Emergence and competition of virus variants in respiratory viral infections.

The emergence of new variants of concern (VOCs) of the SARS-CoV-2 infection is one of the main factors of epidemic progression. Their development can be characterized by three critical stages: virus mutation leading to the appearance of new viable variants; the competition of different variants leading to the production of a sufficiently large number of copies; and infection transmission between individuals and its spreading in the population. The first two stages take place at the individual level (infected individual), while the third one takes place at the population level with possible competition between different variants. This work is devoted to the mathematical modeling of the first two stages of this process: the emergence of new variants and their progression in the epithelial tissue with a possible competition between them. The emergence of new virus variants is modeled with non-local reaction-diffusion equations describing virus evolution and immune escape in the space of genotypes. The conditions of the emergence of new virus variants are determined by the mutation rate, the cross-reactivity of the immune response, and the rates of virus replication and death. Once different variants emerge, they spread in the infected tissue with a certain speed and viral load that can be determined through the parameters of the model. The competition of different variants for uninfected cells leads to the emergence of a single dominant variant and the elimination of the others due to competitive exclusion. The dominant variant is the one with the maximal individual spreading speed. Thus, the emergence of new variants at the individual level is determined by the immune escape and by the virus spreading speed in the infected tissue.

SARS-CoV-2 before and after Omicron: two different viruses and two different diseases?

For the first time in the history of medicine, it has been possible to describe-after a spillover-the evolution of a new human virus spreading in a non-immune population. This allowed not only to observe the subsequent emersion of variants endowed with features providing the virus with an evolutionary advantage, but also the shift of the pathways of virus replication and the acquisition of immunoevasive features. These characteristics had a remarkable influence on the diffusion of the SARS-CoV-2 and on the clinical presentation and prognosis of COVID-19, aspects that are described and commented in this review.

Human Arboviral Infections in Italy: Past, Current, and Future Challenges.

Arboviruses represent a public health concern in many European countries, including Italy, mostly because they can infect humans, causing potentially severe emergent or re-emergent diseases, with epidemic outbreaks and the introduction of endemic circulation of new species previously confined to tropical and sub-tropical regions. In this review, we summarize the Italian epidemiology of arboviral infection over the past 10 years, describing both endemic and imported arboviral infections, vector distribution, and the influence of climate change on vector ecology. Strengthening surveillance systems at a national and international level is highly recommended to be prepared to face potential threats due to arbovirus diffusion.

Rapid System to Detect Variants of SARS-CoV-2 in Nasopharyngeal Swabs.

Currently, the reference method for identifying the presence of variants of SARS-CoV-2 is whole genome sequencing. Although it is less expensive than in the past, it is still time-consuming, and interpreting the results is difficult, requiring staff with specific skills who are not always available in diagnostic laboratories. The test presented in this study aimed to detect, using traditional real-time PCR, the presence of the main variants described for the spike protein of the SARS-CoV-2 genome. The primers and probes were designed to detect the main deletions that characterize the different variants. The amplification targets were deletions in the S gene: 25-27, 69-70, 241-243, and 157-158. In the ORF1a gene, the deletion 3675-3677 was chosen. Some of these mutations can be considered specific variants, while others can be identified by the simultaneous presence of one or more deletions. We avoided using point mutations in order to improve the speed of the test. Our test can help clinical and medical microbiologists quickly recognize the presence of variants in biological samples (particularly nasopharyngeal swabs). The test can also be used to identify variants of the virus that could potentially be more diffusive as well as not responsive to the vaccine.

Conformational Behavior of SARS-Cov-2 Spike Protein Variants: Evolutionary Jumps in Sequence Reverberate in Structural Dynamic Differences.

SARS-CoV-2 has evolved rapidly in the first 3 years of pandemic diffusion. The initial evolution of the virus appeared to proceed through big jumps in sequence changes rather than through the stepwise accumulation of point mutations on already established variants. Here, we examine whether this nonlinear mutational process reverberates in variations of the conformational dynamics of the SARS-CoV-2 Spike protein (S-protein), the first point of contact between the virus and the human host. We run extensive microsecond-scale molecular dynamics simulations of seven distinct variants of the protein in their fully glycosylated state and set out to elucidate possible links between the mutational spectrum of the S-protein and the structural dynamics of the respective variant, at global and local levels. The results reveal that mutation-dependent structural and dynamic modulations mostly consist of increased coordinated motions in variants that acquire stability and in an increased internal flexibility in variants that are less stable. Importantly, a limited number of functionally important substructures (the receptor binding domain, in particular) share the same time of movements in all variants, indicating efficient preorganization for functional regions dedicated to host interactions. Our results support a model in which the internal dynamics of the S-proteins from different strains varies in a way that reflects the observed random and non-stepwise jumps in sequence evolution, while conserving the functionally oriented traits of conformational dynamics necessary to support productive interactions with host receptors.

Mechanics of diffusion-mediated budding and implications for virus replication and infection.

Budding allows virus replication and macromolecular secretion in cells through the formation of a membrane protrusion (bud) that evolves into an envelope. The largest energetic barrier to bud formation is membrane deflection and is trespassed primarily thanks to nucleocapsid-membrane adhesion. Transmembrane proteins (TPs), which later form the virus ligands, are the main promotors of adhesion and can accommodate membrane bending thanks to an induced spontaneous curvature. Adhesive TPs must diffuse across the membrane from remote regions to gather on the bud surface, thus, diffusivity controls the kinetics. This paper proposes a simple model to describe diffusion-mediated budding unravelling important size limitations and size-dependent kinetics. The predicted optimal virion radius, giving the fastest budding, is validated against experiments for coronavirus, HIV, flu and hepatitis. Assuming exponential replication of virions and hereditary size, the model can predict the size distribution of a virus population. This is verified against experiments for SARS-CoV-2. All the above comparisons rely on the premise that budding poses the tightest size constraint. This is true in most cases, as demonstrated in this paper, where the proposed model is extended to describe virus infection via receptor- and clathrin-mediated endocytosis, and via membrane fusion.

Recovering mixtures of fast-diffusing states from short single-particle trajectories.

Single-particle tracking (SPT) directly measures the dynamics of proteins in living cells and is a powerful tool to dissect molecular mechanisms of cellular regulation. Interpretation of SPT with fast-diffusing proteins in mammalian cells, however, is complicated by technical limitations imposed by fast image acquisition. These limitations include short trajectory length due to photobleaching and shallow depth of field, high localization error due to the low photon budget imposed by short integration times, and cell-to-cell variability. To address these issues, we investigated methods inspired by Bayesian nonparametrics to infer distributions of state parameters from SPT data with short trajectories, variable localization precision, and absence of prior knowledge about the number of underlying states. We discuss the advantages and disadvantages of these approaches relative to other frameworks for SPT analysis.

Development and applications of diffusive gradients in thin films for monitoring pharmaceuticals in surface waters.

Pharmaceutical contaminants in surface water have raised significant concerns because of their potential ecological risks. In particular, coronavirus disease 2019 (COVID-19)-related pharmaceuticals can be released to surface water and reduce environmental water quality. Therefore, reliable and robust sampling tools are required for monitoring pharmaceuticals. In this study, passive sampling devices of diffusive gradients in thin films (DGTs) were developed for sampling 35 pharmaceuticals in surface waters. The results demonstrated that hydrophilic-lipophilic balance (HLB) was more suitable for DGT-based devices compared with XAD18 and XDA1 resins. For most pharmaceuticals, the performance of the HLB-DGT devices were independent of pH (5.0-9.0), ionic strength (0.001-0.5 M), and flow velocity (0-400 rpm). The HLB-DGT devices exhibited linear pharmaceutical accumulation for 7 days, and time-weighted average concentrations provided by the HLB-DGT were comparable to those measured by conventional grab sampling. Compared to previous studies, we extended DGT monitoring to include three antiviral drugs used for COVID-19 treatment, which may inspire further exploration on identifying the effects of COVID-19 on ecological and human health.

A generic model for pandemics in networks of communities and the role of vaccination.

The slogan "nobody is safe until everybody is safe" is a dictum to raise awareness that in an interconnected world, pandemics, such as COVID-19, require a global approach. Motivated by the ongoing COVID-19 pandemic, we model here the spread of a virus in interconnected communities and explore different vaccination scenarios, assuming that the efficacy of the vaccination wanes over time. We start with susceptible populations and consider a susceptible-vaccinated-infected-recovered model with unvaccinated ("Bronze"), moderately vaccinated ("Silver"), and very-well-vaccinated ("Gold") communities, connected through different types of networks via a diffusive linear coupling for local spreading. We show that when considering interactions in "Bronze"-"Gold" and "Bronze"-"Silver" communities, the "Bronze" community is driving an increase in infections in the "Silver" and "Gold" communities. This shows a detrimental, unidirectional effect of non-vaccinated to vaccinated communities. Regarding the interactions between "Gold," "Silver," and "Bronze" communities in a network, we find that two factors play a central role: the coupling strength in the dynamics and network density. When considering the spread of a virus in Barabási-Albert networks, infections in "Silver" and "Gold" communities are lower than in "Bronze" communities. We find that the "Gold" communities are the best in keeping their infection levels low. However, a small number of "Bronze" communities are enough to give rise to an increase in infections in moderately and well-vaccinated communities. When studying the spread of a virus in dense Erdos-Rényi and sparse Watts-Strogatz and Barabási-Albert networks, the communities reach the disease-free state in the dense Erdos-Rényi networks, but not in the sparse Watts-Strogatz and Barabási-Albert networks. However, we also find that if all these networks are dense enough, all types of communities reach the disease-free state. We conclude that the presence of a few unvaccinated or partially vaccinated communities in a network can increase significantly the rate of infected population in other communities. This reveals the necessity of a global effort to facilitate access to vaccines for all communities.

Research on the Influence of Information Diffusion on the Transmission of the Novel Coronavirus (COVID-19).

With the rapid development of the Mobile Internet in China, epidemic information is real-time and holographic, and the role of information diffusion in epidemic control is increasingly prominent. At the same time, the publicity of all kinds of big data also provides the possibility to explore the impact of media information diffusion on disease transmission. We explored the mechanism of the influence of information diffusion on the transmission of COVID-19, developed a model of the interaction between information diffusion and disease transmission based on the Susceptible-Infected-Recovered (SIR) model, and conducted an empirical test by using econometric methods. The benchmark result showed that there was a significant negative correlation between the information diffusion and the transmission of COVID-19. The result of robust test showed that the diffusion of both epidemic information and protection information hindered the further transmission of the epidemic. Heterogeneity test results showed that the effect of epidemic information on the suppression of COVID-19 is more significant in cities with weak epidemic control capabilities and higher Internet development levels.

Effect of Human Mobility on the Spatial Spread of Airborne Diseases: An Epidemic Model with Indirect Transmission.

We extended a class of coupled PDE-ODE models for studying the spatial spread of airborne diseases by incorporating human mobility. Human populations are modeled with patches, and a Lagrangian perspective is used to keep track of individuals' places of residence. The movement of pathogens in the air is modeled with linear diffusion and coupled to the SIR dynamics of each human population through an integral of the density of pathogens around the population patches. In the limit of fast diffusion pathogens, the method of matched asymptotic analysis is used to reduce the coupled PDE-ODE model to a nonlinear system of ODEs for the average density of pathogens in the air. The reduced system of ODEs is used to derive the basic reproduction number and the final size relation for the model. Numerical simulations of the full PDE-ODE model and the reduced system of ODEs are used to assess the impact of human mobility, together with the diffusion of pathogens on the dynamics of the disease. Results from the two models are consistent and show that human mobility significantly affects disease dynamics. In addition, we show that an increase in the diffusion rate of pathogen leads to a lower epidemic.

Application of the method of parallel trajectories on modeling the dynamics of COVID-19 third wave.

In this paper, we present a new method for successfully simulating the dynamics of COVID-19, experimentally focusing on the third wave. This method, namely, the Method of Parallel Trajectories (MPT), is based on the recently introduced self-organized diffusion model. According to this method, accurate simulation of the dynamics of the COVID-19 infected population evolution is accomplished by considering not the total data for the infected population, but successive segments of it. By changing the initial conditions with which each segment of the simulation is produced, we achieve close and detailed monitoring of the evolution of the pandemic, providing a tool for evaluating the overall situation and the fine-tuning of the restrictive measures. Finally, the application of the proposed MPT on simulating the pandemic's third wave dynamics in Greece and Italy is presented, verifying the method's effectiveness.

A simple embed over encryption scheme for DICOM images using Bülban Map.

With the onset of any pandemic, the medical image database is bound to increase. These medical images are prone to attack by hackers for their medical data and patient health information. To safeguard these medical images, a new algorithm is proposed. The algorithm involves secretly embedding the patient identification number into the medical image and encrypting the medical image, protecting the patient's identity and the patient's medical condition from hackers. The encryption algorithm involved a single stage of confusion and two stages of diffusion. The confusion operation was performed using the key generated by the Bülban map. The first stage of diffusion was done in the transform domain, using 5/3 transformation. The second diffusion stage was performed in the spatial domain by altering the pixel values using the key. The algorithm was tested on over 30 DICOM (Digital Imaging and Communications in Medicine) images taken from Open Science Framework (OSF), a public database for COVID-19 patients. The algorithm could resist the statistical attacks upon analysis, providing a PSNR of 7.084 dB and entropy of 15.9815 bits for the cipher image. The correlation coefficients for the cipher image were 0.0275, -0.0027, 0.018 in horizontal, vertical and diagonal directions. The keyspace was 2((M-1) ×N)×16, with M the number of rows and N the number of columns in the image. The key sensitivity was high. The test results and metric analysis prove that the algorithm is an effective one for embedding and encryption.

Evidence for a semisolid phase state of aerosols and droplets relevant to the airborne and surface survival of pathogens.

The phase state of respiratory aerosols and droplets has been linked to the humidity-dependent survival of pathogens such as SARS-CoV-2. To inform strategies to mitigate the spread of infectious disease, it is thus necessary to understand the humidity-dependent phase changes associated with the particles in which pathogens are suspended. Here, we study phase changes of levitated aerosols and droplets composed of model respiratory compounds (salt and protein) and growth media (organic-inorganic mixtures commonly used in studies of pathogen survival) with decreasing relative humidity (RH). Efflorescence was suppressed in many particle compositions and thus unlikely to fully account for the humidity-dependent survival of viruses. Rather, we identify organic-based, semisolid phase states that form under equilibrium conditions at intermediate RH (45 to 80%). A higher-protein content causes particles to exist in a semisolid state under a wider range of RH conditions. Diffusion and, thus, disinfection kinetics are expected to be inhibited in these semisolid states. These observations suggest that organic-based, semisolid states are an important consideration to account for the recovery of virus viability at low RH observed in previous studies. We propose a mechanism in which the semisolid phase shields pathogens from inactivation by hindering the diffusion of solutes. This suggests that the exogenous lifetime of pathogens will depend, in part, on the organic composition of the carrier respiratory particle and thus its origin in the respiratory tract. Furthermore, this work highlights the importance of accounting for spatial heterogeneities and time-dependent changes in the properties of aerosols and droplets undergoing evaporation in studies of pathogen viability.

A Simple Queuing Model for Molecular Communications Receivers.

The complexity of molecular communications system, involving a massive number of interacting entities, makes scalability a fundamental property of simulators and modeling tools. A typical scenario is that of targeted drug delivery systems, which makes use of biological nanomachines close to a biological target, able to release molecules in the diseased area. In this paper, we propose a simple but reliable receiver model for diffusion-based molecular communication systems tackling the time needed for analyzing such a system. The proposed model consists of using an equivalent markovian queuing model, which reproduces the aggregate behavior of thousands of receptors spread over the receiver surface. It takes into account not only the fact that the absorption of molecules can occur only through receptors, but also that absorption is not an instantaneous process and may require a significant time during which the receptor is not available to bind to other molecules. Our results, expressed in terms of number of absorbed molecules and average number of busy receptors, demonstrate that the proposed approach is in good agreement with results obtained through particle-based simulations of a large number of receptors, although the time taken for obtaining the results with the proposed model is an order of magnitudes lower than the simulation time. We believe that this model can be the precursor of novel class of models based on similar principles that allow realizing reliable simulations of much larger systems.

Modelling aerosol-based exposure to SARS-CoV-2 by an agent based Monte Carlo method: Risk estimates in a shop and bar.

Present day risk assessment on the spreading of airborne viruses is often based on the classical Wells-Riley model assuming immediate mixing of the aerosol into the studied environment. Here, we improve on this approach and the underlying assumptions by modeling the space-time dependency of the aerosol concentration via a transport equation with a dynamic source term introduced by the infected individual(s). In the present agent-based methodology, we study the viral aerosol inhalation exposure risk in two scenarios including a low/high risk scenario of a "supermarket"/"bar". The model takes into account typical behavioral patterns for determining the rules of motion for the agents. We solve a diffusion model for aerosol concentration in the prescribed environments in order to account for local exposure to aerosol inhalation. We assess the infection risk using the Wells-Riley model formula using a space-time dependent aerosol concentration. The results are compared against the classical Wells-Riley model. The results indicate features that explain individual cases of high risk with repeated sampling of a heterogeneous environment occupied by non-equilibrium concentration clouds. An example is the relative frequency of cases that might be called superspreading events depending on the model parameters. A simple interpretation is that averages of infection risk are often misleading. They also point out and explain the qualitative and quantitative difference between the two cases-shopping is typically safer for a single individual person.

Vaccination Diffusion and Incentive: Empirical Analysis of the US State of Michigan.

Vaccination is the only way to reach herd immunity and help people return to normal life. However, vaccination rollouts may not be as fast as expected in some regions due to individuals' vaccination hesitation. For this reason, in Detroit, Michigan, the city government has offered a $50 prepaid card to people who entice city residents to visit vaccination sites. This study examined vaccination rates in the US using Detroit, Michigan, as the setting. It sought to address two issues. First, we analyzed the vaccination diffusion process to predict whether any region would reach a vaccination completion level that ensures herd immunity. Second, we examined a natural experiment involving a vaccination incentive scheme in Detroit and discovered its causal inference. We collected weekly vaccination data and demographic Census data from the state of Michigan and employed the Bass model to study vaccination diffusion. Also, we used a synthetic control method to evaluate the causal inference of a vaccination incentive scheme utilized in Detroit. The results showed that many Michigan counties-as well as the city of Detroit-would not reach herd immunity given the progress of vaccination efforts. Also, we found that Detroit's incentive scheme indeed increased the weekly vaccination rate by 44.19% for the first dose (from 0.86 to 1.25%) but was ineffective in augmenting the rate of the second dose. The implications are valuable for policy makers to implement vaccination incentive schemes to boost vaccination rates in geographical areas where such rates remain inadequate for achieving herd immunity.

Spatiotemporal evolution analysis of NO2 column density before and after COVID-19 pandemic in Henan province based on SI-APSTE model.

Air pollution is the result of comprehensive evolution of a dynamic and complex system composed of emission sources, topography, meteorology and other environmental factors. The establishment of spatiotemporal evolution model is of great significance for the study of air pollution mechanism, trend prediction, identification of pollution sources and pollution control. In this paper, the air pollution system is described based on cellular automata and restricted agents, and a Swarm Intelligence based Air Pollution SpatioTemporal Evolution (SI-APSTE) model is constructed. Then the spatiotemporal evolution analysis method of air pollution is studied. Taking Henan Province before and after COVID-19 pandemic as an example, the NO2 products of TROPOMI and OMI were analysed based on SI-APSTE model. The tropospheric NO2 Vertical Column Densities (VCDs) distribution characteristics of spatiotemporal variation of Henan province before COVID-19 pandemic were studied. Then the tropospheric NO2 VCDs of TROPOMI was used to study the pandemic period, month-on-month and year-on-year in 18 urban areas of Henan Province. The results show that SI-APSTE model can effectively analyse the spatiotemporal evolution of air pollution by using environmental big data and swarm intelligence, and also can establish a theoretical basis for pollution source identification and trend prediction.

Mechanisms of Airborne Infection via Evaporating and Sedimenting Droplets Produced by Speaking.

For estimating the infection risk from virus-containing airborne droplets, it is crucial to consider the interplay of all relevant physical-chemical effects that affect droplet evaporation and sedimentation times. For droplet radii in the range 70 nm < R < 60 µm, evaporation can be described in the stagnant-flow approximation and is diffusion-limited. Analytical equations are presented for the droplet evaporation rate, the time-dependent droplet size, and the sedimentation time, including evaporation cooling and solute osmotic-pressure effects. Evaporation makes the time for initially large droplets to sediment much longer and thus significantly increases the viral air load. Using recent estimates for SARS-CoV-2 concentrations in sputum and droplet production rates while speaking, a single infected person that constantly speaks without a mouth cover produces a total steady-state air load of more than 104 virions at a given time. In a midsize closed room, this leads to a viral inhalation frequency of at least 2.5 per minute. Low relative humidity, as encountered in airliners and inside buildings in the winter, accelerates evaporation and thus keeps initially larger droplets suspended in air. Typical air-exchange rates decrease the viral air load from droplets with an initial radius larger than 20 µm only moderately.

Criticality in epidemic spread: An application in the case of COVID19 infected population.

Recently, it has been successfully shown that the temporal evolution of the fraction of COVID-19 infected people possesses the same dynamics as the ones demonstrated by a self-organizing diffusion model over a lattice, in the frame of universality. In this brief, the relevant emerging dynamics are further investigated. Evidence that this nonlinear model demonstrates critical dynamics is scrutinized within the frame of the physics of critical phenomena. Additionally, the concept of criticality over the infected population fraction in epidemics (or a pandemic) is introduced and its importance is discussed, highlighting the emergence of the critical slowdown phenomenon. A simple method is proposed for estimating how far away a population is from this "singular" state, by utilizing the theory of critical phenomena. Finally, a dynamic approach applying the self-organized diffusion model is proposed, resulting in more accurate simulations, which can verify the effectiveness of restrictive measures. All the above are supported by real epidemic data case studies.