Zhitong Mai

Bio: Zhitong Mai is an academic researcher from Guangzhou Medical University. The author has contributed to research in topics: Medicine & Virology. The author has an hindex of 1, co-authored 2 publications receiving 771 citations.

Modified SEIR and AI prediction of the epidemics trend of COVID-19 in China under public health interventions

Abstract: Background: The coronavirus disease 2019 (COVID-19) outbreak originating in Wuhan, Hubei province, China, coincided with chunyun, the period of mass migration for the annual Spring Festival. To contain its spread, China adopted unprecedented nationwide interventions on January 23 2020. These policies included large-scale quarantine, strict controls on travel and extensive monitoring of suspected cases. However, it is unknown whether these policies have had an impact on the epidemic. We sought to show how these control measures impacted the containment of the epidemic. Methods: We integrated population migration data before and after January 23 and most updated COVID-19 epidemiological data into the Susceptible-Exposed-Infectious-Removed (SEIR) model to derive the epidemic curve. We also used an artificial intelligence (AI) approach, trained on the 2003 SARS data, to predict the epidemic. Results: We found that the epidemic of China should peak by late February, showing gradual decline by end of April. A five-day delay in implementation would have increased epidemic size in mainland China three-fold. Lifting the Hubei quarantine would lead to a second epidemic peak in Hubei province in mid-March and extend the epidemic to late April, a result corroborated by the machine learning prediction. Conclusions: Our dynamic SEIR model was effective in predicting the COVID-19 epidemic peaks and sizes. The implementation of control measures on January 23 2020 was indispensable in reducing the eventual COVID-19 epidemic size.

Bisabolane-type sesquiterpenoids from Curcuma longa L. exert anti-influenza and anti-inflammatory activities through NF-κB/MAPK and RIG-1/STAT1/2 signaling pathways

Abstract: Influenza is a viral respiratory illness that causes seasonal epidemics and occasional pandemics. Disease severity may be contributed by influenza virus-induced cytokine dysregulation. The study was designed to investigate the isolation and identification of bisabolane-type sesquiterpenoids from Curcuma longa L., their antiviral and anti-inflammatory activities against H1N1 and their potential role in regulating host immune response in vitro. A pair of new bisabolane-type sesquiterpenoids, (6S,7S)-3-hydroxy-3-hydroxymethylbisabola-1,10-diene-9-one (18) together with seventeen known analogs (1–17), was isolated and elucidated from Curcuma longa L. Compounds 2, 11 and 14 could significantly inhibit A/PR/8/34 (H1N1) replication in MDCK cells, and compound 2 could significantly inhibit A/PR/8/34 (H1N1) replication in A549 cells. Compounds 4, 8, 9, 13 and 17 could markedly reduce pro-inflammatory cytokine (TNF-α, IL-6, IL-8 and IP-10) production at the mRNA and protein levels in A549 cells. Compound 4 regulated the levels of steroid biosynthesis, oxidative phosphorylation and protein processing in the endoplasmic reticulum, thereby inhibiting immune responses by proteomics analysis. Furthermore, compound 4 could inhibit the expression of p-NF-κB p65, NF-κB p65, IκBα, p-p38 MAPK, p-IκBα, RIG-1, STAT-1/2 and p-STAT-1/2 in the signaling pathways. These findings indicate that bisabolane-type sesquiterpenoids of C. longa could inhibit the expression of inflammatory cytokines induced by the virus and regulate the activity of NF-κB/MAPK and RIG-1/STAT-1/2 signaling pathways in vitro.

Crucial control measures to contain China's first Delta variant outbreak

Abstract: Abstract The SARS-CoV-2 B.1.617.2 (Delta) variant flared up in late May in Guangzhou, China. Transmission characteristics of Delta variant were analysed for 153 confirmed cases and two complete transmission chains with seven generations were fully presented. A rapid transmission occurred in five generations within 10 days. The basic reproduction number (R0) was 3.60 (95% confidence interval: 2.50–5.30). After redefining the concept of close contact, the proportion of confirmed cases discovered from close contacts increased from 43% to 100%. With the usage of a yellow health code, the potential exposed individuals were self-motivated to take a nucleic acid test and regained public access with a negative testing result. Facing the massive requirement of screening, novel facilities like makeshift inflatable laboratories were promptly set up as a vital supplement and 17 cases were found, with 1 pre-symptomatic. The dynamic adjustment of these three interventions resulted in the decline of Rt from 5.00 to 1.00 within 9 days. By breaking the transmission chain and eliminating the transmission source through extending the scope of the close-contact tracing, health-code usage and mass testing, the Guangzhou Delta epidemic was effectively contained.

Different Circulation Pattern of Multiple Respiratory Viruses in Southern China During the COVID-19 Pandemic

Abstract: China implemented stringent non-pharmaceutical interventions (NPIs) in spring 2020, which has effectively suppressed SARS-CoV-2. In this study, we utilized data from routine respiratory virus testing requests from physicians and examined circulation of 11 other respiratory viruses in Southern China, from January 1, 2018 to December 31, 2020. A total of 58,169 throat swabs from patients with acute respiratory tract infections (ARTIs) were collected and tested. We found that while the overall activity of respiratory viruses was lower during the period with stringent NPIs, virus activity rebounded shortly after the NPIs were relaxed and social activities resumed. Only influenza was effectively suppressed with very low circulation which extended to the end of 2020. Circulation of other respiratory viruses in the community was maintained even during the period of stringent interventions, especially for rhinovirus. Our study shows that NPIs against COVID-19 have different impacts on respiratory viruses.

Glycomic Analysis Reveals That Sialyltransferase Inhibition Is Involved in the Antiviral Effects of Arbidol

Abstract: This study revealed, for the first time, that ST inhibition and the resulted destruction of SA receptors of host cells may be an underlying mechanism for the antiviral activity of ARB. ST inhibition has been proposed as a novel host-targeting antiviral approach recently and several compounds are currently under exploration. ABSTRACT Due to the high mutation rate of influenza virus and the rapid increase of drug resistance, it is imperative to discover host-targeting antiviral agents with broad-spectrum antiviral activity. Considering the discrepancy between the urgent demand of antiviral drugs during an influenza pandemic and the long-term process of drug discovery and development, it is feasible to explore host-based antiviral agents and strategies from antiviral drugs on the market. In the current study, the antiviral mechanism of arbidol (ARB), a broad-spectrum antiviral drug with potent activity at early stages of viral replication, was investigated from the aspect of hemagglutinin (HA) receptors of host cells. N-glycans that act as the potential binding receptors of HA on 16-human bronchial epithelial (16-HBE) cells were comprehensively profiled for the first time by using an in-depth glycomic approach based on TiO2-PGC chip-Q-TOF MS. Their relative levels upon the treatment of ARB and virus were carefully examined by employing an ultra-high sensitive qualitative method based on Chip LC-QQQ MS, showing that ARB treatment led to significant and extensive decrease of sialic acid (SA)-linked N-glycans (SA receptors), and thereby impaired the virus utilization on SA receptors for rolling and entry. The SA-decreasing effect of ARB was demonstrated to result from its inhibitory effect on sialyltransferases (ST), ST3GAL4 and ST6GAL1 of 16-HBE cells. Silence of STs, natural ST inhibitors, as well as sialidase treatment of 16-HBE cells, resulted in similar potent antiviral activity, whereas ST-inducing agent led to the diminished antiviral effect of ARB. These observations collectively suggesting the involvement of ST inhibition in the antiviral effect of ARB. IMPORTANCE This study revealed, for the first time, that ST inhibition and the resulted destruction of SA receptors of host cells may be an underlying mechanism for the antiviral activity of ARB. ST inhibition has been proposed as a novel host-targeting antiviral approach recently and several compounds are currently under exploration. ARB is the first antiviral drug on the market that was found to possess ST inhibiting function. This will provide crucial evidence for the clinical usages of ARB, such as in combination with neuraminidase (NA) inhibitors to exert optimized antiviral effect, etc. More importantly, as an agent that can inhibit the expression of STs, ARB can serve as a novel lead compound for the discovery and development of host-targeting antiviral drugs.

A SIR model assumption for the spread of COVID-19 in different communities

Abstract: In this paper, we study the effectiveness of the modelling approach on the pandemic due to the spreading of the novel COVID-19 disease and develop a susceptible-infected-removed (SIR) model that provides a theoretical framework to investigate its spread within a community Here, the model is based upon the well-known susceptible-infected-removed (SIR) model with the difference that a total population is not defined or kept constant per se and the number of susceptible individuals does not decline monotonically To the contrary, as we show herein, it can be increased in surge periods! In particular, we investigate the time evolution of different populations and monitor diverse significant parameters for the spread of the disease in various communities, represented by China, South Korea, India, Australia, USA, Italy and the state of Texas in the USA The SIR model can provide us with insights and predictions of the spread of the virus in communities that the recorded data alone cannot Our work shows the importance of modelling the spread of COVID-19 by the SIR model that we propose here, as it can help to assess the impact of the disease by offering valuable predictions Our analysis takes into account data from January to June, 2020, the period that contains the data before and during the implementation of strict and control measures We propose predictions on various parameters related to the spread of COVID-19 and on the number of susceptible, infected and removed populations until September 2020 By comparing the recorded data with the data from our modelling approaches, we deduce that the spread of COVID-19 can be under control in all communities considered, if proper restrictions and strong policies are implemented to control the infection rates early from the spread of the disease

Corona Virus (COVID-19) "Infodemic" and Emerging Issues through a Data Lens: The Case of China.

Abstract: Coronavirus (COVID-19) is a humanitarian emergency, which started in Wuhan in China in early December 2019, brought into the notice of the authorities in late December, early January 2020, and, after investigation, was declared as an emergency in the third week of January 2020. The WHO declared this as Public Health Emergency of International Concern (PHEIC) on 31th of January 2020, and finally a pandemic on 11th March 2020. As of March 24th, 2020, the virus has caused a casualty of over 16,600 people worldwide with more than 380,000 people confirmed as infected by it, of which more than 10,000 cases are serious. Mainly based on Chinese newspapers, social media and other digital platform data, this paper analyzes the timeline of the key actions taken by the government and people over three months in five different phases. It found that although there was an initial delay in responding, a unique combination of strong governance, strict regulation, strong community vigilance and citizen participation, and wise use of big data and digital technologies, were some of the key factors in China's efforts to combat this virus. Being inviable and non-measurable (unlike radioactive exposure), appropriate and timely information is very important to form the basic foundation of mitigation and curative measures. Infodemic, as it is termed by WHO, is a key word, where different stakeholder's participation, along with stricter regulation, is required to reduce the impact of fake news in this information age and social media. Although different countries will need different approaches, focusing on its humanitarian nature and addressing infodemic issues are the two critical factors for future global mitigation efforts.

Predictions for COVID-19 with Deep Learning Models of LSTM, GRU and Bi-LSTM

Abstract: COVID-19, responsible of infecting billions of people and economy across the globe, requires detailed study of the trend it follows to develop adequate short-term prediction models for forecasting the number of future cases. In this perspective, it is possible to develop strategic planning in the public health system to avoid deaths as well as managing patients. In this paper, proposed forecast models comprising autoregressive integrated moving average (ARIMA), support vector regression (SVR), long shot term memory (LSTM), bidirectional long short term memory (Bi-LSTM) are assessed for time series prediction of confirmed cases, deaths and recoveries in ten major countries affected due to COVID-19. The performance of models is measured by mean absolute error, root mean square error and r2_score indices. In the majority of cases, Bi-LSTM model outperforms in terms of endorsed indices. Models ranking from good performance to the lowest in entire scenarios is Bi-LSTM, LSTM, GRU, SVR and ARIMA. Bi-LSTM generates lowest MAE and RMSE values of 0.0070 and 0.0077, respectively, for deaths in China. The best r2_score value is 0.9997 for recovered cases in China. On the basis of demonstrated robustness and enhanced prediction accuracy, Bi-LSTM can be exploited for pandemic prediction for better planning and management.