Inefficacious drugs against covid-19: analysis of sales, tweets, and search engines

• Published in: Revista de saúde pública 2024-01, Vol.58, p.06-06
• Main Authors: Brito Junior, Irineu de, Saraiva, Flaviane Azevedo, Bruno, Nathan de Campos, Silva, Roberto Fray da, Hino, Celso Mitsuo, Yoshizaki, Hugo Tsugunobu Yoshida
• Format: Article
• Language: English
• Subjects: Chloroquine; Covid-19; Health Policy & Services; Infodemic; Ivermectin; Original; Pandemic; SARS-CoV-2; Search Engine; Social Media
• ISSN: 0034-8910; 1518-8787; 1518-8787
• DOI: 10.11606/s1518-8787.2024058005413

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. The methodology adopted in this study has four stages: data collection, data processing, exploratory data analysis, and correlation analysis. Spearman's method was used to obtain cross-correlations between each pair of variables. 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. 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 real-time 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.