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.
Rev Saude Publica. 2024 Feb 26;58:06. doi: 10.11606/s1518-8787.2024058005413. eCollection 2024.
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.
评估两种没有被证明对新冠病毒有疗效的药物（伊维菌素和氯喹）的销售情况与其他相关变量（如谷歌搜索量、与这些药物相关的推文数量、新冠病毒导致的病例和死亡人数）之间的相关性。
本研究采用了四个阶段的方法：数据收集、数据处理、探索性数据分析和相关性分析。采用斯皮尔曼方法计算了每对变量之间的交叉相关性。
结果表明变量之间存在相似的行为。峰值出现在同一时期或相近时期。探索性数据分析表明，在开始宣传这些药物用于治疗新冠病毒期间，氯喹出现短缺。这两种药物与其他变量均呈现出高度显著的相关性。此外，当我们采用一个月的滞后时，它们与药物销售的相关性更高。对于氯喹来说，这种相关性体现在死亡人数上。对于伊维菌素来说，这种相关性体现在推文数量、病例数量和死亡人数上。
这些结果为政府、行业和药店的危机管理决策提供了参考。在危机时期，如新冠疫情期间，一些变量可以帮助进行销售预测，尤其是谷歌搜索量和推文，可以实时分析情况。监测社交媒体平台和搜索引擎可以确定公众对药物的使用情况，并更好地预测这些药物需求的潜在高峰。
