Assessing the impacts of vaccination and viral evolution in contact networks.

A key lesson learned with COVID-19 is that public health measures were very different from country to country. In this study, we provide an analysis of epidemic dynamics using three well-known stochastic network models-small-world networks (Watts-Strogatz), random networks (Erdös-Rényi), and scale-free networks (Barabási-Albert)-to assess the impact of different viral strains, lockdown strategies, and vaccination campaigns. We highlight the significant role of highly connected nodes in the spread of infections, particularly within Barabási-Albert networks. These networks experienced earlier and higher peaks in infection rates, but ultimately had the lowest total number of infections, indicating their rapid transmission dynamics. We also found that intermittent lockdown strategies, particularly those with 7-day intervals, effectively reduce the total number of infections, serving as viable alternatives to prolonged continuous lockdowns. When simulating vaccination campaigns, we observed a bimodal distribution leading to two distinct outcomes: pandemic contraction and pandemic expansion. For WS and ER networks, rapid mass vaccination campaigns significantly reduced infection rates compared to slower campaigns; however, for BA networks, differences between vaccination strategies were minimal. To account for the evolution of a virus into a more transmissible strain, we modeled vaccination scenarios that varied vaccine efficacy against the wild-type virus and noted a decline in this efficacy over time against a second variant. Our results showed that vaccination coverage above 40% significantly flattened infection peaks for the wild-type virus, while at least 80% coverage was required to similarly reduce peaks for variant 2. Furthermore, the effect of vaccine efficacy on reducing the peak of variant 2 infection was minimal. Although vaccination strategies targeting hub nodes in scale-free networks did not substantially reduce the total number of infections, they were effective in increasing the probability of preventing pandemic outbreaks. These findings underscore the need to consider the network structure for effective pandemic control.

Confinement tonicity on epidemic spreading.

Emerging and re-emerging pathogens are latent threats in our society with the risk of killing millions of people worldwide, without forgetting the severe economic and educational backlogs. From COVID-19, we learned that self isolation and quarantine restrictions (confinement) were the main way of protection till availability of vaccines. However, abrupt lifting of social confinement would result in new waves of new infection cases and high death tolls. Here, inspired by how an extracellular solution can make water move into or out of a cell through osmosis, we define confinement tonicity. This can serve as a standalone measurement for the net direction and magnitude of flows between the confined and deconfined susceptible compartments. Numerical results offer insights on the effects of easing quarantine restrictions.

Topological data analysis of antibody dynamics of severe and non-severe patients with COVID-19.

The COVID-19 pandemic is a significant public health threat with unanswered questions regarding the immune system's role in the disease's severity level. Here, based on antibody kinetic data of severe and non-severe COVID-19 patients, topological data analysis (TDA) highlights that severity is not binary. However, there are differences in the shape of antibody responses that further classify COVID-19 patients into non-severe, severe, and intermediate cases of severity. Based on the results of TDA, different mathematical models were developed to represent the dynamics between the different severity groups. The best model was the one with the lowest average value of the Akaike Information Criterion for all groups of patients. Our results suggest that different immune mechanisms drive differences between the severity groups. Further inclusion of different components of the immune system will be central for a holistic way of tackling COVID-19.

A new computational approach, based on images trajectories, to identify the subjacent heterogeneity of sperm to the effects of ketanserin.

The identification of kinematic subpopulations is of paramount importance to understanding the biological nature of the sperm heterogeneity. Nowadays, the data of motility parameters obtained by a computer-assisted sperm analysis (CASA) system has been used as input to distinct algorithms to identify kinematic subpopulations. In contrast, the images of the trajectories were depicted only as examples of the patterns of motility in each subpopulation. Here, python code was written to reconstruct the images of trajectories, from their coordinates, then the images of trajectories were used as input to a machine learning clustering algorithm of classification, and the subpopulations were described statistically by the motility parameters. Finally, the images of trajectories in each subpopulation were displayed in a way we called Pollock plots. Semen samples of boar sperm were treated with distinct concentrations of ketanserin (an antagonist of the 5-HT2 receptor of serotonin) and untreated samples were used as a control. The motility of sperm in each sample was analyzed at 0 and 30 min of incubation. Six subpopulations were found. The subpopulation 2 presented the highest values of velocities at 0 or 30 min. After 30 min of incubation, the ketanserin increased the values of the curvilinear velocity at high concentrations, whereas the linearity and the straight velocity decreased. Our computational model permits better identification of the kinematic subpopulations than the traditional approach and provides insights onto the heterogeneity of the response to ketanserin; thus, it could significantly impact the research on the relationship between sperm heterogeneity-fertility.

Predicting Influenza A Virus Infection in the Lung from Hematological Data with Machine Learning.

The tracking of pathogen burden and host responses with minimally invasive methods during respiratory infections is central for monitoring disease development and guiding treatment decisions. Utilizing a standardized murine model of respiratory influenza A virus (IAV) infection, we developed and tested different supervised machine learning models to predict viral burden and immune response markers, i.e., cytokines and leukocytes in the lung, from hematological data. We performed independently in vivo infection experiments to acquire extensive data for training and testing of the models. We show here that lung viral load, neutrophil counts, cytokines (such as gamma interferon [IFN-γ] and interleukin 6 [IL-6]), and other lung infection markers can be predicted from hematological data. Furthermore, feature analysis of the models showed that blood granulocytes and platelets play a crucial role in prediction and are highly involved in the immune response against IAV. The proposed in silico tools pave the path toward improved tracking and monitoring of influenza virus infections and possibly other respiratory infections based on minimally invasively obtained hematological parameters. IMPORTANCE During the course of respiratory infections such as influenza, we do have a very limited view of immunological indicators to objectively and quantitatively evaluate the outcome of a host. Methods for monitoring immunological markers in a host's lungs are invasive and expensive, and some of them are not feasible to perform. Using machine learning algorithms, we show for the first time that minimally invasively acquired hematological parameters can be used to infer lung viral burden, leukocytes, and cytokines following influenza virus infection in mice. The potential of the framework proposed here consists of a new qualitative vision of the disease processes in the lung compartment as a noninvasive tool.

Distinct immunological and molecular signatures underpinning influenza vaccine responsiveness in the elderly.

Seasonal influenza outbreaks, especially in high-risk groups such as the elderly, represent an important public health problem. Prevailing inadequate efficacy of seasonal vaccines is a crucial bottleneck. Understanding the immunological and molecular mechanisms underpinning differential influenza vaccine responsiveness is essential to improve vaccination strategies. Here we show comprehensive characterization of the immune response of randomly selected elderly participants (≥ 65 years), immunized with the adjuvanted influenza vaccine Fluad. In-depth analyses by serology, multi-parametric flow cytometry, multiplex and transcriptome analysis, coupled to bioinformatics and mathematical modelling, reveal distinguishing immunological and molecular features between responders and non-responders defined by vaccine-induced seroconversion. Non-responders are specifically characterized by multiple suppressive immune mechanisms. The generated comprehensive high dimensional dataset enables the identification of putative mechanisms and nodes responsible for vaccine non-responsiveness independently of confounding age-related effects, with the potential to facilitate development of tailored vaccination strategies for the elderly.

Minimizing the epidemic final size while containing the infected peak prevalence in SIR systems.

Mathematical models are critical to understand the spread of pathogens in a population and evaluate the effectiveness of non-pharmaceutical interventions (NPIs). A plethora of optimal strategies has been recently developed to minimize either the infected peak prevalence ( I P P ) or the epidemic final size ( E F S ). While most of them optimize a simple cost function along a fixed finite-time horizon, no consensus has been reached about how to simultaneously handle the I P P and the E F S , while minimizing the intervention's side effects. In this work, based on a new characterization of the dynamical behaviour of SIR-type models under control actions (including the stability of equilibrium sets in terms of herd immunity), we study how to minimize the E F S while keeping the I P P controlled at any time. A procedure is proposed to tailor NPIs by separating transient from stationary control objectives: the potential benefits of the strategy are illustrated by a detailed analysis and simulation results related to the COVID-19 pandemic.

Computational simulations to dissect the cell immune response dynamics for severe and critical cases of SARS-CoV-2 infection.

BACKGROUND: COVID-19 is a global pandemic leading to high death tolls worldwide day by day. Clinical evidence suggests that COVID-19 patients can be classified as non-severe, severe, and critical cases. In particular, studies have highlighted the relationship between lymphopenia and the severity of the illness, where CD8+ T cells have the lowest levels in critical cases. However, a quantitative understanding of the immune responses in COVID-19 patients is still missing. OBJECTIVES: In this work, we aim to elucidate the key parameters that define the course of the disease deviating from severe to critical cases. The dynamics of different immune cells are taken into account in mechanistic models to elucidate those that contribute to the worsening of the disease. METHODS: Several mathematical models based on ordinary differential equations are proposed to represent data sets of different immune response cells dynamics such as CD8+ T cells, NK cells, and also CD4+ T cells in patients with SARS-CoV-2 infection. Parameter fitting is performed using the differential evolution algorithm. Non-parametric bootstrap approach is introduced to abstract the stochastic environment of the infection. RESULTS: The mathematical model that represents the data more appropriately is considering CD8+ T cell dynamics. This model had a good fit to reported experimental data, and in accordance with values found in the literature. The NK cells and CD4+ T cells did not contribute enough to explain the dynamics of the immune responses. CONCLUSIONS: Our computational results highlight that a low viral clearance rate by CD8+ T cells could lead to the severity of the disease. This deregulated clearance suggests that it is necessary immunomodulatory strategies during the course of the infection to avoid critical states in COVID-19 patients.

Network models to evaluate vaccine strategies towards herd immunity in COVID-19.

Vaccination remains a critical element in the eventual solution to the COVID-19 public health crisis. Many vaccines are already being mass produced and supplied in many countries. However, the COVID-19 vaccination programme will be the biggest in history. Reaching herd immunity will require an unprecedented mass immunisation campaign that will take several months and millions of dollars. Using different network models, COVID-19 pandemic dynamics of different countries can be recapitulated such as in Italy. Stochastic computational simulations highlight that peak epidemic sizes in a population strongly depend on the network structure. Assuming a vaccine efficacy of at least 80% in a mass vaccination program, at least 70% of a given population should be vaccinated to obtain herd immunity, independently of the network structure. If the vaccine efficacy reports lower levels of efficacy in practice, then the coverage of vaccination would be needed to be even higher. Simulations suggest that the "Ring of Vaccination" strategy, vaccinating susceptible contact and contact of contacts, would prevent new waves of COVID -19 meanwhile a high percent of the population is vaccinated.

Serum Concentration of the Phytohormone Abscisic Acid Is Associated With Immune-Regulatory Mediators and Is a Potential Biomarker of Disease Severity in Chronic Obstructive Pulmonary Disease.

COPD and asthma are two distinct but sometimes overlapping diseases exhibiting varying degrees and types of inflammation on different stages of the disease. Although several biomarkers are defined to estimate the inflammatory endotype and stages in these diseases, there is still a need for new markers and potential therapeutic targets. We investigated the levels of a phytohormone, abscisic acid (ABA) and its receptor, LANCL2, in COPD patients and asthmatics. In addition, PPAR-γ that is activated by ABA in a ligand-binding domain-independent manner was also included in the study. In this study, we correlated ABA with COPD-propagating factors to define the possible role of ABA, in terms of immune regulation, inflammation, and disease stages. We collected blood from 101 COPD patients, 52 asthmatics, and 57 controls. Bronchoscopy was performed on five COPD patients and 29 controls. We employed (i) liquid chromatography-tandem mass spectrometry and HPLC to determine the ABA and indoleamine 2,3-dioxygenase levels, respectively; (ii) real-time PCR to quantify the gene expression of LANCL2 and PPAR-γ; (iii) Flow cytometry to quantify adipocytokines; and (iv) immunoturbidimetry and ELISA to measure CRP and cytokines, respectively. Finally, a multinomial regression model was used to predict the probability of using ABA as a biomarker. Blood ABA levels were significantly reduced in COPD patients and asthmatics compared to age- and gender-matched normal controls. However, PPAR-γ was elevated in COPD patients. Intriguingly, ABA was positively correlated with immune-regulatory factors and was negatively correlated with inflammatory markers, in COPD. Of note, ABA was increased in advanced COPD stages. We thereby conclude that ABA might be involved in regulation of COPD pathogenesis and might be regarded as a potential biomarker for COPD stages.

Dynamical characterization of antiviral effects in COVID-19.

Mathematical models describing SARS-CoV-2 dynamics and the corresponding immune responses in patients with COVID-19 can be critical to evaluate possible clinical outcomes of antiviral treatments. In this work, based on the concept of virus spreadability in the host, antiviral effectiveness thresholds are determined to establish whether or not a treatment will be able to clear the infection. In addition, the virus dynamic in the host - including the time-to-peak and the final monotonically decreasing behavior - is characterized as a function of the time to treatment initiation. Simulation results, based on nine patient data, show the potential clinical benefits of a treatment classification according to patient critical parameters. This study is aimed at paving the way for the different antivirals being developed to tackle SARS-CoV-2.

Stability analysis in COVID-19 within-host model with immune response.

The 2019 coronavirus disease (COVID-19) is now a global pandemic. Severe Acute Respiratory Syndrome Coronavirus 2 (SARS-CoV-2) is the causative pathogen of COVID-19. Here, we study an in-host model that highlights the effector T cell response to SARS-CoV-2. The stability of a unique positive equilibrium point, with viral load V * , suggests that the virus may replicate fast enough to overcome T cell response and cause infection. This overcoming is the bifurcation point, near which the orders of magnitude for V * can be sensitive to numerical changes in the parameter values. Our work offers a mathematical insight into how SARS-CoV-2 causes the disease.

Uncovering antibody cross-reaction dynamics in influenza A infections.

MOTIVATION: Influenza viruses are a cause of large outbreaks and pandemics with high death tolls. A key obstacle is that flu vaccines have inconsistent performance, in the best cases up to 60% effectiveness, but it can be as low as 10%. Uncovering the hidden pathways of how antibodies (Abs) induced by one influenza strain are effective against another, cross-reaction, is a central vexation for the design of universal flu vaccines. RESULTS: We conceive a stochastic model that successfully represents the antibody cross-reactive data from mice infected with H3N2 influenza strains and further validation with cross-reaction data of H1N1 strains. Using a High-Performance Computing cluster, several aspects and parameters in the model were tested. Computational simulations highlight that changes in time of infection and the B-cells population are relevant, however, the affinity threshold of B-cells between consecutive infections is a necessary condition for the successful Abs cross-reaction. Our results suggest a 3-D reformulation of the current influenza antibody landscape for the representation and modeling of cross-reactive data. AVAILABILITY AND IMPLEMENTATION: The full code as a testing/simulation platform is freely available here: https://github.com/systemsmedicine/Antibody_cross-reaction_dynamics. SUPPLEMENTARY INFORMATION: Supplementary data are available at Bioinformatics online.

In-host Mathematical Modelling of COVID-19 in Humans.

COVID-19 pandemic has underlined the impact of emergent pathogens as a major threat to human health. The development of quantitative approaches to advance comprehension of the current outbreak is urgently needed to tackle this severe disease. Considering different starting times of infection, mathematical models are proposed to represent SARS-CoV-2 dynamics in infected patients. Based on the target cell limited model, the within-host reproductive number for SARS-CoV-2 is consistent with the broad values of human influenza infection. The best model to fit the data was including immune cell response, which suggests a slow immune response peaking between 5 to 10 days post-onset of symptoms. The model with the eclipse phase, time in a latent phase before becoming productively infected cells, was not supported. Interestingly, model simulations predict that SARS-CoV-2 may replicate very slowly in the first days after infection, and viral load could be below detection levels during the first 4 days post infection. A quantitative comprehension of SARS-CoV-2 dynamics and the estimation of standard parameters of viral infections is the key contribution of this pioneering work. These models can serve for future evaluation of control theoretical approaches to tailor new drugs against COVID-19.

Characterization of SARS-CoV-2 dynamics in the host.

While many epidemiological models were proposed to understand and handle COVID-19 pandemic, too little has been invested to understand human viral replication and the potential use of novel antivirals to tackle the infection. In this work, using a control theoretical approach, validated mathematical models of SARS-CoV-2 in humans are characterized. A complete analysis of the main dynamic characteristic is developed based on the reproduction number. The equilibrium regions of the system are fully characterized, and the stability of such regions is formally established. Mathematical analysis highlights critical conditions to decrease monotonically SARS-CoV-2 in the host, as such conditions are relevant to tailor future antiviral treatments. Simulation results show the aforementioned system characterization.

Pinning Control for the p53-Mdm2 Network Dynamics Regulated by p14ARF.

p53 regulates the cellular response to genotoxic damage and prevents carcinogenic events. Theoretical and experimental studies state that the p53-Mdm2 network constitutes the core module of regulatory interactions activated by cellular stress induced by a variety of signaling pathways. In this paper, a strategy to control the p53-Mdm2 network regulated by p14ARF is developed, based on the pinning control technique, which consists into applying local feedback controllers to a small number of nodes (pinned ones) in the network. Pinned nodes are selected on the basis of their importance level in a topological hierarchy, their degree of connectivity within the network, and the biological role they perform. In this paper, two cases are considered. For the first case, the oscillatory pattern under gamma-radiation is recovered; afterward, as the second case, increased expression of p53 level is taken into account. For both cases, the control law is applied to p14ARF (pinned node based on a virtual leader methodology), and overexpressed Mdm2-mediated p53 degradation condition is considered as carcinogenic initial behavior. The approach in this paper uses a computational algorithm, which opens an alternative path to understand the cellular responses to stress, doing it possible to model and control the gene regulatory network dynamics in two different biological contexts. As the main result of the proposed control technique, the two mentioned desired behaviors are obtained.