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Background: With the rapid advancement of the One Health approach, the transmission of human infectious diseases is generally related to environmental and animal health. Coronavirus disease (COVID-19) has been largely impacted by environmental factors regionally and globally and has significantly disrupted human society, especially in low-income regions that border many countries. However, few research studies have explored the impact of environmental factors on disease transmission in these regions., Methods: We used the Xinjiang Uygur Autonomous Region as the study area to investigate the impact of environmental factors on COVID-19 variation using a dynamic disease model. Given the special control and prevention strategies against COVID-19 in Xinjiang, the focus was on social and environmental factors, including population mobility, quarantine rates, and return rates. The model performance was evaluated using the statistical metrics of correlation coefficient (CC), normalized absolute error (NAE), root mean square error (RMSE), and distance between the simulation and observation (DISO) indices. Scenario analyses of COVID-19 in Xinjiang encompassed three aspects: different population mobilities, quarantine rates, and return rates., Results: The results suggest that the established dynamic disease model can accurately simulate and predict COVID-19 variations with high accuracy. This model had a CC value of 0.96 and a DISO value of less than 0.35. According to the scenario analysis results, population mobilities have a large impact on COVID-19 variations, with quarantine rates having a stronger impact than return rates., Conclusion: These results provide scientific insight into the control and prevention of COVID-19 in Xinjiang, considering the influence of social and environmental factors on COVID-19 variation. The control and prevention strategies for COVID-19 examined in this study may also be useful for the control of other infectious diseases, especially in low-income regions that are bordered by many countries., Competing Interests: The authors declare that the research was conducted in the absence of any commercial or financial relationships that could be construed as a potential conflict of interest., (Copyright © 2024 Fu, Liu, Wang, Zhao, Cui, Hu, Zhang, and Wang.)

Background: The COVID-19 pandemic, which began in late 2019, has resulted in the devastating collapse of the social economy and more than 10 million deaths worldwide. A recent study suggests that the pattern of COVID-19 cases will resemble a mini-wave rather than a seasonal surge. In general, COVID-19 has more severe impacts on cities than on rural areas, especially in cities with high population density., Methods: In this study, the background situation of COVID-19 transmission is discussed, including the population number and population density. Moreover, a widely used time series autoregressive integrated moving average (ARIMA) model is applied to simulate and forecast the COVID-19 variations in the six cities. We comprehensively analyze the dynamic variations in COVID-19 in the four first-tier cities of mainland China (BJ: Beijing, SH: Shanghai, GZ: Guangzhou and SZ: Shenzhen), Hong Kong (HK), China and Singapore (SG) from 2020 to 2022., Results: The major results show that the six cities have their own temporal characteristics, which are determined by the different control and prevention measures. The four first-tier cities of mainland China (i.e., BJ, SH, GZ, and SZ) have similar variations with one wave because of their identical "Dynamic COVID-19 Zero" strategy and strict Non-Pharmaceutical Interventions (NPIs). HK and SG have multiple waves primarily caused by the input cases. The ARIMA model has the ability to provide an accurate forecast of the COVID-19 pandemic trend for the six cities, which could provide a useful approach for predicting the short-term variations in infectious diseases.Accurate forecasting has significant value for implementing reasonable control and prevention measures., Conclusions: Our main conclusions show that control and prevention measures should be dynamically adjusted and organically integrated for the COVID-19 pandemic. Moreover, the mathematical models are proven again to provide an important scientific basis for disease control., Competing Interests: The authors declare that the research was conducted in the absence of any commercial or financial relationships that could be construed as a potential conflict of interest., (Copyright © 2023 Ni, Sun, Hu, Cui, Zhang and Zhang.)

Cigarette smoke aggravates severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) infection. However, the underlying mechanisms remain unclear. Here, they show that benzo[a]pyrene in cigarette smoke extract facilitates SARS-CoV-2 infection via upregulating angiotensin-converting enzyme 2 (ACE2) and transmembrane protease serine 2 (TMPRSS2). Benzo[a]pyrene trans-activates the promoters of ACE2 and TMPRSS2 by upregulating nuclear receptor subfamily 4 A number 2 (NR4A2) and promoting its binding of NR4A2 to their promoters, which is independent of functional genetic polymorphisms in ACE2 and TMPRSS2. Benzo[a]pyrene increases the susceptibility of lung epithelial cells to SARS-CoV-2 pseudoviruses and facilitates the infection of authentic Omicron BA.5 in primary human alveolar type II cells, lung organoids, and lung and testis of hamsters. Increased expression of Nr4a2, Ace2, and Tmprss2, as well as decreased methylation of CpG islands at the Nr4a2 promoter are observed in aged mice compared to their younger counterparts. NR4A2 knockdown or interferon-λ2/λ3 stimulation downregulates the expression of NR4A2, ACE2, and TMPRSS2, thereby inhibiting the infection. In conclusion, benzo[a]pyrene enhances SARS-CoV-2 infection by boosting NR4A2-induced ACE2 and TMPRSS2 expression. This study elucidates the mechanisms underlying the detrimental effects of cigarette smoking on SARS-CoV-2 infection and provides prophylactic options for coronavirus disease 2019, particularly for the elderly population., (© 2023 The Authors. Advanced Science published by Wiley-VCH GmbH.)

The evolving SARS-CoV-2 Omicron strain has repeatedly caused widespread disease epidemics, and effective antibody drugs continue to be in short supply. Here, we identified a batch of nanobodies with high affinity for receptor binding domain (RBD) of SARS-CoV-2 spike protein, separated them into three classes using high performance liquid chromatography (HPLC), and then resolved the crystal structure of the ternary complexes of two non-competing nanobodies (NB1C6 and NB1B5) with RBD using X-ray crystallography. The structures showed that NB1B5 and NB1C6 bind to the left and right flank of the RBD, respectively, and that the binding epitopes are highly conserved cryptic sites in all SARS-CoV-2 mutant strains, as well as that NB1B5 can effectively block the ACE2. These two nanobodies were covalently linked into multivalent and bi-paratopic formats, and have a high affinity and neutralization potency for omicron, potentially inhibiting viral escape. The binding sites of these two nanobodies are relatively conserved, which help guide the structural design of antibodies targeting future variants of SARS-CoV-2 to combat COVID-19 epidemics and pandemics., Competing Interests: Declaration of Competing Interest The authors declare that they have no known competing financial interests or personal relationships that could have appeared to influence the work reported in this paper., (Copyright © 2023 Elsevier Inc. All rights reserved.)

Background: The ongoing coronavirus disease 2019 (COVID-19) pandemic caused by the severe acute respiratory syndrome-coronavirus 2 (SARS-CoV-2) and the Omicron variant presents a formidable challenge for control and prevention worldwide, especially for low- and middle-income countries (LMICs). Hence, taking Kazakhstan and Pakistan as examples, this study aims to explore COVID-19 transmission with the Omicron variant at different contact, quarantine and test rates., Methods: A disease dynamic model was applied, the population was segmented, and three time stages for Omicron transmission were established: the initial outbreak, a period of stabilization, and a second outbreak. The impact of population contact, quarantine and testing on the disease are analyzed in five scenarios to analysis their impacts on the disease. Four statistical metrics are employed to quantify the model's performance, including the correlation coefficient (CC), normalized absolute error, normalized root mean square error and distance between indices of simulation and observation (DISO)., Results: Our model has high performance in simulating COVID-19 transmission in Kazakhstan and Pakistan with high CC values greater than 0.9 and DISO values less than 0.5. Compared with the present measures (baseline), decreasing (increasing) the contact rates or increasing (decreasing) the quarantined rates can reduce (increase) the peak values of daily new cases and forward (delay) the peak value times (decreasing 842 and forward 2 days for Kazakhstan). The impact of the test rates on the disease are weak. When the start time of stage II is 6 days, the daily new cases are more than 8 and 5 times the rate for Kazakhstan and Pakistan, respectively (29,573 vs. 3259; 7398 vs. 1108). The impact of the start times of stage III on the disease are contradictory to those of stage II., Conclusions: For the two LMICs, Kazakhstan and Pakistan, stronger control and prevention measures can be more effective in combating COVID-19. Therefore, to reduce Omicron transmission, strict management of population movement should be employed. Moreover, the timely application of these strategies also plays a key role in disease control., (© 2023. The Author(s).)

The emergence of Omicron/BA.1 has brought new challenges to fight against SARS-CoV-2. A large number of mutations in the Spike protein suggest that its susceptibility to immune protection elicited by the existing COVID-19 infection and vaccines may be altered. In this study, we constructed the pseudotyped SARS-CoV-2 variant Omicron. The sensitivity of 28 serum samples from COVID-19 convalescent patients infected with SARS-CoV-2 original strain was tested against pseudotyped Omicron as well as the other variants of concern (VOCs, Alpha, Beta, Gamma, Delta) and variants of interest (VOIs, Lambda, Mu). Our results indicated that the mean neutralization ED50 of these sera against Omicron decreased to 66, which is about 8.4-folds compared to the D614G reference strain (ED50 = 556), whereas the neutralization activity of other VOC and VOI pseudotyped viruses decreased only about 1.2-4.5-folds. The finding from our in vitro assay suggest that Omicron variant may lead to more significant escape from immune protection elicited by previous SARS-CoV-2 infection and perhaps even by existing COVID-19 vaccines.

A steep rise in Omicron reinfection cases suggests that this variant has increased immune evasion ability. To evaluate its antigenicity relationship with other variants, antisera from guinea pigs immunized with spike protein of SARS-CoV-2 variants of concern (VOCs) and variants of interest (VOIs) were cross-tested against pseudotyped variants. The neutralization activity against Omicron was markedly reduced when other VOCs or VOIs were used as immunogens, and Omicron (BA.1)-elicited sera did not efficiently neutralize the other variants. However, a Beta or Omicron booster, when administered as the 4th dose 3-months after the 3rd dose of any of the variants, could elicit broad neutralizing antibodies against all of the current variants including Omicron BA.1. Further analysis with 280 available antigen-antibody structures and quantification of immune escape from 715 reported neutralizing antibodies provide explanations for the observed differential immunogenicity. Three distinct clades predicted using an in silico algorithm for clustering of sarbecoviruses based on immune escape provide key information for rational design of vaccines., (© 2022. The Author(s).)

Because of the evolutionary variants of SARS-CoV-2, development of broad-spectrum neutralizing antibodies resilient to virus escape is urgently needed. We identified a group of high-affinity nanobodies from camels immunized with receptor-binding domain (RBD) of SARS-CoV-2 spike protein and resolved the structures of two non-competing nanobodies (NB1A7 and NB1B11) in complex with RBD using X-ray crystallography. The structures show that NB1A7 targets the highly conserved cryptic epitope shared by SARS-CoV-2 variants and some other coronaviruses and blocks ACE2 receptor attachment of the spike protein, and NB1B11 epitope overlaps with the contacting surface of ACE2 and is different from the binding site of NB1A7. These two nanobodies were covalently linked into multivalent and bi-paratopic formats, which significantly improved the avidity and neutralization potency and may further inhibit viral escape. The results contribute to the structure-guided design of antibodies against future variants of SARS-CoV-2 virus to combat coronavirus epidemics and pandemics., Competing Interests: Declaration of interests The authors declare no competing interests., (Copyright © 2022 Elsevier Ltd. All rights reserved.)

The long‐term protective efficacy of neutralizing antibodies (Nabs) against Omicron subvariants after inactivated booster vaccines remains elusive. During the follow‐up study, 54 healthy volunteers aged 20–31 years received inactivated CoronaVac booster vaccinations and were monitored for 221 days. The dynamic efficacy and durability of Nab against Omicron subvariants BA.1, BA.2, BA.2.12.2, and BA4/5 were assessed using a pseudotyped virus neutralization assay at up to nine time points post immunization. The antibody response against Omicron subvariants was substantially weaker than D614G, with BA.4/5 being the least responsive. The geometric mean titer (GMT) of Nab against Omicron subvariants BA.1, BA.2, BA.2.12.1, and BA.4/5 was 2.2‐, 1.7‐, 1.8‐, and 2.2‐fold lower than that against D614G (ps < 0.0001). The gap in Nab response between Omicron subvariants was pronounced during the 2 weeks–2 months following booster vaccination (ps < 0.05). Seven months post booster, the antibody potency against D614G was maintained at 100% (50% for Nab titers ≥ 100 50% inhibitory dilution [EC50]), whereas at 77.3% for BA.1, 90.9% for BA.2, 86.4% for BA.2.12.1, and 86.4% for BA.4/5 (almost 20% for Nab titers ≥ 100 EC50). Despite the inevitable immune escape, Omicron subvariants maintained sustained and measurable antibody potency post‐booster vaccination during long‐term monitoring, which could help optimize immunization strategies. [ABSTRACT FROM AUTHOR]

The severe acute respiratory syndrome coronavirus 2 (SARS‐CoV‐2) variants, particularly those with multiple mutations in receptor‐binding domain (RBD), pose a critical challenge to the efficacy of coronavirus disease 2019 (COVID‐19) vaccines and therapeutic neutralizing monoclonal antibodies (mAbs). Omicron sublineages BA.1, BA.2, BA.3, as well as the recent emergence of C.1.2, B.1.630, B.1.640.1, and B.1.640.2, have multiple mutations in RBD and may lead to severe neutralizing antibody evasion. It is urgent to evaluate the antigenic change of the above seven variants against mAbs and sera from guinea pigs immunized with variants of concern (VOCs) (Alpha, Beta, Gamma, Delta, Omicron) and variants of interest (VOIs) (Lambda, Mu) immunogens. Only seven out of the 24 mAbs showed no reduction in neutralizing activity against BA.1, BA.2, and BA.3. However, among these seven mAbs, the neutralization activity of XGv337 and XGv338 against C.1.2, B.1.630, B.1.640.1, and B.1.640.2 were decreased. Therefore, only five neutralizing mAbs showed no significant change against these seven variants. Using VOCs and VOIs as immunogens, we found that the antigenicity of variants could be divided into three clusters, and each cluster showed similar antigenicity to different immunogens. Among them, D614G, B.1.640.1, and B.1.630 formed a cluster, C.1.2 and B.1.640.2 formed a cluster, and BA.1, BA.2, and BA.3 formed a cluster. [ABSTRACT FROM AUTHOR]

The ongoing coronavirus disease 2019 (COVID‐19) pandemic has caused more than 150 million cases of infection to date and poses a serious threat to global public health. In this study, global COVID‐19 data were used to examine the dynamical variations from the perspectives of immunity and contact of 84 countries across the five climate regions: tropical, arid, temperate, and cold. A new approach named Yi Hua Jie Mu is proposed to obtain the transmission rates based on the COVID‐19 data between the countries with the same climate region over the Northern Hemisphere and Southern Hemisphere. Our results suggest that the COVID‐19 pandemic will persist over a long period of time or enter into regular circulation in multiple periods of 1–2 years. Moreover, based on the simulated results by the COVID‐19 data, it is found that the temperate and cold climate regions have higher infection rates than the tropical and arid climate regions, which indicates that climate may modulate the transmission of COVID‐19. The role of the climate on the COVID‐19 variations should be concluded with more data and more cautions. The non‐pharmaceutical interventions still play the key role in controlling and prevention this global pandemic. Plain Language Summary: In this work, global COVID‐19 data were used to examine the dynamical variations from the perspectives of immunity and contact over five climate regions: tropical, arid, temperate, cold, and polar. A new approach is proposed to obtain the infection rates based on the COVID‐19 data between the countries with the same climate region over the Northern Hemisphere and Southern Hemisphere. Our results suggest that the COVID‐19 pandemic will persist over a long period of time or enter into regular circulation in multiple periods of 1–2 years. Moreover, it is found that the temperate and cold climate regions have higher infection rates than the tropical and arid climate regions, which indicates that climate may modulate the transmission of COVID‐19. Key Points: A new approached is proposed to predict the future COVID‐19 variations rather than relying on information on other corona virusesCOVID‐19 pandemic will persist in multiple periods of 1–2 yearsThe temperate and cold climate regions have higher infection rates than the tropical and arid climate regions [ABSTRACT FROM AUTHOR]

• The input population and output population are considered. • 108 scenarios are listed from the input population and quarantine strategies. • The second outbreak of disease is obtained. In this study, an epidemic model was developed to simulate and predict the disease variations of Guangdong province which was focused on the period from Jan 27 to Feb 20, 2020. To explore the impacts of the input population and quarantine strategies on the disease variations at different scenarios, four time points were assumed as Feb 6, Feb 16, Feb 24 and Mar 5 2020. The major results suggest that our model can well capture the disease variations with high accuracy. The simulated peak value of the confirmed cases is 1002 at Feb 10, 2020 which is mostly close to the reported number of 1007 at Feb 9, 2020. The disease will become extinction with peak value of 1397 at May 11, 2020. Moreover, the increased numbers of the input population can mainly shorten the disease extinction days and the increased percentages of the exposed individuals of the input population increase the number of cumulative confirmed cases at a small percentage. Increasing the input population and decreasing the quarantine strategy together around the time point of the peak value of the confirmed cases, may lead to the second outbreak. [ABSTRACT FROM AUTHOR]