Co-infection Model Research Articles

Malaria abrogates O'nyong-nyong virus pathologies by restricting virus infection in nonimmune cells.

O'nyongnyong virus (ONNV) is a re-emerging alphavirus previously known to be transmitted by main malaria vectors, thus suggesting the possibility of coinfections with arboviruses in co-endemic areas. However, the pathological outcomes of such infections remain unknown. Using murine coinfection models, we demonstrated that a preexisting blood-stage Plasmodium infection suppresses ONNV-induced pathologies. We further showed that suppression of viremia and virus dissemination are dependent on Plasmodium-induced IFNγ and are associated with reduced infection of CD45- cells at the site of virus inoculation. We further proved that treatment with IFNγ or plasma samples from Plasmodium vivax-infected patients containing IFNγ are able to restrict ONNV infection in human fibroblast, synoviocyte, skeletal muscle, and endothelial cell lines. Mechanistically, the role of IFNγ in restricting ONNV infection was confirmed in in vitro infection assays through the generation of an IFNγ receptor 1 α chain (IFNγR1)-deficient cell line.

HIV/AIDS-Pneumonia Codynamics Model Analysis with Vaccination and Treatment.

In this paper, we proposed and analyzed a realistic compartmental mathematical model on the spread and control of HIV/AIDS-pneumonia coepidemic incorporating pneumonia vaccination and treatment for both infections at each infection stage in a population. The model exhibits six equilibriums: HIV/AIDS only disease-free, pneumonia only disease-free, HIV/AIDS-pneumonia coepidemic disease-free, HIV/AIDS only endemic, pneumonia only endemic, and HIV/AIDS-pneumonia coepidemic endemic equilibriums. The HIV/AIDS only submodel has a globally asymptotically stable disease-free equilibrium if ℛ1 < 1. Using center manifold theory, we have verified that both the pneumonia only submodel and the HIV/AIDS-pneumonia coepidemic model undergo backward bifurcations whenever ℛ2 < 1 and ℛ3 = max{ℛ1, ℛ2} < 1, respectively. Thus, for pneumonia infection and HIV/AIDS-pneumonia coinfection, the requirement of the basic reproduction numbers to be less than one, even though necessary, may not be sufficient to completely eliminate the disease. Our sensitivity analysis results demonstrate that the pneumonia disease transmission rate β2 and the HIV/AIDS transmission rate β1 play an important role to change the qualitative dynamics of HIV/AIDS and pneumonia coinfection. The pneumonia infection transmission rate β2 gives rises to the possibility of backward bifurcation for HIV/AIDS and pneumonia coinfection if ℛ3 = max{ℛ1, ℛ2} < 1, and hence, the existence of multiple endemic equilibria some of which are stable and others are unstable. Using standard data from different literatures, our results show that the complete HIV/AIDS and pneumonia coinfection model reproduction number is ℛ3 = max{ℛ1, ℛ2} = max{1.386, 9.69 } = 9.69 at β1 = 2 and β2 = 0.2 which shows that the disease spreads throughout the community. Finally, our numerical simulations show that pneumonia vaccination and treatment against disease have the effect of decreasing pneumonia and coepidemic disease expansion and reducing the progression rate of HIV infection to the AIDS stage.

Transcriptome Analysis of Atlantic Salmon (Salmo salar) Skin in Response to Sea Lice and Infectious Salmon Anemia Virus Co-Infection Under Different Experimental Functional Diets.

Sea lice (Lepeophtheirus salmonis) are ectoparasitic copepods that cause significant economic loss in marine salmoniculture. In commercial salmon farms, infestation with sea lice can enhance susceptibility to other significant pathogens, such as the highly contagious infectious salmon anemia virus (ISAv). In this study, transcriptomic analysis was used to evaluate the impact of four experimental functional feeds (i.e. 0.3% EPA/DHA+high-ω6, 0.3% EPA/DHA+high-ω6+immunostimulant (IS), 1% EPA/DHA+high-ω6, and 1% EPA/DHA+high-ω3) on Atlantic salmon (Salmo salar) during a single infection with sea lice (L. salmonis) and a co-infection with sea lice and ISAv. The overall objectives were to compare the transcriptomic profiles of skin between lice infection alone with co-infection groups and assess differences in gene expression response among animals with different experimental diets. Atlantic salmon smolts were challenged with L. salmonis following a 28-day feeding trial. Fish were then challenged with ISAv at 18 days post-sea lice infection (dpi), and maintained on individual diets, to establish a co-infection model. Skin tissues sampled at 33 dpi were subjected to RNA-seq analysis. The co-infection’s overall survival rates were between 37%-50%, while no mortality was observed in the single infection with lice. With regard to the infection status, 756 and 1303 consensus differentially expressed genes (DEGs) among the four diets were identified in “lice infection vs. pre-infection” and “co-infection vs. pre-infection” groups, respectively, that were shared between the four experimental diets. The co-infection groups (co-infection vs. pre-infection) included up-regulated genes associated with glycolysis, the interferon pathway, complement cascade activity, and heat shock protein family, while the down-regulated genes were related to antigen presentation and processing, T-cell activation, collagen formation, and extracellular matrix. Pathway enrichment analysis conducted between infected groups (lice infection vs. co-infection) resulted in several immune-related significant GO terms and pathways unique to this group, such as “autophagosome”, “cytosolic DNA-sensing pathway” and “response to type I interferons”. Understanding how experimental functional feeds can impact the host response and the trajectory of co-infections will be an essential step in identifying efficacious intervention strategies that account for the complexities of disease in open cage culture.

Global stability of HIV/HTLV co-infection model with CTL-mediated immunity

&lt;p style='text-indent:20px;'&gt;Mathematical modeling of human immunodeficiency virus (HIV) and human T-lymphotropic virus type Ⅰ (HTLV-I) mono-infections has received considerable attention during the last decades. These two viruses share the same way of transmission between individuals; through direct contact with certain contaminated body fluids. Therefore, a person can be co-infected with both viruses. In the present paper, we construct and analyze a new HIV/HTLV-I co-infection model under the effect of Cytotoxic T lymphocytes (CTLs) immune response. The model describes the interaction between susceptible CD&lt;inline-formula&gt;&lt;tex-math id="M1"&gt;\begin{document}$ 4^{+} $\end{document}&lt;/tex-math&gt;&lt;/inline-formula&gt;T cells, silent HIV-infected cells, active HIV-infected cells, silent HTLV-infected cells, Tax-expressing (active) HTLV-infected cells, free HIV particles, HIV-specific CTLs and HTLV-specific CTLs. The HIV can spread by two routes of transmission, virus-to-cell (VTC) and cell-to-cell (CTC). Both active and silent HIV-infected cells can infect the susceptible CD&lt;inline-formula&gt;&lt;tex-math id="M2"&gt;\begin{document}$ 4^{+} $\end{document}&lt;/tex-math&gt;&lt;/inline-formula&gt;T cells by CTC mechanism. On the other side, HTLV-I has only one mode of transmission via direct cell-to-cell contact. The well-posedness of the model is established by showing that the solutions of the model are nonnegative and bounded. We calculate all possible equilibria and define the key threshold parameters which govern the existence and stability of all equilibria of the model. We explore the global asymptotic stability of all equilibria by utilizing Lyapunov function and LaSalle's invariance principle. We have discussed the influence of CTL immune response on the co-infection dynamics. We have presented numerical simulations to justify the applicability and effectiveness of the theoretical results. In addition, we evaluate the effect of HTLV-I infection on the HIV dynamics and vice versa.&lt;/p&gt;

Global dynamics of IAV/SARS-CoV-2 coinfection model with eclipse phase and antibody immunity.

Coronavirus disease 2019 (COVID-19) and influenza are two respiratory infectious diseases of high importance widely studied around the world. COVID-19 is caused by the severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2), while influenza is caused by one of the influenza viruses, A, B, C, and D. Influenza A virus (IAV) can infect a wide range of species. Studies have reported several cases of respiratory virus coinfection in hospitalized patients. IAV mimics the SARS-CoV-2 with respect to the seasonal occurrence, transmission routes, clinical manifestations and related immune responses. The present paper aimed to develop and investigate a mathematical model to study the within-host dynamics of IAV/SARS-CoV-2 coinfection with the eclipse (or latent) phase. The eclipse phase is the period of time that elapses between the viral entry into the target cell and the release of virions produced by that newly infected cell. The role of the immune system in controlling and clearing the coinfection is modeled. The model simulates the interaction between nine compartments, uninfected epithelial cells, latent/active SARS-CoV-2-infected cells, latent/active IAV-infected cells, free SARS-CoV-2 particles, free IAV particles, SARS-CoV-2-specific antibodies and IAV-specific antibodies. The regrowth and death of the uninfected epithelial cells are considered. We study the basic qualitative properties of the model, calculate all equilibria, and prove the global stability of all equilibria. The global stability of equilibria is established using the Lyapunov method. The theoretical findings are demonstrated via numerical simulations. The importance of considering the antibody immunity in the coinfection dynamics model is discussed. It is found that without modeling the antibody immunity, the case of IAV and SARS-CoV-2 coexistence will not occur. Further, we discuss the effect of IAV infection on the dynamics of SARS-CoV-2 single infection and vice versa.

Stability analysis of SARS-CoV-2/HTLV-I coinfection dynamics model

&lt;abstract&gt;&lt;p&gt;Although some patients with coronavirus disease 2019 (COVID-19) develop only mild symptoms, fatal complications have been observed among those with underlying diseases. Severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) is the causative of COVID-19. Human T-cell lymphotropic virus type-I (HTLV-I) infection can weaken the immune system even in asymptomatic carriers. The objective of the present study is to formulate a new mathematical model to describe the co-dynamics of SARS-CoV-2 and HTLV-I in a host. We first investigate the properties of the model's solutions, and then we calculate all equilibria and study their global stability. The global asymptotic stability is examined by constructing Lyapunov functions. The analytical findings are supported via numerical simulation. Comparison between the solutions of the SARS-CoV-2 mono-infection model and SARS-CoV-2/HTLV-I coinfection model is given. Our proposed model suggest that the presence of HTLV-I suppresses the immune response, enhances the SARS-CoV-2 infection and, consequently, may increase the risk of COVID-19. Our developed coinfection model can contribute to understanding the SARS-CoV-2 and HTLV-I co-dynamics and help to select suitable treatment strategies for COVID-19 patients who are infected with HTLV-I.&lt;/p&gt;&lt;/abstract&gt;

A mathematical study of dynamical model for Japanese encephalitis-dengue co-infection using JE vaccine

A mathematical study of dynamical model for Japanese encephalitis-dengue co-infection using JE vaccine

A mathematical study of dynamical model for Japanese encephalitis-dengue co-infection using JE vaccine

A nonlinear deterministic mathematical model has been described with the co-infection dynamics of Japanese encephalitis (JE) and dengue disease, incorporating the JE vaccine. Basic mathematical results have been discussed along with their stabilities. By using center manifold theory, the model undergoes a backwards bifurcation phenomenon and this has occurred when the basic reproduction number is smaller than unity. Sensitivity analysis has been performed to determine which parameters significantly affect disease dynamics. The effects of these parameters on the transmission of disease were investigated using a numerical simulation. According to the findings, we ascertain that JE-dengue co-infection can be managed with the use of JE vaccine, also minimising JE transmission rate rapidly.

Novel Numerical Estimates of the Pneumonia and Meningitis Epidemic Model via the Nonsingular Kernel with Optimal Analysis

In this article, we investigated a deterministic model of pneumonia‐meningitis coinfection. Employing the Atangana–Baleanu fractional derivative operator in the Caputo framework, we analyze a seven‐component approach based on ordinary differential equations (DEs). Furthermore, the invariant domain, disease‐free as well as endemic equilibria, and the validity of the model’s potential results are all investigated. According to controller design evaluation and modelling, the modulation technique devised is effective in diminishing the proportion of incidences in various compartments. A fundamental reproducing value is generated by exploiting the next generation matrix to assess the properties of the equilibrium. The system’s reliability is further evaluated. Sensitivity analysis is used to classify the impact of each component on the spread or prevention of illness. Using simulation studies, the impacts of providing therapy have been determined. Additionally, modelling the appropriate configuration demonstrated that lowering the fractional order from 1 necessitates a rapid initiation of the specified control technique at the largest intensity achievable and retaining it for the bulk of the pandemic’s duration.

Modeling and Analysis of HIV and Cholera Direct Transmission with Optimal Control

In this study, a mathematical model of the human immunodeficiency virus (HIV) and cholera co infection is constructed and analyzed. The disease‐free equilibrium of the co‐infection model is both locally and globally asymptotically stable if R0 &lt; 1 and unstable if R0 &gt; 1. The only cholera model and only the HIV model show forward bifurcation if the corresponding reproduction numbers attain a value one. The disease‐free equilibria of only the cholera and only the HIV models is locally and globally asymptotically if R0 &lt; 1, and the endemic equilibria of only the cholera model and only the HIV model are locally and globally asymptotically stable if the corresponding reproduction number is equal to one. The endemic equilibrium point of the HIV and cholera model is computed, and stability property is shown with numerical simulations. The computed partial derivatives (∂R0h/∂R0c &gt; 0) show that the increase of one infection contributes to the increase of other infection. Pontryagin’s maximum principle is applied to construct Hamiltonian function, and optimal controls are computed. The optimal system is solved numerically using forward and backward sweep method of Runge Kutta’s fourth‐order methods. The numerical simulations are plotted using MATLAB.

Fractional-order dynamics of Chagas-HIV epidemic model with different fractional operators

&lt;abstract&gt;&lt;p&gt;In this research, we reformulate and analyze a co-infection model consisting of Chagas and HIV epidemics. The basic reproduction number $ R_0 $ of the proposed model is established along with the feasible region and disease-free equilibrium point $ E^0 $. We prove that $ E^0 $ is locally asymptotically stable when $ R_0 $ is less than one. Then, the model is fractionalized by using some important fractional derivatives in the Caputo sense. The analysis of the existence and uniqueness of the solution along with Ulam-Hyers stability is established. Finally, we solve the proposed epidemic model by using a novel numerical scheme, which is generated by Newton polynomials. The given model is numerically solved by considering some other fractional derivatives like Caputo, Caputo-Fabrizio and fractal-fractional with power law, exponential decay and Mittag-Leffler kernels.&lt;/p&gt;&lt;/abstract&gt;

Global Dynamics of a Within‐Host COVID‐19/AIDS Coinfection Model with Distributed Delays

Acquired immunodeficiency syndrome (AIDS) is a spectrum of conditions caused by infection with the human immunodeficiency virus (HIV). Among people with AIDS, cases of COVID‐19 have been reported in many countries. COVID‐19 (coronavirus disease 2019) is caused by the severe acute respiratory syndrome coronavirus 2 (SARS‐CoV‐2). In this manuscript, we are going to present a within‐host COVID‐19/AIDS coinfection model to study the dynamics and influence of the coinfection between COVID‐19 and AIDS. The model is a six‐dimensional delay differential equation that describes the interaction between uninfected epithelial cells, infected epithelial cells, free SARS‐CoV‐2 particles, uninfected CD4+ T cells, infected CD4+ T cells, and free HIV‐1 particles. We demonstrated that the proposed model is biologically acceptable by proving the positivity and boundedness of the model solutions. The global stability analysis of the model is carried out in terms of the basic reproduction number. Numerical simulations are carried out to investigate that if COVID‐19/AIDS coinfected individuals have a poor immune response or a low number of CD4+ T cells, then the viral load of SARS‐CoV‐2 and the number of infected epithelial cells will rise. On the contrary, the existence of time delays can rise the number of uninfected CD4+ T cells and uninfected epithelial cells, thus reducing the viral load within the host.

Co-infection model for Covid-19 and Rubella with vaccination treatment: stability and threshold

This study aimed to explore a co-infection transmission model between Covid-19 and Rubella that involves administering vaccinations for both diseases. These two diseases not only have the same characteristics, but also has the same pattern in terms of the causes of disease, spread, clinical manifestations, and vaccines as prevention efforts. This model provided answers to the question of whether one of these diseases or both will disappear from the human population through several steps in the mathematical modelling analysis, which consists of: 1) critical point analysis, 2) stability analysis, 3) next generation matrix, and 4) threshold value analysis. This research resulted in four critical points, which is a critical point of disease-free, Rubella critical point, Covid-19 critical point, and the critical point for both diseases. Based on the next generation matrix and the disease-free critical point, two basic reproduction numbers were generated, namely for Rubella and for Covid-19. The first condition, R 01 less than 1 and R 02 less than 1, the disease-free critical point will be stable such that Rubella and Covid-19 will disappear from the human population. The second condition, R 01 greater than 1 and R 02 less than 1, the disease-free critical point become unstable, which means that Rubella-infected people will be found in the population. The third condition, R 01 less than 1 and R 02 greater than 1, Covid-19 will be found in the human population.

Dynamic Analysis and Optimal Control of a Multi-Strain Coinfection Model with Amplification and Vaccination

Dynamic Analysis and Optimal Control of a Multi-Strain Coinfection Model with Amplification and Vaccination

Liver Transplantation for Hepatitis D Virus in the United States: A UNOS Study on Outcomes in the MELD Era.

Without available curative therapies for delta hepatitis (hepatitis delta virus [HDV]), hepatic decompensation and hepatocellular carcinoma (HCC) among HDV patients often necessitates liver transplantation (LT). The objective of this study was to evaluate outcomes of LT among hepatitis B virus (HBV)/HDV patients in the United States. We performed the first US-based retrospective study of patients who underwent LT for HDV compared with HBV (monoinfection) in the years 2002-2019. We evaluated posttransplant survival and predictors of survival. We identified a total of 152 HBV/HDV and 5435 HBV patients who underwent LT. HDV patients were younger at transplant (52 versus 55, P < 0.001), less commonly Asian (16% versus 36%, P < 0.001), more likely to be HCV Ab positive (42% versus 28%, P < 0.001), and less likely to be listed for LT with HCC (38% versus 51%, P = 0.001), more likely to have ascites (73% versus 64%, P = 0.019), had worse coagulopathy (mean INR 2.0 versus 1.82, P = 0.04), and were more likely to receive a HCV-positive donor organ (7% versus 3%, P = 0.001). Post-LT overall survival and graft survival were similar between HDV and HBV patients, including among patients with HCC. Older age, HCV coinfection, HCC, and higher model for end-stage liver disease at transplant were associated with higher posttransplant mortality. HDV patients were sicker and more likely to be listed for LT for decompensated disease compared with HBV patients. Post-LT survival was similar between HDV and HBV patients, in contrast to prior international studies that suggested worse post-LT survival in HBV patients due to higher rates of HBV reactivation.

Plasmid co-infection: linking biological mechanisms to ecological and evolutionary dynamics.

As infectious agents of bacteria and vehicles of horizontal gene transfer, plasmids play a key role in bacterial ecology and evolution. Plasmid dynamics are shaped not only by plasmid–host interactions but also by ecological interactions between plasmid variants. These interactions are complex: plasmids can co-infect the same cell and the consequences for the co-resident plasmid can be either beneficial or detrimental. Many of the biological processes that govern plasmid co-infection—from systems that exclude infection by other plasmids to interactions in the regulation of plasmid copy number—are well characterized at a mechanistic level. Modelling plays a central role in translating such mechanistic insights into predictions about plasmid dynamics and the impact of these dynamics on bacterial evolution. Theoretical work in evolutionary epidemiology has shown that formulating models of co-infection is not trivial, as some modelling choices can introduce unintended ecological assumptions. Here, we review how the biological processes that govern co-infection can be represented in a mathematical model, discuss potential modelling pitfalls, and analyse this model to provide general insights into how co-infection impacts ecological and evolutionary outcomes. In particular, we demonstrate how beneficial and detrimental effects of co-infection give rise to frequency-dependent selection on plasmid variants.This article is part of the theme issue ‘The secret lives of microbial mobile genetic elements’.

Animal-Based Factors Prior to Infection Predict Histological Disease Outcome in Porcine Reproductive and Respiratory Syndrome Virus- and Actinobacillus pleuropneumoniae-Infected Pigs.

A large variety of clinical manifestation in individual pigs occurs after infection with pathogens involved in porcine respiratory disease complex (PRDC). Some pigs are less prone to develop respiratory disease symptoms. The variation in clinical impact after infection and the recovery capacity of an individual animal are measures of its resilience. In this paper, we examined which ones of a range of animal-based factors (rectal temperature, body weight, skin lesion scores, behavior, natural antibody serum levels, serum levels of white blood cells, and type of T and granulocyte subsets) when measured prior to infection are related to disease severity. These animal-based factors and the interaction with housing regimen of the piglets (conventional or enriched) were modeled using linear regression to predict disease severity using a dataset acquired from a previous study using a well-established experimental coinfection model of porcine reproductive and respiratory syndrome virus (PRRSV) and Actinobacillus pleuropneumoniae. Both PRRSV and A. pleuropneumoniae are often involved in PRDC. Histological lung lesion score of each animal was used as a measure for PRDC severity after infection. Prior to infection, higher serum levels of lymphocytes (CD3+), naïve T helper (CD3+CD4+CD8−), CD8+ (as well as higher relative levels of CD8+), and memory T helper (CD3+CD4+CD8+) cells and higher relative levels of granulocytes (CD172a) were related to reduced disease severity in both housing systems. Raised serum concentrations of natural IgM antibodies binding to keyhole limpet hemocyanin (KLH) were also related to reduced disease severity after infection. Increased levels of skin lesions at the central body part (after weaning and before infection) were related to increased disease severity in conventional housing systems only. High resisters showed a lower histological lung lesion score, which appeared unrelated to sex. Body temperature, behavior, and growth prior to infections were influenced by housing regimen but could not explain the variation in lung lesion scores after infection. Raised basal lymphocyte counts and lower skin lesion scores are related to reduced disease severity independent of or dependent on housing system, respectively. In conclusion, our study identifies intrinsic animal-based measures using linear regression analysis that predicts resilience to infections in pigs.

Spatio-temporal analysis of coinfection using wavefronts of Escherichia coli O157: H7 in a dairy cattle farm

This study proposes a new methodology to analyze the impacts of host and pathogen dispersal on coinfection dynamics using the example of Escherichia coli O157:H7 in a dairy farm. A multi-strain Susceptible–Infected–Susceptible model is extended to a Reaction–Diffusion (RD) coinfection model of E. coli O157:H7 transmission, which includes intermittent shedding and pathogen growth in the environment. Analysis of the new RD coinfection model consists of existence and stability of constant equilibria, calculation of the basic reproduction number, and existence of traveling and stationary wave solutions. A stationary wave solution of the RD model defines a nonconstant endemic equilibrium. Whereas a traveling wavefront represents an epidemic wave of infection passing through the farm with a specific speed, direction, and amplitude. The numerical simulations of the RD model demonstrate stable traveling and stationary wavefronts of the RD coinfection model. The stationary wavefront connects two constant coexistence equilibria and the traveling wavefront represents the gradual establishment of an endemic coexistence equilibrium in the dairy farm. The significance of this study lies in utilizing the nonlinear wave theory to analyze dynamics of coinfection in a dairy farm both with respect to location and time. Hence, in addition to Turing theory, the nonlinear wave theory can be used to determine the likelihood of acquiring the infection based on the location of cattle at each specific time.

Caspofungin Inhibits Mixed Biofilms of Candida albicans and Methicillin-Resistant Staphylococcus aureus and Displays Effectiveness in Coinfected Galleria mellonella Larvae.

ABSTRACTCandida albicans and Staphylococcus aureus are pathogens commonly isolated from bloodstream infections worldwide. While coinfection by both pathogens is associated with mixed biofilms and more severe clinical manifestations, due to the combined expression of virulence and resistance factors, effective treatments remain a challenge. In this study, we evaluated the activity of echinocandins, especially caspofungin, against mixed biofilms of C. albicans and methicillin-resistant (MRSA) or methicillin-susceptible S. aureus (MSSA) and their effectiveness in vivo using the Galleria mellonella coinfection model. Although caspofungin (CAS) and micafungin (MFG) inhibited the mixed biofilm formation, with CAS exhibiting inhibitory activity at lower concentrations, only CAS was active against preformed mixed biofilms. CAS significantly decreased the total biomass of mixed biofilms at concentrations of ≥2 μg/ml, whereas the microbial viability was reduced at high concentrations (32 to 128 μg/ml), leading to fungus and bacterium cell wall disruption and fungal cell enlargement. Notably, CAS (20 or 50 mg/kg of body weight) treatment led to an increased survival and improved outcomes of G. mellonella larvae coinfected with C. albicans and MRSA, since a significant reduction of fungal and bacterial burden in larval tissues was achieved with induction of granuloma formation. Our results reveal that CAS can be a therapeutic option for the treatment of mixed infections caused by C. albicans and S. aureus, supporting additional investigation.IMPORTANCE Infections by microorganisms resistant to antimicrobials is a major challenge that leads to high morbidity and mortality rates and increased time and cost with hospitalization. It was estimated that 27 to 56% of bloodstream infections by C. albicans are polymicrobial, with S. aureus being one of the microorganisms commonly coisolated worldwide. About 80% of infections are associated with biofilms by single or mixed species that can be formed on invasive medical devices, e.g., catheter, and are considered a dissemination source. The increased resistance to antimicrobials in bacterial and fungal cells when they are in biofilms is the most medically relevant behavior that frequently results in therapeutic failure. Although there are several studies evaluating treatments for polymicrobial infections associated or not with biofilms, there is still no consensus on an effective antimicrobial therapy to combat the coinfection by bacteria and fungi.

A fractional-order model for COVID-19 and tuberculosis co-infection using Atangana–Baleanu derivative

This paper considers and analyzes a fractional order model for COVID-19 and tuberculosis co-infection, using the Atangana–Baleanu derivative. The existence and uniqueness of the model solutions are established by applying the fixed point theorem. It is shown that the model is locally asymptotically stable when the reproduction number is less than one. The global stability analysis of the disease free equilibrium points is also carried out. The model was simulated using data relevant to both diseases in New Delhi, India. Fitting the model to the cumulative confirmed COVID-19 cases for New Delhi from March 1, 2021 to June 26, 2021, COVID-19 and TB contact rates and some other important parameters of the model are estimated. The numerical method used combines the two-step Lagrange polynomial and the fundamental theorem of fractional calculus and has been shown to be highly accurate and efficient, user-friendly and converges quickly to the exact solution even with a large step of discretization. Simulations of the Fractional order model revealed that reducing the risk of COVID-19 infection by latently-infected TB individuals will not only bring down the burden of COVID-19, but will also reduce the co-infection of both diseases in the population. Also, the conditions for the co-existence or elimination of both diseases from the population are established.