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:10.2139/ssrn.3587704

Cited by 21 publications

(23 citation statements)

References 17 publications

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“…The geographic disparity of the COVID-19 incidence rate has been recognized in previous studies (Hu et al, 2020; Liu et al, 2020; Yang et al, 2020). However, most research was conducted at or above the county level, which prevents us from observing how the COVID-19 incidence rate interacts with the various factors at a finer spatial scale.…”

Section: Discussionmentioning

confidence: 54%

Racial segregation, testing sites access, and COVID-19 incidence rate in Massachusetts, USA

Han

Wang

et al. 2020

Preprint

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The U.S. has merely 4% of the world population but 25% of the world's COVID-19 cases. Massachusetts has been in the leading position of total cases since the outbreak in the U.S. Racial residential segregation is a fundamental cause of racial disparities in health. Moreover, disparities of access to health care have a large impact on COVID-19 cases. Thus, this study estimates racial segregation and disparities in testing sites access and employs economic, demographic, and transportation variables at the city/town level in Massachusetts. Spatial regression models are applied to evaluate the relationships between COVID-19 incidence rate and related variables. This is the first study to apply spatial analysis methods across neighborhoods in the U.S. to examine the COVID-19 incidence rate. The findings are: 1) residential segregations of Hispanic and Non-Hispanic Black/African Americans have a significantly positive association with COVID-19 incidence rate, indicating the higher susceptibility of COIVD-19 infections among minority; 2) The Black has the shortest drive time to testing sites, followed by Hispanic, Asian, and Whites. The drive time to testing sites is significantly negatively associated with the COVID-19 incidence rate, implying the importance of testing location being accessed by all populations; 3) Poverty rate and road density are significant explanatory variables. Importantly, overcrowding represented by more than one person per room is a significant variable found to be positively associated with COVID-19 incidence rate, suggesting the effectiveness of social distancing for reducing infection; 4) Different from previous studies, elderly population rate is not statistically significant with incidence rate because the elderly population in Massachusetts is less distributed in the hot spot regions of COVID-19 infections. The findings in this study provide useful insights for policymakers to propose new strategies to contain the COVID-19 transmissions in Massachusetts.

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Section: Discussionmentioning

confidence: 54%

Racial segregation, testing sites access, and COVID-19 incidence rate in Massachusetts, USA

Han

Wang

et al. 2020

Preprint

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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://dataverse.harvard.edu/dataset.xhtml?persistentId=doi:10.7910/DVN/MR5IJN 10.7910/DVN/MR5IJN ). 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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“…The number of (A) detected infections, (B) fatality cases. Zero on the horizontal axis corresponds to the time from which the data ( Hu et al, 2020 ) are taken (January 23), which also coincides with the Wuhan closure. Red circles correspond to the observed Hubei counts.…”

Section: Introductionmentioning

confidence: 96%

A systems biology approach to COVID-19 progression in population

Djordjevic

Rodic

Salom

et al. 2021

Proteomics and Systems Biology

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A number of models in mathematical epidemiology have been developed to account for control measures such as vaccination or quarantine. However, COVID-19 has brought unprecedented social distancing measures, with a challenge on how to include these in a manner that can explain the data but avoid overfitting in parameter inference. We here develop a simple time-dependent model, where social distancing effects are introduced analogous to coarse-grained models of gene expression control in systems biology. We apply our approach to understand drastic differences in COVID-19 infection and fatality counts, observed between Hubei (Wuhan) and other Mainland China provinces. We find that these unintuitive data may be explained through an interplay of differences in transmissibility, effective protection, and detection efficiencies between Hubei and other provinces. More generally, our results demonstrate that regional differences may drastically shape infection outbursts. The obtained results demonstrate the applicability of our developed method to extract key infection parameters directly from publically available data so that it can be globally applied to outbreaks of COVID-19 in a number of countries. Overall, we show that applications of uncommon strategies, such as methods and approaches from molecular systems biology research to mathematical epidemiology, may significantly advance our understanding of COVID-19 and other infectious diseases.

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Building an Open Resources Repository for Covid-19 Research

Cited by 21 publications

References 17 publications

Racial segregation, testing sites access, and COVID-19 incidence rate in Massachusetts, USA

Racial segregation, testing sites access, and COVID-19 incidence rate in Massachusetts, USA

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

A systems biology approach to COVID-19 progression in population

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