 其中多数文章属于地理信息系统和人文地理学领域。确定了体现地理学贡献的四个主要研究题目：流行病传播、社会管理、公众行为和流行病影响。研究结果表明了利用机器学习方法去开展海量文献综述的可行性, 强调了地理学在抗击新冠肺炎的价值。该文献综述有助于决策者制定具备空间思维的政策, 也有助于学者们寻求加强与地理学者合作的未来研究方向。

Impact of socio-economic environment and its interaction on the initial spread of COVID-19 in mainland China.

Coronavirus disease 2019 (COVID-19) has strongly impacted society since it was first reported in mainland China in December 2020. Understanding its spread and consequence is crucial to pandemic control, yet difficult to achieve because we deal with a complex context of social environment and variable human behaviour. However, few efforts have been made to comprehensively analyse the socio-economic influences on viral spread and how it promotes the infection numbers in a region. Here we investigated the effect of socio-economic factors and found a strong linear relationship between the gross domestic product (GDP) and the cumulative number of confirmed COVID-19 cases with a high value of R2 (between 0.57 and 0.88). Structural equation models were constructed to further analyse the social-economic interaction mechanism of the spread of COVID-19. The results show that the total effect of GDP (0.87) on viral spread exceeds that of population influx (0.58) in the central cities of mainland China and that the spread mainly occurred through its interplay with other factors, such as socio-economic development. This evidence can be generalized as socio-economic factors can accelerate the spread of any infectious disease in a megacity environment. Thus, the world is in urgent need of a new plan to prepare for current and future pandemics.

Global evidence of expressed sentiment alterations during the COVID-19 pandemic.

The COVID-19 pandemic has created unprecedented burdens on people's physical health and subjective well-being. While countries worldwide have developed platforms to track the evolution of COVID-19 infections and deaths, frequent global measurements of affective states to gauge the emotional impacts of pandemic and related policy interventions remain scarce. Using 654 million geotagged social media posts in over 100 countries, covering 74% of world population, coupled with state-of-the-art natural language processing techniques, we develop a global dataset of expressed sentiment indices to track national- and subnational-level affective states on a daily basis. We present two motivating applications using data from the first wave of COVID-19 (from 1 January to 31 May 2020). First, using regression discontinuity design, we provide consistent evidence that COVID-19 outbreaks caused steep declines in expressed sentiment globally, followed by asymmetric, slower recoveries. Second, applying synthetic control methods, we find moderate to no effects of lockdown policies on expressed sentiment, with large heterogeneity across countries. This study shows how social media data, when coupled with machine learning techniques, can provide real-time measurements of affective states.

Rapid greening response of China's 2020 spring vegetation to COVID-19 restrictions: Implications for climate change.

The 2019 novel coronavirus pandemic (COVID-19) negatively affected global public health and socioeconomic development. Lockdowns and travel restrictions to contain COVID-19 resulted in reduced human activity and decreased anthropogenic emissions. However, the secondary effects of these restrictions on the biophysical environment are uncertain. Using remotely sensed big data, we investigated how lockdowns and traffic restrictions affected China's spring vegetation in 2020. Our analyses show that travel decreased by 58% in the first 18 days following implementation of the restrictions across China. Subsequently, atmospheric optical clarity increased and radiation levels on the vegetation canopy were augmented. Furthermore, the spring of 2020 arrived 8.4 days earlier and vegetation 17.45% greener compared to 2015-2019. Reduced human activity resulting from COVID-19 restrictions contributed to a brighter, earlier, and greener 2020 spring season in China. This study shows that short-term changes in human activity can have a relatively rapid ecological impact at the regional scale.

How did human dwelling and working intensity change over different stages of COVID-19 in Beijing?

The COVID-19 pandemic has changed human daily activities significantly. Understanding the nature, causes, and extent of these changes is essential to evaluate the pandemic's influence on commerce, transportation, employment, and environment, among others. However, existing studies mainly focus on changes to general human mobility patterns; few have investigated changes in specific human daily activities. Based on one-year longitudinal mobile phone positioning data for more than 31 million users in Beijing, we tracked intensity changes in two basic human daily activities, dwelling and working, over the stages of COVID-19. The results show that during COVID-19 outbreak, human working intensity decreased about 60% citywide, while dwelling intensity decreased about 40% in some work and education areas. After COVID-19 was under control, intensity in most regions has recovered, but that in schools, hotels, entertainment venues, and tourism areas has not. These intensity changes at regional scale are due to behavior changes at individual scale: about 43% of residents left Beijing before COVID-19, while only 16% have returned back; all commuters decreased their commuting times during COVID-19, while only 75% have reverted to normal. The findings reveal variations in human activities caused by COVID-19 that can support targeted urban management in the post-epidemic era.

Effect of different resumption strategies to flatten the potential COVID-19 outbreaks amid society reopens: a modeling study in China.

BACKGROUND: The effect of the COVID-19 outbreak has led policymakers around the world to attempt transmission control. However, lockdown and shutdown interventions have caused new social problems and designating policy resumption for infection control when reopening society remains a crucial issue. We investigated the effects of different resumption strategies on COVID-19 transmission using a modeling study setting. METHODS: We employed a susceptible-exposed-infectious-removed model to simulate COVID-19 outbreaks under five reopening strategies based on China's business resumption progress. The effect of each strategy was evaluated using the peak values of the epidemic curves vis-à-vis confirmed active cases and cumulative cases. Two-sample t-test was performed in order to affirm that the pick values in different scenarios are different. RESULTS: We found that a hierarchy-based reopen strategy performed best when current epidemic prevention measures were maintained save for lockdown, reducing the peak number of active cases and cumulative cases by 50 and 44%, respectively. However, the modeled effect of each strategy decreased when the current intervention was lifted somewhat. Additional attention should be given to regions with significant numbers of migrants, as the potential risk of COVID-19 outbreaks amid society reopening is intrinsically high. CONCLUSIONS: Business resumption strategies have the potential to eliminate COVID-19 outbreaks amid society reopening without special control measures. The proposed resumption strategies focused mainly on decreasing the number of imported exposure cases, guaranteeing medical support for epidemic control, or decreasing active cases.

COVID-19: Challenges to GIS with Big Data

The outbreak of the 2019 novel coronavirus disease (COVID-19) has caused more than 100,000 people infected and thousands of deaths. Currently, the number of infections and deaths is still increasing rapidly. COVID-19 seriously threatens human health, production, life, social functioning and international relations. In the fight against COVID-19, Geographic Information Systems (GIS) and big data technologies have played an important role in many aspects, including the rapid aggregation of multi-source big data, rapid visualization of epidemic information, spatial tracking of confirmed cases, prediction of regional transmission, spatial segmentation of the epidemic risk and prevention level, balancing and management of the supply and demand of material resources, and social-emotional guidance and panic elimination, which provided solid spatial information support for decision-making, measures formulation, and effectiveness assessment of COVID-19 prevention and control. GIS has developed and matured relatively quickly and has a complete technological route for data preparation, platform construction, model construction, and map production. However, for the struggle against the widespread epidemic, the main challenge is finding strategies to adjust traditional technical methods and improve speed and accuracy of information provision for social management. At the data level, in the era of big data, data no longer come mainly from the government but are gathered from more diverse enterprises. As a result, the use of GIS faces difficulties in data acquisition and the integration of heterogeneous data, which requires governments, businesses, and academic institutions to jointly promote the formulation of relevant policies. At the technical level, spatial analysis methods for big data are in the ascendancy. Currently and for a long time in the future, the development of GIS should be strengthened to form a data-driven system for rapid knowledge acquisition, which signifies that GIS should be used to reinforce the social operation parameterization of models and methods, especially when providing support for social management.