Co-infection Model Research Articles

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

<abstract><p>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.</p></abstract>

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 < 1 and unstable if R0 > 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 < 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 > 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

<abstract><p>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.</p></abstract>

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.

COVID-19 and dengue co-infection in Brazil: optimal control and cost-effectiveness analysis.

A mathematical model for the co-interaction of COVID-19 and dengue transmission dynamics is formulated and analyzed. The sub-models are shown to be locally asymptotically stable when the respective reproduction numbers are below unity. Using available data sets, the model is fitted to the cumulative confirmed daily COVID-19 cases and deaths for Brazil (a country with high co-endemicity of both diseases) from February 1, 2021 to September 20, 2021. The fitting was done using the fmincon function in the Optimization Toolbox of MATLAB. Parameters denoting the COVID-19 contact rate, death rate and loss of infection acquired immunity to COVID-19 were estimated using the two data sets. The model is then extended to include optimal control strategies. The appropriate conditions for the existence of optimal control and the optimality system for the co-infection model are established using the Pontryagin’s Principle. Different control strategies and their cost-effectiveness analyses were considered and simulated for the model, which include: controls against incident dengue and COVID-19 infections, control against co-infection with a second disease and treatment controls for both dengue and COVID-19. Highlights of the simulation results show that: (1) dengue prevention strategy could avert as much as 870,000 new COVID-19 infections; (2) dengue only control strategy or COVID-19 only control strategy significantly reduces new co-infection cases; (3) the strategy implementing control against incident dengue infection is the most cost-effective in controlling dengue and COVID-19 co-infections.

Effect of density dependence on coinfection dynamics: part 2

In this paper we continue the stability analysis of the model for coinfection with density dependent susceptible population introduced in Andersson et al. (Effect of density dependence on coinfection dynamics. arXiv:2008.09987, 2020). We consider the remaining parameter values left out from Andersson et al. (Effect of density dependence on coinfection dynamics. arXiv:2008.09987, 2020). We look for coexistence equilibrium points, their stability and dependence on the carrying capacity K. Two sets of parameter value are determined, each giving rise to different scenarios for the equilibrium branch parametrized by K. In both scenarios the branch includes coexistence points implying that both coinfection and single infection of both diseases can exist together in a stable state. There are no simple explicit expression for these equilibrium points and we will require a more delicate analysis of these points with a new bifurcation technique adapted to such epidemic related problems. The first scenario is described by the branch of stable equilibrium points which includes a continuum of coexistence points starting at a bifurcation equilibrium point with zero single infection strain #1 and finishing at another bifurcation point with zero single infection strain #2. In the second scenario the branch also includes a section of coexistence equilibrium points with the same type of starting point but the branch stays inside the positive cone after this. The coexistence equilibrium points are stable at the start of the section. It stays stable as long as the product of K and the rate {bar{gamma }} of coinfection resulting from two single infections is small but, after this it can reach a Hopf bifurcation and periodic orbits will appear.

Helicobacter pylori and Epstein-Barr Virus Coinfection Stimulates Aggressiveness in Gastric Cancer through the Regulation of Gankyrin.

ABSTRACTPersistent coinfection with Helicobacter pylori and Epstein-Barr virus (EBV) promotes aggressive gastric carcinoma (GC). The molecular mechanisms underlying the aggressiveness in H. pylori and EBV-mediated GC are not well characterized. We investigated the molecular mechanism involved in H. pylori- and EBV-driven proliferation of gastric epithelial cells. Results showed that the coinfection is significantly more advantageous to the pathogens as coinfection creates a microenvironment favorable to higher pathogen-associated gene expression. The EBV latent genes ebna1 and ebna3c are highly expressed in the coinfection compared to lone EBV infection at 12 and 24 h. The H. pylori-associated genes 16S rRNA, cagA, and babA were also highly expressed during coinfection compared to H. pylori alone. In addition, upregulation of gankyrin, which is a small oncoprotein, modulates various cell signaling pathways, leading to oncogenesis. Notably, the knockdown of gankyrin decreased the cancer properties of gastric epithelial cells. Gankyrin showed a similar expression pattern as that of ebna3c at both transcript and protein levels, suggesting a possible correlation. Further, EBV and H. pylori created a microenvironment that induced cell transformation and oncogenesis through dysregulation of the cell cycle regulatory (ccnd1, dapk3, pcna, and akt), GC marker (abl1, tff-2, and cdx2), cell migration (mmp3 and mmp7), DNA response (pRB, pten, and p53), and antiapoptotic (bcl2) genes in infected gastric epithelial cells through gankyrin. Our study provides a new insight into the interplay of two oncogenic agents (H. pylori and EBV) that leads to an enhanced carcinogenic activity in gastric epithelial cells through overexpression of gankyrin.IMPORTANCE In the present study, we evaluated the synergistic effects of EBV and H. pylori infection on gastric epithelial cells in various coinfection models. These coinfection models were among the first to depict the exposures of gastric epithelial cells to EBV followed by H. pylori; however, coinfection models exist that narrated the scenario upon exposure to H. pylori followed by that to EBV. We determined that a coinfection by EBV and H. pylori enhanced the expression of oncogenic protein gankyrin. The interplay between EBV and H. pylori promoted the oncogenic properties of AGS cells like elevated focus formation, cell migration, and cell proliferation through gankyrin. EBV and H. pylori mediated an enhanced expression of gankyrin, which further dysregulated cancer-associated genes such as cell migratory, tumor suppressor, DNA damage response, and proapoptotic genes.

Butyrate, Forskolin, and Lactose Synergistically Enhance Disease Resistance by Inducing the Expression of the Genes Involved in Innate Host Defense and Barrier Function.

The rising concern of antimicrobial resistance highlights a need for effective alternatives to antibiotics for livestock production. Butyrate, forskolin, and lactose are three natural products known to induce the synthesis of host defense peptides (HDP), which are a critical component of innate immunity. In this study, the synergy among butyrate, forskolin, and lactose in enhancing innate host defense, barrier function, and resistance to necrotic enteritis and coccidiosis was investigated. Our results indicated that the three compounds synergistically augmented the expressions of multiple HDP and barrier function genes in chicken HD11 macrophages. The compounds also showed an obvious synergy in promoting HDP gene expressions in chicken jejunal explants. Dietary supplementation of a combination of 1 g/kg sodium butyrate, 10 mg/kg forskolin-containing plant extract, and 10 g/kg lactose dramatically improved the survival of chickens from 39% to 94% (p < 0.001) in a co-infection model of necrotic enteritis. Furthermore, the three compounds largely reversed growth suppression, significantly alleviated intestinal lesions, and reduced colonization of Clostridium perfringens or Eimeria maxima in chickens with necrotic enteritis and coccidiosis (p < 0.01). Collectively, dietary supplementation of butyrate, forskolin, and lactose is a promising antibiotic alternative approach to disease control and prevention for poultry and possibly other livestock species.

Enhancing reservoir control in the co-dynamics of HIV-VL: from mathematical modeling perspective

HIV patients are vulnerable to developing active visceral leishmaniasis (VL). To understand this complication, we studied a mathematical model for HIV and visceral leishmaniasis coinfection. In this approach, we reckoned two distinct equilibria: the disease-free and the endemic equilibria. The local and global stability of the disease-free equilibrium were thoroughly investigated. To further support the qualitative findings, we performed simulations to quantify the changes of the dynamical behavior of the full model for variation of relevant parameters. Increasing the rate of VL recovery (phi _{1}), the recovery rate for VL–HIV Co-infection (phi _{2}), removing reservoirs (c_{1}), minimizing the contact rate (beta _{h}) are important in controlling the transmission of individual and co-infection disease of VL and HIV. In conclusion, possible measures should be implemented to reduce the number of infected individuals. Therefore, we recommend that policy makers and stakeholders incorporate these measures during planing and implementation phases to control the transmission of VL–HIV co-infection.