COVID-19 in Spain and India: Comparing Policy Implications by Analyzing Epidemiological and Social Media Data

TL;DR

This study compares COVID-19 policy effectiveness in Spain and India by analyzing epidemiological data and social media sentiment, revealing how citizen ideology influences outbreak control and future case predictions.

Contribution

It introduces a data-driven approach combining social media analysis and epidemiological data to compare public health policies across countries with similar demographics.

Findings

Twitter sentiment correlates with new case trends

Policy-related social media analysis can predict outbreak trajectories

Citizen ideology impacts policy adherence and effectiveness

Abstract

The COVID-19 pandemic has forced public health experts to develop contingent policies to stem the spread of infection, including measures such as partial/complete lockdowns. The effectiveness of these policies has varied with geography, population distribution, and effectiveness in implementation. Consequently, some nations (e.g., Taiwan, Haiti) have been more successful than others (e.g., United States) in curbing the outbreak. A data-driven investigation into effective public health policies of a country would allow public health experts in other nations to decide future courses of action to control the outbreaks of disease and epidemics. We chose Spain and India to present our analysis on regions that were similar in terms of certain factors: (1) population density, (2) unemployment rate, (3) tourism, and (4) quality of living. We posit that citizen ideology obtainable from twitter conversations can provide insights into conformity to policy and suitably reflect on future case predictions. A milestone when the curves show the number of new cases diverging from each other is used to define a time period to extract policy-related tweets while the concepts from a causality network of policy-dependent sub-events are used to generate concept clouds. The number of new cases is predicted using sentiment scores in a regression model. We see that the new case predictions reflects twitter sentiment, meaningfully tied to a trigger sub-event that enables policy-related findings for Spain and India to be effectively compared.