Scenario analysis for programmatic tuberculosis control in Bangladesh: a mathematical modelling study

Abstract

Tuberculosis (TB) is a major public health problem in Bangladesh. Although the National TB control program of Bangladesh is implementing a comprehensive expansion of TB control strategies, logistical challenges exist, and there is significant uncertainty concerning the disease burden. Mathematical modelling of TB is considered one of the most effective ways to understand the dynamics of infection transmission and allows quantification of parameters in different settings, including Bangladesh. In this study, we present a two-strain mathematical modelling framework to explore the dynamics of drug-susceptible (DS) and multidrug-resistant (MDR) TB in Bangladesh. We calibrated the model using DS and MDR-TB annual incidence data from Bangladesh from years 2001 to 2015. Further, we performed a sensitivity analysis of the model parameters and found that the contact rate of both strains had the largest influence on the basic reproduction numbers [Formula: see text] and [Formula: see text] of DS and MDR-TB, respectively. Increasingly powerful intervention strategies were developed, with realistic impact and coverage determined with the help of local staff. We simulated for the period from 2020 to 2035. Here, we projected the DS and MDR-TB burden (as measured by the number of incident cases and mortality) under a range of intervention scenarios to determine which of these scenario is the most effective at reducing burden. Of the single-intervention strategies, enhanced case detection is the most effective and prompt in reducing DS and MDR-TB incidence and mortality in Bangladesh and that with GeneXpert testing was also highly effective in decreasing the burden of MDR-TB. Our findings also suggest combining additional interventions simultaneously leads to greater effectiveness, particularly for MDR-TB, which we estimate requires a modest investment to substantially reduce, whereas DS-TB requires a strong sustained investment.

Figure 1

Reported Bangladesh TB annual incidence data as estimated by the WHO (red dots) and the corresponding best fit (blue solid curve): (left) drug-susceptible (DS) TB and (right) multidrug-resistant (MDR) TB.

Figure 2

Schematic diagram of TB model for Bangladesh TB setting. System states and flow parameters are defined in the Methods section. Values for each of the model parameters can be found in Table 1.

Figure 3

PRCC values depicting the sensitivity of the drug-susceptible basic reproduction number ${\text{R}}_{0\text{s}}$ with respect to the parameters $\mathrm{\beta },\mathrm{\alpha },\mathrm{\varphi },\mathrm{\delta },{\mathrm{\gamma }}_{\text{s}},{\mathrm{\tau }}_{\text{s}},\mathrm{\rho }$ and ${\mathrm{\eta }}_{\text{s}}$.

Figure 4

PRCC values depicting the sensitivity of the multidrug-resistant basic reproduction number ${\text{R}}_{0\text{m}}$ with respect to the parameters $\mathrm{\beta },\mathrm{\alpha },\mathrm{\varphi },\mathrm{\delta },{\mathrm{\gamma }}_{\text{m}},{\mathrm{\tau }}_{\text{m}},\mathrm{\kappa },{\mathrm{\eta }}_{\text{m}}$ and $\text{c}$.

Figure 5

Impact of the four single intervention strategies on TB burden (left-hand side DS-TB annual incidence and right-hand side MDR-TB annual incidence): (A1 and A2) varying detection rate, (B1 and B2) varying DS-TB treatment rate, (C1 and C2) varying MDR-TB treatment rate, and (D1 and D2) varying Drug-susceptibility testing rate.

Figure 6

Impact of the four single intervention strategies on TB mortality (left-hand side DS-TB annual mortality and right-hand side MDR-TB annual mortality): (A1 and A2) varying detection rate, (B1 and B2) varying DS-TB treatment rate, (C1 and C2) varying MDR-TB treatment rate, and (D1 and D2) varying Drug-susceptibility testing rate.

Figure 7

Combination intervention strategy and its effect on (A) DS-TB and (B) MDR-TB annual incidence in Bangladesh.

Figure 8

Combination intervention strategy and its effect on (A) DS-TB and (B) MDR-TB annual mortality in Bangladesh.

Figure 9

The effects of the strain-specific basic reproduction number on the dynamics of model (2).