ArticleArtificial intelligence in medicine2015
Synthesis of a high resolution social contact network for Delhi with application to pandemic planning.
Article in Artificial intelligence in medicine, 2015. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 8 papers, 1 of them a synthesis that pooled it.
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Who cites it
8 citing papers in PubMed, 1 synthesis or guideline pooled it.
- Effectiveness of workplace social distancing measures in reducing influenza transmission: a systematic review.BMC public health · 2018Pooled it
- A comparison of contact patterns derived from the population structure in agent-based models and empirical contact survey data.PLoS computational biology · 2026Article
- Computational decision-support tools for urban design to improve resilience against COVID-19 and other infectious diseases: A systematic review.Progress in planning · 2023Article
- Epidemic dynamics in census-calibrated modular contact network.Network modeling and analysis in health informatics and bioinformatics · 2023Article
- Artificial Intelligence-Empowered Mobilization of Assessments in COVID-19-like Pandemics: A Case Study for Early Flattening of the Curve.International journal of environmental research and public health · 2020Article
- Disparities in spread and control of influenza in slums of Delhi: findings from an agent-based modelling study.BMJ open · 2018Article
- Combining Participatory Influenza Surveillance with Modeling and Forecasting: Three Alternative Approaches.JMIR public health and surveillance · 2017Article
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4 authors.
Funding
Abstract
objectiveWe 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.
methodsA 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.
results(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.
conclusionA 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.
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