Water Transmission Increases the Intensity of COVID-19 Outbreaks.

India suffered from a devastating 2021 spring outbreak of coronavirus disease 2019 (COVID-19), surpassing any other outbreaks before. However, the reason for the acceleration of the outbreak in India is still unknown. We describe the statistical characteristics of infected patients from the first case in India to June 2021, and trace the causes of the two outbreaks in a complete way, combined with data on natural disasters, environmental pollution and population movements etc. We found that water-to-human transmission accelerates COVID-19 spreading. The transmission rate is 382% higher than the human-to-human transmission rate during the 2020 summer outbreak in India. When syndrome coronavirus 2 (SARS-CoV-2) enters the human body directly through the water-oral transmission pathway, virus particles and nitrogen salt in the water accelerate viral infection and mutation rates in the gastrointestinal tract. Based on the results of the attribution analysis, without the current effective interventions, India could have experienced a third outbreak during the monsoon season this year, which would have increased the severity of the disaster and led to a South Asian economic crisis.

Water Transmission Increases the Intensity of COVID-19 Outbreaks

India suffered from a devastating 2021 spring outbreak of coronavirus disease 2019 (COVID-19), surpassing any other outbreaks before. However, the reason for the acceleration of the outbreak in India is still unknown. We describe the statistical characteristics of infected patients from the first case in India to June 2021, and trace the causes of the two outbreaks in a complete way, combined with data on natural disasters, environmental pollution and population movements etc. We found that water-to-human transmission accelerates COVID-19 spreading. The transmission rate is 382% higher than the human-to-human transmission rate during the 2020 summer outbreak in India. When syndrome coronavirus 2 (SARS-CoV-2) enters the human body directly through the water-oral transmission pathway, virus particles and nitrogen salt in the water accelerate viral infection and mutation rates in the gastrointestinal tract. Based on the results of the attribution analysis, without the current effective interventions, India could have experienced a third outbreak during the monsoon season this year, which would have increased the severity of the disaster and led to a South Asian economic crisis.

The impact of crowd gatherings on the spread of COVID-19.

Crowd gatherings are an important cause of COVID-19 outbreaks. However, how the scale, scene and other factors of gatherings affect the spread of the epidemic remains unclear. A total of 184 gathering events worldwide were collected to construct a database, and 99 of them with a clear gathering scale were used for statistical analysis of the impact of these factors on the disease incidence among the crowd in the study. The results showed that the impact of small-scale (less than 100 people) gathering events on the spread of COVID-19 in the city is also not to be underestimated due to their characteristics of more frequent occurrence and less detection and control. In our dataset, 22.22% of small-scale events have an incidence of more than 0.8. In contrast, the incidence of most large-scale events is less than 0.4. Gathering scenes such as "Meal" and "Family" occur in densely populated private or small public places have the highest incidence. We further designed a model of epidemic transmission triggered by crowd gathering events and simulated the impact of crowd gathering events on the overall epidemic situation in the city. The simulation results showed that the number of patients will be drastically reduced if the scale and the density of crowds gathering are halved. It indicated that crowd gatherings should be strictly controlled on a small scale. In addition, it showed that the model well reproduce the epidemic spread after crowd gathering events better than does the original SIER model and could be applied to epidemic prediction after sudden gathering events.

Comparison of PM2.5 and CO2 Concentrations in Large Cities of China during the COVID-19 Lockdown.

Estimating the impacts on PM2.5 pollution and CO2 emissions by human activities in different urban regions is important for developing efficient policies. In early 2020, China implemented a lockdown policy to contain the spread of COVID-19, resulting in a significant reduction of human activities. This event presents a convenient opportunity to study the impact of human activities in the transportation and industrial sectors on air pollution. Here, we investigate the variations in air quality attributed to the COVID-19 lockdown policy in the megacities of China by combining in-situ environmental and meteorological datasets, the Suomi-NPP/VIIRS and the CO2 emissions from the Carbon Monitor project. Our study shows that PM2.5 concentrations in the spring of 2020 decreased by 41.87% in the Yangtze River Delta (YRD) and 43.30% in the Pearl River Delta (PRD), respectively, owing to the significant shutdown of traffic and manufacturing industries. However, PM2.5 concentrations in the Beijing-Tianjin-Hebei (BTH) region only decreased by 2.01% because the energy and steel industries were not fully paused. In addition, unfavorable weather conditions contributed to further increases in the PM2.5 concentration. Furthermore, CO2 concentrations were not significantly affected in China during the short-term emission reduction, despite a 19.52% reduction in CO2 emissions compared to the same period in 2019. Our results suggest that concerted efforts from different emission sectors and effective long-term emission reduction strategies are necessary to control air pollution and CO2 emissions.

The oscillation-outbreaks characteristic of the COVID-19 pandemic.

The evolution of the COVID-19 pandemic features the alternation of oscillations and abrupt rises. The oscillations are attributable to weekly and seasonal modulations, while abrupt rises are stimulated by mass gatherings.

Environmental Indicator for COVID-19 Non-Pharmaceutical Interventions.

A novel coronavirus (COVID-19) has caused viral pneumonia worldwide, posing a major threat to international health. Our study reports that city lockdown is an effective way to reduce the number of new cases and the nitrogen dioxide (NO2) concentration can be used as an environmental lockdown indicator to evaluate the effectiveness of lockdown measures. The airborne NO2 concentration steeply decreased over the vast majority of COVID-19-hit areas since the lockdown. The total number of newly confirmed cases reached an inflection point about two weeks since the lockdown and could be reduced by about 50% within 30 days of the lockdown. The stricter lockdown will help newly confirmed cases to decline earlier and more rapidly, and at the same time, the reduction rate of NO2 concentration will increase. Our research results show that NO2 satellite observations can help decision makers effectively monitor and manage non-pharmaceutical interventions in the epidemic.

Optimal parameterization of COVID-19 epidemic models

ABSTRACT At the time of writing, coronavirus disease 2019 (COVID-19) is seriously threatening human lives and health throughout the world. Many epidemic models have been developed to provide references for decision-making by governments and the World Health Organization. To capture and understand the characteristics of the epidemic trend, parameter optimization algorithms are needed to obtain model parameters. In this study, the authors propose using the Levenberg–Marquardt algorithm (LMA) to identify epidemic models. This algorithm combines the advantage of the Gauss–Newton method and gradient descent method and has improved the stability of parameters. The authors selected four countries with relatively high numbers of confirmed cases to verify the advantages of the Levenberg–Marquardt algorithm over the traditional epidemiological model method. The results show that the Statistical-SIR (Statistical-Susceptible–Infected–Recovered) model using LMA can fit the actual curve of the epidemic well, while the epidemic simulation of the traditional model evolves too fast and the peak value is too high to reflect the real situation. 摘要 现如今, 新冠肺炎(COVID-19)严重威胁着世界各国人民的生命健康.许多流行病学模型已经被用于为政策制定者和世界卫生组织提供决策参考.为了更加深刻的理解疫情趋势的变化特征, 许多参数优化算法被用于反演模型参数.本文提议使用结合了高斯-牛顿法和梯度下降法的Levenberg–Marquardt(LMA)算法来优化模型参数.使用四个病例数相对较多的国家来验证这一算法的优势:相较于传统流行病学模型模拟曲线过早过快的到达峰值, 应用LMA的Statistical-SIR(Statistical-Susceptible–Infected–Recovered)模型可以更好地拟合实际疫情曲线.

Improvement of the Global Prediction System of the COVID-19 Pandemic based on the ensemble empirical mode decomposition and autoregressive–moving-average model in a hybrid approach

ABSTRACT In 2020, the COVID-19 pandemic spreads rapidly around the world. To accurately predict the number of daily new cases in each country, Lanzhou University has established the Global Prediction System of the COVID-19 Pandemic (GPCP). In this article, the authors use the ensemble empirical mode decomposition (EEMD) model and autoregressive–moving-average (ARMA) model to improve the prediction results of GPCP. In addition, the authors also conduct direct predictions for those countries with a small number of confirmed cases or are in the early stage of the disease, whose development trends of the pandemic do not fully comply with the law of infectious diseases and cannot be predicted by the GPCP model. Judging from the results, the absolute values of the relative errors of predictions in countries such as Cuba have been reduced significantly and their prediction trends are closer to the real situations through the method mentioned above to revise the prediction results out of GPCP. For countries such as El Salvador with a small number of cases, the absolute values of the relative errors of prediction become smaller. Therefore, this article concludes that this method is more effective for improving prediction results and direct prediction. 摘要 2020年, 新型冠状病毒肺炎(COVID-19)在世界范围内迅速传播.为准确预测各国每日新增发病人数, 兰州大学开发了COVID-19流行病全球预测系统(GPCP).在本文的研究中, 我们使用集合经验模态分解(EEMD)模型和自回归-移动平均(ARMA)模型对GPCP的预测结果进行改进, 并对发病人数较少或处于发病初期, 不完全符合传染病规律, GPCP模型无法预测的国家进行直接预测.从结果来看, 使用该方法修正预测结果, 古巴等国家预测误差均大幅下降, 且预测趋势更接近真实情况.对于萨尔瓦多等发病人数较少的国家直接进行预测, 相对误差较小, 预测结果较为准确.该方法对于改进预测结果和直接预测均较为有效.

Impact of city lockdown on the air quality of COVID-19-hit of Wuhan city.

A series of strict lockdown measures were implemented in the areas of China worst affected by coronavirus disease 19, including Wuhan, to prevent the disease spreading. The lockdown had a substantial environmental impact, because traffic pollution and industrial emissions are important factors affecting air quality and public health in the region. After the lockdown, the average monthly air quality index (AQI) in Wuhan was 59.7, which is 33.9% lower than that before the lockdown (January 23, 2020) and 47.5% lower than that during the corresponding period (113.6) from 2015 to 2019. Compared with the conditions before the lockdown, fine particulate matter (PM2.5) decreased by 36.9% and remained the main pollutant. Nitrogen dioxide (NO2) showed the largest decrease of approximately 53.3%, and ozone (O3) increased by 116.6%. The proportions of fixed-source emissions and transported external-source emissions in this area increased. After the lockdown, O3 pollution was highly negatively correlated with the NO2 concentration, and the radiation increase caused by the PM2.5 reduction was not the main reason for the increase in O3. This indicates that the generation of secondary pollutants is influenced by multiple factors and is not only governed by emission reduction.