India is estimated to have nearly twenty-seven lakh tuberculosis patients1. This data does not include those who do not show any symptoms – but can spread the bacteria. The epidemiology of tuberculosis in India is further complicated by the growing number of TB cases resistant to multiple drugs.
When available data is not sufficient to understand epidemiology, mathematical modelling can help. And that is what Saurav Mandal and his lab from JNU, Delhi did2. They considered three genes that are known to confer drug resistance. Small changes in the nucleotide sequences at the genome level slowly add up, leading to the emergence of drug resistant strains. Since these changes are unpredictable, the researchers used stochastic modelling.
They considered five types of individuals in any population: infected, non-infected, suspected, latent and recovered. The rates of transmission between the different types may be different. So the team used different unknown constants for the rates of each type of transmission in the differential equations they formulated.
Using the equations, Mandal and team ran simulations with different parameters on a dummy population and examined the spread of the disease for equilibrium points in the population. They changed the transmission rate parameters and found that, with different values, the equilibrium points in the population fluctuate.
The team then examined variations in TB occurrence for fifty years and analysed the time-series. And there was a temporal pattern: tuberculosis hotspots re-emerge every five years.
“Currently, TB control strategies are based on detection and cure. But our study suggests that minimizing the amplification of resistance should also be a priority for public health policies”, says Zubbair Malik, School of Computation & Integrative Sciences, JNU.
“The other takeaway is that region-specific control strategies can prevent TB from becoming a global threat”, says R. K. Brojen Singh, his mentor.
 Computational Biology and Chemistry, 87:107250 (2020);
Manish Kumar Tekam