In 2020, just before measures were established by governments to minimize the spread of SARS-CoV-2, such as social distancing, quarantine, lockdowns, and internal movement restrictions, people began to buy some products in quantities much superior to their regular needs. This behavior is called panic buying and is a typical human response in times of crisis and uncertainty. This study compares retail sale levels during the period in which panic purchases occurred to the sales levels before and after that period. We analyzed the sales of five groups of products from 144 stores of two major retailers in São Paulo, Brazil. Several regression models were used to evaluate the data obtained from demographic information, actual sales, per capita income, and product sales transactions. The results show that sales before, during, and after the panic buying period varied according to the product type and increased according to the average per capita income of each store’s influence area. These results may assist policymakers in introducing public policies and managing resources during a crisis that requires social distancing and lockdowns.

OBJECTIVE: Assess the correlation between the sales of two drugs with no proven efficacy against covid-19, ivermectin and chloroquine, and other relevant variables, such as Google® searches, number of tweets related to these drugs, number of cases and deaths resulting from covid-19. METHODS: Th e m ethodology a dopted i n t his s tudy h as f our s tages: d ata c ollection, d ata processing, exploratory data analysis, and correlation analysis. Spearman’s method was used to obtain cross-correlations between each pair of variables. RESULTS: The results show similar behaviors between variables. Peaks occurred in the same or near periods. The exploratory data analysis showed shortage of chloroquine in the period corresponding to the beginning of advertising for the application of these drugs against covid-19. Both drugs showed a high and statistically significant correlation with the other variables. Also, some of them showed a higher correlation with drug sales when we employed a one-month lag. In the case of chloroquine, this was observed for the number of deaths. In the case of ivermectin, this was observed for the number of tweets, cases, and deaths. CONCLUSIONS: The results contribute to decision making in crisis management by governments, industries, and stores. In times of crisis, as observed during the covid-19 pandemic, some variables can help sales forecasting, especially Google® and tweets, which provide a realtime analysis of the situation. Monitoring social media platforms and search engines would allow the determination of drug use by the population and better prediction of potential peaks in the demand for these drugs.