A pilot study using financial transactions’ spatial information to define high-risk neighborhoods and distribution pattern of COVID-19

Abstract

Background Development of surveillance systems based on big data sources with spatial information is necessitated more than ever during this pandemic. Here, we present our pilot results of a new technique for the incorporation of spatial information of transactions and a vital registry of COVID-19 to evaluate the disease spread. Methods We merged two databases of laboratory-confirmed national COVID-19 registry of Iran and financial transactions of point-of-sale devices from February to March 2020 as our training data sources. Spatial information was used for the visualization of maps and movements of sick individuals. We used the point-of-sale devices-related guild to check for the dynamics of financial transactions and effectiveness of quarantines. Findings In the study period, 174,428 confirmed cases were in the COVID-19 registry with accompanying transactions information. In total, 13,924,982 financial transactions were performed by them, with a mean of 1.2 per day for each person. All guilds had a decreasing pattern of “risky” transactions except for grocery stores and pharmacies. The latter showed a decreasing pattern by impose of lockdowns. Different cities were the hotspot of disease transmission as many “high-risk” transactions were performed in them, among which Tehran (mainly its central neighborhoods) and southern cities of Lake Urmia predominated. Lockdowns indicated that the disease gradually became less transmissible. Interpretation Financial transactions can be readily used for epidemics surveillance. Semi real-time results of such iterations can be informative for policy makers, guild owners, and general population to prepare safer commuting and merchandise spaces.