Building an Open Resources Repository for COVID-19 Research

Hu, Tao; Guan, Weihe Wendy; Zhu, Xinyan; Shao, Yuanzheng; Liu, Lingbo; Du, Jing; Liu, Hongqiang; Zhou, Huan; Wang, Jialei; She, Bing; Zhang, Luyao; Li, Zhibin; Wang, Peixiao; Tang, Yicheng; Hou, Ruizhi; Li, Yun; Sha, Dexuan; Yang, Yifan; Lewis, Benjamin; Kakkar, Devika; Bao, Shuming

doi:10.2478/dim-2020-0012

Cited by 44 publications

(12 citation statements)

References 22 publications

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“…The socioeconomic patterns are analyzed at a finer scale using weekly confirmed cases in Massachusetts by city/town (https://www.mass.gov/info-details/covid-19-response-reporting#covid-19cases-by-city/town-) on 29 April 2020 including 350 cities/towns (Hu et al 2020a) and 42 of them have uncertain numbers. Four variables, including poverty rate, educational attainment, elderly people rate, and income, are used to analyze the socioeconomic impact.…”

Section: Social Implicationsmentioning

confidence: 99%

Taking the pulse of COVID-19: a spatiotemporal perspective

Yang

Sha

Liu

et al. 2020

International Journal of Digital Earth

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105

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Section: Social Implicationsmentioning

confidence: 99%

Taking the pulse of COVID-19: a spatiotemporal perspective

Yang

Sha

Liu

et al. 2020

International Journal of Digital Earth

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105

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“…Data on the reported cases were collected and processed at the China Data Center and shared on the dataverse platform of Harvard University, which does not require additional data preprocessing( Hu et al, 2020 ; Yang et al, 2020 ). Therefore, we mainly conducted data preprocessing for information on healthcare workers suffering from COVID-19, which was obtained via retrospective analyses.…”

Section: Study Area and Data Sourcesmentioning

confidence: 99%

Exploring the impact of under-reported cases on the COVID-19 spatiotemporal distributions using healthcare workers infection data

Wang

Bao

et al. 2022

Cities

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A timely understanding of the spatiotemporal pattern and development trend of COVID-19 is critical for timely prevention and control. However, the under-reporting of casesis widespread in fields associated with public health. It is also possible to draw biased inferences and formulate inappropriate prevention and control policies if the phenomenon of under-reporting is not taken into account. Therefore, in this paper, a novel framework was proposed to explore the impact of under-reporting on COVID-19 spatiotemporal distributions, and empirical analysis was carried out using infection data of healthcare workers in Wuhan and Hubei (excluding Wuhan). The results show that (1) the lognormal distribution was the most suitable to describe the evolution of epidemic with time; (2) the estimated peak infection time of the reported cases lagged the peak infection time of the healthcare worker cases, and the estimated infection time interval of the reported cases was smaller than that of the healthcare worker cases. (3) The impact of under-reporting cases on the early stages of the pandemic was greater than that on its later stages, and the impact on the early onset area was greater than that on the late onset area. (4) Although the number of reported cases was lower than the actual number of cases, a high spatial correlation existed between the cumulatively reported cases and healthcare worker cases. The proposed framework of this study is highly extensible, and relevant researchers can use data sources from other counties to carry out similar research.

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“…Daily counts of confirmed COVID19 cases, aggregated at the city level for 339 city-specific polygons in China, were obtained from the Harvard Dataverse China Data Lab (https ://datav erse.harva rd.edu/datas et.xhtml ?persi stent Id=doi:10.7910/DVN/MR5IJ N). The construction of the China dataset is described by Hu et al (2020). Spatially explicit quantities derived from these geocoded time series were gridded in equal area projections (China: Sinusoidal, USA: Molleweide) at 500 m resolution to coregister with the reprojected VIIRS grids.…”

Section: Datamentioning

confidence: 99%

Spatiotemporal evolution of COVID-19 infection and detection within night light networks: comparative analysis of USA and China

Small

Sousa

2021

Appl Netw Sci

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The spatial distribution of population affects disease transmission, especially when shelter in place orders restrict mobility for a large fraction of the population. The spatial network structure of settlements therefore imposes a fundamental constraint on the spatial distribution of the population through which a communicable disease can spread. In this analysis we use the spatial network structure of lighted development as a proxy for the distribution of ambient population to compare the spatiotemporal evolution of COVID-19 confirmed cases in the USA and China. The Visible Infrared Imaging Radiometer Suite (VIIRS) Day/Night Band sensor on the NASA/NOAA Suomi satellite has been imaging night light at ~ 700 m resolution globally since 2012. Comparisons with sub-kilometer resolution census observations in different countries across different levels of development indicate that night light luminance scales with population density over ~ 3 orders of magnitude. However, VIIRS’ constant ~ 700 m resolution can provide a more detailed representation of population distribution in peri-urban and rural areas where aggregated census blocks lack comparable spatial detail. By varying the low luminance threshold of VIIRS-derived night light, we depict spatial networks of lighted development of varying degrees of connectivity within which populations are distributed. The resulting size distributions of spatial network components (connected clusters of nodes) vary with degree of connectivity, but maintain consistent scaling over a wide range (5 × to 10 × in area & number) of network sizes. At continental scales, spatial network rank-size distributions obtained from VIIRS night light brightness are well-described by power laws with exponents near −2 (slopes near −1) for a wide range of low luminance thresholds. The largest components (104 to 105 km2) represent spatially contiguous agglomerations of urban, suburban and periurban development, while the smallest components represent isolated rural settlements. Projecting county and city-level numbers of confirmed cases of COVID-19 for the USA and China (respectively) onto the corresponding spatial networks of lighted development allows the spatiotemporal evolution of the epidemic (infection and detection) to be quantified as propagation within networks of varying connectivity. Results for China show rapid nucleation and diffusion in January 2020 followed by rapid decreases in new cases in February. While most of the largest cities in China showed new confirmed cases approaching zero before the end of February, most of these cities also showed distinct second waves of cases in March or April. Whereas new cases in Wuhan did not approach zero until mid-March, as of December 2020 it has not yet experienced a second wave of cases. In contrast, the results for the USA show a wide range of trajectories, with an abrupt transition from slow increases in confirmed cases in a small number of network components in January and February, to rapid geographic dispersion to a larger number of components shortly before mobility reductions occurred in March. Results indicate that while most of the upper tail of the network had been exposed by the end of March, the lower tail of the component size distribution has only shown steep increases since mid-June.

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Building an Open Resources Repository for COVID-19 Research

Cited by 44 publications

References 22 publications

Taking the pulse of COVID-19: a spatiotemporal perspective

Taking the pulse of COVID-19: a spatiotemporal perspective

Exploring the impact of under-reported cases on the COVID-19 spatiotemporal distributions using healthcare workers infection data

Spatiotemporal evolution of COVID-19 infection and detection within night light networks: comparative analysis of USA and China

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