The Study of the Effects of Mobility Trends on the Statistical Models of the COVID-19 Virus Spreading

Gondauri, Davit; Batiashvili, Mikheil

doi:10.29333/ejgm/8212

Cited by 26 publications

(21 citation statements)

References 11 publications

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“…Even after this date, the Rt value was very close to 1, yet it was above this threshold in some moments. This result is like the 21 days found by [25] and the 15-20 days found by [26]. It is noteworthy that these studies use different methodologies to the current study, and a direct comparison would not be entirely adequate.…”

Section: Discussionsupporting

confidence: 54%

Data-Driven Approach to Understand the Mobility Patterns of the Portuguese Population during the COVID-19 Pandemic

Tamagusko

Ferreira

2020

Sustainability

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SARS-CoV-2 emerged in late 2019. Since then, it has spread to several countries, becoming classified as a pandemic. So far, there is no definitive treatment or vaccine, so the best solution is to prevent transmission between individuals through social distancing. However, it is not easy to measure the effectiveness of these distance measures. Therefore, this study uses data from Google COVID-19 Community Mobility Reports to understand the Portuguese population’s mobility patterns during the COVID-19 pandemic. In this study, the Rt value was modeled for Portugal. In addition, the changepoint was calculated for the population mobility patterns. Thus, the mobility pattern change was used to understand the impact of social distance measures on the dissemination of COVID-19. As a result, it can be stated that the initial Rt value in Portugal was very close to 3, falling to values close to 1 after 25 days. Social isolation measures were adopted quickly. Furthermore, it was observed that public transport was avoided during the pandemic. Finally, until the emergence of a vaccine or an effective treatment, this is the new normal, and it must be understood that new patterns of mobility, social interaction, and hygiene must be adapted to this reality.

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Section: Discussionsupporting

confidence: 54%

Data-Driven Approach to Understand the Mobility Patterns of the Portuguese Population during the COVID-19 Pandemic

Tamagusko

Ferreira

2020

Sustainability

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“…Travel quarantine delayed epidemic progression there by 3–5 days ( Chinazzi et al, 2020 ). Gondauri and Batiashvili (2020) , analyzing the mobility in nine countries, found that trips performed one day are directly related to COVID-19 cases after 15–20 days. According to Cot et al (2021) , this lag period is two to five weeks.…”

Section: Literature Reviewmentioning

confidence: 99%

Spatial association of mobility and COVID-19 infection rate in the USA: A county-level study using mobile phone location data

Tokey

2021

Journal of Transport & Health

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Introduction Human mobility has been a central issue in the discussion from the beginning of COVID-19. While the body of literature on the relationship of COVID transmission and mobility is large, studies mostly captured a relatively short timeframe. Moreover, spatial non-stationarity has garnered less attention in these explorative models. Therefore, the major concern of this study is to see the relationship of mobility and COVID on a broader temporal scale and after mitigating this methodological gap. Objective In response to this concern, this study first explores the spatiotemporal pattern of mobility indicators. Secondly, it attempts to understand how mobility is related to COVID infection rate and how this relationship has been changed over time and space after controlling several sociodemographic characteristics, spatial heterogeneity, and policy-related changes during different phases of Coronavirus. Data and method This study uses GPS-based mobility data for a wider time frame of six months (March 20-August’20) divided into four tiers and carries analysis for all the US counties (N = 3142). Space-time cube is used to generate the spatiotemporal pattern. For the second objective, Ordinary Least Square (OLS), Spatial Error Model (SEM), and Geographically Weighted Regression (GWR) were used. Result The spatial-temporal pattern suggests that the trip rate, out-of-county trip rate, and miles/person traveled were mostly plummeted till the first wave reached its peak, and subsequently, all of these mobility matrices started to rise. From spatial models, infection rates were found negatively correlated with miles traveled and out-of-county trips. Highly COVID infected areas mostly had more people working from home, low percentages of aged people and educated people, and high percentages of poor people. Conclusion This study, with necessary policy implications, provides a comprehensive understanding of the shifting pattern of mobility and COVID. Spatial models outperform OLS with better fits and non-clustered residuals.

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“…Di antara kebijakan tersebut adalah social distancing atau Pembatasan Sosial Berskala Besar (PSBB), work from home dan school from home. Kebijakan-kebijakan tersebut bertujuan untuk menekan mobilitas penduduk untuk meminimalkan pertambahan jumlah kasus harian COVID-19 sebagaimana kesimpulan sementara pada kebijakan serupa di beberapa negara lain di dunia (Badr, 2020;Buckee, 2020;Gondauri, 2020;Wilson, 2020).…”

Section: Pendahuluanunclassified

Tinjauan Big Data Mobilitas Penduduk Pada Masa Social Distancing Dan New Normal Serta Keterkaitannya Dengan Jumlah Kasus Covid-19

Firdaus¹,

Wijayanto²

2021

semnasoffstat

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Berbagai kebijakan Pemerintah Indonesia telah digulirkan untuk mengatasi dampak penyebaran Pandemi Coronavirus Disease (COVID-19). Di antara kebijakan tersebut meliputi social distancing atau Pembatasan Sosial Berskala Besar (PSBB), work from home, school from home, dst. Dengan tujuan untuk menekan mobilitas penduduk, berbagai kebijakan tersebut diharapkan dapat meminimalkan pertambahan jumlah kasus harian COVID-19. Penelitian ini berfokus pada identifikasi dan perbandingan pola mobilitas penduduk selama masa social distancing dan new normal serta mencoba menganalisis keterkaitan antara mobilitas penduduk dengan kasus COVID- 19, khususnya di wilayah Provinsi DKI Jakarta. Periode penelitian dimulai sejak mulai diberlakukannya social distancing pada pertengahan bulan Maret hingga akhir Juli 2020. Dengan menggunakan independent two-sample t-test, terdapat perbedaan yang signifikan antara rata- rata mobilitas penduduk maupun peningkatan kasus COVID-19 pada masa social distancing dan pada masa new normal di Jakarta. Penerapan social distancing (PSBB) di DKI Jakarta secara umum mampu menurunkan mobilitas penduduk dan menekan penambahan kasus COVID-19.

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The Study of the Effects of Mobility Trends on the Statistical Models of the COVID-19 Virus Spreading

Cited by 26 publications

References 11 publications

Data-Driven Approach to Understand the Mobility Patterns of the Portuguese Population during the COVID-19 Pandemic

Data-Driven Approach to Understand the Mobility Patterns of the Portuguese Population during the COVID-19 Pandemic

Spatial association of mobility and COVID-19 infection rate in the USA: A county-level study using mobile phone location data

Tinjauan Big Data Mobilitas Penduduk Pada Masa Social Distancing Dan New Normal Serta Keterkaitannya Dengan Jumlah Kasus Covid-19

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