Infection Dynamics Research Articles

Fractional-Order Mathematical Modeling of Methicillin- Resistant Staphylococcus aureus Transmission in Hospitals

This article investigates the environmental contamination and antibiotic exposure effect on the transmission dynamics of the Methicillin-Resistant Staphylococcus aureus (MRSA) model in hospitals using the fractional Adams–Bashforth–Moulton Method (ABMM). This epidemic model simulates the dynamics of patient populations, bacterial contamination, and healthcare worker safety under varying conditions. This model provides critical insights into the interactions between hospital practices, environmental factors, and infection dynamics, demonstrating the importance of symmetry in balancing hospital admission and discharge rates to manage infection spread effectively. The analysis extends to the impact of environmental bacterial density and hospital admission rates on patient colonization. Increasing admission rates introduce more susceptible patients, exacerbating infection spread when bacterial density is high. Conversely, lower admission rates and bacterial density result in a more controlled infection environment. The model further investigates how varying discharge rates influence colonization dynamics, highlighting that effective discharge practices can mitigate infection spread, especially in high-bacterial density scenarios. It must be noted that this model is studied fractionally for the first time. Overall, this model provides critical insights into the interactions between hospital practices, environmental factors, and infection dynamics, offering valuable guidance for infection control strategies and hospital policy formulation. By adjusting fractional order constant (σ) values and analyzing different scenarios, this research aids in understanding and managing bacterial infections in healthcare settings. The proposed method is able to provide the results presented in the figures within this study considering the influence of many factors.

On existence of traveling wave of an HBV infection dynamics model: a novel approach

On existence of traveling wave of an HBV infection dynamics model: a novel approach

Seasonal succession, host associations and biochemical roles of aquatic viruses in a eutrophic lake plagued by cyanobacterial blooms

Viruses are implicated as important biogeochemical mediators and ecological drivers in freshwater ecosystems. However, the dynamics of viruses and host associations over the seasons and blooming periods in eutrophic freshwater ecosystems remain elusive. From the water microbiomes of planktonic biomass from Lake Taihu, a large and eutrophic lake that suffered from yearly Microcystis-dominated harmful algal blooms (HABs) in China, we recovered 41,997 unique viral clusters spanning a wide taxonomic range, including 15,139 Caudovirales clusters targeting bacteria and 1,044 NCLDV clusters targeting eukaryotes. The viral community exhibited distinct seasonal succession driven by microbial communities (mainly Cyanobacteria and Planctomycetes) and environmental factors (mainly nutrients and temperature). Host prediction highlighted a more distinct viral impact on bacteria-driven nitrogen pathways than phosphate cycling through infection. HAB-induced microbial and environmental variations affected viral strategies including lifestyles, host range, and virus-encoded auxiliary metabolic genes (vAMGs) distributions. Viruses infecting Proteobacteria and Actinobacteria showed enhanced lysogenic lifestyle and condensed host ranges during HAB peak in summer, while viruses infecting Bacteroidota selected the opposite strategy. Notably, vAMGs were most abundant before HAB outbreak in spring, compensating for host bacterial metabolism including carbohydrates metabolism, photosynthesis, and phosphate regulation. The elucidated relationship between viruses, host microbes, and bloom-associated environment suggested prominent biochemical roles in the eutrophic freshwater ecosystems.

Stochastic analysis of Ebola infection in small zoonotic niches.

The size of fruit bat colonies ranges from dozens to hundreds of thousands of individuals, depending on the species. While a deterministic modelling approach is appropriate for large colonies, the role of population fluctuations can be all-important for small colonies. From this perspective, we analyse the infection dynamics in small zoonotic niches due to filoviruses, e.g. Ebola. To this end, we perform stochastic numerical simulations and analytical calculations. The inherent stochasticity in ecological processes may play a significant role in driving small populations towards extinction. Here, we reveal that fluctuations can either lead to virus eradication or to sustain infection compared with the deterministic dynamics, depending on the size of the zoonotic niche. Altogether, our findings reveal non-trivial stochastic effects, which can shed light on the infection dynamics in small- and medium-sized bat colonies and help design preventive measures for zoonotic diseases.

The impact of temperature on non-typhoidal Salmonella and Campylobacter infections: an updated systematic review and meta-analysis of epidemiological evidence

As temperatures rise, the transmission and incidence of enteric infections such as those caused by Salmonella and Campylobacter increase. This study aimed to review and synthesise the available evidence on the effects of exposure to ambient temperatures on non-typhoidal Salmonella and Campylobacter infections. A systematic search was conducted for peer-reviewed epidemiological studies published between January 1990 and March 2024, in PubMed, Scopus, Embase, and Web of Science databases. Original observational studies using ecological time-series, case-crossover or case-series study designs reporting the association between ambient temperature and non-typhoidal Salmonella and Campylobacter infections in the general population were included. A random-effects meta-analysis was performed to pool the relative risks (RRs) per 1°C temperature increase, and further meta regression, and subgroup analyses by climate zone, temperature metrics, temporal resolution, lag period, and continent were conducted. The Navigation Guide systematic review methodology framework was used to assess the quality and strength of evidence. The study protocol was registered with the International Prospective Register of Systematic Reviews (PROSPERO). Out of 3472 results, 44 studies were included in this systematic review encompassing over one million cases each of Salmonella and Campylobacter infections. Geographically, the 44 studies covered 27 countries across five continents and most of the studies were from high income countries. The meta-analysis incorporated 23 Salmonella studies (65 effect estimates) and 15 Campylobacter studies (24 effect estimates). For each 1°C rise in temperature, the risk of non-typhoidal Salmonella and Campylobacter infections increased by 5% (RR: 1.05, 95% CI: 1.04-1.06), and 5% (RR: 1.05, 95% CI: 1.04-1.07%), respectively, with varying risks across different climate zones. The overall evidence was evaluated as being of "high" quality, and the strength of the evidence was determined to be "sufficient" for both infections. These findings emphasise the relationship between temperature and the incidence of Salmonella and Campylobacter infections. It is crucial to exercise caution when generalising these findings, given the limited number of studies conducted in low and middle-income countries. Nevertheless, the results demonstrate the importance of implementing focused interventions and adaptive measures, such as the establishment of localised early warning systems and preventive strategies that account for climatic fluctuations. Furthermore, our research emphasises the ongoing need for surveillance and research efforts to monitor and understand the changing dynamics of temperature-related enteric infections in the context of climate change. Australian Research Council Discovery Projects grant (ARC DP200102571) Program.

Seasonal haemosporidian detection in mosquitoes (Diptera: Culicidae) and their interactions with vertebrate hosts in a Mexican cloud forest.

Studies on avian haemosporidians in Mexico and around the world reveal poor knowledge about the vectors that transmit avian haemosporidian parasites and their association with vertebrate hosts. Seasonal variations given by changes in temperature and precipitation have significant repercussions on the dynamics of vector-borne infections. The seasonal dynamics of major haemosporidian parasites and their main vectors are mostly unknown. This study aimed to determine the presence of avian haemosporidians in hematophagous mosquitoes and to characterize mosquito-vertebrate host interactions during a year in a peri-urban cloud forest in Xalapa City, Veracruz, Mexico. We analyzed 12 species and a total of 1170 hematophagous mosquitoes. We found the presence of Plasmodium and Haemoproteus genera in the blood meals of mosquitoes. The highest haemosporidian detection and mosquito richness were in June (dry-warm season), whereas the highest mosquito abundance was in October (humid-warm season). We recorded three new haemosporidian lineages in this study. Analysis of blood meals showed that mosquitoes fed on different vertebrate groups (amphibians, reptiles, birds, and humans). This study contributed to the knowledge about the distribution of hematophagous mosquito-haemosporidian-vertebrate host interactions.

T. Muris Infection Dynamics of a Fresh, Wild Isolate: Is the Established E Isolate Still Relevant?

For decades, parasitic worms such as Trichuris muris have been maintained in laboratory animals, providing insights into host-parasite interactions and host immune responses. The most used T. muris isolate is the E isolate, established in the laboratory in 1954. However, one concern with these model systems is the potential for laboratory-induced selection and therefore changes in host-parasite interactions. To address these concerns, we compare the E isolate with a recently isolated T. muris isolate (M isolate), established from wild house mice (Mus musculus domesticus, Isle of May, UK), in their capacity to infect laboratory mice. High dose infection of C57BL/6 mice revealed that significantly more parasites of the M isolate survived to the adult stage compared to the E isolate. Worm persistence was associated with heightened TNF-α and IL-10 secretion upon parasite-specific re-stimulation, and higher serum IgG1 and IgG2c levels, concomitant with an increase in T-bet+ and ICOS+ CD4+ T effector-memory cells. Differences in host response to the isolates were not as pronounced during low dose infection. Our study highlights the need for regular evaluation of lab-maintained parasite isolates against freshly isolated parasites to understand whether the established lab strains remain relevant model systems for our understanding of parasitic infections.

Exploring the interplay between Porphyromonas gingivalis KGP gingipain, herpes virus MicroRNA-6, and Icp4 transcript in periodontitis: Computational and clinical insights.

Porphyromonas gingivalis, a major pathogen in periodontitis, produces KGP (Lys-gingipain), a cysteine protease that enhances bacterial virulence by promoting tissue invasion and immune evasion. Recent studies highlight microRNAs' role in viral latency, potentially affecting lytic replication through host mechanisms. Herpes virus (HSV) establishes latency via interactions between microRNA-6 (miRH-6) and the ICP4 transcription factor in neural ganglia. This suggests a potential link between periodontitis and HSV-induced latency. This study aims to identify and validate the insilico inhibitory interaction of P. gingivalis KGP with ICP4 transcripts and correlate the presence of viral latency-associated transcript micro-RNA-6 with periodontitis. Computational docking analysis was performed to investigate the potential interaction between ICP4 and KGP gingipain. The binding energy and RMSD ligand values were calculated to determine the interaction's strength. Ten patients with recurrent clinical attachment loss despite conventional therapy were included in the clinical study. Subgingival tissue samples were collected post-phase I therapy, and HSV microRNA-6 presence was detected via polymerase chain reaction and confirmed through gel electrophoresis. Computational docking identified the ICP4-KGP gingipain complex with the lowest binding energy (-288.29 kJ mol^1) and an RMSD ligand of 1.5 Angstroms, indicating strong interaction potential. Gel electrophoresis confirmed miRH-6 presence in all samples. The identification of miRNA-6 in periodontitis patients and the strong interaction potential between P. gingivalis KGP gingipain and ICP4 transcripts indicate a possible link between bacterial virulence factors and viral latency dynamics in periodontal tissues. These results highlight the complex interplay between oral pathogens, viral microRNAs, and host immune responses in periodontitis.

Microbial distribution in Mudbank regions off Alappuzha, South-West coast of India.

The coastal waters of Kerala, in the South Eastern Arabian Sea (SEAS), are unique during the Southwest monsoon season due to the concurrent occurrence of two physical processes, the upwelling and Mudbanks. However, little is known about the viral ecology and activity in a system where upwelling and Mudbanks coexist, though it is generally recognized that microbial assemblages play a vital role in the food web dynamics of marine systems, particularly in upwelling. Water samples were taken from monsoon and pre-monsoon period from three locations, (M1, M2, and M3) off Alappuzha, on the southwest coast of India to examine the viral activity and distribution. The dissolved oxygen levels showed the incursion of hypoxic waters in all the stations during the peak upwelling period. Upwelling signals were prominent in all the stations, but Mudbank and upwelling co-occurred at M2 alone during monsoon. The abundance of viruses ranged from 0.86 to 15.68 × 106 Viral-like Particles (VLPs mL-1) and prokaryotic abundance ranged from 2.73-16.26 × 105 cells mL-1. Viral and prokaryotic abundance was significantly higher in the monsoon compared to pre and late-monsoon. Based on Transmission electron microscopy (TEM) results, the non-tailed viruses constituted the major (43%) proportion of the total viruses in this study region. However, the viral production rates and viral-mediated bacterial mortality were high in the pre-monsoon compared to the monsoon and late-monsoon periods. There was no obvious effect of Mudbanks on viral dynamics and the observed variations in virological and hydrological features were governed mainly by coastal upwelling.

The effects of weather and mobility on respiratory viruses before and during the COVID-19 pandemic

Abstract Background The flu season is caused by a combination of different pathogens, including influenza viruses, which cause the flu, and non-influenza respiratory viruses, that cause common colds or influenza-like illness. These viruses exhibit similar dynamics and, given that outbreaks occur mostly in the winter and there is almost no circulation during the summer, in temperate regions, meteorological conditions have historically been regarded as a principal modulator of their epidemiology. However, after the emergence of SARS-CoV2, in late 2019, the dynamics of these respiratory viruses were strongly perturbed worldwide: some infections displayed near-eradication, while others experienced temporal shifts or occurred “off-season”. This disruption raised questions regarding the dominant role of weather while also providing an unique opportunity to investigate the roles of other determinants in their epidemiological dynamics. Methods Weather, mobility and epidemiological surveillance data was collected for Influenza, RSV, hCOV and hMPV, from Canada and the USA, from 2016 to 2023. Statistical analysis and modeling were employed to test the effects of weather and mobility on viral dynamics, before and during the COVID-19 pandemic. Results Using Beta Regressions, we found that whereas in the pre-COVID-19 pandemic period, weather had a strong effect, in the pandemic period, this effect was strongly reduced post-pandemic with mobility playing a more significant role. Conclusions These results, together with previous studies, dispute the general belief that respiratory viral dynamics are mostly dictated by weather and indicate that behavioral changes resulting from the non-pharmacological interventions implemented to control SARS-CoV2, played a key role. This disruption of past dynamical equilibrium raises important questions regarding the factors that modulate them, particularly in a context of climate change. Key messages • We took advantage of the disruption caused by COVID-19 to study dynamics of other respiratory viruses and to disentangle the effects of weather from those of human behavior. • While before 2020 cold temperatures were highly correlated with incidence, afterwards cold weather was no longer a necessary condition and human mobility became central.

Analysis of control measures inducing the evolution of infectious viral diseases

With the continuation of the infectious disease, pathogens mutate and interact with the resident pathogen. In this paper, we develop an SIR model that considers the evolution of viral virulence under the regulation of different defense strengths. By using infectious disease dynamics and evolutionary adaptive dynamics, we obtain the evolutionary criteria allowing for evolutionary branching and continuously stable strategy across varying intensities of prevention and control measures. Our study finds that robust prevention and control measures can contribute to a rapid evolutionary trend toward higher virulence. We perform a critical function analysis to reveal the evolutionary consequences of trade-offs of arbitrary shape and analyze a diverse range of evolutionary dynamics. Furthermore, we investigate the coexistence of two different strains and construct an evolutionary model to obtain their invasion fitness functions and the evolutionarily singular coalition of the dimorphism. We contemplate the presence of secondary branching on a significantly extended evolutionary time scale. Last, we conduct the numerical simulations to prove our theoretical findings. This reveals the law that the evolutionary state of pathogens is affected by the intensity of prevention and control strategies. The results and methods can be used as references for studying the effects of other factors on virus evolution.

An interdisciplinary approach: Exploring the five dimensions of the tools deployed by EUVABECO

Abstract To effectively address the complexities of vaccination across the EU, EUVABECO’s 14 partners are implementing a comprehensive, interdisciplinary approach. Twelve pilot projects will be implemented from September 2024 to August 2025 in six MS (BE, DE, EL, LV, LU, PL, PT). These pilots will test innovative tools across five domains-medical, social, industrial, modelling and forecasting, and digital-to optimize vaccination outcomes: Medical/Clinical Tool: A decision support system that aids patients and healthcare professionals by providing current, evidence-based information tailored to individuals, thereby facilitating informed vaccination decisions. Social Tool: Data linkage on an individual level, to enhance the targeting and monitoring of vaccine campaigns, and ensuring equitable access to vaccines, especially for vulnerable populations. Industrial Tool: An electronic vaccine leaflet that boosts the flexibility and responsiveness of vaccine distribution by offering dynamic, real-time information in local languages to both healthcare providers and patients - regardless of the source and point of use of the vaccine. Modelling and Forecasting Tool: A disease simulator that models infection dynamics and evaluates the impact of public health interventions, crucial for strategic planning and resource allocation. Digital Tool: A citizen-held digital vaccination card that enables individuals to maintain a comprehensive, accessible record of their own vaccination histories, enhancing continuity of care across borders.

Rich dynamics of a hepatitis C virus infection model with logistic proliferation and time delays

AbstractIn this paper, we study a dynamic model of hepatitis C virus (HCV) infection with density‐dependent proliferation of uninfected and infected hepatocytes and two time delays, which is derived from a three‐dimensional model by the quasi‐steady‐state approximation. The model can exhibit forward bifurcation or backward bifurcation, and an explicit control threshold parameter is obtained for the case of backward bifurcation. It is shown that if the proliferation rate of infected hepatocytes is greater than the proliferation rate of uninfected hepatocytes by a certain amount, it becomes more difficult for the virus to be removed. The model has rich dynamical properties: (i) In some parameter regions, bistability can occur; (ii) both time delays (virus‐to‐cell delay) and (cell‐to‐cell delay) can lead to Hopf bifurcations; (iii) same length of time delays and can lead to at most one stability switch, but different time delays can lead to multiple stability switches. Several sufficient conditions for the global stability of the disease‐free equilibrium and the endemic equilibrium are obtained for both forward and backward bifurcation scenarios. In particular, several sharp results on global stability are obtained. Theoretical and numerical results portray the complexity of viral evolutionary dynamics in chronic HCV‐infected patients.

Living without pain: A 10-year study of congenital insensitivity to pain with anhidrosis.

Congenital Insensitivity to Pain with Anhidrosis (CIPA) is a rare hereditary neuropathy caused by NTRK1 gene mutations, predisposing patients to recurrent infections and chronic wounds. Long-term studies on microbial and clinical outcomes in CIPA are limited. This study presents analysis of infection patterns, antibiotic resistance, and clinical outcomes in CIPA patients. A comprehensive ten-year retrospective cohort study was conducted at Soroka University Medical Center, Beer Sheva, Israel, from January 2014 to January 2023. Electronic medical records were reviewed to identify 63 CIPA patients, all were of consanguineous Bedouin families. Data collection included demographic details, clinical presentations, genetic analysis, documentation of infections, wound sites, hospital duration, and surgical interventions. Staphylococcus aureus, notably methicillin-resistant, dominated, with Gram-negative bacteria common in lower limbs. The study noted reduced extended-spectrum beta-lactamase bacteria and linked demographic factors to infection traits, antibiotic resistance, and surgical needs. This study provides valuable insights into the clinical and microbial patterns of CIPA, highlighting dynamic shifts in microbial compositions and antibiotic resistance profiles over time. Staphylococcus aureus, and Gram-negative bacteria particularly in lower limb infections, pose significant challenges in patient management. The findings underscore the importance of tailored approaches to address evolving microbial profiles and optimize patient care in CIPA. Key Message: This is the largest cohort study on CIPA to date, highlighting the dominance of Staphylococcus aureus, including methicillin-resistant strains, and significant Gram-negative bacterial infections in lower limbs. Contribution to Literature: It draws parallels between infection dynamics in CIPA and diabetic foot ulcers, emphasizing similar challenges due to neuropathy and ischemia, enhancing understanding of infection susceptibility and management in neuropathic conditions. The findings inform clinical practices by detailing infection and resistance patterns, supporting the development of targeted treatment strategies to improve outcomes for CIPA and similar conditions.

Decoding Infectious Disease Dynamics: A Stochastic Approach to Population Heterogeneity and Epidemic Outcomes

Decoding Infectious Disease Dynamics: A Stochastic Approach to Population Heterogeneity and Epidemic Outcomes

Clusters of African countries based on the social contacts and associated socioeconomic indicators relevant to the spread of the epidemic

IntroductionIt is well known that social contact patterns differ from country to country. This variation coincides with significant socioeconomic heterogeneity that complicates the design of effective nonpharmaceutical interventions. This study examined how socioeconomic heterogeneity in selected African countries might be factored in to explain better patterns of social contact mix between countries.MethodsWe used a standardized contact matrix for 32 African countries, estimated in (Prem et al. in PLoS Comput Biol 17(7):e1009098, 2021). We scaled the matrices using an epidemic model from (Röst et al. in Viruses 12(7):708, 2020). We also analyzed aggregated data from the World Bank country website. The data includes 28 variables; social, economic, environmental, institutional, governance, health and well-being, education, gender inequality, and other development-related indicators that describe countries. Principal component analysis was used to visualize socioeconomic similarities between countries and identify the indicators for maximum variation. The (2D)2PCA\\documentclass[12pt]{minimal} \\usepackage{amsmath} \\usepackage{wasysym} \\usepackage{amsfonts} \\usepackage{amssymb} \\usepackage{amsbsy} \\usepackage{mathrsfs} \\usepackage{upgreek} \\setlength{\\oddsidemargin}{-69pt} \\begin{document}$(2D)^{2} PCA$\\end{document} approach was used to reduce the dimension of synthetic contact matrices for each country to avoid the dimensionality curse. Then, hierarchical aggregate clustering was used to identify groups of countries with similar social patterns, taking into account the country’s socioeconomic performance.ResultsOur model yielded four meaningful clusters, each with a few distinguishing features. Social contacts varied between groups, but were generally similar within each set. The socioeconomic performance of the country influenced the clusters.ConclusionsOur results suggest that integrating socioeconomic factors into social contacts can better explain infectious disease transmission dynamics and that similar interventions can be implemented in countries within the cluster

Metagenomic time series reveals a Western English Channel viral community dominated by members with strong seasonal signals.

Marine viruses are key players of ocean biogeochemistry, profoundly influencing microbial community ecology and evolution. Despite their importance, few studies have explored continuous inter-seasonal viral metagenomic time series in marine environments. Viral dynamics are complex, influenced by multiple factors such as host population dynamics and environmental conditions. To disentangle the complexity of viral communities, we developed an unsupervised machine learning framework to classify viral contigs into "chronotypes" based on temporal abundance patterns. Analysing an inter-seasonal monthly time series of surface viral metagenomes from the Western English Channel, we identified chronotypes and compared their functional and evolutionary profiles. Results revealed a consistent annual cycle with steep compositional changes from winter to summer and steadier transitions from summer to winter. Seasonal chronotypes were enriched in potential auxiliary metabolic genes of the ferrochelatases and 2OG-Fe(II) oxygenase orthologous groups compared to non-seasonal types. Chronotypes clustered into four groups based on their correlation profiles with environmental parameters, primarily driven by temperature and nutrients. Viral contigs exhibited a rapid turnover of polymorphisms, akin to Red Queen dynamics. However, within seasonal chronotypes, some sequences exhibited annual polymorphism recurrence, suggesting that a fraction of the seasonal viral populations evolve more slowly. Classification into chronotypes revealed viral genomic signatures linked to temporal patterns, likely reflecting metabolic adaptations to environmental fluctuations and host dynamics. This novel framework enables the identification of long-term trends in viral composition, environmental influences on genomic structure, and potential viral interactions.

Indirect and direct effects of nighttime light on COVID-19 mortality using satellite image mapping approach

The COVID-19 pandemic has highlighted the importance of understanding environmental factors in disease transmission. This study aims to explore the spatial association between nighttime light (NTL) from satellite imagery and COVID-19 mortality. It particularly examines how NTL serves as a pragmatic proxy to estimate human interaction in illuminated nocturnal area, thereby impacting viral transmission dynamics to neighboring areas, which is defined as spillover effect. Analyzing 43,199 COVID-19 deaths from national mortality data during January 2020 and October 2022, satellite-derived NTL data, and various environmental and socio-demographic covariates, we employed the Spatial Durbin Error Model to estimate the direct and indirect effect of NTL on COVID-19 mortality. Higher NTL was initially directly linked to increased COVID-19 mortality but this association diminished over time. The spillover effect also changed: during the early 3rd wave (December 2020 – February 2021), a unit (nanoWatts/sr/cm2) increase in NTL led to a 7.9% increase in neighboring area mortality (p = 0.013). In contrast, in the later 7th wave (July – September 2022), dominated by Omicron, a unit increase in NTL resulted in an 8.9% decrease in mortality in neighboring areas (p = 0.029). The shift from a positive to a negative spillover effect indicates a change in infection dynamics during the pandemic. The study provided a novel approach to assess nighttime human activity and its influence on disease transmission, offering insights for public health strategies utilizing satellite imagery, particularly when direct data collection is impractical while the collection from space is readily available.

Modeling and analysis using piecewise hybrid fractional operator in time scale measure for ebola virus epidemics under Mittag–Leffler kernel

This emerging infectious disease poses one of the most severe threats to public health in these locations, but there are not many reliable therapies yet. In this work, we developed the Ebola virus dynamics and control factors epidemic model with a piecewise hybrid fractional Operator in time scale measure insight of Mittag–Leffler kernel. Patterns and structures that repeat at various scales are the focus of fractal analysis, which has applications in complex systems such as biological ones. Both qualitatively and statistically, a proposed model with the Lipschitz criteria and linear growth is examined, considering positive solutions, boundedness, and uniqueness at equilibrium points with Leray-Schauder results under time scale ideas. The regulation for linear responses approach will be used by Chaos Control to stabilize the system after its equilibrium points. A fractional-order framework with a controlled design will be considered, where solutions are bounded in the feasible domain of relations of different compartments. Ulam–Hyers stability results in the solution are treated when function (constant or rising) for the component of qualitative inquiry in generalized form. The dynamical behaviors of the suggested model are discussed with the Newton polynomial approach used to implement on model in the sense of classical piecewise and Mittag–Leffler kernel at different fractional order values. The model shows that solutions are stable and confined within a feasible range, ensuring reliability. Through detailed simulations, it effectively captures how different interventions and infection rates influence Ebola spread. This fractional-order model enhances understanding of Ebola transmission, providing a strong basis for predicting outbreaks and planning effective control measures, with practical applications for analyzing real-world data.

In silico assessment of CAR macrophages activity against SARS-CoV-2 infection

Macrophage engineering with chimeric antigen receptor is a promising technique first applied to the treatment of tumours and recently suggested as a possible immunotherapeutic route against the COVID-19 disease. Four immunotherapies based on engineered macrophages have been tested in vitro revealing promising, with one of them acting without increasing the cytokines level. We present a mathematical model aimed at the evaluation of both the SARS-CoV-2 virions dynamics and the cytokines production induced, while such newly developed constructs interact with the immune system once administered. The importance of the study lies both in monitoring the dynamics of the infection and in evaluating the cytokine production, since clinical studies show that in critical COVID-19 patients an abnormal cytokines production occurs, a concern to be accounted for in designing appropriate therapeutic strategies. The mathematical model was built in the context of the continuum approach of the mass conservation, while the numerical simulations have been performed introducing parameters deduced from the experiments, using the finite element method. The model simulations allow to analyse and to compare the immune mechanisms underlying the virus dynamics, deepening the investigation for two selected immunotherapies, suggesting that a synergistic work of involved cytokines with phagocytic activity of macrophages occurs. The best SARS-CoV-2 clearance relies not only on the phagocytic capacity of the engineered macrophages, but also on the production of T-lymphocytes, pro- and anti-inflammatory cytokines which in the two cases examined in depth can decrease by 99.7 %, 99.6 % and 69 % respectively, passing from the most effective immunotherapy to the least effective one. This study is the first mathematical model that analyses the dynamics of macrophages engineered to fight the COVID-19, and paves the way for their possible exploitation against such a challenging disease, going beyond existing models involving other immune cells.