Influence of Environmental Factors and Genome Diversity on Cumulative COVID-19 Cases in the Highland Region of China: Comparative Correlational Study

BACKGROUND

The novel coronavirus SARS-CoV-2 caused the global COVID-19 pandemic. Emerging reports support lower mortality and reduced case numbers in highland areas; however, comparative studies on the cumulative impact of environmental factors and viral genetic diversity on COVID-19 infection rates have not been performed to date.

OBJECTIVE

The aims of this study were to determine the difference in COVID-19 infection rates between high and low altitudes, and to explore whether the difference in the pandemic trend in the high-altitude region of China compared to that of the lowlands is influenced by environmental factors, population density, and biological mechanisms.

METHODS

We examined the correlation between population density and COVID-19 cases through linear regression. A zero-shot model was applied to identify possible factors correlated to COVID-19 infection. We further analyzed the correlation of meteorological and air quality factors with infection cases using the Spearman correlation coefficient. Mixed-effects multiple linear regression was applied to evaluate the associations between selected factors and COVID-19 cases adjusting for covariates. Lastly, the relationship between environmental factors and mutation frequency was evaluated using the same correlation techniques mentioned above.

RESULTS

Among the 24,826 confirmed COVID-19 cases reported from 40 cities in China from January 23, 2020, to July 7, 2022, 98.4% (n=24,430) were found in the lowlands. Population density was positively correlated with COVID-19 cases in all regions (ρ=0.641, P=.003). In high-altitude areas, the number of COVID-19 cases was negatively associated with temperature, sunlight hours, and UV index (P=.003, P=.001, and P=.009, respectively) and was positively associated with wind speed (ρ=0.388, P<.001), whereas no correlation was found between meteorological factors and COVID-19 cases in the lowlands. After controlling for covariates, the mixed-effects model also showed positive associations of fine particulate matter (PM2.5) and carbon monoxide (CO) with COVID-19 cases (P=.002 and P<.001, respectively). Sequence variant analysis showed lower genetic diversity among nucleotides for each SARS-CoV-2 genome (P<.001) and three open reading frames (P<.001) in high altitudes compared to 300 sequences analyzed from low altitudes. Moreover, the frequencies of 44 nonsynonymous mutations and 32 synonymous mutations were significantly different between the high- and low-altitude groups (P<.001, mutation frequency>0.1). Key nonsynonymous mutations showed positive correlations with altitude, wind speed, and air pressure and showed negative correlations with temperature, UV index, and sunlight hours.

CONCLUSIONS

By comparison with the lowlands, the number of confirmed COVID-19 cases was substantially lower in high-altitude regions of China, and the population density, temperature, sunlight hours, UV index, wind speed, PM2.5, and CO influenced the cumulative pandemic trend in the highlands. The identified influence of environmental factors on SARS-CoV-2 sequence variants adds knowledge of the impact of altitude on COVID-19 infection, offering novel suggestions for preventive intervention.

Post-Coronavirus Disease 2019 Syndrome in Japan: An Observational Study Using a Medical Database

Introduction

In Japan, the clinical information on post-COVID-19 syndrome, including nursing care requirements, is limited. The present study investigated the incidence of acute and post-COVID-19 symptoms, including nursing care requirements, when different SARS-CoV2 strains were prevalent and vaccination statuses changed to mass vaccination programs in Japan.

Methods

Electronic health records of 122,045 patients diagnosed with COVID-19 between January 1, 2020, and June 30, 2022, were obtained from the Tokushukai Group Medical Database. Patient data was divided into three observation periods. Using the International Statistical Classification of Diseases and Related Health Problems 10 codes, typical symptoms of acute (within two weeks after diagnosis) and post-COVID-19 (2-12 weeks after diagnosis) were extracted. Moreover, the nursing care requirements of patients who visited the hospital before and after the COVID-19 diagnosis were examined.

Results

Original and alpha strains were prevalent in Period 1, wherein most of the population was unvaccinated. The delta strain was prevalent in Period 2, wherein approximately 70% of the population was vaccinated. The omicron strain was prevalent in Period 3, wherein approximately 70% of the population completed the two vaccination doses. Headache, malaise/fatigue, depression, and disuse syndrome were detected in acute and post-COVID-19. The incidence of depression and disuse syndrome in post-COVID-19 increased with age, with the highest incidence in the 60-85-year group. Moreover, increased high-level nursing care requirements were observed after COVID-19 in the 60-85-year-age group.

Conclusions

A lower incidence of acute and post-COVID-19 symptoms in Japan is linked to increased population vaccination coverage. However, differences in viral strains may be involved. Moreover, a reduction in long-term quality of life exists in older adult patients after COVID-19. These data provide fundamental information for preventing and treating post-COVID-19 syndrome in Japan.

Mathematical Modeling of SARS-CoV-2 Transmission between Minks and Humans Considering New Variants and Mink Culling

We formulated and studied mathematical models to investigate control strategies for the outbreak of the disease caused by SARS-CoV-2, considering the transmission between humans and minks. Two novel models, namely SEIR and SVEIR, are proposed to incorporate human-to-human, human-to-mink, and mink-to-human transmission. We derive formulas for the reproduction number R0 for both models using the next-generation matrix technique. We fitted our model to the daily number of COVID-19-infected cases among humans in Denmark as an example, and using the best-fit parameters, we calculated the values of R0 to be 1.58432 and 1.71852 for the two-strain and single-strain models, respectively. Numerical simulations are conducted to investigate the impact of control measures, such as mink culling or vaccination strategies, on the number of infected cases in both humans and minks. Additionally, we investigated the possibility of the mutated virus in minks being transmitted to humans. Our results indicate that to control the disease and spread of SARS-CoV-2 mutant strains among humans and minks, we must minimize the transmission and contact rates between mink farmers and other humans by quarantining such individuals. In order to reduce the virus mutation rate in minks, culling or vaccination strategies for infected mink farms must also be implemented. These measures are essential in managing the spread of SARS-CoV-2 and its variants, protecting public health, and mitigating the potential risks associated with human-to-mink transmission.

Adaptive Multi-Factor Quantitative Analysis and Prediction Models: Vaccination, Virus Mutation and Social Isolation on COVID-19

Different countries have adopted various control measures for the COVID-19 pandemic in different periods, and as the virus continues to mutate, the progression of the pandemic and preventive measures adopted have varied dynamically over time. Thus, quantitative analysis of the dynamic impact of different factors such as vaccination, mutant virus, social isolation, etc., on transmission and predicting pandemic progress has become a difficult task. To overcome the challenges above and enable governments to formulate reasonable countermeasures against the ongoing COVID-19 pandemic, we integrate several mathematical methods and propose a new adaptive multifactorial and geographically diverse epidemiological model based on a modified version of the classical susceptible-exposed-infectious-recovered (SEIR) model. Based on public datasets, a multi-center study was carried out considering 21 regions. First, a retrospective study was conducted to predict the number of infections over the next 30 days in 13 representative pandemic areas worldwide with an accuracy of 87.53%, confirming the robustness of the proposed model. Second, the impact of three scenarios on COVID-19 was quantified based on the scalability of the model: two different vaccination regimens were analyzed, and it was found that the number of infections would progressively decrease over time after vaccination; variant virus caused a 301.55% increase in infections in the United Kingdom; and 3-tier social lockdown in the United Kingdom reduced the infections by 47.01%. Third, we made short-term prospective predictions for the next 15 and 30 days for six countries with severe COVID-19 transmission and the predicted trend is accurate. This study is expected to inform public health responses. Code and data are publicly available at https://github.com/yuanyuanpei7/covid-19.

Immune Profile of SARS-CoV-2 Variants of Concern

The spread of the current Sars-Cov-2 pandemics leads to the development of mutations that are constantly monitored because they could affect the efficacy of vaccines. Three recently identified mutated strains, known as variants of concern, are rapidly spreading worldwide. Here, we study possible effects of these mutations on the immune response to Sars-Cov-2 infection using NetTepi a computational method based on artificial neural networks that considers binding and stability of peptides obtained by proteasome degradation for widely represented HLA class I alleles present in human populations as well as the T-cell propensity of viral peptides that measures their immune response. Our results show variations in the number of potential highly ranked peptides ranging between 0 and 20% depending on the specific HLA allele. The results can be useful to design more specific vaccines.

The virological impacts of SARS-CoV-2 D614G mutation

The coronavirus diseases 2019 (COVID-19) caused by the infection of severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) emerged in December 2019 has caused more than 140 million infections worldwide by the end of April 2021. As an enveloped single-stranded positive-sense RNA virus, SARS-CoV-2 underwent constant evolution that produced novel variants carrying mutation conferring fitness advantages. The current prevalent D614G variant, with glycine substituted for aspartic acid at position 614 in the spike glycoprotein, is one of such variants that became the main circulating strain worldwide in a short period of time. Over the past year, intensive studies from all over the world had defined the epidemiological characteristics of this highly contagious variant and revealed the underlying mechanisms. This review aims at presenting an overall picture of the impacts of D614G mutation on virus transmission, elucidating the underlying mechanisms of D614G in virus pathogenicity, and providing insights into the development of effective therapeutics.

B-Cell Memory Responses to Variant Viral Antigens

A central feature of vertebrate immune systems is the ability to form antigen-specific immune memory in response to microbial challenge and so provide protection against future infection. In conflict with this process is the ability that many viruses have to mutate their antigens to escape infection- or vaccine-induced antibody memory responses. Mutable viruses such as dengue virus, influenza virus and of course coronavirus have a major global health impact, exacerbated by this ability to evade immune responses through mutation. There have been several outstanding recent studies on B-cell memory that also shed light on the potential and limitations of antibody memory to protect against viral antigen variation, and so promise to inform new strategies for vaccine design. For the purposes of this review, the current understanding of the different memory B-cell (MBC) populations, and their potential to recognize mutant antigens, will be described prior to some examples from antibody responses against the highly mutable RNA based flaviviruses, influenza virus and SARS-CoV-2.

Evaluating the effect of virus mutation on the transmission of avian influenza H7N9 virus in China based on dynamical model

In 2017, the low pathogenic avian influenza H7N9 virus in China had mutated into high pathogenicity to domestic poultry, and led to a large number of poultry death and human cases. To evaluate the effect of virus mutation on the transmission of avian influenza H7N9 virus, this paper takes Guangdong province for the research area, takes domestic poultry, virus in the domestic poultry survival environment and human beings for the research objects, and establishes a non-autonomous dynamical model. By fitting model with the newly confirmed human cases in Guangdong province, the model we established is confirmed and applied to explain the dynamics of historical human cases. By carrying on parameter estimation, it is deduced that at least 5279376 human beings in Guangdong province had been infected with avian influenza H7N9 virus from March 2013 to September 2017, but most of them were not confirmed, since they had no obvious symptoms or had been cured as common influenza. And comparing with the low pathogenic avian influenza H7N9 virus (H7N9 LPAIV), the transmission rate of the highly pathogenic avian influenza H7N9 virus (H7N9 HPAIV) to human is almost unchanged, but to domestic poultry is about 3.87 times higher. Also, we calculate the basic reproduction number ℜ₀ = 1.3042, which indicates that the virus will persist in Guangdong province with time. Besides, we also perform some sensitivity analysis of the newly confirmed human cases and ℜ₀ in terms of model parameters and conclude that reducing the birth population of domestic poultry, speeding up the circulation of domestic poultry in the market and raising the rate of disease-related death of domestic poultry are benefit to control the transmission of the avian influenza H7N9 virus.

Heterogeneity of the Epstein-Barr Virus (EBV) Major Internal Repeat Reveals Evolutionary Mechanisms of EBV and a Functional Defect in the Prototype EBV Strain B95-8

Epstein-Barr virus (EBV) is a ubiquitous pathogen of humans that can cause several types of lymphoma and carcinoma. Like other herpesviruses, EBV has diversified through both coevolution with its host and genetic exchange between virus strains. Sequence analysis of the EBV genome is unusually challenging because of the large number and lengths of repeat regions within the virus. Here we describe the sequence assembly and analysis of the large internal repeat 1 of EBV (IR1; also known as the BamW repeats) for more than 70 strains. The diversity of the latency protein EBV nuclear antigen leader protein (EBNA-LP) resides predominantly within the exons downstream of IR1. The integrity of the putative BWRF1 open reading frame (ORF) is retained in over 80% of strains, and deletions truncating IR1 always spare BWRF1. Conserved regions include the IR1 latency promoter (Wp) and one zone upstream of and two within BWRF1. IR1 is heterogeneous in 70% of strains, and this heterogeneity arises from sequence exchange between strains as well as from spontaneous mutation, with interstrain recombination being more common in tumor-derived viruses. This genetic exchange often incorporates regions of <1 kb, and allelic gene conversion changes the frequency of small regions within the repeat but not close to the flanks. These observations suggest that IR1-and, by extension, EBV-diversifies through both recombination and breakpoint repair, while concerted evolution of IR1 is driven by gene conversion of small regions. Finally, the prototype EBV strain B95-8 contains four nonconsensus variants within a single IR1 repeat unit, including a stop codon in the EBNA-LP gene. Repairing IR1 improves EBNA-LP levels and the quality of transformation by the B95-8 bacterial artificial chromosome (BAC).IMPORTANCE Epstein-Barr virus (EBV) infects the majority of the world population but causes illness in only a small minority of people. Nevertheless, over 1% of cancers worldwide are attributable to EBV. Recent sequencing projects investigating virus diversity to see if different strains have different disease impacts have excluded regions of repeating sequence, as they are more technically challenging. Here we analyze the sequence of the largest repeat in EBV (IR1). We first characterized the variations in protein sequences encoded across IR1. In studying variations within the repeat of each strain, we identified a mutation in the main laboratory strain of EBV that impairs virus function, and we suggest that tumor-associated viruses may be more likely to contain DNA mixed from two strains. The patterns of this mixing suggest that sequences can spread between strains (and also within the repeat) by copying sequence from another strain (or repeat unit) to repair DNA damage.