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Discrete Dynamics in Nature & Society ; : 1-14, 2021.
Article in English | Academic Search Complete | ID: covidwho-1566411


In this study, a deterministic SEQIR model with standard incidence and the corresponding stochastic epidemic model are explored. In the deterministic model, the reproduction number is given, and the local asymptotic stability of the equilibria is proved. When the reproduction number is less than unity, the disease-free equilibrium is locally asymptotically stable, whereas the endemic equilibrium is locally asymptotically stable in the case of a reproduction number greater than unity. A stochastic expansion based on a deterministic model is studied to explore the uncertainty of the spread of infectious diseases. Using the Lyapunov function method, the existence and uniqueness of a global positive solution are considered. Then, the extinction conditions of the epidemic and its asymptotic property around the endemic equilibrium are obtained. To demonstrate the application of this model, a case study based on COVID-19 epidemic data from France, Italy, and the UK is presented, together with numerical simulations using given parameters. [ FROM AUTHOR] Copyright of Discrete Dynamics in Nature & Society is the property of Hindawi Limited and its content may not be copied or emailed to multiple sites or posted to a listserv without the copyright holder's express written permission. However, users may print, download, or email articles for individual use. This may be abridged. No warranty is given about the accuracy of the copy. Users should refer to the original published version of the material for the full . (Copyright applies to all s.)

Chemosphere ; 278: 130406, 2021 Sep.
Article in English | MEDLINE | ID: covidwho-1169125


During the 2019 novel coronavirus (COVID-19) pandemic, many countries took strong lockdown policy to reduce disease spreading, resulting in mitigating the ambient air pollution due to less traffic and industrial emissions. However, limited studies focused on the household air pollution especially in rural area, the potential risk induced by indoor air pollution exposure was unknown during this period. This field study continuously measured real-time PM2.5 levels in kitchen, living room, and outdoor in the normal days (Period-1) and the days of COVID-19 lockdown overlapping the Chinese Spring Festival (Period-2) in rural homes in China. The average daily PM2.5 concentrations increased by 17.4 and 5.1 µg/m3 in kitchen and living room during Period-2, respectively, which may be due to more fuel consumption for cooking and heating caused by larger family sizes than those during the normal days. The ambient PM2.5 concentration in rural areas in Period-2 decreased by 6.7 µg/m3 compared to the Period-1, less than the drop in urban areas (26.8 µg/m3). An increase of mass fraction of very fine particles in ambient air was observed during lockdown overlapping annual festival days, which could be explained by the residential solid fuel burning. Due to higher indoor air pollution level and longer time spent in indoor environments, daily personal exposure to PM2.5 was 134 ± 40 µg/m3 in Period-2, which was significantly higher than that during in Period-1 (126 ± 27 µg/m3, p < 0.05). The increase of personal PM2.5 exposure during Period-2 could potentially have negative impact on human health, indicating further investigations should be performed to estimate the health impact of global COVID-19 lockdown on community, especially in rural homes using solid fuels as the routine fuels.

Air Pollutants , Air Pollution, Indoor , COVID-19 , Air Pollutants/analysis , Air Pollution, Indoor/analysis , China , Communicable Disease Control , Cooking , Environmental Monitoring , Family Characteristics , Holidays , Humans , Particulate Matter/analysis , Rural Population , SARS-CoV-2
PLoS One ; 15(12): e0244351, 2020.
Article in English | MEDLINE | ID: covidwho-1004462


The COVID-19 pandemic is currently spreading widely around the world, causing huge threats to public safety and global society. This study analyzes the spatiotemporal pattern of the COVID-19 pandemic in China, reveals China's epicenters of the pandemic through spatial clustering, and delineates the substantial effect of distance to Wuhan on the pandemic spread. The results show that the daily new COVID-19 cases mostly occurred in and around Wuhan before March 6, and then moved to the Grand Bay Area (Shenzhen, Hong Kong and Macau). The total COVID-19 cases in China were mainly distributed in the east of the Huhuanyong Line, where the epicenters accounted for more than 60% of the country's total in/on 24 January and 7 February, half in/on 31 January, and more than 70% from 14 February. The total cases finally stabilized at approximately 84,000, and the inflection point for Wuhan was on 14 February, one week later than those of Hubei (outside Wuhan) and China (outside Hubei). The generalized additive model-based analysis shows that population density and distance to provincial cities were significantly associated with the total number of the cases, while distances to prefecture cities and intercity traffic stations, and population inflow from Wuhan after 24 January, had no strong relationships with the total number of cases. The results and findings should provide valuable insights for understanding the changes in the COVID-19 transmission as well as implications for controlling the global COVID-19 pandemic spread.

COVID-19/epidemiology , COVID-19/transmission , Models, Biological , Pandemics , Cities/epidemiology , Hong Kong/epidemiology , Humans , Macau/epidemiology , Spatial Analysis