COVID-19 and tuberculosis: A mathematical model based forecasting in Delhi, India

Abstract

Background: There is emerging evidence that patients with Latent Tuberculosis Infection(LTBI) and Tuberculosis(TB) disease have an increased risk of the SARS-CoV-2 infection and predisposition towards developing severe COVID-19 pneumonia. In this study we attempted to estimate the number of TB patients infected with SARS-CoV-2 and have severe disease during the COVID-19 epidemic in Delhi, India.

Methods: Susceptible-Exposed-Infectious-Recovered (SEIR) model was used to estimate the number of COVID-19 cases in Delhi. Assuming the prevalence of TB in Delhi to be 0.55%, 53% of SARS-CoV2 infected TB cases to present with severe disease we estimated the number of SARS-CoV2 infected TB cases and the number of severe patients. The modelling used estimated R₀ for two scenarios, without any intervention and with public health interventions.

Results: We observed that the peak of SARS-CoV-2-TB co-infected patients would occur on the 94th day in absence of public health interventions and on 138th day in presence of interventions. There could be 20,880 SARS-CoV-2 infected TB cases on peak day of epidemic when interventions are implemented and 27,968 cases in the absence of intervention. Among them, there could be 14,823 patients with severe disease when no interventions are implemented and 11,066 patients with severe disease in the presence of intervention.

Conclusion: The importance of primary prevention measures needs to be emphasized especially in TB patients. The TB treatment centres and hospitals needs to be prepared for early diagnosis and management of severe COVID-19 in TB patients.

Keywords: COVID-19; Epidemic; Mathematical modelling; SARS-CoV-2.