Dynamic and classifier-based model SARS-CoV-2 Omicron spillover risk assessment in China

The coronavirus disease 2019 (COVID-19) continues to have a huge impact on health care and economic systems around the world. The first question to ponder is to understand the flow of COVID-19 in the spatial and temporal dimensions. We collected 7 Omicron clusters outbreaks in China since the outbreak of COVID-19 as of August 2022, selected outbreak cases from different Provinces and cities, and collected variable indicators that affect spillover outcomes, such as distance, migration index, PHSM index, daily reported cases number and so on. First, variables influencing spillover outcome events were assessed and analyzed retrospectively by constructing an infectious disease dynamics model and a classifier model, and secondly, the association between explanatory variables and spillover outcome events was constructed by fitting a logistics function. This study incorporates 7 influencing factors and classifies the spillover risk level into 3 levels. If different outbreak sites could be classified into different levels of spillover, it may reduce the pressure of epidemic prevention in some cities due to the lack of a uniform standard, which might be more conducive to achieving the goal of "dynamic zero".

A comparative study on the three calculation methods for reproduction numbers of COVID-19.

Objective: This study uses four COVID-19 outbreaks as examples to calculate and compare merits and demerits, as well as applicational scenarios, of three methods for calculating reproduction numbers. Method: The epidemiological characteristics of the COVID-19 outbreaks are described. Through the definition method, the next-generation matrix-based method, and the epidemic curve and serial interval (SI)-based method, corresponding reproduction numbers were obtained and compared. Results: Reproduction numbers (R eff ), obtained by the definition method of the four regions, are 1.20, 1.14, 1.66, and 1.12. Through the next generation matrix method, in region H R eff = 4.30, 0.44; region P R eff = 6.5, 1.39, 0; region X R eff = 6.82, 1.39, 0; and region Z R eff = 2.99, 0.65. Time-varying reproduction numbers (R t ), which are attained by SI of onset dates, are decreasing with time. Region H reached its highest R t = 2.8 on July 29 and decreased to R t < 1 after August 4; region P reached its highest R t = 5.8 on September 9 and dropped to R t < 1 by September 14; region X had a fluctuation in the R t and R t < 1 after September 22; R t in region Z reached a maximum of 1.8 on September 15 and decreased continuously to R t < 1 on September 19. Conclusion: The reproduction number obtained by the definition method is optimal in the early stage of epidemics with a small number of cases that have clear transmission chains to predict the trend of epidemics accurately. The effective reproduction number R eff , calculated by the next generation matrix, could assess the scale of the epidemic and be used to evaluate the effectiveness of prevention and control measures used in epidemics with a large number of cases. Time-varying reproduction number R t , obtained via epidemic curve and SI, can give a clear picture of the change in transmissibility over time, but the conditions of use are more rigorous, requiring a greater sample size and clear transmission chains to perform the calculation. The rational use of the three methods for reproduction numbers plays a role in the further study of the transmissibility of COVID-19.

A comparative study on the three calculation methods for reproduction numbers of COVID-19

Objective This study uses four COVID-19 outbreaks as examples to calculate and compare merits and demerits, as well as applicational scenarios, of three methods for calculating reproduction numbers. Method The epidemiological characteristics of the COVID-19 outbreaks are described. Through the definition method, the next-generation matrix-based method, and the epidemic curve and serial interval (SI)-based method, corresponding reproduction numbers were obtained and compared. Results Reproduction numbers (Reff), obtained by the definition method of the four regions, are 1.20, 1.14, 1.66, and 1.12. Through the next generation matrix method, in region H Reff = 4.30, 0.44;region P Reff = 6.5, 1.39, 0;region X Reff = 6.82, 1.39, 0;and region Z Reff = 2.99, 0.65. Time-varying reproduction numbers (Rt), which are attained by SI of onset dates, are decreasing with time. Region H reached its highest Rt = 2.8 on July 29 and decreased to Rt < 1 after August 4;region P reached its highest Rt = 5.8 on September 9 and dropped to Rt < 1 by September 14;region X had a fluctuation in the Rt and Rt < 1 after September 22;Rt in region Z reached a maximum of 1.8 on September 15 and decreased continuously to Rt < 1 on September 19. Conclusion The reproduction number obtained by the definition method is optimal in the early stage of epidemics with a small number of cases that have clear transmission chains to predict the trend of epidemics accurately. The effective reproduction number Reff, calculated by the next generation matrix, could assess the scale of the epidemic and be used to evaluate the effectiveness of prevention and control measures used in epidemics with a large number of cases. Time-varying reproduction number Rt, obtained via epidemic curve and SI, can give a clear picture of the change in transmissibility over time, but the conditions of use are more rigorous, requiring a greater sample size and clear transmission chains to perform the calculation. The rational use of the three methods for reproduction numbers plays a role in the further study of the transmissibility of COVID-19.

Role of spike compensatory mutations in the interspecies transmission of SARS-CoV-2.

SARS-CoV-2, the virus responsible for COVID-19 in humans, can efficiently infect a large number of animal species. Like any virus, and particularly RNA viruses, SARS-CoV-2 undergoes mutations during its life cycle some of which bring a selective advantage, leading to the selection of a given lineage. Minks are very susceptible to SARS-CoV-2 and owing to their presence in mass rearing, they make a good model for studying the relative importance of mutations in viral adaptation to host species. Variants, such as the mink-selected SARS-CoV-2 Y453F and D614G or H69del/V70del, Y453F, I692V and M1229I were identified in humans after spreading through densely caged minks. However, not all mink-specific mutations are conserved when the virus infects human populations back. Many questions remain regarding the interspecies evolution of SARS-CoV-2 and the dynamics of transmission leading to the emergence of new variant strains. We compared the human and mink ACE2 receptor structures and their interactions with SARS-CVoV-2 variants. In minks, ACE2 presents a Y34 amino acid instead of the H34 amino acid found in the human ACE2. H34 is essential for the interaction with the Y453 residue of the SARS-CoV-2 Spike protein. The Y453F mink mutation abolishes this conflict. A series of 18 mutations not involved in the direct ACE2 interaction was observed in addition to the Y453F and D614G in 16 different SARS-CoV-2 strains following bidirectional infections between humans and minks. These mutations were not random and were distributed into five different functional groups having an effect on the kinetics of ACE2-RD interaction. The interspecies transmission of SARS-CoV-2 from humans to minks and back to humans, generated specific mutations in each species which improved the affinity for the ACE2 receptor either by direct mutation of the core 453 residue or by associated compensatory mutations.

[SARS-CoV-2 has no origin]. / Le virus SARS-CoV-2 n'a pas « d'origine ¼.

Since the beginning of the COVID-19 pandemic, the question of the origin of this virus has been the subject of a vivid controversy. It is the question of the "origin" itself which is biased. Darwin showed that there is no determined origin to any animal or plant species, simply an evolutionary and selective process. The same is true for viruses, there is no origin, but an evolutionary process. Viruses circulate from host to host, animals or humans. Pandemic viruses are already circulating in humans and evolving before the onset of disease. This evolutionary process then continues and gives rise to successive variants. The solution is not to target the disease or the putative causative agent but rather to address the process of disease emergence.

Title: Le virus SARS-CoV-2 n'a pas « d'origine ¼. Abstract: Depuis le début de la pandémie de COVID-19, la question de l'origine de ce virus fait l'objet d'une vive polémique. C'est la question de « l'origine ¼ qui est biaisée. Darwin a montré qu'il n'y a pas d'origine déterminée à aucune espèce animale ou végétale, simplement un processus évolutif et sélectif. Il en est de même pour les virus, il n'y a pas d'origine, mais un processus évolutif. Les virus circulent d'hôte à hôte, animaux ou humains. Les virus pandémiques circulent déjà chez l'homme et évoluent avant l'apparition d'une maladie. Ce processus évolutif se poursuit et donne naissance à des variants successifs. La solution n'est pas de cibler la maladie ou le possible agent causal, mais plutôt de cibler le processus d'émergence de la maladie lui-même.

Epidemiological Characteristics and Transmissibility of Human Immunodeficiency Virus in Nanning City, China, 2001-2020.

Background: Human immunodeficiency virus (HIV) is a single-stranded RNA virus that can weaken the body's cellular and humoral immunity and is a serious disease without specific drug management and vaccine. This study aimed to evaluate the epidemiologic characteristics and transmissibility of HIV. Methods: Data on HIV follow-up were collected in Nanning City, Guangxi Zhuang Autonomous, China. An HIV transmission dynamics model was built to simulate the transmission of HIV and estimate its transmissibility by comparing the effective reproduction number (Reff ) at different stages: the rapid growth period from January 2001 to March 2005, slow growth period from April 2005 to April 2011, and the plateau from May 2011 to December 2019 of HIV in Nanning City. Results: High-risk areas of HIV prevalence in Nanning City were mainly concentrated in suburbs. Furthermore, high-risk groups were those of older age, with lower income, and lower education levels. The Reff in each stage (rapid growth, slow growth, and plateau) were 2.74, 1.62, and 1.15, respectively, which suggests the transmissibility of HIV in Nanning City has declined and prevention and control measures have achieved significant results. Conclusion: Over the past 20 years, the HIV incidence in Nanning has remained at a relatively high level, but its development trend has been curbed. Transmissibility was reduced from 2.74 to 1.15. Therefore, the prevention and treatment measures in Nanning City have achieved significant improvement.

There is no "origin" to SARS-CoV-2.

Since the beginning of the COVID-19 pandemic in 2020 caused by SARS-CoV-2, the question of the origin of this virus has been a highly debated issue. Debates have been, and are still, very disputed and often violent between the two main hypotheses: a natural origin through the "spillover" model or a laboratory-leak origin. Tenants of these two options are building arguments often based on the discrepancies of the other theory. The main problem is that it is the initial question of the origin itself which is biased. Charles Darwin demonstrated in 1859 that all species are appearing through a process of evolution, adaptation and selection. There is no determined origin to any animal or plant species, simply an evolutionary and selective process in which chance and environment play a key role. The very same is true for viruses. There is no determined origin to viruses, simply also an evolutionary and selective process in which chance and environment play a key role. However, in the case of viruses the process is slightly more complex because the "environment" is another living organism. Pandemic viruses already circulate in humans prior to the emergence of a disease. They are simply not capable of triggering an epidemic yet. They must evolve in-host, i.e. in-humans, for that. The evolutionary process which gave rise to SARS-CoV-2 is still ongoing with regular emergence of novel variants more adapted than the previous ones. The real relevant question is how these viruses can emerge as pandemic viruses and what the society can do to prevent the future emergence of pandemic viruses.

The optimal vaccination strategy to control COVID-19: a modeling study in Wuhan City, China.

BACKGROUND: Reaching optimal vaccination rates is an essential public health strategy to control the coronavirus disease 2019 (COVID-19) pandemic. This study aimed to simulate the optimal vaccination strategy to control the disease by developing an age-specific model based on the current transmission patterns of COVID-19 in Wuhan City, China. METHODS: We collected two indicators of COVID-19, including illness onset data and age of confirmed case in Wuhan City, from December 2, 2019, to March 16, 2020. The reported cases were divided into four age groups: group 1, ≤ 14 years old; group 2, 15 to 44 years old; group 3, 44 to 64 years old; and group 4, ≥ 65 years old. An age-specific susceptible-exposed-symptomatic-asymptomatic-recovered/removed model was developed to estimate the transmissibility and simulate the optimal vaccination strategy. The effective reproduction number (Reff) was used to estimate the transmission interaction in different age groups. RESULTS: A total of 47 722 new cases were reported in Wuhan City from December 2, 2019, to March 16, 2020. Before the travel ban of Wuhan City, the highest transmissibility was observed among age group 2 (Reff = 4.28), followed by group 2 to 3 (Reff = 2.61), and group 2 to 4 (Reff = 1.69). China should vaccinate at least 85% of the total population to interrupt transmission. The priority for controlling transmission should be to vaccinate 5% to 8% of individuals in age group 2 per day (ultimately vaccinated 90% of age group 2), followed by 10% of age group 3 per day (ultimately vaccinated 90% age group 3). However, the optimal vaccination strategy for reducing the disease severity identified individuals ≥ 65 years old as a priority group, followed by those 45-64 years old. CONCLUSIONS: Approximately 85% of the total population (nearly 1.2 billion people) should be vaccinated to build an immune barrier in China to safely consider removing border restrictions. Based on these results, we concluded that 90% of adults aged 15-64 years should first be vaccinated to prevent transmission in China.

Understanding the origin of COVID-19 requires to change the paradigm on zoonotic emergence from the spillover to the circulation model.

While the COVID-19 pandemic continues to spread with currently more than 117 million cumulated cases and 2.6 million deaths worldwide as per March 2021, its origin is still debated. Although several hypotheses have been proposed, there is still no clear explanation about how its causative agent, SARS-CoV-2, emerged in human populations. Today, scientifically-valid facts that deserve to be debated still coexist with unverified statements blurring thus the knowledge on the origin of COVID-19. Our retrospective analysis of scientific data supports the hypothesis that SARS-CoV-2 is indeed a naturally occurring virus. However, the spillover model considered today as the main explanation to zoonotic emergence does not match the virus dynamics and somehow misguided the way researches were conducted. We conclude this review by proposing a change of paradigm and model and introduce the circulation model for explaining the various aspects of the dynamic of SARS-CoV-2 emergence in humans.

Spread of Mink SARS-CoV-2 Variants in Humans: A Model of Sarbecovirus Interspecies Evolution.

The rapid spread of SARS-CoV-2 variants has quickly spanned doubts and the fear about their ability escape vaccine protection. Some of these variants initially identified in caged were also found in humans. The claim that these variants exhibited lower susceptibility to antibody neutralization led to the slaughter of 17 million minks in Denmark. SARS-CoV-2 prevalence tests led to the discovery of infected farmed minks worldwide. In this study, we revisit the issue of the circulation of SARS-CoV-2 variants in minks as a model of sarbecovirus interspecies evolution by: (1) comparing human and mink angiotensin I converting enzyme 2 (ACE2) and neuropilin 1 (NRP-1) receptors; (2) comparing SARS-CoV-2 sequences from humans and minks; (3) analyzing the impact of mutations on the 3D structure of the spike protein; and (4) predicting linear epitope targets for immune response. Mink-selected SARS-CoV-2 variants carrying the Y453F/D614G mutations display an increased affinity for human ACE2 and can escape neutralization by one monoclonal antibody. However, they are unlikely to lose most of the major epitopes predicted to be targets for neutralizing antibodies. We discuss the consequences of these results for the rational use of SARS-CoV-2 vaccines.

Origin of COVID-19: Dismissing the Mojiang mine theory and the laboratory accident narrative.

The origin of SARS-CoV-2 is still the subject of a controversial debate. The natural origin theory is confronted to the laboratory leak theory. The latter is composite and comprises contradictory theories, one being the leak of a naturally occurring virus and the other the leak of a genetically engineered virus. The laboratory leak theory is essentially based on a publication by Rahalkar and Bahulikar in 2020 linking SARS-CoV-2 to the Mojiang mine incident in 2012 during which six miners fell sick and three died. We analyzed the clinical reports. The diagnosis is not that of COVID-19 or SARS. SARS-CoV-2 was not present in the Mojiang mine. We also bring arguments against the laboratory leak narrative.

COVID-19 and emerging infectious diseases: The society is still unprepared for the next pandemic.

The whole human society was caught unprepared by the emergence of SARS-CoV-2 and the related COVID-19 pandemic. This should have not been. We already had on hand all information to organize properly and prevent this emergence. However, this information was never translated into preparedness because the current system of sanitary crises management is not adapted. We keep implementing a medical, symptomatic, post-emergence approach which cannot stop an emerging pandemic. The only preventive action considered is the screening for viruses in the wild but it is not efficient since pandemic viruses do not exist in the wild, and indeed, have never been found. The emergence of a viral pandemic is the result of a double accident: the in-host evolution of the causative virus and its amplification to the epidemic threshold by societal factors. To be prepared the society should target this societal dimension of emerging diseases and organize accordingly. Unfortunately, the society is not organized that way and is still unprepared and vulnerable.

Emergence of Bat-Related Betacoronaviruses: Hazard and Risks.

The current Coronavirus Disease 2019 (COVID-19) pandemic, with more than 111 million reported cases and 2,500,000 deaths worldwide (mortality rate currently estimated at 2.2%), is a stark reminder that coronaviruses (CoV)-induced diseases remain a major threat to humanity. COVID-19 is only the latest case of betacoronavirus (ß-CoV) epidemics/pandemics. In the last 20 years, two deadly CoV epidemics, Severe Acute Respiratory Syndrome (SARS; fatality rate 9.6%) and Middle East Respiratory Syndrome (MERS; fatality rate 34.7%), plus the emergence of HCoV-HKU1 which causes the winter common cold (fatality rate 0.5%), were already a source of public health concern. Betacoronaviruses can also be a threat for livestock, as evidenced by the Swine Acute Diarrhea Syndrome (SADS) epizootic in pigs. These repeated outbreaks of ß-CoV-induced diseases raise the question of the dynamic of propagation of this group of viruses in wildlife and human ecosystems. SARS-CoV, SARS-CoV-2, and HCoV-HKU1 emerged in Asia, strongly suggesting the existence of a regional hot spot for emergence. However, there might be other regional hot spots, as seen with MERS-CoV, which emerged in the Arabian Peninsula. ß-CoVs responsible for human respiratory infections are closely related to bat-borne viruses. Bats are present worldwide and their level of infection with CoVs is very high on all continents. However, there is as yet no evidence of direct bat-to-human coronavirus infection. Transmission of ß-CoV to humans is considered to occur accidentally through contact with susceptible intermediate animal species. This zoonotic emergence is a complex process involving not only bats, wildlife and natural ecosystems, but also many anthropogenic and societal aspects. Here, we try to understand why only few hot spots of ß-CoV emergence have been identified despite worldwide bats and bat-borne ß-CoV distribution. In this work, we analyze and compare the natural and anthropogenic environments associated with the emergence of ß-CoV and outline conserved features likely to create favorable conditions for a new epidemic. We suggest monitoring South and East Africa as well as South America as these regions bring together many of the conditions that could make them future hot spots.

COVID-19: Time to exonerate the pangolin from the transmission of SARS-CoV-2 to humans.

The emergence of COVID-19 has triggered many works aiming at identifying the animal intermediate potentially involved in the transmission of SARS-CoV-2 to humans. The presence of SARS-CoV-2-related viruses in Malayan pangolins, in silico analysis of the ACE2 receptor polymorphism and sequence similarities between the Receptor Binding Domain (RBD) of the spike proteins of pangolin and human Sarbecoviruses led to the proposal of pangolin as intermediary. However, the binding affinity of the pangolin ACE2 receptor for SARS-CoV-2 RBD was later on reported to be low. Here, we provide evidence that the pangolin is not the intermediate animal at the origin of the human pandemic. Moreover, data available do not fit with the spillover model currently proposed for zoonotic emergence which is thus unlikely to account for this outbreak. We propose a different model to explain how SARS-CoV-2 related coronaviruses could have circulated in different species, including humans, before the emergence of COVID-19.