Mathematical Modelling Indicates Th-cell Targeted Antibody-Dependent Cellular Cytotoxic Is a Crucial Obstacle Hurdling HIV Vaccine Development (preprint)

HIV poses a significant threat to human health. Although some progress has been made in the development of an HIV vaccine, there is currently no reported success in achieving an effective and fully functional vaccine for HIV. This highlights the challenges involved in HIV vaccine development. Through mathematical modeling, we have conducted a systematic study on the impact of antibody-dependent cellular cytotoxicity (ADCC) on HIV-specific immune responses. Unlike other viral infections, the ADCC effect following HIV infection may cause significant damage to the follicular center Th cells, leading to apoptosis of follicular center cells and rapid death of effector Th cells. This impedes the generation of neutralizing antibodies and creates barriers to viral clearance, thereby contributing to long-term infection. Another challenge posed by this effect is the substantial reduction in vaccine effectiveness, as effective antigenic substances such as gp120 bind to Th cell surfaces, resulting in the apoptosis of follicular center Th cells due to ADCC, hindering antibody regeneration. To address this issue, we propose the concept of using bispecific antibodies. By genetically editing B cells to insert the bispecific antibody gene, which consists of two parts targeting the CD4 binding site of HIV, such as the broadly neutralizing antibody 3BNC117, and the other targeting antibodies against other viruses, such as the spike protein of SARS-CoV-2. We can simultaneously enhance the levels of two pathogen-specific antibodies through stimulation with non-HIV-antigens corresponding to the other part of the chimeric antibody, such as the spike protein. This study contributes to the elucidation of the pathophysiology of HIV, while also providing a theoretical framework for the successful development of an HIV vaccine.

Bioinformatic Analysis of Defective Viral Genomes in SARS-CoV-2 and Its Impact on Population Infection Characteristics (preprint)

DVGs (Defective Viral Genomes) and SIP (Semi-Infectious Particle) are commonly present in RNA virus infections. In this study, we analyzed high-throughput sequencing data and found that DVGs or SIPs are also widely present in SARS-CoV-2. Comparison of SARS-CoV-2 with various DNA viruses revealed that the SARS-CoV-2 genome is more susceptible to damage and has greater sequencing sample heterogeneity. Variability analysis at the whole-genome sequencing depth showed a higher coefficient of variation for SARS-CoV-2, and DVG analysis indicated a high proportion of splicing sites, suggesting significant genome heterogeneity and implying that most virus particles assembled are enveloped with incomplete RNA sequences. We further analyzed the characteristics of different strains in terms of sequencing depth and DVG content differences and found that as the virus evolves, the proportion of intact genomes in virus particles increases, which can be significantly reflected in third-generation sequencing data, while the proportion of DVG gradually decreases. Specifically, the proportion of intact genome of Omicron was greater than that of Delta and Alpha strains. This can well explain why Omicron strain is more infectious than Delta and Alpha strains. We also speculate that this improvement in completeness is due to the enhancement of virus assembly ability, as the Omicron strain can quickly realize the binding of RNA and capsid protein, thereby shortening the exposure time of exposed virus RNA in the host environment and greatly reducing its degradation level. Finally, by using mathematical modeling, we simulated how DVG effects under different environmental factors affect the infection characteristics and evolution of the population. We can explain well why the severity of symptoms is closely related to the amount of virus invasion and why the same strain causes huge differences in population infection characteristics under different environmental conditions. Our study provides a new approach for future virus research and vaccine development.

A Deterministic Agent-based Model with Antibody Dynamics Information in COVID-19 Epidemic Simulation (preprint)

Accurate prediction of the temporal and spatial characteristics of COVID-19 infection can provide favorable guidance for epidemic prevention and control. We first introduce individual antibody dynamics into an agent-based model. Antibody dynamics model can well explain the antibody fading effects through time. Based on this model, we further developed an agent-based approach which considers the dynamic behaviors of each individual antibodies. The method can effectively reflect the dynamic interaction between the antibody and the virus in each host body in the overall population. Using this method, we can accurately predict the temporal and spatial characteristics of the epidemic. It can quantitatively calculate the number and spatial distribution of infected persons with different symptoms at different times. At the same time, our model can predict the prevention and control effect of different prevention and control measures. At present, China's dynamic zero strategies mainly include large-scale nucleic acid test, isolation of positive infected persons and their close contacts. Our model demonstrates that for a less infectious and more virulent variant, this approach can achieve good preventive effect. The effect of reducing social contacts and quarantining only positive infected persons is relatively weaker on epidemic control. This can explain why China's targeted epidemic-control measures had an excellent performance in 2020 and 2021. However, our model also warns that for the highly infectious and less virulent variant, targeted epidemic-control measures can no longer achieve effective control of the epidemic. Therefore, we must choose to quarantine potential infected groups in a wider range (such as the quarantine of secondary close contact and tertiary close contact) or coexist with the virus. Furthermore, our model has a strong traceability ability, which can effectively conduct epidemiological investigation to unearth patient number zero based on the early epidemic distribution. In the end, our model expands the traditional approaches of epidemiological simulation and provides an alternative in epidemic modeling.

More or less deadly? A mathematical model that predicts SARS-CoV-2 evolutionary direction. (preprint)

SARS-CoV-2 has caused tremendous deaths world wild. It is of great value to predict the evolutionary direction of SARS-CoV-2. In this paper, we proposed a novel mathematical model that could predict the evolutionary trend of SARS-CoV-2. We focus on the mutational effects on viral assembly capacity. A robust coarse-grained mathematical model is constructed to simulate the virus dynamics in host body. Both virulence and transmissibility can be quantified in this model. The relationship between virulence and transmissibility can be simulated. A delicate equilibrium point that optimizing the transmissibility can be numerically obtained. Based on this model, we predict the virulence of SARS-CoV-2 might further decrease accompany with an enhancement of transmissibility. However, this trend is not continuous, its virulence will not disappear but remains at a relatively stable range. We can also explain the cross-species transmission phenomenon of certain RNA virus based on this model. A small-scale model which simulates the virus packing process is also proposed. It can be explained why small number of mutations would lead to a significant divergence in clinical performance, both in the overall particle formation quantity and virulence. This research provides a mathematical attempt in elucidating the evolutionary driving force in RNA virus evolution.

Antibody Dynamics Simulation -Theory and Application (preprint)

Infectious disease such as COVID-19 poses a considerable threat to public health when a pandemic strain emerges. Constructing a reliable mathematical model helps us quantitatively explain the kinetic characteristics of antibody-virus interactions, which could provide a reasonable prediction toward many sensitive concerns faced by the public, such as how to calculate protection time provided by the specific vaccine. A novel and robust model is developed to integrate antibody dynamics with virus dynamics in the host body. Our model is based on a comprehensive understanding of immunology principles rather than a simple data-fitting attempt by arbitrarily mathematical function selection. The physical-based mechanism would bring this model more reliable and broader prediction performance. This model gives quantitative insights between antibody dynamics and virus loading in the host body. Based on this model, we can estimate the antibody dynamic parameters with high fidelity. We could solve lots of critical problems, such as the calculation of vaccine protection time. We can also explain lots of mysterious phenomena such as antibody inferences, self-reinfection, chronic infection, etc. We suggest the best strategy in prolonging the vaccine protection time is not repeated inoculation but a directed induction of fast binding antibodies. Eventually, it will also inform the future construction of the mathematical model and help us fight against those infectious diseases.

Statistical Analysis Supports UTR（untranslated region）Deletion Theory in SARS-CoV-2 (preprint)

The statistical analysis found that the mortality rate of COVID-19 infection experienced a significant decline in the early stage of the epidemic. We suspect that the sharp deterioration of virus toxicity is related to point mutation and the deletion of the untranslated region of the virus genome. Through sequence analysis of mega-genome data, we found that the genome length of COVID-19 was deleted, which mainly occurred in the untranslated regions at both ends. Sequence similarity analysis further indicated that short UTR length strain emerged by deleting strain with long sequence length. This process is irreversible; the genome with a short sequence length could not restore to the long sequence length. By studying the relationship between genome length and mortality, we found a good correlation between them statistically, which demonstrated that the deletion of the untranslated region of the virus significantly affected the toxicity of the virus. We extracted the viral genome length of patients with different symptoms from the GISAID database for analysis to confirm this relationship. It discovered that the viral genome length of hospitalized patients was significantly more extensive than that of asymptomatic patients. In contrast, the viral genome length of asymptomatic patients was considerably longer than that of ordinary patients with symptoms. To further prove this idea, we performed a genome-level mutation scanning to systematically evaluate the influence of mutations at each position on virulence. After Pearson correlation analysis and chi-square investigation, UTR deletion was the primary driving force in alternating virus virulence. All those statistical evidence support the UTR deletion theory in SARS-COV-2. In the end, we proposed a mathematical model to explain why its UTR deletion was not continuous, which indicated humans could not eliminate SARS-COV-2 in a short time without robust intervention procedures.

If we cannot eliminate them, should we tame them? Mathematics underpinning the dose effect of virus infection and its application on covid-19 virulence evolution (preprint)

There is a dose effect in the infection process, that is, different initial virus invasion loads will lead to nonlinear changes in infection probability. Experiments already proved that there was a sigmoid functional relationship between virus infection probability and inoculum dose. By means of mathematical simulation of stochastic process, we theoretically demonstrate that there is a sigmoid function relationship between them. At the same time, our model found three factors that influence the severity of infection symptoms, those are virus toxicity, virus invasion dose and host immunity respectively. Therefore, the mortality rate cannot directly reflect the change of virus toxicity, but is the result of the comprehensive action of these three factors. Protective measures such as masks can effectively reduce the severity of infection while reducing the probability of infection. Based on the sigmoid function relationship between virus infection probability and initial virus invasion dose, we deduce that for highly infectious viruses, such as SARS-COV-2, the evolution of its toxicity is closely related to the host population density, and its toxicity will first increase and then decrease with the increase of host population density. That is to say, on the basis of extremely low host population density, increasing population density is beneficial to the development of virus towards strong toxicity. However, this trend is not sustainable, and there is a turning point of population density. Beyond this turning point, increasing population density will be beneficial to the development of virus towards weak toxicity. This theory can well explain the differences of mortality in Covid-19 in different countries. Countries with high population density and extremely low population density often correspond to lower mortality, while countries with population density in the range of 20-100/km2 often have higher mortality. At the same time, we propose that social distance and masks can effectively accelerate the evolution of virus towards low toxicity, so we should not give up simple and effective protection measures while emphasizing vaccination. HighlightsThrough mathematical simulation of random process, we prove the sigmoid function relationship between virus infection probability and initial virus invasion dose theoretically. Our model found three factors that influence the severity of infection symptoms: virus toxicity, virus invasion dose and host immunity. This can help explain why the average infection age was declining as the epidemic went through. With the increase of host population density, virus toxicity will increase at first and then decrease, which will explain the difference of mortality in different population density areas. From the mathematical level, social distance, masks and other protective measures were proved to be positive in promoting the virus evolving into the less toxicity one. Vaccination could also promote virus virulence attenuation.

A Continuous Bayesian Model for the Stimulation COVID-19 Epidemic Dynamics (preprint)

It is of great theoretical and application value to accurately forecast the spreading dynamics of COVID-19 epidemic. We first proposed and established a Bayesian model to predict the epidemic spreading behavior. In this model, the infection probability matrix is estimated according to the individual contact frequency in certain population group. This infection probability matrix is highly correlated with population geographic distribution, population age structure and so on. This model can effectively avoid the prediction malfunction by using the traditional ordinary differential equation methods such as SIR (susceptible, infectious and recovered) model and so on. Meanwhile, it would forecast the epidemic distribution and predict the epidemic hot spots geographically at different time. According to the results revealed by Bayesian model, the effect of population geographical distribution should be considered in the prediction of epidemic situation, and there is no simple derivation relationship between the threshold of group immunity and the virus reproduction number R 0 . If we further consider the virus mutation effect and the antibody attenuation effect, with a large global population spatial distribution, it will be difficult for us to eliminate Covid-19 in a short time even with vaccination endeavor. Covid-19 may exist in human society for a long time, and the epidemic caused by re-infection is characterized by a wild-geometric && low-probability distribution with no epidemic hotspots.