Age-structured Mathematical Model Research Articles

Age Structured Deterministic Model of Diphtheria Infection

Age-structured mathematical model of diphtheria infection has been formulated with specific epidemiological classes such as S1, susceptible infant at time t (0-1years), S2, susceptible school children population at time t, V, vaccination population at time t, E, exposed population at time t, I1, asymptomatic infection population at time t, I2, symptomatic infection population at time t, ID, detected infectious humans at time t (asymptomatic and symptomatic) population through testing, R, recovered population at time t. It was established through theorems and proofs that the model is epidemiologically meaningful, and that all its state variables are positive (non-negative) at time t>0 in the domain ℘, and that the domain ℘ is indeed bounded. Using the next generation matrix, the reproduction ratio Rb of the system was determined. Using dynamical system theory, it was established that the system is locally stable. A matrix-theoretic method was used in the construction of an appropriate Lyapunov function for the global stability analysis of the formulated model, and also established that the system is globally asymptotically stable if Rb≤1 and unstable otherwise.

Analysis of Age-Structured Mathematical Model of Malaria Transmission Dynamics via Classical and ABC Fractional Operators

Malaria is a complex disease with many factors influencing the transmission dynamics, including age. This research analyzes the transmission dynamics of malaria by developing an age-structured mathematical model using the classical integer order and Atangana–Baleanu–Caputo fractional operators. The analysis of the model focused on several important aspects. The existence and uniqueness of solutions of fractional order were explored based on some fixed-point theorems,such as Banach and Krasnoselski. The Positivity and boundedness of the solutions were also investigated. Furthermore, through mathematical analysis techniques, we analyzed different types of stability results, and the results showed that the disease-free equilibrium point of the model is proved to be both locally and globally asymptotically stable if the basic reproduction number is less than one, whereas the endemic equilibrium point of the model is both locally and globally asymptotically stable if the basic reproduction number is greater than one. The findings from the sensitivity analysis revealed that the most sensitive parameters, essential for controlling or eliminating malaria are mosquito biting rate, density-dependent natural mortality rate, clinical recovery rate, and recruitment rate for mosquitoes. Numerical simulations are also performed to examine the behavior of the model for different values of the fractional-order alpha,and the result revealed that as the value α reduces from 1, the spread of the endemic grows slower. By incorporating these findings, this research helps to clarify the dynamics of malaria and provides information on how to create efficient control measures.

Covid19Vaxplorer: A free, online, user-friendly COVID-19 vaccine allocation comparison tool.

There are many COVID-19 vaccines currently available, however, Low- and middle-income countries (LMIC) still have large proportions of their populations unvaccinated. Decision-makers must decide how to effectively allocate available vaccines (e.g. boosters or primary series vaccination, which age groups to target) but LMIC often lack the resources to undergo quantitative analyses of vaccine allocation, resulting in ad-hoc policies. We developed Covid19Vaxplorer (https://covid19vaxplorer.fredhutch.org/), a free, user-friendly online tool that simulates region-specific COVID-19 epidemics in conjunction with vaccination with the purpose of providing public health officials worldwide with a tool for vaccine allocation planning and comparison. We developed an age-structured mathematical model of SARS-CoV-2 transmission and COVID-19 vaccination. The model considers vaccination with up to three different vaccine products, primary series and boosters. We simulated partial immunity derived from waning of natural infection and vaccination. The model is embedded in an online tool, Covid19Vaxplorer that was optimized for its ease of use. By prompting users to fill information through several windows to input local parameters (e.g. cumulative and current prevalence), epidemiological parameters (e.g basic reproduction number, current social distancing interventions), vaccine parameters (e.g. vaccine efficacy, duration of immunity) and vaccine allocation (both by age groups and by vaccination status). Covid19Vaxplorer connects the user to the mathematical model and simulates, in real time, region-specific epidemics. The tool then produces key outcomes including expected numbers of deaths, hospitalizations and cases, with the possibility of simulating several scenarios of vaccine allocation at once for a side-by-side comparison. We provide two usage examples of Covid19Vaxplorer for vaccine allocation in Haiti and Afghanistan, which had as of Spring 2023, 2% and 33% of their populations vaccinated, and show that for these particular examples, using available vaccine as primary series vaccinations prevents more deaths than using them as boosters.

Impact of a potential Chlamydia vaccine in the USA: mathematical modelling analyses

IntroductionChlamydia trachomatis (CT) infection is a global health challenge. New approaches are needed to control CT disease burden.MethodsAn age-structured deterministic mathematical model calibrated to nationally representative population-based data was developed...

Evaluating the cost-effectiveness of HPV vaccination for adolescent girls in Japan: A comparison of 2-valent, 4-valent, and 9-valent HPV vaccines with consideration of cross-protection

ObjectiveIn April 2023, the Japanese Health Ministry panel approved the inclusion of the 9-valent human papillomavirus (9vHPV) vaccine in the National Immunization Program, alongside the 2-valent (2vHPV) and 4-valent HPV (4vHPV) vaccines. In response to this, we evaluated the cost-effectiveness of these three vaccines for routine immunization of girls aged 12–16 in Japan, considering the cross-protection of 2vHPV and 4vHPV vaccines. MethodsWe constructed an age-structured mathematical model for HPV transmission, aiming to quantify the economic and epidemiological effects of various HPV vaccination strategies over a 70-year period in Japan. We determined incremental costs and quality-adjusted life years (QALYs) for each strategy, applying a 3% annual discount. Univariate and probabilistic sensitivity analyses were conducted to assess the uncertainty of our model results, with all evaluations done in 2023. ResultsOur projections indicate that the HPV vaccination program in Japan will significantly reduce the incidence of HPV-related diseases. All HPV vaccination strategies, using the 2vHPV, 4vHPV, and 9vHPV vaccines, were found to be cost-effective compared to no vaccination, with incremental cost-effectiveness ratios of ¥971,447/QALY, ¥1,237,297/QALY, and ¥742,084/QALY, respectively. Direct comparisons between vaccines demonstrated that the 9vHPV vaccination was more cost-effective than the 2vHPV vaccination, whereas 4vHPV vaccination was dominated by 2vHPV vaccination. ConclusionsOur study validates the cost-effectiveness of implementing the 9vHPV vaccine as the primary option over the 2vHPV or 4vHPV vaccine for girls in Japan. These findings underscore the need to improve the acceptance rate and coverage of HPV vaccinations in the country.

Treatment as prevention for hepatitis C virus in the Middle East and North Africa: a modeling study.

Direct-acting antivirals opened an opportunity for eliminating hepatitis C virus (HCV) infection in the Middle East and North Africa (MENA), the region most affected by HCV infection. Impact of HCV treatment as prevention (HCV-TasP) was investigated in 19 MENA countries. An age-structured mathematical model was used to assess program impact using epidemiologic and programming measures. The model was fitted to a database of systematically gathered HCV antibody prevalence data. Two main scenarios were investigated for the treatment roll-out to achieve (i) 80% reduction in HCV incidence by 2030, and (ii) incidence rate < 1 per 100,000 person-years by 2030. In the target-80%-incidence-reduction scenario, number of treatments administrated by 2030 ranged from 2,610 in Lebanon to 180,416 in Sudan with a median of 53,079, and treatment coverage ranged between 40.2 and 78.4% with a median of 60.4%. By 2030, prevalence of chronic infection ranged between 0.0 and 0.3% with a median of 0.1%, and incidence rate, per 100,000 person-years, ranged between 0.9 and 16.3 with a median of 3.2. Program-attributed reduction in incidence rate ranged between 47.8 and 81.9% with a median of 68.5%, and number of averted infections ranged between 401 and 68,499 with a median of 8,703. Number of treatments needed to prevent one new infection ranged from 1.7 in Oman to 25.9 in Tunisia with a median of 6.5. In the target incidence rate < 1 per 100,000 person-years scenario, number of treatments administrated by 2030 ranged from 3,470 in Lebanon to 211,912 in Sudan with a median of 54,479, and treatment coverage ranged between 55.5 and 95.9% with a median of 87.5%. By 2030, prevalence of chronic infection was less than 0.1%, and incidence rate, per 100,000 person-years, reached less than 1. Program-attributed reduction in incidence rate ranged between 61.0 and 97.5% with a median of 90.7%, and number of averted infections ranged between 559 and 104,315 with a median of 12,158. Number of treatments needed to prevent one new infection ranged from 1.3 in Oman to 25.9 in Tunisia with a median of 5.5. HCV-TasP is an effective and indispensable prevention intervention to control MENA's HCV epidemic and to achieve elimination by 2030.

Optimal control and cost-effectiveness analysis of age-structured malaria model with asymptomatic carrier and temperature variability

This paper presents an age-structured mathematical model for malaria transmission dynamics with asymptomatic carrier and temperature variability. The temperature variability function is fitted to the temperature data, and the malaria model is then fitted to the malaria cases and validated to check its suitability. Time-dependent controls were considered, including Long Lasting Insecticide Nets, treatment of symptomatic, screening and treatment of asymptomatic carriers and spray of insecticides. Pontryagin's Maximum Principle is used to derive the necessary conditions for optimal control of the disease. The numerical simulations of the optimal control problem reveal that the strategy involving the combination of all four controls is the most effective in reducing the number of infected individuals. Furthermore, the cost-effectiveness analysis shows that treatment of symptomatic, screening and treatment of asymptomatic carriers and insecticide spraying is the most cost-effective strategy to implement to control malaria transmission when available resources are limited.

ON MATHEMATICAL MODELING OF THE TRANSMISSION OF GRAPEVINE LEAFROLL-ASSOCIATED VIRUS 3 BY THE VINE MEALYBUG, PLANOCOCCUS FICUS

Grapevine leafroll disease (GLD) is the most common and economically destructive grapevine viral disease in South African vineyards and throughout the world. There are many GLD-associated virus variants with Grapevine leafroll-associated virus 3 (GLRaV-3) being the main causative agent of GLD. The vine mealybug, Planococcus ficus, is the most widespread and problematic vector of GLRaV-3. Roguing, pesticides and sanitary measures are common control strategies used in South African vineyards. In this paper, we propose an age-structured mathematical model for the transmission of (GLRaV-3) by Planococcus ficus in the South African context. The model is investigated in terms of the transmission thresholds and control strategies. A simplified model is used to shed light into the qualitative dynamics of transmission and assess the effectiveness of roguing as the main control strategy for GLRaV-3 spread.

Study of optimal vaccination strategies for early COVID-19 pandemic using an age-structured mathematical model: A case study of the USA.

In this paper we study different vaccination strategies that could have been implemented for the early COVID-19 pandemic. We use a demographic epidemiological mathematical model based on differential equations in order to investigate the efficacy of a variety of vaccination strategies under limited vaccine supply. We use the number of deaths as the metric to measure the efficacy of each of these strategies. Finding the optimal strategy for the vaccination programs is a complex problem due to the large number of variables that affect the outcomes. The constructed mathematical model takes into account demographic risk factors such as age, comorbidity status and social contacts of the population. We perform simulations to assess the performance of more than three million vaccination strategies which vary depending on the vaccine priority of each group. This study focuses on the scenario corresponding to the early vaccination period in the USA, but can be extended to other countries. The results of this study show the importance of designing an optimal vaccination strategy in order to save human lives. The problem is extremely complex due to the large amount of factors, high dimensionality and nonlinearities. We found that for low/moderate transmission rates the optimal strategy prioritizes high transmission groups, but for high transmission rates, the optimal strategy focuses on groups with high CFRs. The results provide valuable information for the design of optimal vaccination programs. Moreover, the results help to design scientific vaccination guidelines for future pandemics.

Assessing the cost-effectiveness of annual COVID-19 booster vaccination in South Korea using a transmission dynamic model.

We evaluated the cost-effectiveness of South Korea's planned annual coronavirus disease 2019 (COVID-19) booster campaign scheduled for October 2023. An age-structured mathematical model was used to analyze the public impacts and cost-effectiveness of vaccination across three vaccination strategies: uniform allocation and prioritizing those over 65 or those over 50 years old. We calculated the incremental cost per quality-adjusted life year (QALY) from both healthcare and societal perspectives. The maximum vaccine cost for cost-effectiveness was also identified. Our analysis highlights the cost-effectiveness of South Korea's annual COVID-19 vaccination program in mitigating health and economic impacts. The most cost-effective strategy is uniform vaccine allocation, offering the lowest incremental cost-effectiveness ratio (ICER) at US$ 25,787/QALY. However, with a relatively high attack rate, the strategy prioritizing individuals over 65 years emerges as more cost-effective, lowering the ICER to US$ 13,785/QALY. Prioritizing those over 50 was less cost-effective. All strategies were cost-saving from a societal perspective, with cost-effectiveness being more sensitive to vaccine price than to its effectiveness. Our results imply a potential strategy shift in current vaccination plan, with uniform vaccine distribution being more cost-effective than prioritizing older adults. Early estimation of viral transmissibility and vaccine effectiveness is crucial in determining the most cost-effective vaccine allocation approach.

Modelling the impact of maternal pneumococcal vaccination on infant pneumococcal disease in low-income settings

Pneumococcal disease is a leading cause of mortality in young children. The largest burden of pneumococcal disease is in the first six months of life before protection from a complete schedule of direct immunisation is possible. Maternal pneumococcal vaccination has been proposed as a strategy for protection in this period of early childhood; however, limited clinical trial data exists. In this study, we developed an age-structured compartmental mathematical model to estimate the impact of maternal pneumococcal vaccination. Our model demonstrates how maternal pneumococcal vaccination could prevent 73% (range 49–88%) of cases in those aged <1 month and 55% (range 36–66%) in those 1–2 months old. This translates to an estimated 17% reduction in deaths due to invasive pneumococcal disease in children under five. Overall, this study demonstrates the potential for maternal pneumococcal vaccination to meaningfully reduce the burden of infant pneumococcal disease, supporting the case for appropriate field-based clinical studies.

Analyzing inherent biases in SARS-CoV-2 PCR and serological epidemiologic metrics

BackgroundProspective observational data show that infected persons with the severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) remain polymerase chain reaction (PCR) positive for a prolonged duration, and that detectable antibodies develop slowly with time. We aimed to analyze how these effects can bias key epidemiological metrics used to track and monitor SARS-CoV-2 epidemics.MethodsAn age-structured mathematical model was constructed to simulate progression of SARS-CoV-2 epidemics in populations. PCR testing to diagnose infection and cross-sectional surveys to measure seroprevalence were also simulated. Analyses were conducted on simulated outcomes assuming a natural epidemic time course and an epidemic in presence of interventions.ResultsThe prolonged PCR positivity biased the epidemiological measures. There was a lag of 10 days between the true epidemic peak and the actually-observed peak. Prior to epidemic peak, PCR positivity rate was twofold higher than that based only on current active infection, and half of those tested positive by PCR were in the prolonged PCR positivity stage after infection clearance. Post epidemic peak, PCR positivity rate poorly predicted true trend in active infection. Meanwhile, the prolonged PCR positivity did not appreciably bias estimation of the basic reproduction number R0. The time delay in development of detectable antibodies biased measured seroprevalence. The actually-observed seroprevalence substantially underestimated true prevalence of ever infection, with the underestimation being most pronounced around epidemic peak.ConclusionsCaution is warranted in interpreting PCR and serological testing data, and any drawn inferences need to factor the effects of the investigated biases for an accurate assessment of epidemic dynamics.

Modeling the population-level impact of treatment on COVID-19 disease and SARS-CoV-2 transmission

Different COVID-19 treatment candidates are under development, and some are becoming available including two promising drugs from Merck and Pfizer. This study provides conceptual frameworks for the effects of three types of treatments, both therapeutic and prophylactic, and to investigate their population-level impact, to inform drug development, licensure, decision-making, and implementation. Different drug efficacies were assessed using an age-structured mathematical model describing SARS-CoV-2 transmission and disease progression, with application to the United States as an illustrative example. Severe and critical infection treatment reduces progression to COVID-19 severe and critical disease and death with small number of treatments needed to avert one disease or death. Post-exposure prophylaxis treatment had a large impact on flattening the epidemic curve, with large reductions in infection, disease, and death, but the impact was strongly age dependent. Pre-exposure prophylaxis treatment had the best impact and effectiveness, with immense reductions in infection, disease, and death, driven by the robust control of infection transmission. Effectiveness of both pre-exposure and post-exposure prophylaxis treatments was disproportionally larger when a larger segment of the population was targeted than a specific age group. Additional downstream potential effects of treatment, beyond the primary outcome, enhance the population-level impact of both treatments. COVID-19 treatments are an important modality in controlling SARS-CoV-2 disease burden. Different types of treatment act synergistically for a larger impact, for these treatments and vaccination.

Vaccine failure, seasonality and demographic changes associate with mumps outbreaks in Jiangsu Province, China: Age-structured mathematical modelling study

Measles, mumps and rubella (MMR) vaccine program was introduced in Jiangsu province of China in May 2008 and has been greatly contributed to decreasing of mumps cases. However, mumps has been resurging since May 2015. A number of studies have put forward that the resurgence of mumps is due to vaccine failure. In this paper, we investigated the other reasons for the resurging of mumps, such as the changes in seasonal transmission patterns and demographic structures, by using an age-structured mathematical model. We divided the history (January 2005 to May 2019) of mumps epidemics of Jiangsu province into three different stages: No vaccine stage (January 2005 to December 2008), effectively controlled stage (January 2009 to December 2014) and resurgence stage (January 2015 to May 2019). The features of mumps epidemics in three stages are compared under different demographic structures with same physical contact rate. The mumps transmission rate was increased in summer and dropped in November in stage III compared with that in stage I. The changes in demographic structures give a good explanation why the mumps outbreaked among children around 10 years old in stage I and around 5 years old in stage III. We have a conclusion that the vaccine failure, changes in seasonality and demographic structures were associated with the mumps outbreaks in recent years in Jiangsu province, China. We give the patterns of mumps dynamics considering age, vaccine, seasonality and demographic structures, which can help health program planners to implement more preventive interventions in mumps control during the period of higher risk of infection.

Type 2 diabetes epidemic and key risk factors in Qatar: a mathematical modeling analysis

IntroductionWe aimed to characterize and forecast type 2 diabetes mellitus (T2DM) disease burden between 2021 and 2050 in Qatar where 89% of the population comprises expatriates from over 150 countries.Research...

Assessing the best time interval between doses in a two-dose vaccination regimen to reduce the number of deaths in an ongoing epidemic of SARS-CoV-2

The SARS-CoV-2 pandemic is a major concern all over the world and, as vaccines became available at the end of 2020, optimal vaccination strategies were subjected to intense investigation. Considering their critical role in reducing disease burden, the increasing demand outpacing production, and that most currently approved vaccines follow a two-dose regimen, the cost-effectiveness of delaying the second dose to increment the coverage of the population receiving the first dose is often debated. Finding the best solution is complex due to the trade-off between vaccinating more people with lower level of protection and guaranteeing higher protection to a fewer number of individuals. Here we present a novel extended age-structured SEIR mathematical model that includes a two-dose vaccination schedule with a between-doses delay modelled through delay differential equations and linear optimization of vaccination rates. By maintaining the minimum stock of vaccines under a given production rate, we evaluate the dose interval that minimizes the number of deaths. We found that the best strategy depends on an interplay between the vaccine production rate and the relative efficacy of the first dose. In the scenario of low first-dose efficacy, it is always better to vaccinate the second dose as soon as possible, while for high first-dose efficacy, the best strategy of time window depends on the production rate and also on second-dose efficacy provided by each type of vaccine. We also found that the rate of spread of the infection does not affect significantly the thresholds of the best window, but is an important factor in the absolute number of total deaths. These conclusions point to the need to carefully take into account both vaccine characteristics and roll-out speed to optimize the outcome of vaccination strategies.

COVID-19 epidemic in New York City: development of an age group-specific mathematical model to predict the outcome of various vaccination strategies

BackgroundSince December 14, 2020, New York City (NYC) has started the first batch of COVID-19 vaccines. However, the shortage of vaccines is currently an inevitable problem. Therefore, optimizing the age-specific COVID-19 vaccination is an important issue that needs to be addressed as a priority.ObjectiveCombined with the reported COVID-19 data in NYC, this study aimed to construct a mathematical model with five age groups to estimate the impact of age-specific vaccination on reducing the prevalence of COVID-19.MethodsWe proposed an age-structured mathematical model and estimated the unknown parameters based on the method of Markov Chain Monte Carlo (MCMC). We also calibrated our model by using three different types of reported COVID-19 data in NYC. Moreover, we evaluated the reduced cumulative number of deaths and new infections with different vaccine allocation strategies.ResultsCompared with the current vaccination strategy in NYC, if we gradually increased the vaccination coverage rate for only one age groups from March 1, 2021 such that the vaccination coverage rate would reach to 40% by June 1, 2021, then as of June 1, 2021, the cumulative deaths in the 75–100 age group would be reduced the most, about 72 fewer deaths per increased 100,000 vaccinated individuals, and the cumulative new infections in the 0–17 age group would be reduced the most, about 21,591 fewer new infections per increased 100,000 vaccinated individuals. If we gradually increased the vaccination coverage rate for two age groups from March 1, 2021 such that the vaccination coverage rate would reach to 40% by June 1, 2021, then as of June 1, 2021, the cumulative deaths in the 65–100 age group would be reduced the most, about 36 fewer deaths per increased 100,000 vaccinated individuals, and the cumulative new infections in the 0–44 age group would be reduced the most, about 17,515 fewer new infections per increased 100,000 vaccinated individuals. In addition, if we had an additional 100,000 doses of vaccine for 0–17 and 75–100 age groups as of June 1, 2021, then the allocation of 80% to the 0–17 age group and 20% to the 75–100 age group would reduce the maximum numbers of new infections and deaths simultaneously in NYC.ConclusionsThe COVID-19 burden including deaths and new infections would decrease with increasing vaccination coverage rate. Priority vaccination to the elderly and adolescents would minimize both deaths and new infections.

Optimal harvest strategy based on a discrete age-structured model with monthly fishing effort for chub mackerel, Scomber japonicus, in South Korea

Currently, the catch of fishery resources in South Korea has fallen more than 40%, from 1.73 million tons in 1986 to 1.01 million tons in 2018. In particular, the amount of chub mackerel, one of the most favored fish species by South Koreans, caught in 2017 was 103,870 tons, which was only 25% of the total catch, compared with 415,003 tons in 1996. The total allowable catch system and the closed season are currently in place as a countermeasure to the continued decline in fishery resources; however, their effectiveness is questionable. Our study aims to maximize fishing profits by controlling the monthly fishing effort while maintaining the amount of chub mackerel. Discrete age-structured mathematical model was established with an optimal harvest control system. Density-dependent mortality was applied at the juvenile stage to reflect fish mortality because it is highly dependent on population density in the underage phase. The conditions of the parameters guaranteeing the existence of optimal harvest strategies were demonstrated and obtained using Pontryagin's maximum principle. We compared an optimal harvest strategies and the actual harvest strategies of fishing effort from July 2010 to June 2020. In addition, we compared the profit and biomass of the optimal harvest strategies when designating one month in a year as closed season. We also conducted sensitivity analysis by varying the price of chub mackerel and cost of fishing effort. As a result, the optimal harvest strategies are to have the highest amount of fishing effort from August to September and gradually reduce the amount of effort. The optimal harvest strategies could improve the maximum economic yield and the maximum sustainable yield by 11.9% and 10.9%, respectively, over the observed strategy. To the best of our knowledge, forecasting analysis of the maximum economic yield and closed season conducted based on optimal harvest control system for Chub mackerel in South Korea. The appropriate allocation of monthly fishing effort can increase sustainable catch and profit. In addition, it is better to implement a closed season in July to maximize the profit of fishing and in June to the resource recovery.

Non-pharmaceutical interventions and COVID-19 vaccination strategies in Senegal: a modelling study

BackgroundWhen vaccines against the novel COVID-19 were available in Senegal, many questions were raised. How long should non-pharmaceutical interventions (NPIs) be maintained during vaccination roll-out? What are the best vaccination...

Impact of diabetes mellitus on tuberculosis epidemiology in Indonesia: A mathematical modeling analysis

We investigated and forecasted the impact of diabetes mellitus (DM) on tuberculosis (TB) epidemiology in Indonesia between 2020 and 2050. A recently-developed age-structured TB-DM dynamic mathematical model was utilized to assess the impact of DM on TB epidemiology. Model parameters were informed by systematic reviews and meta-analyses. Sensitivity and uncertainty analyses were conducted to assess robustness of predictions. The proportion of TB incident cases attributed to DM increased from 18.8% (95% uncertainty interval (UI): 12.6%–24.3%) in 2020, to 20.9% (95% UI: 14.7%–27.1%) in 2030, and 25.8% (95% UI: 17.7%–32.2%) in 2050. The proportion of TB-related deaths attributed to DM increased from 24.3% (95% UI: 18.7%–29.1%) in 2020, to 27.7% (95% UI: 22.4%–32.4%) in 2030, and 34.3% (95% UI: 27.6%–38.0%) in 2050. Most of the impact of DM on TB transmission has risen because of faster progression to TB disease, increased risk of reinfection, and increased infectiousness, with higher bacterial loads. Sensitivity and uncertainty analyses affirmed the predictions. TB-DM synergy is projected to increase in Indonesia over the next three decades with DM becoming a major driver of TB incidence and deaths. Joint TB-DM management and programs could offer significant reductions in TB incidence and mortality, making post-2015 End TB targets more feasible.