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.

Forecasting the changes between endemic and epidemic phases of a contagious disease, with the example of COVID-19 (preprint)

Predicting the endemic/epidemic transition during the temporal evolution of a contagious disease. Methods: Defining indicators for detecting the transition endemic/epidemic, with four scalars to be compared, calculated from the daily reported news cases: coefficient of variation, skewness, kurtosis, and entropy. The indicators selected are related to the shape of the empirical distribution of the new cases observed over 14 days. This duration has been chosen to smooth out the effect of weekends when fewer new cases are registered. For finding a forecasting variable, we have used the PCA (principal component analysis), whose first principal component (a linear combination of the selected indicators) explains a large part of the observed variance and can then be used as a predictor of the phenomenon studied (here the occurrence of an epidemic wave). Results: A score has been built from the four proposed indicators using a Principal Component Analysis (PCA), which allows an acceptable level of forecasting performance by giving a realistic retro-predicted date for the rupture of the stationary endemic model corresponding to the entrance in the epidemic exponential growth phase. This score is applied to the retro-prediction of the limits of the different phases of the COVID-19 outbreak in successive endemic/epidemic transitions and three countries, France, India, and Japan. Conclusion: We provided a new forecasting method for predicting an epidemic wave occurring after an endemic phase for a contagious disease.

Vaccine efficacy for COVID-19 outbreak in New York City (preprint)

This article aims to study the COVID-19 data for New York City. We use both the daily number of second does vaccination and the daily number of reported cases for New York City. This article provides a method to combine an epidemic model and such data. We explore the influence of vaccine efficacy on our results.

Modelling of COVID-19 pandemic vis-à-vis some socio-economic factors (preprint)

The impacts of COVID-19 outbreak on socio-economic status of countries across the globe cannot be overemphasized as we examine the role it played in various countries. A lot of people were out of jobs, many households were careful of their spending and a greater social fracture of the population in fourteen different countries has emerged. We considered periods of infection spread during the first and second wave in Organization for Economic Co-operation and Development (OECD) countries and countries in Africa, that is developed and developing countries alongside their social-economic data. We established a mathematical and statistical relationship between Theil and Gini index, then we studied the relationship between the data from epidemiology and socio-economic determinants using several machine learning and deep learning methods. High correlations were observed between some of the socio-economic and epidemiologic parameters and we predicted three of the socio-economic variables in order to validate our results. These result shows a sharp difference between the first and second wave of the pandemic confirming the real dynamics of the spread of the outbreak in several countries and ways by which it was mitigated.

Functional Data Analysis: Transition from Daily Observation of COVID-19 Prevalence in France to Functional Curves (preprint)

In this paper we use the technique of functional data analysis to model daily hospitalized, deceased, ICU cases and return home patient numbers along the COVID-19 outbreak, considered as functional data across different departments in France while our response variables are numbers of vaccinations, deaths, infected, recovered and tests in France. These sets of data were considered before and after vaccination started in France. We used some smoothing techniques to smooth our data set, then analysis based on functional principal components method was performed, clustering using k-means techniques was done to understand the dynamics of the pandemic in different French departments according to their geographical location on France map and we also performed canonical correlations analysis between variables. Finally, we made some predictions to assess the accuracy of the method using functional linear regression models.

Analysis of daily reproduction rates of COVID-19 using Current Health Expenditure as Gross Domestic Product percentage (CHE/GDP) across countries (preprint)

(1) BackgroundImpact and severity of coronavirus pandemic on health infrastructure vary across countries. We examine the role percentage health expenditure plays in various countries in terms of their preparedness and see how countries improved their public health policy in the first and second wave of the coronavirus pandemic; (2) MethodsWe considered the infectious period during the first and second wave of 195 countries with their Current Health Expenditure as Gross Domestic Product percentage (CHE/GDP). Exponential model was used to calculate the slope of the regression line while the ARIMA model was used to calculate the initial autocorrelation slope and also to forecast new cases for both waves. The relationship between epidemiologic and CHE/GDP data was used for processing ordinary least square multivariate modeling and classifying countries into different groups using PC analysis, K-means and Hierarchical clustering; (3) ResultsResults show that some countries with high CHE/GDP improved their public health strategy against virus during the second wave of the pandemic; (4) ConclusionsResults revealed that countries who spend more on health infrastructure improved in the tackling of the pandemic in the second wave as they were worst hit in the first wave. This research will help countries to decide on how to increase their CHE/GDP in order to tackle properly other pandemic waves of the present Covid-19 outbreak and future diseases that may occur. We are also opening up a debate on the crucial role socio-economic determinants play during the exponential phase of the pandemic modelling.

Data Analysis and Forecasting of COVID-19 Pandemic in Kuwait (preprint)

The first COVID 19 case of Kuwait was announced on 24th February, 2020 and the daily new cases increases exponentially since then until May, 2020 when the first wave started to decline. The same exponential dynamics has been observed between January and March, 2021. The forecast of new cases and death recorded daily is crucial so that health experts and citizens can be guided in order to avoid escalation of the pandemic. We propose a deterministic method to predict the basic reproduction number Ro of first and second wave of COVID-19 cases in Kuwait and also to forecast the daily new cases and death of the pandemic in the country. Forecasting has been done using ARIMA model, Exponential smoothing model, Holts method, Prophet forecasting model and machine learning models like log-linear, polynomial and support vector regressions. The results presented aligned with other methods used to predict Ro in first and second waves and the forecasting clearly shows the trend of the pandemic in Kuwait. The deterministic prediction of Ro is a good forecasting tool available during the exponential phase of the contagion, which shows an increasing trend during the beginning of the first and second waves of the pandemic in Kuwait.

What can we learn from COVID-19 data by using epidemic models with unidentified infectious cases? (preprint)

Background: The COVID-19 epidemic, which started in late December 2019 and rapidly spread throughout the world, was accompanied by an unprecedented release of reported case data. Our objective is to propose a fresh look at this data by coupling a phenomenological description to the epidemiological dynamics. Methods: We use a phenomenological model to describe and regularize the data. This model can be matched by a single mathematical model reproducing the epidemiological dynamics with a time-dependent transmission rate. We provide a method to compute this transmission rate and reconstruct the changes in the social interactions between people as well as changes in host-pathogen interactions. This method is applied to the cumulative case data of 8 different geographic areas. Findings: We reconstruct the transmission rate from the data, therefore we are in position to understand the contribution of the dynamical effects of social interactions (contacts between individuals) and the contribution of the dynamics of the epidemic. We deduce from the comparison of several instantaneous reproduction numbers that the social effects are the most important in the dynamic of COVID-19. We obtain an instantaneous reproduction number that stays below $3.5$ from early beginning of the epidemic. Conclusion: The instantaneous reproduction number staying below $3.5$ implies that it is sufficient to vaccinate $71\%$ of the population in each state or country considered in our study. Therefore assuming the vaccines will remain efficient against the new variants, and to be more confident it is sufficient to vaccinate $75-80\%$ to get rid of COVID-19 in each state or country. Funding: This research was funded by the Agence Nationale de la Recherche in France (Project name: MPCUII (PM) and (QG))

SI Epidemic Model Applied to COVID-19 Data in Mainland China (preprint)

The article is devoted to the parameters identification in the SI model. We consider several methods, starting with an exponential fit of the early cumulative data of SARS-CoV2 in mainland China. The present methodology provides a way to compute the parameters at the early stage of the epidemic. Next, we establish an identifiability result. Then we use the Bernoulli-Verhulst model as a phenomenological model to fit the data and derive some results on the parameters identification. The last part of the paper is devoted to some numerical algorithms to fit a daily piecewise constant rate of transmission.

Estimation of Daily Reproduction Rates in COVID-19 Outbreak. (preprint)

(1) Background: The estimation of daily reproduction rates throughout the infectivity period is rarely considered and only their sum Ro is calculated to quantify the level of virulence of an infectious agent; (2) Methods: We give the equation of the discrete dynamics of epidemic growth and we obtain an estimation of the daily reproduction rates, by using a technique of deconvolution of the series of observed new cases of Covid-19; (3) Results: We give both simulation results as well as estimations for several countries for the Covid-19 outbreak; (4) Conclusions: We discuss the role of the noise on the precision of the estimation and we open on perspectives of forecasting methods to predict the distribution of daily reproduction rates along the infectivity period.

SI epidemic model applied to COVID-19 data in mainland China (preprint)

The article is devoted to the parameters identification in the SI model. We consider several methods, starting with an exponential fit of the early cumulative data of Sars-CoV2 in mainland China. The present methodology provides a way to compute the parameters at the early stage of the epidemic. Next, we establish an identifiability result. Then we use the Bernoulli-Verhulst model as a phenomenological model to fit the data and derive some results on the parameters identification. The last part of the paper is devoted to some numerical algorithms to fit a daily piecewise constant rate of transmission.

Factors associated with the spatial heterogeneity of COVID-19 in France: a nationwide ecological study (preprint)

Like in many countries and regions, spread of the COVID 19 pandemic has exhibited important spatial heterogeneity across France, one of the most affected countries so far. To better understand factors associated with incidence, mortality and lethality heterogeneity across the 96 administrative departments of metropolitan France, we thus conducted a geoepidemiological analysis based on publicly available data, using hierarchical ascendant classification (HAC) on principal component analysis (PCA) of multidimensional variables, and multivariate analyses with generalized additive models (GAM). Our results confirm a marked spatial heterogeneity of in-hospital COVID 19 incidence and mortality, following the North East / South West diffusion of the epidemic. The delay elapsed between the first COVID-19 associated death and the onset of the national lockdown on March 17th, 2020, appeared positively associated with in-hospital incidence, mortality and lethality. Mortality was also strongly associated with incidence. Mortality and lethality rates were significantly higher in departments with older population, but they were not significantly associated with the number of intensive-care beds available in 2018. We did not find any significant association between incidence, mortality or lethality rates and incidence of new chloroquine and hydroxychloroquine dispensations in pharmacies either, nor between COVID 19 incidence and climate, nor between economic indicators and in-hospital COVID 19 incidence or mortality. This ecological study highlights the impact of population age structure, epidemic spread and transmission mitigation policies in COVID-19 morbidity or mortality heterogeneity.

Summer COVID-19 third wave: faster high altitude spread suggests high UV adaptation (preprint)

We present spread parameters for first and second waves of the COVID-19 pandemy for USA states, and third wave for 32 regions (19 countries and 13 states of the USA) detected beginning of August 2020. USA first/second wave spreads increase/decrease with population density, are uncorrelated with temperature and median population age. Pooling all 32 regions, third wave spread is slower than for first wave, similar to second wave, and increases with mean altitude (second wave slopes decrease above 900m). Apparently, viruses adapted in spring (second wave) to high temperatures and infecting the young, and in summer (third) waves for spread at altitudes above 1000m. Third wave slopes are not correlated to temperature, so patterns with elevation presumably indicate resistance to relatively high UV regimes. Environmental trends of the COVID-19 pandemy change at incredible rates, making predictions based on classical epidemiological knowledge particularly uncertain.

SARS-CoV-2 and miRNA-like inhibition power (preprint)

(1) Background: RNA viruses and especially coronaviruses could act inside host cells not only by building their own proteins, but also by perturbing the cell metabolism. We show the possibility of miRNA-like inhibitions by the SARS-CoV-2 concerning for example the hemoglobin and type I interferons syntheses, hence highly perturbing oxygen distribution in vital organs and immune response as described by clinicians; (2) Methods: We compare RNA subsequences of SARS-CoV-2 protein S and RNA-dependent RNA polymerase genes to mRNA sequences of beta-globin and type I interferons; (3) Results: RNA subsequences longer than eight nucleotides from SARS-CoV-2 genome could hybridize subsequences of the mRNA of beta-globin and of type I interferons; (4) Conclusions: Beyond viral protein production, Covid-19 might affect vital processes like host oxygen transport and immune response.

Inverted covariate effects for mutated 2nd vs 1st waveCovid-19: high temperature spread biased for young (preprint)

(1) BackgroundHere, we characterize COVID-19 2nd waves, following a study presenting negative associations between 1st wave COVID-19 spread parameters and temperature; (2) MethodsVisual examinations of daily increase in confirmed COVID-19 cases in 124 countries, determined 1st and 2ndwaves in 28 countries; (3) Results1st wave spread rate increases with country mean elevation, temperature, time since wave onset, and median age. Spread rates decrease above 1000m, indicating high UV decrease spread rate. For 2nd waves, associations are opposite: viruses adapted to high temperature and to infect young populations. Earliest 2nd waves started April 5-7 at mutagenic high elevations (Armenia, Algeria). 2nd waves occurred also at warm-to-cold season transition (Argentina, Chile). Spread decreases in most (77%) countries. Death-to-total case ratios decrease during the 2ndwave, also when comparing with the same period for countries where the 1st wave is ongoing. In countries with late 1st wave onset, spread rates fit better 2nd than 1st wave-temperature patterns; In countries with ageing populations (examples: Japan, Sweden, Ukraine), 2nd waves only adapted to spread at higher temperatures, not to infect children. (4) Conclusions1st wave viruses evolved towards lower spread and mortality. 2nd wave mutant COVID-19 strain(s) adapted to higher temperature, infecting children and replace (also in cold conditions) 1st wave COVID-19 strains. Counterintuitively, low spread strains replace high spread strains, rendering prognostics and extrapolations uncertain.