Revealing geographic transmission pattern of COVID-19 using neighborhood-level simulation with human mobility data and SEIR model: A case study of South Carolina

Ning, Huan; Li, Zhenlong; Qiao, Shan; Zeng, Chengbo; Zhang, Jiajia; Olatosi, Bankole; Li, Xiaoming

doi:10.1016/j.jag.2023.103246

Cited by 5 publications

(5 citation statements)

References 23 publications

(26 reference statements)

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“…These developments were closely intertwined with the imposition of strict limitations. There exists a causal link between the mechanisms of pandemic control and nocturnal light (Kim, 2022; Ning et al., 2023). The primary reason behind the overall decrease in nocturnal light was the reduction in nighttime illumination from residential, commercial, and industrial establishments on the ground (Zhao et al., 2022).…”

Section: Discussionmentioning

confidence: 99%

“…A frequently employed technique for evaluating the impact of pandemic management across various dimensions is the utilization of statistical data. Many scholars conduct research on pandemics utilizing statistical data for geographic analysis (Filonchyk et al., 2021; Mărgărint et al., 2023; Masters et al., 2022), establishing evaluation criteria (Wen et al., 2022), analyzing variations in pandemic transmission (Ning et al., 2023), elucidating diverse control strategies (F. Guo et al., 2022), scrutinizing the spatiotemporal progression of the pandemic (Gaisie et al., 2022), and delineating the spatiotemporal pattern (Wadhwa & Thakur, 2022). However, statistical data may lag due to limitations within the statistical system, and simultaneously, the representativeness or accessibility of the data may be inadequate.…”

Section: Introductionmentioning

confidence: 99%

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Spatiotemporal Dynamics of COVID‐19 Pandemic City Lockdown: Insights From Nighttime Light Remote Sensing

Jiang,

Liu

2024

GeoHealth

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The global COVID‐19 outbreak severely hampered the growth of the global economy, prompting the implementation of the strictest prevention policies in China. Establishing a significant relationship between changes in nighttime light and COVID‐19 lockdowns from a geospatial perspective is essential. In light of nighttime light remote sensing, we evaluated the spatiotemporal dynamic effects of COVID‐19 city lockdowns on human activity intensity in the Zhengzhou region. Prior to the COVID‐19 outbreak, nighttime light in the Zhengzhou region maintained a significant growth trend, even under regular control measures. However, following the October 2022 COVID‐19 lockdown, nighttime light experienced a substantial decrease. In the central area of Zhengzhou, nighttime light decreased by at least 18% compared to pre‐lockdown levels, while in the sub‐center, the decrease was around 14%. The areas where nighttime light decreased the most in the central region were primarily within a 15 km radius, while in the sub‐center, the decrease was concentrated within a 5 km radius. These changes in both statistical data and nighttime light underscored the significant impact of the COVID‐19 lockdown on economic activities in the Zhengzhou region.

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Section: Discussionmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

Spatiotemporal Dynamics of COVID‐19 Pandemic City Lockdown: Insights From Nighttime Light Remote Sensing

Jiang,

Liu

2024

GeoHealth

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“…Prior research also suggests an association between tourism and SARS-CoV-2 infection. For example, when modeling metro areas in South Carolina, Charleston’s transmission rate was two times higher than other metros in the state [ 24 ]. Findings suggested that transportation and tourism activities in the Charleston area influenced this higher rate of transmission.…”

Section: Discussionmentioning

confidence: 99%

Unique SARS-CoV-2 Variants, Tourism Metrics, and B.1.2 Emergence in Early COVID-19 Pandemic: A Correlation Analysis in South Dakota

Nahian,

Huber,

McFadden

2023

IJERPH

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The severe acute respiratory syndrome coronavirus-2 (SARS-CoV-2) virus, which is the source of the coronavirus disease 2019 (COVID-19), was declared a pandemic in the March of 2020. Travel and tourism were severely impacted as restrictions were imposed to help slow the disease spread, but some states took alternative approaches to travel restrictions. This study investigated the spread of COVID-19 in South Dakota during the early pandemic period to better understand how tourism affected the movement of the virus within the region. Sequences from the fall of 2020 were retrieved from public sources. CDC and other sources were used to determine infections, deaths, and tourism metrics during this time. The data were analyzed using correlation and logistic regression. This study found that the number of unique variants per month was positively correlated with hotel occupancy, but not with the number of cases or deaths. Interestingly, the emergence of the B.1.2 variant in South Dakota was positively correlated with increased case numbers and deaths. Data show that states with a shelter-in-place order were associated with a slower emergence of the B.1.2 variant compared to states without such an order, including South Dakota. Findings suggest complex relationships between tourism, SARS-CoV-2 infections, and mitigation strategies. The unique approach that South Dakota adopted provided insights into the spread of the disease in areas without state-wide restrictions. Our results suggest both positive and negative aspects of this approach. Finally, our data highlight the need for future surveillance efforts, including efforts focused on identifying variants with known increased transmission potential to produce effective population health management.

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“…For instance, in urban sciences, the data has been used for analyzing human mobility patterns within and between various regions [7], evaluating transportation infrastructures and planning [8], analyzing transportation equity and socioeconomic disparities [9,10], and assessing the accessibility of bus rapid transit [11]. Similarly, SafeGraph data has been extensively used in public health studies, including tracking the spread of infectious diseases [12,13], monitoring social distancing behaviors, examining the effectiveness of control measures during the COVID-19 pandemic [14,15], and investigating the impacts of nonpharmaceutical intervention [16]. In addition, such datasets play a pivotal role in environmental sciences, such as understanding factors influencing long-term park visitation [17], estimating visitors' demographic status and their patterns in national parks [18,19], and examining how urban socio-physical system impact on the resilience of cities [20].…”

Section: Introductionmentioning

confidence: 99%

Understanding the bias of mobile location data across spatial scales and over time: A comprehensive analysis of SafeGraph data in the United States

Li,

Ning,

Jing

et al. 2024

PLoS ONE

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Mobile location data has emerged as a valuable data source for studying human mobility patterns in various contexts, including virus spreading, urban planning, and hazard evacuation. However, these data are often anonymized overviews derived from a panel of traced mobile devices, and the representativeness of these panels is not well documented. Without a clear understanding of the data representativeness, the interpretations of research based on mobile location data may be questionable. This article presents a comprehensive examination of the potential biases associated with mobile location data using SafeGraph Patterns data in the United States as a case study. The research rigorously scrutinizes and documents the bias from multiple dimensions, including spatial, temporal, urbanization, demographic, and socioeconomic, over a five-year period from 2018 to 2022 across diverse geographic levels, including state, county, census tract, and census block group. Our analysis of the SafeGraph Patterns dataset revealed an average sampling rate of 7.5% with notable temporal dynamics, geographic disparities, and urban-rural differences. The number of sampled devices was strongly correlated with the census population at the county level over the five years for both urban (r > 0.97) and rural counties (r > 0.91), but less so at the census tract and block group levels. We observed minor sampling biases among groups such as gender, age, and moderate-income, with biases typically ranging from -0.05 to +0.05. However, minority groups such as Hispanic populations, low-income households, and individuals with low levels of education generally exhibited higher levels of underrepresentation bias that varied over space, time, urbanization, and across geographic levels. These findings provide important insights for future studies that utilize SafeGraph data or other mobile location datasets, highlighting the need to thoroughly evaluate the spatiotemporal dynamics of the bias across spatial scales when employing such data sources.

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Revealing geographic transmission pattern of COVID-19 using neighborhood-level simulation with human mobility data and SEIR model: A case study of South Carolina

Cited by 5 publications

References 23 publications

Spatiotemporal Dynamics of COVID‐19 Pandemic City Lockdown: Insights From Nighttime Light Remote Sensing

Spatiotemporal Dynamics of COVID‐19 Pandemic City Lockdown: Insights From Nighttime Light Remote Sensing

Unique SARS-CoV-2 Variants, Tourism Metrics, and B.1.2 Emergence in Early COVID-19 Pandemic: A Correlation Analysis in South Dakota

Understanding the bias of mobile location data across spatial scales and over time: A comprehensive analysis of SafeGraph data in the United States

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