Cybercrimes and crime against women increased while other crimes declined during the COVID-19 lockdown
Aditi Jain || 13 Sep 2021

COVID-19 pandemic brought the world to a grinding halt. Nations shut down the non-essential services to cut down the transmission of this new unknown disease. Whilst hospitalizations and mortality were the direct consequences of this undesirable pandemic phase, people suffered due to loss of jobs and for the poor, it became difficult even to make the two ends meet. Experts debated on whether the damage to the economy will be worse than the spread of infection and associated hospitalization and mortality. If we turn the pages of history, it shows that the devastating influenza flu posed lockdown of 1918 also led to a 38% decline in criminal cases in Chicago, US. However, we are 100 years past that time and nature and tactics used in a crime have changed since then.

Nandan Sudarsanam, an AI Scientist at IIT Madras and a member of Robert Bosch Centre of Data Science and Artificial Intelligence (RBCDSAI) wondered if people resort to crimes out of desperation during such testing economic phases or if the crime rates decrease due to restricted movement of criminals during such periods? He also pondered over if the fear of catching the virus could have also played a role in decreasing the crime rates during the pandemic. Nandan and his team at IIT Madras decided that it will be worthwhile to gauge the criminal behaviour more closely during these unconventional times. The team decided to study the impact COVID-19 induced lockdown on crime rate using the data science approach.

Researchers decided to study if the incidences of different types of crimes increased or dropped during the lockdown. As the movement was restricted during the lockdown periods, the researchers also checked if the number of distress calls also translated to the actual FIRs made of the crime. Studying this part helped researchers gain an understanding of whether the restricted movement also restricted the reporting of crimes.

For this study, the team used the data of distress calls and crime registration before complete lockdown January 1, 2018 to March 22nd, 2020), during complete lockdown (March 23rd to April 30th, 2020), during partial lockdown (May 1st to June 8th, 2020) and when restrictions were lifted completely in Tamil Nadu (November 24th to December 31st) received from the State Crime Records Bureau and State Police Master Control Room. This data was from 1,346 police stations across Tamil Nadu. The researchers used the Bayesian time series model to compare incidences of crimes during these four periods and studied seven classes of crime namely: property offences; crimes against women, children and elders; cases of missing persons and unidentified dead bodies; vehicle- and traffic-related offences; emerging crimes; and public nuisance events, offences against the human body.

The results showed that vehicle and traffic violations, property offences and missing person cases were considerably low when the movement of people was restricted, however, the incidences of cybercrimes, bank/ATM frauds and crime against women during complete and partial lockdown increased during the lockdown. The team was intrigued to find that though there were more distress calls received from vulnerable populations like women and the elderly, this trend of increase in the number of distress calls did not translate into the FIRs or registration of crime. The researchers believe that the restricted movement was the main factor due to which women and the elderly were unable to register formal complaints of crime against them.

This study, performed using crime data at these unconventional times, brings to light the aspects of criminal psychology and motivators for the crime. The knowledge obtained can help police and other government entities frame better policies and take actions to reduce crime.


Kandaswamy Paramasivan, Dr. Nandan Sudarsanam, Sivapriya Vellaichamy, Karysa K. Norris, and Rahul Subburaj.


Coronavirus, Stay-at-home Orders, Bayesian Inference, cybercrime, Crime Registration, crime against women.