Reproduction Number Research Articles

Insights into the community structure and environmental functions of water hyacinth rhizobiome in urban river ecosystem.

Water hyacinth (WH) is a widespread floating invasive aquatic plant with a prolific reproductive and dispersion rate. With the aid of its root-associated microbes, WH significantly modulates the ecosystem's functioning. Despite their irrevocable importance, the WH microbiome remains unexplored in detail. Here, we present a shotgun sequencing analysis of WH rhizobiome predominant in urban rivers and their surrounding water to unveil the diversity drivers and functional relationship. The core microbiome of the WH mainly consisted of the methane-metabolizing archaebacteria and sulfate-reducing bacteria, which are probably driving the methane and sulfur metabolic flux along the vegetative zone in the water. The beta diversity analysis revealed temporal variations (River WH_2020 vs. WH_2022) (R of 0.8 to 1 and R2 of 0.17 to 0.41), which probably could be attributed to the transient taxa as there was a higher sharing of core bacteria (48%). Also, the WH microbiome significantly differed (R = 0.46 to 1.0 and R2 of 0.18 to 0.52) from its surrounding water. Further, the functional analysis predicted 140 pollutant-degrading enzymes (PDEs) well-implicated in various xenobiotic pollutant degradation, including hydrocarbons, plastics, and aromatic dyes. These PDEs were mapped to bacterial genera such as Hydrogenophaga, Ideonella, Rubrivivax, Dechloromonas, and Thauera, which are well-reported for facilitating the metabolism of xenobiotic compounds. The higher prevalence of metal and biocide resistance genes further highlighted the persistence of resistant microbes assisting WH in bioremediation applications.

Evolution of COVID-19 dynamics in Guangdong Province, China: an endemic-epidemic modeling study

BackgroundFrom January 2020 to June 2022, strict interventions against COVID-19 were implemented in Guangdong Province, China. However, the evolution of COVID-19 dynamics remained unclear in this period.ObjectivesThis study aims to investigate the evolution of within- and between-city COVID-19 dynamics in Guangdong, specifically during the implementation of rigorous prevention and control measures. The intent is to glean valuable lessons that can be applied to refine and optimize targeted interventions for future crises.MethodsData of COVID-19 cases and synchronous interventions from January 2020 to June 2022 in Guangdong Province were collected. The epidemiological characteristics were described, and the effective reproduction number (Rt) was estimated using a sequential Bayesian method. Endemic-epidemic multivariate time-series model was employed to quantitatively analyze the spatiotemporal component values and variations, to identify the evolution of within- and between-city COVID-19 dynamics.ResultsThe incidence of COVID-19 in Guangdong Province was 12.6/100,000 population (15,989 cases) from January 2020 to June 2022. The Rt predominantly remained below 1 and increased to a peak of 1.39 in Stage 5. As for the evolution of variations during the study period, there were more spatiotemporal components in stage 1 and 5. All components were fewer from Stage 2 to Stage 4. Results from the endemic-epidemic multivariate time-series model revealed a strong follow-up impact from previous infections in Dongguan, Guangzhou and Zhanjiang, with autoregressive components of 0.48, 0.45 and 0.36, respectively. Local risk was relatively high in Yunfu, Shanwei and Shenzhen, with endemic components of 1.17, 1.04 and 0.71, respectively. The impact of the epidemic on the neighboring regions was significant in Zhanjiang, Shenzhen and Zhuhai, with epidemic components of 2.14, 1.92, and 1.89, respectively.ConclusionThe findings indicate the presence of spatiotemporal variation of COVID-19 in Guangdong Province, even with the implementation of strict interventions. It’s significant to prevent transmissions within cities with dense population. Preventing spatial transmissions between cities is necessary when the epidemic is severe. To better cope with future crises, interventions including vaccination, medical resource allocation and coordinated non-pharmaceutical interventions were suggested.

Complex dynamics of an SIHR epidemic model with variable hospitalization rate depending on unoccupied hospital beds

In this paper, we propose an susceptible-infectious-hospitalized-recovered (SIHR) epidemic model with a nonlinear hospitalization rate depending on the number of unoccupied hospital beds. Note that the number of all hospital beds is used as a measure of all available medical resources. The basic reproduction number is calculated using the next-generation matrix method. We analyze the existence of endemic equilibria and discuss the global stability of the disease-free equilibrium. Existence and stability of endemic equilibria indicate possible occurrences of bifurcations. We confirm the appearance of backward bifurcation, saddle–node bifurcation, Hopf bifurcation, and Bogdanov–Takens bifurcation using normal form theory and central manifold theory. Numerical simulations show that the dynamic behavior of the model undergoes a transition from forward bifurcation to backward bifurcation and saddle–node bifurcation when the number of total hospital beds is reduced. Our findings suggest that when the number of total hospital beds falls below a threshold, backward bifurcation will occur, meaning that the disease cannot be eliminated even if the basic reproduction number is below unity. Therefore, the number of hospital beds should be increased beyond the bed threshold during an outbreak of a disease, which has important implications for disease control.

A deterministic Analysis of an Age-Gender structured model for malaria transmission dynamics

Objectives: Children below five years and women especially pregnant women are at high risk of malaria infection and death due to their weak immunity and exposition to mosquitoes. Different studies have been undertaken considering only the age structured model and other factors but did not take into account the gender. The objective of this work is to develop and analyze the malaria transmission model including this structure, to contribute on the existing measures and mechanisms to eradicate malaria in Rwanda.Methods: The dynamic malaria transmission model considering age and gender structure was developed and analyzed. To study the dynamics of disease, the basic reproduction number was analytically computed and numerically estimated and the normalized forward sensitivity index was used to highlight its sensitive parameters.Results: The most positive sensitive parameters in the model are the force of infection and infection rate for vectors and Infection rate for female of 5 years and above. The most negative sensitive parameters are the per capita death rate for vectors and humans.Conclusion: To control the spread of malaria in Rwanda, the biting and infection rate should be decreased.Therefore, women must comply with government measures against malaria and educate children about it.

Optimal control analysis on the spread of COVID-19: Impact of contact transmission and environmental contamination

The study investigates the intricate dynamics of SARS-CoV-2 transmission, with a particular focus on both close-contact interactions and environmental factors. Using advanced mathematical modeling and epidemiological analysis, explored the effects of these transmission pathways on the spread of COVID-19. The equilibrium points for both the disease-free and endemic states calculated and evaluated their global stability. Additionally, the basic reproduction number (R0) is derived to quantify the transmission potential of the virus.To ensure model accuracy, numerical simulations are performed using MATLAB, utilizing daily COVID-19 case data from India. Parameter values are sourced from existing literature, with certain parameters estimated through fitting the model to observed data. Crucially, the model incorporates environmental transmission factors, such as surface contamination and airborne spread. The inclusion of these factors provides a more comprehensive understanding of the virus’s spread, demonstrating the importance of interventions like use of face masks, environmental sanitization, vaccine efficacy, availability of treatment resources underappreciated when focusing solely on direct human contact. A sensitivity analysis is conducted to assess the impact of different parameters on R0, with results visualized through heat maps to identify the most influential factors.Furthermore, Pontryagin’s maximum principle is employed to develop an optimal control model, enabling the formulation of effective intervention strategies. By analysing both interpersonal and environmental transmission mechanisms, this study offers a more holistic framework for understanding SARS-CoV-2 transmission. The insights gained are critical for informing public health strategies, emphasizing the necessity of addressing both direct contact and environmental sources of infection to more effectively manage current and future outbreaks.

Epidemiologic Quantities for Monkeypox Virus Clade I from Historical Data with Implications for Current Outbreaks, Democratic Republic of the Congo.

We used published data from outbreak investigations of monkeypox virus clade I in the Democratic Republic of the Congo to estimate the distributions of critical epidemiological parameters. We estimated a mean incubation period of 9.9 days (95% credible interval [CrI] 8.5-11.5 days) and a mean generation time of 17.2 days (95% CrI 14.1-20.9 days) or 11.3 days (95% CrI 9.4-14.0 days), depending on the considered dataset. Presymptomatic transmission was limited. Those estimates suggest generally slower transmission dynamics in clade I than in clade IIb. The time-varying reproduction number for clade I in the Democratic Republic of the Congo was estimated to be below the epidemic threshold in the first half of 2024. However, in the South Kivu Province, where the newly identified subclade Ib has been associated with sustained human-to-human transmission, we estimated an effective reproduction number above the epidemic threshold (95% CrI 0.96-1.27).

Ecoepidemic modelling and dynamics of alveolar echinococcosis transmission

Alveolar echinococcosis, transmitted between definitive hosts and intermediate hosts via predation, threatens the health of humans and causes great economic losses in western China. In order to explore the transmission mechanism of this disease, an eco-epidemiological lifecycle model is formulated to illustrate interactions between two hosts. The basic and demographic reproduction numbers are developed to characterize the stability of the disease-free and endemic equilibria as well as bifurcation dynamics. The existence of forward bifurcation and Hopf bifurcation are confirmed and are used to explain the threshold transmission dynamics. Numerical simulations and bifurcation diagrams are also presented to depict rich dynamics of the model. Numerical analysis suggests that improving the control rate of voles will reduce the risk of transmission, while the high predation rate of foxes may also lead to a lower transmission risk, which is different from the predictions of previous studies. The evaluation of three control measures on voles implies that, when the fox’s predation rate is low (high), the chemical (integrated) control will be more effective.

Minimal wave speed for a two-group epidemic model with nonlocal dispersal and spatial-temporal delay

In this paper, a two-group SIR reaction-diffusion epidemic model with nonlocal dispersal and spatial-temporal delay based on within-group and inter-group transmission mechanisms is investigated. The basic reproduction number R0 is calculated using the method of next-generation matrix. The critical wave speed cm* is determined by analyzing the distribution of roots of the characteristic equation. When R0>1 and wave speed c⩾cm*, the existence of traveling waves connecting disease-free and endemic steady states is obtained by constructing sub- and super-solutions and a Lyapunov functional, and applying Schauder’s fixed-point theorem and a limit argument. When R0>1 and 0<c<cm*, the nonexistence of traveling waves connecting disease-free and endemic steady states is proven by contradiction and two-sided Laplace transform. This indicates that the critical wave speed cm* is exactly the minimum wave speed. Numerical simulations are carried out to illustrate theoretical results. The dependence of the minimal speed cm* on time delay, diffusion rates and contact rates is discussed, showing that the longer the latent period and the lower the diffusion rates of infected individuals and the inter-group transmission rates between groups, the slower the spread of disease.

Inverse uncertainty quantification for stochastic systems by resampling. Applications to modeling of alcohol consumption and infection by HIV

A random differential equation, or stochastic differential equation with parametric uncertainty, is a classical differential equation whose input values (coefficients, initial conditions, etc.) are random variables. Given data, the probability distributions of the input random parameters must be appropriately inferred, before proceeding to simulate the model’s output. This task is called inverse uncertainty quantification. In this paper, the goal is to study the applicability of the Bayesian bootstrap to draw inferences on the posterior distributions of the parameters, by resampling the residuals of the deterministic least-squares optimization with Dirichlet weights. The method is based on repeated deterministic calibrations. Thus, to alleviate the curse of dimensionality, the technique may be combined with the principle of maximum entropy for densities, when there are some parameters that are not optimized deterministically. For illustration of the methodology, two case studies on important health topics are conducted, with stochastic fitting to data. The first one, on past alcohol consumption in Spain, taking social contagion into account. The second one, on HIV evolution considering CD4+ T cells and viral load, with a patient in clinical follow-up. All these applied models are built from a compartmental viewpoint, with a randomized basic reproduction number that controls the long-term behavior of the system.

A clustered-LPSEIRS malware propagation model in complex networks

In this paper, we present a new malware propagation model that integrates epidemic spread, clustering, and link prediction techniques, tailored for complex network networks. Our model is based on the clustered-link prediction-susceptible-exposed-infected-recovered (clustered-LPSEIRS) epidemic model, which simulates malware dissemination within the network. Our findings reveal a significant decrease in the rate of malware spread compared to the traditional SEIR model, with this enhancement in containment attributed to the integration of clustering and link prediction methods. We also compute the basic reproduction ratio (R0\\documentclass[12pt]{minimal} \\usepackage{amsmath} \\usepackage{wasysym} \\usepackage{amsfonts} \\usepackage{amssymb} \\usepackage{amsbsy} \\usepackage{mathrsfs} \\usepackage{upgreek} \\setlength{\\oddsidemargin}{-69pt} \\begin{document}$$R_{0}$$\\end{document}) for our model, providing insights into the potential ramifications of malware within the network. By examining parameter variations, we enhance our understanding of the model's behavior under diverse scenarios. Additionally, we assess the influence of clustering and link prediction on mitigating malware spread, emphasizing its effectiveness in diminishing the overall impact.

Analyzing steady-state equilibria and bifurcations in a time-delayed SIR epidemic model featuring Crowley-Martin incidence and Holling type II treatment rates

This article presents a time-delayed SIR epidemiological model that has been quantitatively examined. The model incorporates a logistic growth function for the susceptible population, a Crowley-Martin type incidence, and Holling type II treatment rates. We investigated two separate time delays. The first delay refers to the rate at which new infections occur, allowing us to evaluate the impact of the latent period. The second delay relates to the rate of treatment for those who have contracted the infection, which allows us to examine the consequences of postponed access to therapy. The investigation of the steady-state behavior of the model emphasizes two equilibria, namely, the infection-free equilibrium and the endemic equilibrium. The determination of critical values involves the use of the fundamental reproduction number, denoted R0, which serves as a predictive measure to determine the potential elimination of a disease within a specific population. Using the fundamental reproduction number, it can be shown that infection-free equilibrium exhibits local asymptotic stability when the value of R0 is less than 1. In contrast, when R0 exceeds 1, the infection-free equilibrium becomes unstable in the context of the time-delayed system. Furthermore, an analysis of the steady-state dynamics of the endemic equilibrium indicates the appearance of oscillations and periodic solutions with the Hopf bifurcation for all feasible combinations of two-time delays as the bifurcated parameter. In sensitivity analysis, a sensitivity index is utilized to evaluate the relative modification in the fundamental reproduction number caused by each parameter. In summary, numerical simulations are employed to offer empirical evidence for the theoretical findings.

Serologic Evidence for Early SARS-CoV-2 Circulation in Lima, Peru, 2020.

During early 2021, Peru had the highest COVID-19-associated per-capita mortality rate. Socioeconomic inequality, insufficiently prepared healthcare, and surveillance systems are factors explaining the mortality rate, which can be severely worsened by early undetected SARS-CoV-2 circulation. We tested 1,441 individuals with fever sampled during August 2019-May 2021, several months before the first SARS-CoV-2 seroprevalence study available so far in Lima, Peru, for SARS-CoV-2-specific antibodies. The testing algorithm included a chemiluminescence immunoassay and surrogate virus neutralization test. Early positive samples (N = 24) from January-March 2020 were further tested using a plaque-reduction neutralization test (PRNT) and avidity test against the SARS-CoV-2 spike and nucleoprotein. None of the early samples were PRNT-confirmed, in contrast to 81.8% (18/22) of a subsample from April 2020 onward (Fisher exact test; P <0.0001). Therefore, we excluded non-PRNT-confirmed samples from subsequent analyses. The SARS-CoV-2 antibody detection rate was 0.9% in mid-April 2020 (1/104; 95% CI: 0.1-5.8%), suggesting viral circulation in early-middle March 2020, consistent with the first molecular detection of SARS-CoV-2 in Peru on March 2020. Mean avidity increase of 62-77% to 81-94% from all PRNT-confirmed SARS-CoV-2-positive samples during early 2020 were consistent with onset of SARS-CoV-2 circulation during late February/March 2020. Early circulation was also confirmed in a susceptible, exposed, infected, and recovered mathematical model that calculated an effective reproduction number >1 during February-March 2020. Early introduction of SARS-CoV-2 thus contributed to the high COVID-19 mortality rate in Peru. Emphasizing the role of diagnostic confirmation in understanding the pandemic's trajectory, this study highlights the importance of early detection and accurate testing in managing infectious disease outbreaks.

Infection with pathogenic fungi is a major threat to the mass production of the parasitoid wasp Habrobracon hebetor: 20 generations under surveillance

Pathogen infection is one of the most important challenges threatening the success of mass rearing of natural enemies either in insectaries or in augmentative biological control programs. In this investigation, we studied the biological and population growth parameters of a naturally infected colony of the parasitoid wasp Habrobracon hebetor (Say) (Hymenoptera: Braconidae) over 20 generations (G1inf − G20inf) of rearing on Ephestia kuehniella (Zeller) (Pyralidae: Lepidoptera) under laboratory conditions. Based on morphological characteristics and ITS sequences, two types of fungal colonies were isolated from the diseased females and identified as Thermothielavioides terrestris and Aspergillus flavus. The results showed that although no significant differences were found in the female longevity and fecundity of H. hebetor on E. kuehniella up to G15inf, the duration of the immature period was extended as the number of generations increased. In addition, the intrinsic rate of increase (r) and finite rate of increase (λ) had no significant differences up to G8inf. The net reproductive rate (R0) of G15inf had no significant differences with G2inf, G8inf, and G10inf. On the other hand, in G15inf, R0 was significantly higher than G4inf, G6inf, and G20inf. The findings underscore the negative impact of fungal infection on population growth parameters compared with uninfected population. This study showed that naturally fungal infection of the parasitoid affected the population growth and life table parameters through mass rearing, compared and discussed with the healthy parasitoid.

Automated counting and classifying Daphnia magna using machine vision

Daphnia magna (D. magna) is a model organism widely used in aquatic ecotoxicology research due to its sensitivity to environmental changes. The survival and reproduction rates of D. magna are easily affected by toxic environments. However, their small size, fragility, and transparency, especially in neonate stages, make them challenging to count accurately. Traditionally, counting adult and neonate D. magna relies on manual separation and visual observation, which is not only tedious but also prone to inaccuracies. Previous attempts to aid counting with optical sensors have faced issues such as inducing stress damage due to vertical movement and an inability to distinguish between adults and neonates. With the advancement of deep learning technologies, our study employs a simple light source culture device and utilizes the Mask2Former model to analyze D. magna against the background. Additionally, the U-Net model is used for comparative analysis. We also applied OpenCV technology for automatic counting of adult and neonate D. magna. The model's results were compared against manual counting performed by experienced technicians. Our approach achieves an average relative accuracy of 99.72 % for adult D. magna and 98.30 % for neonate. This method not only enhances counting accuracy but also provides a fast and reliable technique for studying the survival and reproduction rates of D. magna as a model organism.

Ecotoxicity of alkaline residue from the pulp and paper industry to soil faunal organisms

The increased production and expanding demand for cellulosic products have required companies to provide solutions for the proper disposal of generated residues without compromising soil quality. In this context, the current research aimed at evaluating the ecotoxicological impacts of dregs application in subtropical soils. The experiments were carried out with the earthworm Eisenia andrei, the springtail Folsomia candida, and the potworm Enchytraeus crypticus in two subtropical soils (Neosol and Cambisol) collected in the southern region of Brazil. The considerable differences in texture and organic matter content motivated the choosing of these soils. The reproductive rate of all organisms was influenced, with varying EC50 values depending on the soil type. However, more pronounced effects were observed for Neosol. The springtail F. candida proved to be the most sensitive to contamination (EC50 = 5.8 g kg−1 for Neosol and 48.5 g kg−1 for Cambisol), followed by the earthworm E. andrei (EC50 = 62.4 g kg−1 for Neosol and 129.5 g kg−1 for Cambisol) and the potworm E. crypticus (EC50 = 67.2 g kg−1 for Neosol and 230.4 g kg−1 for Cambisol). As a result, while dregs can be used to correct soil acidity, they have been shown to have negative effects on important organisms responsible for functions critical to maintaining ecosystem quality.

Biological and population parameters of Telenomus remus and Trichogramma atopovirilia as biological control agents for Spodoptera frugiperda

Maize production faces a significant threat from Spodoptera frugiperda (J. E. Smith) worldwide; historically, Trichogramma pretiosum Riley has served as the primary augmentative egg parasitoid for its biological control. However, since 2004, Telenomus remus Nixon has emerged as a viable global alternative. On a regional scale, in Mexico, Trichogramma atopovirilia (Oatman & Platner) was theorized to outperform T. pretiosum, proving superior in both laboratory and field conditions in 2020. To further the field efficacy of the augmentative use of parasitoids as biological agents of S. frugiperda, we conducted comparisons of biological and population parameters under laboratory-controlled conditions to determine whether T. remus would be more effective than T. atopovirilia, the best Trichogramma parasitoid in Mexico for S. frugiperda. We found that the former parasitoid had statistically significant longer female and male longevity, as well as a higher proportion of female offspring, and greater numerical values in net reproduction rate, intrinsic rate of increase, and finite rate of increase, but no differences were found in the number of days required for both parasitoids to develop from egg to female or egg to male, as well as in the mean generation time and doubling time. Under the conditions of these trials, T. remus had a 47.02% greater intrinsic rate of increase (rm) and may thus be a more effective biological control agent for S. frugiperda than T. atopovirilia. However, these biological and population parameter comparisons are the first execution worldwide; further studies are needed to validate these promising T. remus results.

The antidepressant, sertraline, impacts growth and reproduction in the benthic deposit feeder, Tubifex tubifex

Among emerging contaminants, pharmaceuticals are considered one of the most pertinent substances that may threaten aquatic ecosystems. Pharmaceuticals are designed to be directed at specific metabolic- and molecular pathways. Thus, they are assumed to be still biologically active when entering the ecosystem and may result in unpremeditated impacts on non-target organisms. One of the most widely used selective serotonin reuptake inhibitors, sertraline (an antidepressant), is regularly found in aquatic environments. However, knowledge about the effects, and in particular, of sediment-associated sertraline in benthic invertebrates is limited. We examined the impacts of chronic exposure (28 d) to sediment-associated sertraline (3.3, 33, 330 μg/g dw sed.) on survival, growth and reproduction in the deposit-feeding oligochaete, Tubifex tubifex. Sertraline significantly decreased T. tubifex survival and growth. Worms exposed to high sertraline concentrations (330 μg/g) had a lower growth rate and reproduction, as indicated by a significantly lower number of cumulated cocoons. Worms exposed to an environmentally relevant concentration (3.3 μg/g) decreased growth but maintained a reproduction rate similar to that of the control. The implications are that adult worms exposed to high sertraline concentrations presumably required more energy for maintenance and detoxification, thereby reducing available energy for reproduction and growth. This represents a trade-off between survival, reproduction and growth. In contrast, T. tubifex exposed to environmentally relevant concentrations allocated more energy to reproduction by slightly increasing the number of cocoons produced and reducing growth. However, the quantity and quality of offspring may be impacted as we observed fewer juveniles in the environmentally relevant treatment than in the control. Overall, the results indicate that sediment-associated sertraline is bioavailable and negatively impacts T. tubifex survival, growth, and reproduction even at environmentally relevant concentrations.

Spotlight hunting of invasive deer (Axis axis) selects for individuals with greater reproductive value in a protected area of north-eastern Argentina

Spotlight hunting of invasive deer (Axis axis) selects for individuals with greater reproductive value in a protected area of north-eastern Argentina

Mathematical analysis of Ebola considering transmission at treatment centres and survivor relapse using fractal-fractional Caputo derivatives in Uganda

In this article, we seek to formulate a robust mathematical model to study the Ebola disease through fractal-fractional operators. The study thus incorporates the transmission rate in the treatment centers and the relapse rate, since the Ebola virus persists or mostly hides in the immunologically protected sites of survivors. The Ebola virus disease (EVD) is one of the infectious diseases that has recorded a high death rate in countries where it is endemic, and Uganda is not an exception. The world at large has suffered from this deadly disease since 1976 when it was declared epidemic by the World Health Organization. The study employed fractal-fractional operators to identify the epidemiological patterns of EVD, especially in treatment centers and relapse. Memory loss and relapse are mostly observed in EVD survivors and this justifies the use of fractional operators to capture the true dynamics of the disease. Through dynamical analysis, the model is proven to be positive and bounded in the region. The model is further explicitly shown to have a solution that is unique and stable. The reproduction number was duly computed by using the next-generation matrix approach. By taking EVD epidemic cases in Uganda, the study fitted all parameters to real data. It has been shown through sensitivity index analysis that the transmission rate outside treatment centers and relapse have a significant effect on the endemic state of the disease, as they lead to an increase in the basic reproduction ratio.

On a Symmetry-Based Structural Deterministic Fractal Fractional Order Mathematical Model to Investigate Conjunctivitis Adenovirus Disease

In the last few years, the conjunctivitis adenovirus disease has been investigated by using the concept of mathematical models. Hence, researchers have presented some mathematical models of the mentioned disease by using classical and fractional order derivatives. A complementary method involves analyzing the system of fractal fractional order equations by considering the set of symmetries of its solutions. By characterizing structures that relate to the fundamental dynamics of biological systems, symmetries offer a potent notion for the creation of mechanistic models. This study investigates a novel mathematical model for conjunctivitis adenovirus disease. Conjunctivitis is an infection in the eye that is caused by adenovirus, also known as pink eye disease. Adenovirus is a common virus that affects the eye’s mucosa. Infectious conjunctivitis is most common eye disease on the planet, impacting individuals across all age groups and demographics. We have formulated a model to investigate the transmission of the aforesaid disease and the impact of vaccination on its dynamics. Also, using mathematical analysis, the percentage of a population which needs vaccination to prevent the spreading of the mentioned disease can be investigated. Fractal fractional derivatives have been widely used in the last few years to study different infectious disease models. Hence, being inspired by the importance of fractal fractional theory to investigate the mentioned human eye-related disease, we derived some adequate results for the above model, including equilibrium points, reproductive number, and sensitivity analysis. Furthermore, by utilizing fixed point theory and numerical techniques, adequate requirements were established for the existence theory, Ulam–Hyers stability, and approximate solutions. We used nonlinear functional analysis and fixed point theory for the qualitative theory. We have graphically simulated the outcomes for several fractal fractional order levels using the numerical method.