Ashish Vijay Tendulkar
Machine Learning, Text mining, Natural Language Processing (NLP), Bioinformatics and Computational Biology
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Cohorting to isolate asymptomatic spreaders: An agent-based simulation study on the Mumbai Suburban Railway
Sharad Shriram
Nidhin Vaidhiyan
Gaurav Aggarwal
Jiangzhuo Chen
Srini Venkatramanan
Lijing Wang
Aniruddha Adiga
Adam Sadilek
Madhav Marathe
Rajesh Sundaresan
AMAAS 2021 (2021), pp. 1680
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The Mumbai Suburban Railways, \emph{locals}, are a key transit infrastructure of the city and is crucial for resuming normal economic activity. To reduce disease transmission, policymakers can enforce reduced crowding and mandate wearing of masks. \emph{Cohorting} -- forming groups of travelers that always travel together, is an additional policy to reduce disease transmission on \textit{locals} without severe restrictions. Cohorting allows us to: (i) form traveler bubbles, thereby decreasing the number of distinct interactions over time; (ii) potentially quarantine an entire cohort if a single case is detected, making contact tracing more efficient, and (iii) target cohorts for testing and early detection of symptomatic as well as asymptomatic cases. Studying impact of cohorts using compartmental models is challenging because of the ensuing representational complexity. Agent-based models provide a natural way to represent cohorts along with the representation of the cohort members with the larger social network. This paper describes a novel multi-scale agent-based model to study the impact of cohorting strategies on COVID-19 dynamics in Mumbai. We achieve this by modeling the Mumbai urban region using a detailed agent-based model comprising of 12.4 million agents. Individual cohorts and their inter-cohort interactions as they travel on locals are modeled using local mean field approximations. The resulting multi-scale model in conjunction with a detailed disease transmission and intervention simulator is used to assess various cohorting strategies. The results provide a quantitative trade-off between cohort size and its impact on disease dynamics and well being. The results show that cohorts can provide significant benefit in terms of reduced transmission without significantly impacting ridership and or economic \& social activity.
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Protein Word Detection using Text Segmentation Techniques
G. Devi
Sutanu Chakraborti
BioNLP 2017, Vancouver, Canada, August 4, 2017, Association for Computational Linguistics, pp. 238-246 (to appear)
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Literature in Molecular Biology is abundant with linguistic metaphors. In particular, there have been works in the past that attempt to draw parallels between linguistics and biology, driven by the fundamental premise that proteins have a language of their own. Since word detection is fundamental to the decipherment of any unknown language, we attempt to establish a problem mapping from natural language text to protein sequences at the level of words. Towards this end, we explore the use of an unsupervised text segmentation algorithm to the task of extracting ”biological words” from protein sequences. We also demonstrate the effectiveness of using domain knowledge to
complement data driven approaches in the text segmentation task, as well as in its biological
counterpart.
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