Causal associations between type 1 diabetes and COVID-19 infection and prognosis: a two-sample Mendelian randomization study.

INTRODUCTION: It has been suggested that type 1 diabetes was associated with increased COVID-19 morbidity and mortality. However, their causal relationship is still unclear. Herein, we performed a two-sample Mendelian randomization (MR) to investigate the causal effect of type 1 diabetes on COVID-19 infection and prognosis. RESEARCH DESIGN AND METHODS: The summary statistics of type 1 diabetes were obtained from two published genome-wide association studies of European population, one as a discovery sample including 15 573 cases and 158 408 controls, and the other data as a replication sample consisting of 5913 cases and 8828 controls. We first performed a two-sample MR analysis to evaluate the causal effect of type 1 diabetes on COVID-19 infection and prognosis. Then, reverse MR analysis was conducted to determine whether reverse causality exists. RESULTS: MR analysis results showed that the genetically predicted type 1 diabetes was associated with higher risk of severe COVID-19 (OR=1.073, 95% CI: 1.034 to 1.114, pFDR=1.15×10-3) and COVID-19 death (OR=1.075, 95% CI: 1.033 to 1.119, pFDR=1.15×10-3). Analysis of replication dataset showed similar results, namely a positive association between type 1 diabetes and severe COVID-19 (OR=1.055, 95% CI: 1.029 to 1.081, pFDR=1.59×10-4), and a positively correlated association with COVID-19 death (OR=1.053, 95% CI: 1.026 to 1.081, pFDR=3.50×10-4). No causal association was observed between type 1 diabetes and COVID-19 positive, hospitalized COVID-19, the time to the end of COVID-19 symptoms in the colchicine treatment group and placebo treatment group. Reverse MR analysis showed no reverse causality. CONCLUSIONS: Type 1 diabetes had a causal effect on severe COVID-19 and death after COVID-19 infection. Further mechanistic studies are needed to explore the relationship between type 1 diabetes and COVID-19 infection and prognosis.

Sociodemographic Variation in Children's Health Behaviors During the COVID-19 Pandemic.

Background: Societal changes during the COVID-19 pandemic may affect children's health behaviors and exacerbate disparities. This study aimed to describe children's health behaviors during the COVID-19 pandemic, how they vary by sociodemographic characteristics, and the extent to which parent coping strategies mitigate the impact of pandemic-related financial strain on these behaviors. Methods: This study used pooled data from 50 cohorts in the Environmental influences on Child Health Outcomes Program. Children or parent proxies reported sociodemographic characteristics, health behaviors, and parent coping strategies. Results: Of 3315 children aged 3-17 years, 49% were female and 57% were non-Hispanic white. Children of parents who reported food access as a source of stress were 35% less likely to engage in a higher level of physical activity. Children of parents who changed their work schedule to care for their children had 82 fewer min/day of screen time and 13 more min/day of sleep compared with children of parents who maintained their schedule. Parents changing their work schedule were also associated with a 31% lower odds of the child consuming sugar-sweetened beverages. Conclusions: Parents experiencing pandemic-related financial strain may need additional support to promote healthy behaviors. Understanding how changes in parent work schedules support shorter screen time and longer sleep duration can inform future interventions.

Understanding Rumor Sharing Behavior During COVID-19: The Dominant Motivation and the Potential Consequences for Life Satisfaction.

During the COVID-19 pandemic, rumors were shared widely and quickly, leading to unfortunate consequences. To explore the dominant motivation underlying such rumor sharing behavior and the potential consequences for sharers' life satisfaction, two studies were conducted. Study 1 was based on representative popular rumors that circulated throughout Chinese society during the pandemic to examine the dominant motivation underlying rumor sharing behavior. Study 2 employed a longitudinal design to further test the dominant motivation underlying rumor sharing behavior and its effects on life satisfaction. The results of these two studies generally supported our hypotheses that people chose to share rumors during the pandemic mainly for the purpose of fact-finding. Regarding the effects of rumor sharing behavior on life satisfaction, although sharing wish rumors (i.e., rumors expressing hopes) had no effect on sharers' life satisfaction, sharing dread rumors (i.e., rumors reflecting fears) and aggression rumors (i.e., rumors implying aggression and hatred) reduced sharers' life satisfaction. This research lends support to the integrative model of rumor and provides practical implications for mitigating the spread of rumors.

Rumour type matters: The effect of different types of rumours on coping, subjective well-being, and interpersonal trust during the COVID-19 pandemic.

Rumours circulated quickly online and offline during the COVID-19 pandemic, but empirical research on the subject is limited. Combining qualitative (Study 1, content analysis was conducted on 2344 actual rumours extracted from a rumour-refuting website) and quantitative methods (Study 2, a three-wave study with 10-day intervals), the current study suggests that (1) rumours during the pandemic can be categorised into three types, that is, wish, dread, and aggression rumours, and (2) exposure to different types of rumours is associated with coping consequences, subjective well-being (comprising positive affect, negative affect, and life satisfaction), and interpersonal trust in different ways. Generally, wish rumours seem benign, while dread and aggression rumours are malicious. Specifically, wish rumours are believed to assist coping and to be positively associated with positive affect and interpersonal trust. In contrast, dread rumours are believed not to assist coping and to be marginally significantly and positively associated with negative affect and negatively associated with interpersonal trust. Meanwhile, aggression rumours are believed not to assist coping and are marginally significantly and positively associated with negative affect. All other relationships are nonsignificant. The results of the current study will help national governments and international agencies design and evaluate rumour control strategies and policies.

Gene function and cell surface protein association analysis based on single-cell multiomics data.

Single-cell transcriptomics provides researchers with a powerful tool to resolve the transcriptome heterogeneity of individual cells. However, this method falls short in revealing cellular heterogeneity at the protein level. Previous single-cell multiomics studies have focused on data integration rather than exploiting the full potential of multiomics data. Here we introduce a new analysis framework, gene function and protein association (GFPA), that mines reliable associations between gene function and cell surface protein from single-cell multimodal data. Applying GFPA to human peripheral blood mononuclear cells (PBMCs), we observe an association of epithelial mesenchymal transition (EMT) with the CD99 protein in CD4 T cells, which is consistent with previous findings. Our results show that GFPA is reliable across multiple cell subtypes and PBMC samples. The GFPA python packages and detailed tutorials are freely available at https://github.com/studentiz/GFPA.

Modeling and analyzing single-cell multimodal data with deep parametric inference.

The proliferation of single-cell multimodal sequencing technologies has enabled us to understand cellular heterogeneity with multiple views, providing novel and actionable biological insights into the disease-driving mechanisms. Here, we propose a comprehensive end-to-end single-cell multimodal analysis framework named Deep Parametric Inference (DPI). DPI transforms single-cell multimodal data into a multimodal parameter space by inferring individual modal parameters. Analysis of cord blood mononuclear cells (CBMC) reveals that the multimodal parameter space can characterize the heterogeneity of cells more comprehensively than individual modalities. Furthermore, comparisons with the state-of-the-art methods on multiple datasets show that DPI has superior performance. Additionally, DPI can reference and query cell types without batch effects. As a result, DPI can successfully analyze the progression of COVID-19 disease in peripheral blood mononuclear cells (PBMC). Notably, we further propose a cell state vector field and analyze the transformation pattern of bone marrow cells (BMC) states. In conclusion, DPI is a powerful single-cell multimodal analysis framework that can provide new biological insights into biomedical researchers. The python packages, datasets and user-friendly manuals of DPI are freely available at https://github.com/studentiz/dpi.

Antibody persistence and safety after heterologous boosting with orally aerosolised Ad5-nCoV in individuals primed with two-dose CoronaVac previously: 12-month analyses of a randomized controlled trial.

Antibody persistence and safety up to 12 months of heterologous orally administered adenovirus type-5 vector-based COVID-19 vaccine (Ad5-nCoV) in individuals who were primed with two-dose inactivated SARS-CoV-2 vaccine (CoronaVac) previously, has not been reported yet. This randomized, open-label, single-centre trial included Chinese adults who have received two-dose CoronaVac randomized to low-dose or high-dose aerosolised Ad5-nCoV group, or CoronaVac group. In this report, we mainly evaluated the geometric mean titres (GMTs) of neutralizing antibodies (NAbs) against live wild-type SARS-CoV-2 virus and omicron BA.4/5 pseudovirus at 12 months after the booster dose and the incidence of serious adverse events (SAEs) till month 12. Of 419 participants, all were included in the safety analysis and 120 (28.64%) were included in the immunogenicity analysis. Serum NAb GMT against live wild-type SARS-CoV-2 was 204.36 (95% CI 152.91, 273.14) in the low-dose group and 171.38 (95% CI 121.27, 242.19) in the high-dose group at month 12, significantly higher than the GMT in the CoronaVac group (8.00 [95% CI 4.22, 15.17], p < 0.0001). Serum NAb GMT against omicron BA.4/5 pseudovirus was 40.97 (95% CI 30.15, 55.67) in the low-dose group and 35.08 (95% CI 26.31, 46.77) in the high-dose group at month 12, whereas the GMT in the CoronaVac group was below the lower limit of detection. No vaccine-related SAEs were observed. Orally administered aerosolised Ad5-nCoV following two-dose CoronaVac priming has a good safety profile and is persistently more immunogenic than three-dose CoronaVac within 12 months after the booster dose.Trial registration: ClinicalTrials.gov identifier: NCT05043259..

Multisystem inflammatory syndrome (MIS-C) with SARS-CoV-2 omicron variant BA.2.38 in a four-year-old Chinese girl: A case report.

We report a severe COVID-19 complicated with MIS-C in a girl treated by the author in China, and discuss the current research status and progress in the diagnosis and therapy of MIS-C in children. The patient was a 4-year-old child previously healthy who was referred to the hospital with a complaint of fever, finally, Multisystem inflammatory syndrome was diagnosed with COVID-19.

Trust in government buffers the negative effect of rumor exposure on people's emotions.

Although we are surrounded by various kinds of rumors during the coronavirus disease pandemic, little is known about their primary content, what effect they might have on our emotions, and the potential factors that may buffer their effect. Combining qualitative (study 1 extracted 1907 rumors from top rumor-refuting websites using the Python Web Crawler and conducted content analysis) and quantitative (study 2 conducted an online survey adopting a three-wave design, N = 444) research methods, the current study revealed that government-related rumors accounted for the largest proportion of rumors during the outbreak stage of the pandemic and were positively associated with the public's negative emotions. We also found that trust in government negatively moderated the relationship between government-related rumors and negative emotions. Specifically, when people had low trust in government, exposure to government-related rumors was positively associated with negative emotions. However, when people had high trust in government, the association was non-significant. For positive emotions, we found no significant effects of government-related rumors. The findings highlight the importance of rumor control during public emergencies and cultivating public trust in government in the long run. Supplementary Information: The online version contains supplementary material available at 10.1007/s12144-022-03508-x.

TCM2COVID: A resource of anti-COVID-19 traditional Chinese medicine with effects and mechanisms.

In China, traditional Chinese medicine (TCM) has been widely used for coronavirus infectious disease 2019 (COVID-19) prevention, treatment, and recovery and has played a part in the battle against the disease. A variety of TCM treatments have been recommended for different stages of COVID-19. But, to the best of our knowledge, a comprehensive database for storing and organizing anti-COVID TCM treatments is still lacking. Herein, we developed TCM2COVID, a manually curated resource of anti-COVID TCM formulas, natural products (NPs), and herbs. The current version of TCM2COVID (1) documents over 280 TCM formulas (including over 300 herbs) with detailed clinical evidence and therapeutic mechanism information; (2) records over 80 NPs with detailed potential therapeutic mechanisms; and (3) launches a useful web server for querying, analyzing and visualizing documented formulas similar to those supplied by the user (formula similarity analysis). In summary, TCM2COVD provides a user-friendly and practical platform for documenting, querying, and browsing anti-COVID TCM treatments, and will help in the development and elucidation of the mechanisms of action of new anti-COVID TCM therapies to support the fight against the COVID-19 epidemic. TCM2COVID is freely available at http://zhangy-lab.cn/tcm2covid/.

Effect of a Functional Phospholipid Metabolome-Protein Association Pathway on the Mechanism of COVID-19 Disease Progression.

This study aimed to explore the clinical practice of phospholipid metabolic pathways in COVID-19. In this study, 48 COVID-19 patients and 17 healthy controls were included. Patients were divided into mild (n=40) and severe (n=8) according to their severity. Phospholipid metabolites, TCA circulating metabolites, eicosanoid metabolites, and closely associated enzymes and transfer proteins were detected in the plasma of all individuals using metabolomics and proteomics assays, respectively. 30 of the 33 metabolites found differed significantly (P<0.05) between patients and healthy controls (P<0.05), with D-dimmer significantly correlated with all of the lysophospholipid metabolites (LysoPE, LysoPC, LysoPI and LPA). In particular, we found that phosphatidylinositol (PI) and phosphatidylcholine (PC) could identify patients from healthy controls (AUC 0.771 and 0.745, respectively) and that the severity of the patients could be determined (AUC 0.663 and 0.809, respectively). The last measurement before discharge also revealed significant changes in both PI and PC. For the first time, our study explores the significance of the phospholipid metabolic system in COVID-19 patients. Based on molecular pathway mechanisms, three important phospholipid pathways related to Ceramide-Malate acid (Cer-SM), Lysophospholipid (LPs), and membrane function were established. Clinical values discovered included the role of Cer in maintaining the inflammatory internal environment, the modulation of procoagulant LPA by upstream fibrinolytic metabolites, and the role of PI and PC in predicting disease aggravation.

A Potent Neutralizing Nanobody Targeting the Spike Receptor-Binding Domain of SARS-CoV-2 and the Structural Basis of Its Intimate Binding.

The continuous spread of severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) around the world has raised unprecedented challenges to the human society. Antibodies and nanobodies possessing neutralization activity represent promising drug candidates. In this study, we report the identification and characterization of a potent SARS-CoV-2 neutralizing nanobody that targets the viral spike receptor-binding domain (S-RBD). The nanobody, termed as Nb-007, engages SARS-CoV-2 S-RBD with the two-digit picomolar binding affinity and shows outstanding virus entry-inhibition activity. The complex structure of Nb-007 bound to SARS-CoV-2 S-RBD reveals an epitope that is partially overlapping with the binding site for the human receptor of angiotensin-converting enzyme 2 (ACE2). The nanobody therefore exerts neutralization by competing with ACE2 for S-RBD binding, which is further ascertained by our in-vitro biochemical analyses. Finally, we also show that Nb-007 reserves promising, though compromised, neutralization activity against the currently-circulating Delta variant and that fusion of the nanobody with Fc dramatically increases its entry-inhibition capacity. Taken together, these data have paved the way of developing Nb-007 as a drug-reserve for potential treatment of SARS-CoV-2 related diseases.

The strategies of "early detection, early report, early assessment, and early control" in vector prevention and control

Since the outbreak of coronavirus disease 2019 (COVID-19), the social prevention and control measures of "early detection, early report, early isolation, and early treatment" have been widely used in the public health field and are widely accepted by the general public. In the practice of integrated vector management, Henan province gives full play to the advantages of mobilization and coordination of the Patriotic Health Campaign, establishes a work path of "early detection, early report, early assessment, and early control", the strategies of "four early". It defines the responsibilities of government, departments, territories, and individuals, and clarifies the working concepts of integrated vector management, which helps to form societal forces and promote the development of vector control in Henan province. This article analyzes the strategies of "four early" in integrated vector management in Henan province, in order to provide a reference for vector management strategies in China.

An engineered 5-helix bundle derived from SARS-CoV-2 S2 pre-binds sarbecoviral spike at both serological- and endosomal-pH to inhibit virus entry.

Severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) and related sarbecoviruses enter host cells by receptor-recognition and membrane-fusion. An indispensable step in fusion is the formation of 6-helix bundle by viral spike heptad repeats 1 and 2 (HR1 and HR2). Here, we report the construction of 5-helix bundle (5HB) proteins for virus infection inhibition. The optimal construct inhibits SARS-CoV-2 pseudovirus entry with sub-micromolar IC50. Unlike HR2-based peptides that cannot bind spike in the pre-fusion conformation, 5HB features with the capability of binding to pre-fusion spike. Furthermore, 5HB binds viral HR2 at both serological- and endosomal-pH, highlighting its entry-inhibition capacity when SARS-CoV-2 enters via either cell membrane fusion or endosomal route. Finally, we show that 5HB could neutralize S-mediated entry of the predominant SARS-CoV-2 variants and a wide spectrum of sarbecoviruses. These data provide proof-of-concept evidence that 5HB might be developed for the prevention and treatment of SARS-CoV-2 and other emerging sarbecovirus infections.

[Relationship between blood electrolytes and prognosis of patients with severe coronavirus disease 2019].

OBJECTIVE: To analyze the relationship between blood electrolytes and the prognosis of patients with severe coronavirus disease 2019 (COVID-19) and to provide assistance for clinical decision-making. METHODS: The clinical data of patients with severe COVID-19 admitted to intensive care unit (ICU) of the Wuhan Third Hospital by the Shanghai aid-Hubei medical team from January 21 to March 4, 2020 were collected. Excluding ineligible patients, 110 patients were finally enrolled. The patients' gender, age, temperature, heart rate, systolic and diastolic blood pressure, clinical symptoms at admission, time of symptom onset, duration of fever, and relevant indicators at admission to ICU (including blood potassium, chloride, sodium, calcium, phosphorus, and magnesium, etc.) and prognosis were analyzed. The patients were grouped by blood potassium or calcium levels or blood potassium/calcium ratio. The Kaplan-Meier survival curves were used to analyze the survival of patients in each group. The relationship between the potassium/calcium ratio and the prognosis was analyzed using restricted cubic spline plots. The relationship between each index in the different models and the prognosis was analyzed using Cox regression models. RESULTS: Among 110 severe COVID-19 patients, 78 cases survived, and 32 cases died. Compared with the surviving group, patients in the death group had higher blood potassium levels [mmol/L: 4.25 (3.80, 4.65) vs. 3.90 (3.60, 4.20), P < 0.05] and lower blood calcium levels (mmol/L: 2.00±0.14 vs. 2.19±0.18, P < 0.05). The Kaplan-Meier survival curves showed that patients in the potassium > 4.2 mmol/L group had a worse prognosis than the potassium < 3.8 mmol/L group and the potassium 3.8-4.2 mmol/L group (P = 0.011), patients in the calcium > 2.23 mmol/L group had a better prognosis than the calcium < 2.03 mmol/L group and the calcium 2.03-2.23 mmol/L group, and the lower calcium group had a worse prognosis (P = 0.000 15). Cox regression analysis showed that the hazard ratio (HR) of blood potassium and calcium were 2.08 and 0.01, respectively, in model 1 (single blood potassium or calcium) and in model 2 (model 1 plus age and gender), the HR of blood potassium and calcium were 1.98 and 0.01 respectively, which were significantly associated with patient prognosis (all P < 0.05). Patients in the group with the potassium/calcium ratio > 1.9 had higher blood potassium levels and a higher proportion of mechanical ventilation, lower calcium levels and lower proportion of survival, and longer time of ICU admission compared with the groups with the potassium/calcium ratio < 1.7 and 1.7-1.9. The Kaplan-Meier survival curves showed that the survival rate of the potassium/calcium ratio > 1.9 group was the lowest (P < 0.000 1), and there was no statistically significant difference in survival between the potassium/calcium ratio < 1.7 group and the potassium/calcium ratio 1.7-1.9 group. A restricted cubic spline plot corrected for age and gender showed that patients in the potassium/calcium ratio > 1.8 group had HR values > 1. Cox regression analysis corrected for other indicators showed that the potassium/calcium ratio was still associated with patient prognosis (HR = 4.85, P = 0.033). CONCLUSION: Blood potassium, calcium, and the potassium/calcium ratio at ICU admission are related to the prognosis of patients with severe COVID-19, and the potassium/calcium ratio is an independent risk factor for the death of patients. The higher the potassium/calcium ratio, the worse the prognosis of patients.

Global, regional, and national burden of mortality associated with short-term temperature variability from 2000-19: a three-stage modelling study.

BACKGROUND: Increased mortality risk is associated with short-term temperature variability. However, to our knowledge, there has been no comprehensive assessment of the temperature variability-related mortality burden worldwide. In this study, using data from the MCC Collaborative Research Network, we first explored the association between temperature variability and mortality across 43 countries or regions. Then, to provide a more comprehensive picture of the global burden of mortality associated with temperature variability, global gridded temperature data with a resolution of 0·5°â€ˆ× 0·5° were used to assess the temperature variability-related mortality burden at the global, regional, and national levels. Furthermore, temporal trends in temperature variability-related mortality burden were also explored from 2000-19. METHODS: In this modelling study, we applied a three-stage meta-analytical approach to assess the global temperature variability-related mortality burden at a spatial resolution of 0·5°â€ˆ× 0·5° from 2000-19. Temperature variability was calculated as the SD of the average of the same and previous days' minimum and maximum temperatures. We first obtained location-specific temperature variability related-mortality associations based on a daily time series of 750 locations from the Multi-country Multi-city Collaborative Research Network. We subsequently constructed a multivariable meta-regression model with five predictors to estimate grid-specific temperature variability related-mortality associations across the globe. Finally, percentage excess in mortality and excess mortality rate were calculated to quantify the temperature variability-related mortality burden and to further explore its temporal trend over two decades. FINDINGS: An increasing trend in temperature variability was identified at the global level from 2000 to 2019. Globally, 1 753 392 deaths (95% CI 1 159 901-2 357 718) were associated with temperature variability per year, accounting for 3·4% (2·2-4·6) of all deaths. Most of Asia, Australia, and New Zealand were observed to have a higher percentage excess in mortality than the global mean. Globally, the percentage excess in mortality increased by about 4·6% (3·7-5·3) per decade. The largest increase occurred in Australia and New Zealand (7·3%, 95% CI 4·3-10·4), followed by Europe (4·4%, 2·2-5·6) and Africa (3·3, 1·9-4·6). INTERPRETATION: Globally, a substantial mortality burden was associated with temperature variability, showing geographical heterogeneity and a slightly increasing temporal trend. Our findings could assist in raising public awareness and improving the understanding of the health impacts of temperature variability. FUNDING: Australian Research Council, Australian National Health & Medical Research Council.

De Novo design of potential inhibitors against SARS-CoV-2 Mpro.

The impact of the ravages of COVID-19 on people's lives is obvious, and the development of novel potential inhibitors against SARS-CoV-2 main protease (Mpro), which has been validated as a potential target for drug design, is urgently needed. This study developed a model named MproI-GEN, which can be used for the de novo design of potential Mpro inhibitors (MproIs) based on deep learning. The model was mainly composed of long-short term memory modules, and the last layer was re-trained with transfer learning. The validity (0.9248), novelty (0.9668), and uniqueness (0.0652) of the designed potential MproI library (PMproIL) were evaluated, and the results showed that MproI-GEN could be used to design structurally novel and reasonable molecules. Additionally, PMproIL was filtered based on machine learning models and molecular docking. After filtering, the potential MproIs were verified with molecular dynamics simulations to evaluate the binding stability levels of these MproIs and SARS-CoV-2 Mpro, thereby illustrating the inhibitory effects of the potential MproIs against Mpro. Two potential MproIs were proposed in this study. This study provides not only new possibilities for the development of COVID-19 drugs but also a complete pipeline for the discovery of novel lead compounds.

Computational and Mathematical Methods in Medicine Prediction of COVID-19 in BRICS Countries: An Integrated Deep Learning Model of CEEMDAN-R-ILSTM-Elman.

Since the outbreak of COVID-19, BRICS countries have experienced different epidemic spread due to different health conditions, social isolation measures, vaccination rates, and other factors. A descriptive analysis is conducted for the spread of the epidemic in the BRICS countries. Considering the nonlinear and nonstationary characteristics of COVID-19 data, a principle of decomposition-reconstruction(R)-prediction-integration is proposed. Correspondingly, this paper constructs an integrated deep learning prediction model of CEEMDAN-R-ILSTM-Elman. Specifically, the prediction model is integrated by complete ensemble empirical mode decomposition (CEEMDAN), improved long-term and short-term memory network (ILSTM), and Elman neural network. First, the data is decomposed by adopting CEEMDAN. Then, by calculating the permutation entropy and average period, the decomposed eigenmode component IMFs are reconstructed into four sequences of high, medium, low level, and trend term. Thus, ILSTM and Elman algorithms are used for component sequence prediction, whose results are integrated as the final results. The ILSTM is established based on the LSTM model and the improved beetle antennae search algorithm (IBAS). The ILSTM mainly considers that the prediction accuracy of LSTM model is vulnerable to the influence of parameter selection. The IBAS with adaptive step size is used to automatically optimize the super parameters of LSTM model and to improve the modeling efficiency and prediction accuracy. Experimental results indicate that compared with other benchmark models, CEEMDAN-R-ILSTM-Elman integrated model predicts the number of newly confirmed cases of COVID-19 in BRICS countries with higher accuracy and lower error. Strict social policies have a greater impact on the infection rate and mortality rate of the epidemic. During July-August 2021, epidemic spread in BRICS countries will slow down, and the overall situation is still quite severe.