  India locked down 1.3 billion people on March 25, 2020 in the wake of
COVID-19 pandemic. The economic cost of it was estimated at USD 98 billion,
while the social costs are still unknown. This study investigated how
government formed reactive policies to fight coronavirus across its policy
sectors. Primary data was collected from the Press Information Bureau (PIB) in
the form press releases of government plans, policies, programme initiatives
and achievements. A text corpus of 260,852 words was created from 396 documents
from the PIB. An unsupervised machine-based topic modelling using Latent
Dirichlet Allocation (LDA) algorithm was performed on the text corpus. It was
done to extract high probability topics in the policy sectors. The
interpretation of the extracted topics was made through a nudge theoretic lens
to derive the critical policy heuristics of the government. Results showed that
most interventions were targeted to generate endogenous nudge by using external
triggers. Notably, the nudges from the Prime Minister of India was critical in
creating herd effect on lockdown and social distancing norms across the nation.
A similar effect was also observed around the public health (e.g., masks in
public spaces; Yoga and Ayurveda for immunity), transport (e.g., old trains
converted to isolation wards), micro, small and medium enterprises (e.g., rapid
production of PPE and masks), science and technology sector (e.g., diagnostic
kits, robots and nano-technology), home affairs (e.g., surveillance and
lockdown), urban (e.g. drones, GIS-tools) and education (e.g., online
learning). A conclusion was drawn on leveraging these heuristics are crucial
for lockdown easement planning.
