A novel prediction model based on decomposition-integration and error correction for COVID-19 daily confirmed and death cases.

Coronavirus disease (COVID-19) has infected billion people around the world and affected the economy, but most countries are considering reopening, so the COVID-19 daily confirmed and death cases have increased greatly. It is very necessary to predict the COVID-19 daily confirmed and death cases in order to help every country formulate prevention policies. To enhance the prediction performance, this paper proposes a prediction model based on improved variational mode decomposition by sparrow search algorithm (SVMD), improved kernel extreme learning machine by Aquila optimizer algorithm (AO-KELM) and error correction idea, named SVMD-AO-KELM-error for short-term prediction of COVID-19 cases. Firstly, to solve mode number and penalty factor selection of variational mode decomposition (VMD), an improved VMD based on sparrow search algorithm (SSA), named SVMD, is proposed. SVMD decomposes the COVID-19 case data into some intrinsic mode function (IMF) components and residual is considered. Secondly, to properly selected regularization coefficients and kernel parameters of kernel extreme learning machine (KELM) and improve the prediction performance of KELM, an improved KELM by Aquila optimizer (AO) algorithm, named AO-KELM, is proposed. Each component is predicted by AO-KELM. Then, the prediction error of IMF and residual are predicted by AO-KELM to correct prediction results, which is error correction idea. Finally, prediction results of each component and error prediction results are reconstructed to get final prediction results. Through the simulation experiment of the COVID-19 daily confirmed and death cases in Brazil, Mexico, and Russia and comparison with twelve comparative models, simulation experiment gives that SVMD-AO-KELM-error has best prediction accuracy. It also proves that the proposed model can be used to predict the pandemic COVID-19 cases and offers a novel approach for COVID-19 cases prediction.

Epidemic of the SARS-CoV-2 Omicron variant in Shanghai, China in 2022: Transient and persistent effects on Out-of-hospital cardiac arrests.

OBJECTIVE: To investigate transient and persistent effects of the Shanghai Omicron epidemic in 2022 on the incidence, characteristics, and outcomes of out-of-hospital cardiac arrest (OHCA). METHODS: This retrospective study examined electronic records of patients admitted to the Shanghai Emergency Medical Center during five periods: pre-epidemic, 1 January 2018 to 31 December 2019; low COVID-19 incidence, 1 January 2020 to 27 March 2022; Omicron epidemic, 28 March to 31 May 2022; early post-epidemic, 1 June to 31 July 2022; and late post-epidemic, 1 August to 30 September 2022. Clinicodemographic characteristics and outcomes of OHCA cases were compared between the pre-epidemic and other periods. RESULTS: A total of 55,104 OHCAs were included. The monthly number of OHCAs in the Omicron epidemic was 2.1 times the number in the pre-epidemic (1702 vs 793), while the number in the early post-epidemic was 1.9 times the number in the pre-epidemic (1515 vs 793). Compared to the pre-epidemic, OHCA during or after the epidemic was more likely to involve individuals with hypertension, coronary artery disease, heart failure or stroke. The probability that circulation would spontaneously resume after OHCA was significantly lower during the epidemic than before it (aOR 0.61, 95% CI 0.41-0.90; P = 0.012). However, this difference disappeared by the early post-epidemic. CONCLUSION: The monthly number of OHCAs doubled during the Omicron epidemic in Shanghai, and it remained elevated for another two months. OHCA affected individuals with cardiovascular and cerebrovascular diseases more during and after the epidemic than before it.

A new hybrid prediction model of cumulative COVID-19 confirmed data.

Establishing an accurate and efficient prediction model is of great significance for governments and other social organizations to formulate prevention and control policies and curb the explosive spread of the pandemic. To improve prediction accuracy of cumulative COVID-19 confirmed data, a new hybrid prediction model based on gradient-based optimizer variational mode decomposition (GVMD), extreme learning machine (ELM), and autoregressive integrated moving average (ARIMA), named GVMD-ELM-ARIMA, is proposed. To solve the problem of selecting the k value and the penalty factor α in variational mode decomposition (VMD), this paper proposes gradient-based optimizer variational mode decomposition (GVMD), which realizes the self-adaptive determination of k value and α value. Firstly, GVMD decomposes the cumulative COVID-19 confirmed data into some intrinsic mode functions (IMFs) and a residual component (IMFr). Secondly, IMFs are predicted by ELM. Then, IMFr is predicted by ARIMA. Finally, the final prediction results are obtained by reconstructing the prediction result of IMFs and IMFr. The cumulative COVID-19 confirmed data of the United States, India and Russia is used to verify its effectiveness. Taking the United States as an example, compared with the average MAPE, RMSE and MAE of the single model, the average MAPE of the hybrid model is reduced by 47.27%, the average RMSE is reduced by 44.50%, and the average MAE is reduced by 55.34%. Compared with GVMD-ELM-ELM, GVMD-ELM-ARIMA proposed in this paper reduces the MAPE by 60%, the RMSE by 56.85%, and the MAE by 61.61%. The experimental results show that GVMD-ELM-ARIMA has best prediction accuracy, and it provides a new method for predicting the cumulative COVID-19 confirmed data.

A highly powerful nonspecific strategy to reduce COVID-19 deaths.

The coronavirus disease 2019 (COVID-19) pandemic caused by the coronavirus severe acute respiratory syndrome coronavirus 2 remains risky worldwide. We elucidate here that good IDM (isolation, disinfection, and maintenance of health) is powerful to reduce COVID-19 deaths based on the striking differences in COVID-19 case fatality rates among various scenarios. IDM means keeping COVID-19 cases away from each other and from other people, disinfecting their living environments, and maintaining their health through good nutrition, rest, and treatment of symptoms and pre-existing diseases (not through specific antiviral therapy). Good IDM could reduce COVID-19 deaths by more than 85% in 2020 and more than 99% in 2022. This is consistent with the fact that good IDM can minimize co-infections and maintain body functions and the fact that COVID-19 has become less pathogenic (this fact was supported with three novel data in this report). Although IDM has been frequently implemented worldwide to some degree, IDM has not been highlighted sufficiently. Good IDM is relative, nonspecific, flexible, and feasible in many countries, and can reduce deaths of some other relatively mild infectious diseases. IDM, vaccines, and antivirals aid each other to reduce COVID-19 deaths. The IDM concept and strategy can aid people to improve their health behavior and fight against COVID-19 and future pandemics worldwide.

An Application of Artificial Neural Network to Evaluate the Influence of Weather Conditions on the Variation of PM2.5-Bound Carbonaceous Compositions and Water-Soluble Ionic Species

Previous studies have determined biomass burning as a major source of air pollutants in the ambient air in Thailand. To analyse the impacts of meteorological parameters on the variation of carbonaceous aerosols and water-soluble ionic species (WSIS), numerous statistical models, including a source apportionment analysis with the assistance of principal component analysis (PCA), hierarchical cluster analysis (HCA), and artificial neural networks (ANNs), were employed in this study. A total of 191 sets of PM2.5 samples were collected from the three monitoring stations in Chiang-Mai, Bangkok, and Phuket from July 2020 to June 2021. Hotspot numbers and other meteorological parameters were obtained using NOAA-20 weather satellites coupled with the Global Land Data Assimilation System. Although PCA revealed that crop residue burning and wildfires are the two main sources of PM2.5, ANNs highlighted the importance of wet deposition as the main depletion mechanism of particulate WSIS and carbonaceous aerosols. Additionally, Mg2+ and Ca2+ were deeply connected with albedo, plausibly owing to their strong hygroscopicity as the CCNs responsible for cloud formation.

Frameshift Mutations in the Microevolution and Macroevolution of Viruses (preprint)

Some nucleotide insertions or deletions (indels) in protein-coding open reading frames lead to frameshift mutations (FSMs) which can change amino acid sequences drastically. FSMs are widely distributed in the genomes of many organisms. However, few studies have been reported regarding frequencies of FSMs in microevolution or macroevolution. Many viruses evolve much more rapidly than cellular organisms, and they are hence suitable to investigate frequencies of FSMs in microevolution or macroevolution. In this report, we identified 667 FSMs in gene sequences of 13 virus families and each FSM changed approximately 11 amino acid residues on average. Of the FSMs, 89.21% were 2-indel compensatory FSMs, and the remaining were 1-, 3-, 4-, 5-indel FSMs. We found that FSMs usually occurred more frequently in the viruses of the same family with smaller sequence identities, and FSMs occurred in the sequences of with identities of 60.0-69.9% more frequently than in the sequences with identities of 90.0-99.9% or 80.0-89.9% by approximately 34.9 or 13.1 times on average. We also found FSMs occurred at different frequencies among genes in the same virus genome, among species in the same virus family, or among virus families (e.g., more frequently in Coronaviridae than in Orthomyxoviridae). These results suggest that FSMs are more frequent in the inter-species or macroevolution than in the intra-species or microevolution of viruses. They provide novel evidence for the hopeful monster hypothesis in evolutionary biology. They inspire researchers to investigate the roles, frequencies, features, and functions of FSMs in other viruses and cellular organisms.

The Heavy Particulate Matter Pollution During the COVID-19 Lockdown Period in the Guanzhong Basin, China.

Nationwide restrictions on human activities (lockdown) in China since 23 January 2020, to control the 2019 novel coronavirus disease pandemic (COVID-19), has provided an opportunity to evaluate the effect of emission mitigation on particulate matter (PM) pollution. The WRF-Chem simulations of persistent heavy PM pollution episodes from 20 January to 14 February 2020, in the Guanzhong Basin (GZB), northwest China, reveal that large-scale emission reduction of primary pollutants has not substantially improved the air quality during the COVID-19 lockdown period. Simultaneous reduction of primary precursors during the lockdown period only decreases the near-surface PM2.5 mass concentration by 11.6% (12.6 µg m-3), but increases ozone (O3) concentration by 9.2% (5.5 µg m-3) in the GZB. The primary organic aerosol and nitrate are the major contributor to the decreased PM2.5 in the GZB, with the reduction of 28.0% and 21.8%, respectively, followed by EC (10.1%) and ammonium (7.2%). The increased atmospheric oxidizing capacity by the O3 enhancement facilitates the secondary aerosol (SA) formation in the GZB, increasing secondary organic aerosol and sulphate by 6.5% and 3.3%, respectively. Furthermore, sensitivity experiments suggest that combined emission reduction of NOX and VOCs following the ratio of 1:1 is conducive to lowering the wintertime SA and O3 concentration and further alleviating the PM pollution in the GZB.

Allosteric inhibition of SARS-CoV-2 3CL protease by colloidal bismuth subcitrate.

The SARS-CoV-2 3-chymotrypsin-like protease (3CLpro or Mpro) is a key cysteine protease for viral replication and transcription, making it an attractive target for antiviral therapies to combat the COVID-19 disease. Here, we demonstrate that bismuth drug colloidal bismuth subcitrate (CBS) is a potent inhibitor for 3CLpro in vitro and in cellulo. Rather than targeting the cysteine residue at the catalytic site, CBS binds to an allosteric site and results in dissociation of the 3CLpro dimer and proteolytic dysfunction. Our work provides direct evidence that CBS is an allosteric inhibitor of SARS-CoV-2 3CLpro.

Mitigating NOX emissions does not help alleviate wintertime particulate pollution in Beijing-Tianjin-Hebei, China.

Stringent mitigation measures have reduced wintertime fine particulate matter (PM2.5) concentrations by 42.2% from 2013 to 2018 in the Beijing-Tianjin-Hebei (BTH) region, but severe PM pollution still frequently engulfs the region. The observed nitrate aerosols have not exhibited a significant decreasing trend and constituted a major fraction (about 20%) of the total PM2.5, although the surface-measured NO2 concentration has decreased by over 20%. The contributions of nitrogen oxides (NOX) emissions mitigation to the nitrate and PM2.5 concentrations and how to alleviate nitrate aerosols efficiently under the current situation still remains elusive. The WRF-Chem model simulations of a persistent and heavy PM pollution episode in January 2019 in the BTH reveal that NOX emissions mitigation does not help lower wintertime nitrate and PM2.5 concentrations under current conditions in the BTH. A 50% reduction in NOX emissions only decreases nitrate mass by 10.3% but increases PM2.5 concentrations by 3.2%, because the substantial O3 increase induced by NOX mitigation offsets the HNO3 loss and enhances sulfate and secondary organic aerosols formation. Our results are further consolidated by the occurrence of severe PM pollution in the BTH during the COVID-19 outbreak, with a significant reduction in NO2 concentration. Mitigation of NH3 emissions constitutes the priority measure to effectively lower the nitrate and PM2.5 concentrations in the BTH under current conditions, with 35.5% and 12.7% decrease, respectively, when NH3 emissions are reduced by 50%.

Unexpected air pollution with marked emission reductions during the COVID-19 outbreak in China.

The absence of motor vehicle traffic and suspended manufacturing during the coronavirus disease 2019 (COVID-19) pandemic in China enabled assessment of the efficiency of air pollution mitigation. Up to 90% reduction of certain emissions during the city-lockdown period can be identified from satellite and ground-based observations. Unexpectedly, extreme particulate matter levels simultaneously occurred in northern China. Our synergistic observation analyses and model simulations show that anomalously high humidity promoted aerosol heterogeneous chemistry, along with stagnant airflow and uninterrupted emissions from power plants and petrochemical facilities, contributing to severe haze formation. Also, because of nonlinear production chemistry and titration of ozone in winter, reduced nitrogen oxides resulted in ozone enhancement in urban areas, further increasing the atmospheric oxidizing capacity and facilitating secondary aerosol formation.