B0810
Title: Analyzing government health data to explore the COVID-19 management strategies
Authors: Soudeep Deb - Indian Institute of Management Bangalore (India)
Rishideep Roy - University of Essex (United Kingdom)
Deepti Ganapathy - Indian Institute of Management Bangalore (India) [presenting]
Abstract: Suppose government communication has a significant impact during a pandemic in mobilizing more than a billion people in India. In that case, key trigger nodes must determine how the communication takes place to manage the flow of information and ensure citizens follow lockdown measures. Trustworthy information is a key determinant of knowledge, attitudes and ultimately, behaviour, especially when phenomena are unknown; it is crucial for the government to manage information flow in the best possible way. In light of the above, a suitable statistical methodology is developed to measure and monitor the impact of government communication during health emergencies. Using preferential attachment model to understand the probabilistic structure of how new nodes (every announcement) are getting attached to the six themes of information over times, we model the time series of the probabilities using vector autoregressive process to quantify the effects of COVID-19 cases and deaths on the importance of the themes in this communication process.Through the aforementioned real-life application, it is shown that the proposed model can help deploy interventions that mitigate and protect against future acute health events. The methodology is generalizable and can be adapted to similar high-risk events.
