Panic buying and hoarding express common human behavior in times of crisis. Early in COVID-19, as the pandemic crisis intensified, toilet paper was one of the emblematic cases of panic buying. Using a Geographic Information System (GIS) to cross official per capita income data and real toilet paper transactions obtained from groceries spread around the city of São Paulo (Brazil), this study compares sales levels during the period in which panic purchases took place to the sales levels off that period. As expected, that data disclose noticeable panic buying. Regression analysis reveals that there is a significant positive correlation between average income per capita and panic buying. The results also indicate that panic buying happens in every income class, including low-income ones and contribute to enhancing the understanding of demand behavior during periods of crisis.

Background: Transportation demand forecasting is an essential activity for logistics operators and carriers. It leverages business operation decisions, infrastructure, management, and resource planning activities. Since 2015, there has been an increase in the use of deep learning models in this domain. However, there is a gap in works comparing traditional statistics and deep learning models for transportation demand forecasts. This work aimed to perform a case study of aggregated transportation demand forecasts in 54 distribution centers of a Brazilian carrier. Methods: A computational simulation and case study methods were applied, exploring the characteristics of the datasets through autoregressive integrated moving average (ARIMA) and its variations, in addition to a deep neural network, long short-term memory, known as LSTM. Eight scenarios were explored while considering different data preprocessing methods and evaluating how outliers, training and testing dataset splits during cross-validation, and the relevant hyperparameters of each model can affect the demand forecast. Results: The long short-term memory networks were observed to outperform the statistical methods in ninety-four percent of the dispatching units over the evaluated scenarios, while the autoregressive integrated moving average modeled the remaining five percent. Conclusions: This work found that forecasting transportation demands can address practical issues in supply chains, specially resource planning management.

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.