Evaluating Strategies for Pandemic Response in Delhi Using Realistic Social Networks

We analyze targeted layered containment strategies to contain an influenza pandemic in the National Capital Territory of India (NCT-I, including New Delhi and its surrounding areas). A key contribution of our work is to synthesize a realistic individual-based social contact network for NCT-I using a wide variety of open source and commercial data. New techniques were developed to infer daily activities for individuals using aggregate data published in transportation science, combined with human development surveys and targeted local surveys. The resulting social contact network is the first such network constructed for any urban region of India. The time varying spatially explicit network has over 13 million people and 200 million people-people contacts. The network has several interesting similarities and differences as compared to similar networks for US cities. As a second step, we use a high performance computing based modeling environment to study how an influenza-like illness (ILI) would spread over the NCT-I network. We also analyze well understood pharmaceutical and non-pharmaceutical containment strategies to control a pandemic outbreak. Our methodology builds on earlier work in this area. The results suggests that: (i) pharmaceutical containment strategies typically are more effective than the non-pharmaceutical ones for NCT-I residents, (ii) the epidemic dynamics of the region are strongly influenced by activity pattern and demographic structure of the local residents, (iii) a high resolution social contact network helps us make better public health policy. To the best of our knowledge this is the first such study in the Indian sub-continent.