Faculty Publications
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Publications by NITK Faculty
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Item An agent-based model for intervention planning among communities during epidemic outbreaks(Springer Verlag service@springer.de, 2016) Ponnambalam, L.; Rekha, A.G.; Laxminarayan, Y.We developed an agent-based model containing 50 communities, replicating the 50 states of USA. The age distribution, approximate household size and the socio-structural determinants of each community were modeled based on the US Census. The agent-based model was validated using in-silico seroprevalence data collection. Medical seeking behavior of individuals was parameterized based on the socio-structural determinants of the community. The interventions proposed in literature were tested and the optimal intervention strategy to counter an epidemic outbreak has been identified. In addition, we included novel interventions like coordination among the communities and increasing the awareness of individuals in the lower ranked communities based on information exchange between communities. © Springer Science+Business Media Singapore 2016.Item Analyzing Derived Network Feature Importance to Identify Location Influence in LBSN(Institute of Electrical and Electronics Engineers Inc., 2023) Shetty, R.D.; Dewangan, S.K.; Bhattacharjee, S.Location-based social networks (LBSN) data is of greater importance in various domains of studies, such as viral marketing, location recommendation, influence maximization problem, etc. Identifying influential or hotspot locations of disease spread is of great importance during epidemic outbreaks, in case of limited vaccination, to make timely decisions about stay-at-home policy, restricting the transportation/human movement from one geographical location to another, etc. LBSN data can be used to construct the location-based weighted spatio-temporal network with its defined feature sets, including its rich collection of user movement information with respect to space and time. Here, we design two algorithms to generate spatio-temporal edge weights in such networks. We then examine four benchmark algorithms to identify the importance of these derived edge features to evaluate location influence in the context of contagious disease spread. It is observed that deriving edge features play an important role in the application scenario, especially to understand the need of dense network compared to the sparse network, generated from the LBSN data. © 2023 IEEE.