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Journal Article Epublish 1 citation

PubMed: 38422280 DOI: 10.11606/s1518-8787.2024058005413

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

Rev Saude Publica 58:06

Authors: Irineu de Brito Junior, Flaviane Azevedo Saraiva, Nathan de Campos Bruno, Roberto Fray da Silva, Celso Mitsuo Hino, Hugo Tsugunobu Yoshida Yoshizaki

Affiliations

  • Universidade Estadual Paulista "Júlio de Mesquita Filho". Instituto de Ciência e Tecnologia. Departamento de Engenharia Ambiental. São José dos Campos, SP, Brasil. Universidade de São Paulo. Escola Politécnica. Programa de Mestrado em Engenharia de Sistemas Logísticos. São Paulo, SP, Brasil.
  • Universidade de São Paulo. Escola Politécnica. Departamento de Engenharia de Produção. São Paulo, SP, Brasil.
  • Universidade Estadual Paulista "Júlio de Mesquita Filho". Instituto de Ciência e Tecnologia. Departamento de Engenharia Ambiental. São José dos Campos, SP, Brasil.
  • Universidade de São Paulo. Instituto de Estudos Avançados. São Paulo, SP, Brasil.
  • Universidade de São Paulo. Escola Politécnica. Programa de Mestrado em Engenharia de Sistemas Logísticos. São Paulo, SP, Brasil.

Abstract

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

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.

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 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.

MeSH

Ivermectin, Search Engine, Pharmaceutical Preparations, Chloroquine, Data Collection, Public Health, Covid-19, Data Analysis