Impacts of geographic factors and population density on the COVID-19 spreading under the lockdown policies of China

Sun, Zhibin; Zhang, Hui; Yang, Yifei; Wan, Hua; Wang, Yixiang

doi:10.1016/j.scitotenv.2020.141347

Cited by 140 publications

(116 citation statements)

References 20 publications

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“…Phase 1 is for China's lockdown, which has the largest drop in electricity production at −12% in February 2020 compared to February 2019, and China constitutes 28% of the global total CO 2 emission. China was the first country to implement the national lockdown at the end of January 2020, which led to a nationwide restriction of activities [19]. By early March 2020, other regions around the world also charted positive and negative changes: United States (+0.002% electricity production at 14% of global CO 2 emission) and Europe-Germany, United Kingdom, Italy, France, and Poland (−13% electricity production at 6% global CO 2 emission).…”

Section: Methodsmentioning

confidence: 99%

Atmospheric Carbon Dioxide and Electricity Production Due to Lockdown

et al. 2020

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We analyzed real-time measurements of atmospheric carbon dioxide (CO2), with total electricity production and nationwide restrictions phases in China, the United States of America, Europe, and India due to the novel coronavirus COVID-19 pandemic and its effects on atmospheric CO2. A decline of 3.7% in the global energy demand at about 150 million tonnes of oil equivalent (Mtoe) in the first quarter (Q1) of 2020 was recorded compared to Q1 2019 due to the cutback on international economic activities. Our results showed that: (1) electricity production for the same period in 2018, 2019, and 2020 shrunk at an offset of 9.20%, which resulted in a modest reduction (−1.79%) of atmospheric CO2 to the 2017–2018 CO2 level; (2) a non-seasonal, abrupt, and brief atmospheric CO2 decrease by 0.85% in mid-February 2020 could be due to Phase 1 restrictions in China. The results indicate that electricity production reduction is significant to the short-term variability of atmospheric CO2. It also highlights China’s significant contribution to atmospheric CO2, which suggests that, without the national restriction of activities, CO2 concentration is set to exceed 2019 by 1.79%. Due to the lockdown, it quickly decreased and sustained for two months. The results underscore atmospheric CO2 reductions on the monthly time scale that can be achieved if electricity production from combustible sources was slashed. The result could be useful for cost-benefit analyses on the decrease in electricity production of combustible sources and the impact of this reduction on atmospheric CO2.

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Section: Methodsmentioning

confidence: 99%

Atmospheric Carbon Dioxide and Electricity Production Due to Lockdown

et al. 2020

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“…Apart from it, different social factors can be used as inputs and correlation can be done with the disease and social factors. Such analysis was contributed in some studies that focus on various geographical factors, climate and population density with its influence on infection spreading nature ( Sobral et al, 2020 , Sun et al, 2020 ). Thus, many such correlations would help in providing different aspects to map the disease transmission dynamics and its characteristics with respect to various environmental and social factors and also, to tune the response accordingly.…”

Section: Unleashing the Practical Potentiality Of Prediction During Cmentioning

confidence: 99%

Forecasting of COVID-19 cases using deep learning models: Is it reliable and practically significant?

Devaraj¹,

et al. 2021

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“…Data on positive cases were obtained from the GitHub page of The Civil Protection Department (URL: https://github.com/pcm-dpc/COVID- 19).…”

Section: Parametersmentioning

confidence: 99%

“…In many studies conducted since the beginning of the SARS-CoV-2 outbreak, the population density has been considered to be related to the number of cases, mainly because a high population density can facilitate the spread of the virus [14][15][16][17][18]. However, there are also other studies that did not find a correlation between the density and cases [12,[19][20][21][22]. It is presumed that a high population density can facilitate the spread of the virus due to more people engaging in social interactions to a larger extent, thereby making social distancing harder to maintain and causing public transportation to be more overcrowded.…”

Section: The Territory and Orography Of The Province Of Rovigo/ulss 5mentioning

confidence: 99%

“…SARS-CoV-2 is a new coronavirus, which we will discuss in Section 3.1. These cases turned out to be directly related to the Coronavirus disease-2019 (COVID- 19) pandemic that was first reported in December 2019 in Wuhan, China. The first cases in the Veneto region were registered in the village of Vo' near Padua, 40 km away from Rovigo, on 21 February.…”

Section: Introductionmentioning

confidence: 99%

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Early Phase Management of the SARS-CoV-2 Pandemic in the Geographic Area of the Veneto Region, in One of the World’s Oldest Populations

Camerotto¹,

Sartorio²,

Mazzetto

et al. 2020

IJERPH

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The first cases of Coronavirus disease-2019 (COVID-19) were reported on 21 February in the small town of Vo’ near Padua in the Veneto region of Italy. This event led to 19,286 infected people in the region by 30 June 2020 (39.30 cases/10,000 inhabitants). Meanwhile, Rovigo Local Health Unit n. 5 (ULSS 5), bordering areas with high epidemic rates and having one of the world’s oldest populations, registered the lowest infection rates in the region (19.03 cases/10,000 inhabitants). The aim of this study was to describe timing and event management by ULSS 5 in preventing the propagation of infection within the timeframe spanning from 21 February to 30 June. Our analysis considered age, genetic clusters, sex, orography, the population density, pollution, and economic activities linked to the pandemic, according to the literature. The ULSS 5 Health Director General’s quick decision-making in the realm of public health, territorial assistance, and retirement homes were key to taking the right actions at the right time. Indeed, the number of isolated cases in the Veneto region was the highest among all the Italian regions at the beginning of the epidemic. Moreover, the implementation of molecular diagnostic tools, which were initially absent, enabled health care experts to make quick diagnoses. Quick decision-making, timely actions, and encouraging results were achieved thanks to a solid chain of command, despite a somewhat unclear legislative environment. In conclusion, we believe that the containment of the epidemic depends on the time factor, coupled with a strong sense of awareness and discretion in the Health Director General’s decision-making. Moreover, real-time communication with operating units and institutions goes hand in hand with the common goal of protecting public health.

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Impacts of geographic factors and population density on the COVID-19 spreading under the lockdown policies of China

Cited by 140 publications

References 20 publications

Atmospheric Carbon Dioxide and Electricity Production Due to Lockdown

Atmospheric Carbon Dioxide and Electricity Production Due to Lockdown

Forecasting of COVID-19 cases using deep learning models: Is it reliable and practically significant?

Early Phase Management of the SARS-CoV-2 Pandemic in the Geographic Area of the Veneto Region, in One of the World’s Oldest Populations

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