Depression is a common mental health disorder characterized by persistent low mood, sadness, and a loss of interest or pleasure in daily activities lasting for at least two weeks. Individuals with depression may also experience feelings of worthlessness, hopelessness, and, in severe cases, thoughts or behaviours related to self-harm. Depression is among the leading contributors to the burden of mental illness in India and is closely associated with suicide risk. The disorder arises from a complex interplay of biological, social, cultural, and economic factors, while alcohol and substance use can further worsen symptoms.[1]
Mental health disorders account for an estimated 2,443 disability-adjusted life years (DALYs) per 10,000 population in India, and the age-adjusted suicide rate is 21.1 per 100,000 population.[2] In 2017, approximately 197 million people (14.3% of the population) were living with a mental health disorder, including 46 million people (3.3% of the population) with depressive disorders.[2] These figures highlight the substantial burden of depression and the need for evidence-based approaches to understand its determinants and guide prevention and intervention strategies.
Our focus is on developing a mathematical framework to understand depression dynamics and evaluate the impact of key risk factors at national and subnational levels.
We developed a compartmental mathematical model of depression that incorporates both internal and external risk factors influencing disease progression. The model defines transitions between different mental health states through a system of governing differential equations, enabling a quantitative assessment of depression dynamics. The model is currently identifying and evaluating with suitable data sources and is expected to provide insights into the impact of risk factors on disease burden, supporting evidence-based mental health interventions and public health planning.