Understanding COVID-19 transmission through Bayesian probabilistic modeling and GIS-based Voronoi approach: a policy perspective

Bherwani, Hemant; Anjum, Saima; Kumar, Suman; Gautam, Sneha; Gupta, Ankit; Kumbhare, Himanshu; Anshul, Avneesh; Kumar, Rakesh

doi:10.1007/s10668-020-00849-0

Cited by 68 publications

(69 citation statements)

References 42 publications

Supporting

Mentioning

Contrasting

Unclassified

Order By: Relevance

“…This demonstrates the importance of implementing prevention measures before the fast growth region in order to delay the spread of the virus. Recent studies in India show similar conclusions ( Bherwani et al, 2020 ). On the other hand, countries in Asia vary in T D and I CR due to the way the pandemic evolved in that continent, since it showed first in China, South Korea, and Japan, and much later in the southern region.…”

Section: Resultssupporting

confidence: 73%

Decrease of mobility, electricity demand, and NO2 emissions on COVID-19 times and their feedback on prevention measures

Corpus-Mendoza

Ruiz-Segoviano

Rodriguez-Contreras

et al. 2021

Science of The Total Environment

View full text Add to dashboard Cite

The spread of coronavirus disease 2019 (COVID-19) on 2020 has affected human activities in a way never documented in modern history. As a consequence of the prevention measures implemented to contain the virus, cities around the world are experiencing a decrease in urban mobility and electricity demand that have positively affected the air quality. The most extreme cases for cities around the world show a decrease of 90, 40, and 70% in mobility, electricity demand, and NO 2 emissions respectively. At the same time, the inspection of these changes along the evaluation of COVID-19 incidence curves allow to obtain feedback about the timely execution of prevention measures for this and future global events. In this case, we identify and discuss the early effort of Latin-American countries to successfully delay the spread of the virus by implementing prevention measures before the fast growth of COVID-19 cases in comparison to European countries.

show abstract

Section: Resultssupporting

confidence: 73%

Decrease of mobility, electricity demand, and NO2 emissions on COVID-19 times and their feedback on prevention measures

Corpus-Mendoza

Ruiz-Segoviano

Rodriguez-Contreras

et al. 2021

Science of The Total Environment

View full text Add to dashboard Cite

show abstract

“…Mumbai has the highest population density and has the highest COVID-19 cases. Bherwani et al (2020) also reported that the western region of Maharashtra which has a high population has recorded high COVID-19 confirmed cases, while the south region which has low population has evidently reported low infected cases. A similar picture was reported from Algeria (Kadi and Khelfaoui 2020), Turkey (Şahin 2020;Coşkun et al 2020), Wuhan city of China (Pansini and Fornacca 2020), the US state and some Europian countries (Babbitt et al 2020) where their population density creates the potential for higher person-to-person contagion rate, thus the higher rate of spread of this virus.…”

Section: Relationship Between Demographic Factors Air Pollutions Anmentioning

confidence: 91%

Pre-to-post lockdown impact on air quality and the role of environmental factors in spreading the COVID-19 cases - a study from a worst-hit state of India

et al. 2020

View full text Add to dashboard Cite

The present study aims to examine the changes in air quality during different phases of the COVID-19 pandemic, including the lockdown (LD 1-4) and unlock period (UL 1-2) (post-lockdown) as compared to pre-lockdown (PL 1-3) and to establish the relationships of the environmental and demographic variables with COVID-19 cases in the state of Maharashtra, the worst-hit state in India. Atmospheric pollutants such as PM 2.5 , PM 10 , NOx, and CO were substantially reduced during the lockdown and unlock phases with the greatest reduction in cities having larger traffic volumes. Compared with the immediate pre-lockdown period (PL 3), the averaged PM 2.5 and PM 10 reduced by up to 51% and 47% respectively during the lockdown periods, which resulted in 'satisfactory' level of air quality index (AQI) as a result of reduced vehicular traffic and industrial closing. These parameters continued to reduce as much as 80% during the unlock periods due to the additive impact of weather (rainfall and temperature) combined with the lockdown conditions. Kendall's correlation matrix showed a significant negative correlation between temperature and air pollutants (r= − 0.35 to − 057). Conversely, SO 2 and O 3 did not improve, and in some cases, they increased during the lockdown and unlocking. COVID-19 spreading incidences were strongly and positively correlated with temperature (r < 0.62) and dew point (r < 0.73). Thus, this indicates that the increase in temperature and dew point cannot weaken the transmission of this virus. The number of COVID-19 cases relative to air pollutants was negatively correlated (r = − 0.33 to − 0.74), which may be a mere coincidence as a result of lockdown. However, based on pre-lockdown air quality data and demographic factors, it was found that particulate matter (PM 2.5 and PM 10) and population density are closely linked with higher morbidity and mortality although a more in-depth research is required in this direction to validate this finding. The onset of COVID-19 has allowed us to determine that 'immediate' changes in air quality within densely populated/industrialized areas can improve livelihood based on pollution mitigation. These findings could be used by policymakers to set new benchmarks for air pollution that would improve the quality of life for major sectors of the World's population. COVID-19 has shown us that we can make changes when necessary, and findings may pave the way for future research to inform policy on the tough choices we will have to make between quality of life and survival. Also, our results will enrich the ongoing discussion on the role of environmental factors on the transmission of COVID-19 and will help to take necessary steps for its control.

show abstract

“…Thiessen Polygons are created from a set of feature points, therefore, every individual polygon specifies an effect area around its input feature point (Wheeler et al 2010 ). Therefore, Thiessen Polygon uses the basic method of analysing neighborhood and proximity (Bherwani et al 2020a , b ). In this study, Thiessen Polygons were prepared separately for population density and COVID-19 positive cases for each state for understanding the significance of correlation.…”

Section: Methodsmentioning

confidence: 99%

“…In the context of COVID-19, statistical spatial models examined the widespread infection rate in India. Model studies have also been used to understand the risk assessment of COVID-19 spread in different regions (Bherwani et al 2020a , b ; Das et al 2020 ; Gupta et al 2020 ). However, the correlation analysis for the different variables in the COVID-19 data series is an important aspect.…”

Section: Introductionmentioning

confidence: 99%

Nexus between population density and novel coronavirus (COVID-19) pandemic in the south Indian states: A geo-statistical approach

Arif

Sengupta

2020

Environ Dev Sustain

View full text Add to dashboard Cite

The unprecedented growth of the novel coronavirus (SARS-CoV-2) as a severe acute respiratory syndrome escalated to the coronavirus disease 2019 (COVID-19) pandemic. It has created an unanticipated global public health crisis that is spreading rapidly in India as well, posing a serious threat to 1350 million persons. Among the factors, population density is foremost in posing a challenge in controlling the COVID-19 contagion. In such extraordinary times, evidence-based knowledge is the prime requisite for pacifying the effect. In this piece, we have studied the district wise transmissions of the novel coronavirus in five south Indian states until 20th July 2020 and its relationship with their respective population density. The five states are purposefully selected for their records in better healthcare infrastructure vis-à-vis other states in India. The study uses Pearson’s correlation coefficient to account for the direct impact of population density on COVID-19 transmission rate. Response surface methodology approach is used to validate the correlation between density and transmission rate and spatiotemporal dynamics is highlighted using Thiessen polygon method. The analysis has found that COVID-19 transmission in four states (Kerala, Tamil Nadu, Karnataka and Telangana) strongly hinges upon the spatial distribution of population density. In addition, the results indicate that the long-term impacts of the COVID-19 crisis are likely to differ with demographic density. In conclusion, those at the helm of affairs must take cognizance of the vulnerability clusters together across districts.

show abstract

Understanding COVID-19 transmission through Bayesian probabilistic modeling and GIS-based Voronoi approach: a policy perspective

Cited by 68 publications

References 42 publications

Decrease of mobility, electricity demand, and NO2 emissions on COVID-19 times and their feedback on prevention measures

Decrease of mobility, electricity demand, and NO2 emissions on COVID-19 times and their feedback on prevention measures

Pre-to-post lockdown impact on air quality and the role of environmental factors in spreading the COVID-19 cases - a study from a worst-hit state of India

Nexus between population density and novel coronavirus (COVID-19) pandemic in the south Indian states: A geo-statistical approach

Contact Info

Product

Resources

About