COVID-19 Infection In China Research Articles

Effects of meteorological conditions and air pollution on COVID-19 transmission: Evidence from 219 Chinese cities

The spatial distribution of the COVID-19 infection in China cannot be explained solely by geographical distance and regulatory stringency. In this research we investigate how meteorological conditions and air pollution, as concurring factors, impact COVID-19 transmission, using data on new confirmed cases from 219 prefecture cities from January 24 to February 29, 2020. Results revealed a kind of nonlinear dose-response relationship between temperature and coronavirus transmission. We also found that air pollution indicators are positively correlated with new confirmed cases, and the coronavirus further spreads by 5–7% as the AQI increases by 10 units. Further analysis based on regional divisions revealed that in northern China the negative effects of rising temperature on COVID-19 is counteracted by aggravated air pollution. In the southern cities, the ambient temperature and air pollution have a negative interactive effect on COVID-19 transmission, implying that rising temperature restrains the facilitating effects of air pollution and that they jointly lead to a decrease in new confirmed cases. These results provide implications for the control and prevention of this disease and for the anticipation of another possible pandemic.

Prediction of the Number of Patients Infected with COVID-19 Based on Rolling Grey Verhulst Models.

The outbreak of a novel coronavirus (SARS-CoV-2) has caused a large number of residents in China to be infected with a highly contagious pneumonia recently. Despite active control measures taken by the Chinese government, the number of infected patients is still increasing day by day. At present, the changing trend of the epidemic is attracting the attention of everyone. Based on data from 21 January to 20 February 2020, six rolling grey Verhulst models were built using 7-, 8- and 9-day data sequences to predict the daily growth trend of the number of patients confirmed with COVID-19 infection in China. The results show that these six models consistently predict the S-shaped change characteristics of the cumulative number of confirmed patients, and the daily growth decreased day by day after 4 February. The predicted results obtained by different models are very approximate, with very high prediction accuracy. In the training stage, the maximum and minimum mean absolute percentage errors (MAPEs) are 4.74% and 1.80%, respectively; in the testing stage, the maximum and minimum MAPEs are 4.72% and 1.65%, respectively. This indicates that the predicted results show high robustness. If the number of clinically diagnosed cases in Wuhan City, Hubei Province, China, where COVID-19 was first detected, is not counted from 12 February, the cumulative number of confirmed COVID-19 cases in China will reach a maximum of 60,364–61,327 during 17–22 March; otherwise, the cumulative number of confirmed cases in China will be 78,817–79,780.

Covid-19 Enfeksiyonu, Postmortem Süreç ve Defin İşlemleri

Çin'de COVID-19 enfeksiyonunun Aralık 2019 tarihinde patlak vermesi ile virüs tüm dünyada hızla yayılan bir pandemi başlatarak, ciddi sağlık sorunları ve ölümlere neden olmuştur. Mart 2020 başı itibariyle Dünya genelinde yüzün üzerinde ülkede görüldüğü bildirilmiştir. Ülkemizde ilk vaka Mart başında saptanmış, günümüze kadar binin üzerinde ölüm bildirilmiştir. Bu süreçte adli tıp çalışanları olası bir COVID-19 ölümünde ciddi bulaş riski ile karşı karşıya kalmaktadır. Bu çalışma COVID-19 hastalığına neden olan virusün tanımı ve epidemiyolojisi, ölüm sonrası COVID-19 tanısının nasıl konacağı, ölü muayenesi ve otopsi odasında olası riskler ve bu risklerin nasıl azaltılacağı konusunda bir derleme niteliğindedir. Ayrıca COVID-19 enfeksiyonu şüphesinde ölü defin işlemleri ve ölüm belgesi düzenleme ile ilgili ülkemizde yaşanan sıkıntılar tartışılmış ve öneriler sunulmuştur.

Population Movement, City Closure in Wuhan, and Geographical Expansion of the COVID-19 Infection in China in January 2020.

BackgroundThe unprecedented outbreak of 2019-nCoV pneumonia infection in Wuhan City caused global concern, the outflowing population from Wuhan was believed to be a main reason for the rapid and large-scale spread of the disease, so the government implemented a city closure measure to prevent its transmission considering the large amount of travelling before the Chinese New Year.MethodsBased on the daily reported new cases and the population movement data between January 1 and 31, we examined the effects of population outflow from Wuhan on the geographical expansion of the infection in other provinces and cities of China, as well as the impacts of the city closure in Wuhan in different scenarios of closing dates.ResultsWe observed a significantly positive association between population movement and the number of the 2019-nCoV cases. The spatial distribution of cases per unit outflow population indicated that some areas with large outflow population might have been underestimated for the infection, such as Henan and Hunan provinces. Further analysis revealed that if the city closure policy was implemented two days earlier, 1420 (95% CI: 1059, 1833) cases could have been prevented, and if two days later, 1462 (95% CI: 1090, 1886) more cases would be possible.ConclusionsOur findings suggest that population movement might be one important trigger for the transmission of 2019-nCoV infection in China, and the policy of city closure is effective to control the epidemic.

Detection of SARS-CoV-2 in Different Types of Clinical Specimens

This study describes results of PCR and viral RNA testing for SARS-CoV-2 in bronchoalveolar fluid, sputum, feces, blood, and urine specimens from patients with COVID-19 infection in China to identify possible means of non-respiratory transmission.

"7C" principle and "Alcohol" method towards persevering control of COVID-19 infection in China

"7C" principle and "Alcohol" method towards persevering control of COVID-19 infection in China

Mobility, Exposure, and Epidemiological Timelines of COVID-19 Infections in China outside Hubei Province

The 2019 coronavirus disease (COVID-19) is pseudonymously linked to more than 20 million cases in the world as of August 2020. High-quality data are needed but lacking in the understanding of and fighting against COVID-19. We provide a complete and updating hand-coded line-list dataset containing detailed information of the cases in China and outside the epicenter Hubei Province. The data are extracted from public disclosures by local health authorities, starting from January 19. This dataset contains by far the richest features for the characterization of COVID-19’s epidemiological properties, including individual cases’ demographic information, travel history, potential virus exposure scenario, contacts with known infections, and timelines of symptom onset, quarantine, infection confirmation, and hospitalization. These cases can be considered the baseline COVID-19 transmissibility under extreme mitigation measures, and therefore, a reference for comparative scientific investigation and public policymaking.

Influence of COVID-19 Event on Air Quality and their Association in Mainland China

The outbreak of 2019 novel coronavirus (COVID-19) has hugely impacted the world and becomes a global public threat. To prevent the spread of COVID-19, human activities are largely restricted in China in early February, 2020. The influence of strict COVID-19 control policies on air quality and the potential influence of particulate matter concentration on COVID-19 infection in China are of great interest. This study analyzes the concentrations of six major air pollutants in 366 urban areas across mainland China during January 1 to April 30 in 2017–2020. Results show that strict COVID-19 control policies have significantly improved the air quality in many provinces. Compared to 2019, national mean concentrations of PM2.5, PM10, SO2, NO2 and CO in 2020 decrease by 14%, 15%, 12%, 16% and 12%, respectively, while the concentration of O3 increases by 9%. Generally, the diurnal variation of PM2.5 and PM10 concentrations remains unchanged during COVID-19 and their concentrations are high in the morning and evening while low in the afternoon. Correlation analysis shows that daily COVID-19 infections are positively correlated with PM2.5 concentration in many provinces, indicating a potential risk of aerosol transmission in high PM2.5 environment. Thus it is suggested to stay at home in highly polluted days and go out in the afternoon to reduce the risk of infection due to aerosol transmission.

Influence of COVID-19 Event on Air Quality and their Association in Mainland China

The outbreak of 2019 novel coronavirus (COVID-19) has hugely impacted the world and becomes a global public threat. To prevent the spread of COVID-19, human activities are largely restricted in China in early February, 2020. The influence of strict COVID-19 control policies on air quality and the potential influence of particulate matter concentration on COVID-19 infection in China are of great interest. This study analyzes the concentrations of six major air pollutants in 366 urban areas across mainland China during January 1 to April 30 in 2017–2020. Results show that strict COVID-19 control policies have significantly improved the air quality in many provinces. Compared to 2019, national mean concentrations of PM2.5, PM10, SO2, NO2 and CO in 2020 decrease by 14%, 15%, 12%, 16% and 12%, respectively, while the concentration of O3 increases by 9%. Generally, the diurnal variation of PM2.5 and PM10 concentrations remains unchanged during COVID-19 and their concentrations are high in the morning and evening while low in the afternoon. Correlation analysis shows that daily COVID-19 infections are positively correlated with PM2.5 concentration in many provinces, indicating a potential risk of aerosol transmission in high PM2.5 environment. Thus it is suggested to stay at home in highly polluted days and go out in the afternoon to reduce the risk of infection due to aerosol transmission.