(i) TLC strategies produce the mildest and most delayed epidemic out-break than any of the individual interventions; (ii) the epidemic dynamics of Delhi appear to be strongly influenced by the activity patterns and the demographic structure of its local residents; and (iii) a high resolution social contact network helps in analyzing effective public health policies.
We aim to understand quantitatively how targeted-layered containment (TLC) strategies contain an influenza pandemic in a populous urban area such as Delhi, India using networked epidemiology.
A high resolution synthetic network is constructed based on surveyed data. It captures the underlying contact structure of a certain population and can be used to quantitatively analyze public health policy effectiveness. To the best of our knowledge, this study is the first of its kind in the Indian sub-continent.
A key contribution of our work is a methodology for the synthesis of a realistic individual-based social contact network for Delhi 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 literature in combination 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. This time varying, spatially explicit network has over 13 million people and more than 200 million people-people contacts. The network has several interesting similarities and differences when compared with similar networks of US cities. Additionally, we use a high performance agent-based modeling environment to study how an influenza-like illness would spread over Delhi. We also analyze well understood pharmaceutical and non-pharmaceutical containment strategies, or a combination thereof (also known as TLCs), to control a pandemic outbreak.