Dataset for country profile and mobility analysis in the assessment of COVID-19 pandemic

Dantas, Marcel da Câmara Ribeiro; Alves, Gisliany; Gomes, Rafael Beserra; Bezerra, Leonardo C. T.; Lima, Luciana Conceição de; Silva, Ivanovitch

doi:10.1016/j.dib.2020.105698

Cited by 14 publications

(9 citation statements)

References 2 publications

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“…In the same direction, inconclusive results have been found regarding population density [ 6 , 7 , 8 ]. Previous studies have reported the association between population physical distancing and COVID-19 spreading dynamics [ 9 , 10 , 11 ], as well as other weather conditions such as humidity and temperature [ 12 ]. These links might lie behind the local outbreaks of the pandemic in certain agri-food sectors such as meat and leather and fur industries [ 13 , 14 ].…”

Section: Introductionmentioning

confidence: 99%

Community Risk Factors in the COVID-19 Incidence and Mortality in Catalonia (Spain). A Population-Based Study

Zaldo-Aubanell

López

Bach

et al. 2021

IJERPH

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The heterogenous distribution of both COVID-19 incidence and mortality in Catalonia (Spain) during the firsts moths of the pandemic suggests that differences in baseline risk factors across regions might play a relevant role in modulating the outcome of the pandemic. This paper investigates the associations between both COVID-19 incidence and mortality and air pollutant concentration levels, and screens the potential effect of the type of agri-food industry and the overall land use and cover (LULC) at area level. We used a main model with demographic, socioeconomic and comorbidity covariates highlighted in previous research as important predictors. This allowed us to take a glimpse of the independent effect of the explanatory variables when controlled for the main model covariates. Our findings are aligned with previous research showing that the baseline features of the regions in terms of general health status, pollutant concentration levels (here NO2 and PM10), type of agri-food industry, and type of land use and land cover have modulated the impact of COVID-19 at a regional scale. This study is among the first to explore the associations between COVID-19 and the type of agri-food industry and LULC data using a population-based approach. The results of this paper might serve as the basis to develop new research hypotheses using a more comprehensive approach, highlighting the inequalities of regions in terms of risk factors and their response to COVID-19, as well as fostering public policies towards more resilient and safer environments.

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Section: Introductionmentioning

confidence: 99%

Community Risk Factors in the COVID-19 Incidence and Mortality in Catalonia (Spain). A Population-Based Study

Zaldo-Aubanell

López

Bach

et al. 2021

IJERPH

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“…The present work established two hypotheses for the mobility trend of the population in ten South American countries: Countries that have adopted stricter social distancing measures have a lower level of movement of people during the pandemic (H1), and mobility is occurring randomly in space during the pandemic (H2). To evaluate these hypotheses, we used Google Community Mobility Reports dataset to construct a mobility index that summarises the Google categories into a single one [39]. In order to compare the level of population mobility with the strictness degree of social distancing measures in each country, we used the Covid stringency index from Oxford Covid-19 Government Response Tracker (OxCGRT).…”

Section: Introductionmentioning

confidence: 99%

Evaluating Social Distancing Measures and Their Association with the Covid-19 Pandemic in South America

Alves

Lima

Silva

et al. 2021

IJGI

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Social distancing is a powerful non-pharmaceutical intervention used as a way to slow the spread of the SARS-CoV-2 virus around the world since the end of 2019 in China. Taking that into account, this work aimed to identify variations on population mobility in South America during the pandemic (15 February to 27 October 2020). We used a data-driven approach to create a community mobility index from the Google Covid-19 Community Mobility and relate it to the Covid stringency index from Oxford Covid-19 Government Response Tracker (OxCGRT). Two hypotheses were established: countries which have adopted stricter social distancing measures have also a lower level of circulation (H1), and mobility is occurring randomly in space (H2). Considering a transient period, a low capacity of governments to respond to the pandemic with more stringent measures of social distancing was observed at the beginning of the crisis. In turn, considering a steady-state period, the results showed an inverse relationship between the Covid stringency index and the community mobility index for at least three countries (H1 rejected). Regarding the spatial analysis, global and local Moran indices revealed regional mobility patterns for Argentina, Brazil, and Chile (H1 rejected). In Brazil, the absence of coordinated policies between the federal government and states regarding social distancing may have played an important role for several and extensive clusters formation. On the other hand, the results for Argentina and Chile could be signals for the difficulties of governments in keeping their population under control, and for long periods, even under stricter decrees.

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“…Kabir and Peter [2] , [3] show the online forecasting mechanism for every 24 h and survey about risk perception and health behavior among Nigerian using Nigerian Government Data. Jing [4] presented a dataset containing 33-death case information from Wuhan for estimating death rate while Marcel [5] presented a combinational dataset of country profile and mobility analysis. Two separate datasets are brought by Cam-Tu [6] , [7] about Vietnamese students and teachers' learning habits and perceived support during COVID-19.…”

Section: Data Descriptionmentioning

confidence: 99%

Covid-19 Dataset: Worldwide spread log including countries first case and first death

et al. 2020

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The article represents coronavirus spread log history. The duration, coronavirus takes to spread from one country to another country, could be seen in this dataset and could predicted the same for future pandemics through this dataset. It is highly dependent on the cabalistic number of variables that is the main navel of these datasets. Information for this dataset is collected from trusted websites, local and international popular newspapers. This coronavirus dataset not only help to track the spreading route of coronavirus but also can be used for predicting the possible spreading route of similar future pandemics. This dataset consists of 186 countries' useful data related to COVID-19 pandemic from November 17, 2019, to May 16, 2020, with 8 unique variables that provide the information of the nature of the spread of COVID-19. The datasets mainly focus on two major fields, first one is First Case which consists of information of Date of First Case(s), Number of confirm Case(s) at First Day, Age of the patient(s) of First Case, Last Visited Country and the other one First Death information consist of Date of First Death and Age of the Patient who died first for every Country mentioning corresponding Continent.This dataset also can perform a bunch of predictions using Machine Learning applications, like -how fast the virus is spreading, affect rate, death rate, death rate and able to represent comparison between other pandemics. Using this dataset, any similar pandemic spreadness could be predicted earlier and necessary precaution measures could be taken.

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Dataset for country profile and mobility analysis in the assessment of COVID-19 pandemic

Cited by 14 publications

References 2 publications

Community Risk Factors in the COVID-19 Incidence and Mortality in Catalonia (Spain). A Population-Based Study

Community Risk Factors in the COVID-19 Incidence and Mortality in Catalonia (Spain). A Population-Based Study

Evaluating Social Distancing Measures and Their Association with the Covid-19 Pandemic in South America

Covid-19 Dataset: Worldwide spread log including countries first case and first death

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