Department of Global Health, School of Public Health, Boston University, Boston, MA, United States.
Biostatistics and Epidemiology Data Analytics Center, School of Public Health, Boston University, Boston, MA, United States.
J Med Internet Res. 2020 Dec 15;22(12):e24425. doi: 10.2196/24425.
The epidemic of misinformation about COVID-19 transmission, prevention, and treatment has been going on since the start of the pandemic. However, data on the exposure and impact of misinformation is not readily available.
We aim to characterize and compare the start, peak, and doubling time of COVID-19 misinformation topics across 8 countries using an exponential growth model usually employed to study infectious disease epidemics.
COVID-19 misinformation topics were selected from the World Health Organization Mythbusters website. Data representing exposure was obtained from the Google Trends application programming interface for 8 English-speaking countries. Exponential growth models were used in modeling trends for each country.
Searches for "coronavirus AND 5G" started at different times but peaked in the same week for 6 countries. Searches for 5G also had the shortest doubling time across all misinformation topics, with the shortest time in Nigeria and South Africa (approximately 4-5 days). Searches for "coronavirus AND ginger" started at the same time (the week of January 19, 2020) for several countries, but peaks were incongruent, and searches did not always grow exponentially after the initial week. Searches for "coronavirus AND sun" had different start times across countries but peaked at the same time for multiple countries.
Patterns in the start, peak, and doubling time for "coronavirus AND 5G" were different from the other misinformation topics and were mostly consistent across countries assessed, which might be attributable to a lack of public understanding of 5G technology. Understanding the spread of misinformation, similarities and differences across different contexts can help in the development of appropriate interventions for limiting its impact similar to how we address infectious disease epidemics. Furthermore, the rapid proliferation of misinformation that discourages adherence to public health interventions could be predictive of future increases in disease cases.
自疫情开始以来，一直存在关于 COVID-19 传播、预防和治疗的错误信息的流行。然而，有关错误信息暴露和影响的数据并不容易获得。
我们旨在使用通常用于研究传染病流行的指数增长模型，描述和比较 8 个国家 COVID-19 错误信息主题的开始、峰值和倍增时间。
从世界卫生组织的 Mythbusters 网站上选择 COVID-19 错误信息主题。代表暴露的数据从 Google Trends 应用程序编程接口获得，用于 8 个英语国家。对每个国家的趋势使用指数增长模型进行建模。
6 个国家的“冠状病毒 AND 5G”搜索开始时间不同，但在同一周达到峰值。对于所有错误信息主题，5G 的搜索也具有最短的倍增时间，在尼日利亚和南非最短（大约 4-5 天）。几个国家的“冠状病毒 AND 姜”搜索开始时间相同（2020 年 1 月 19 日那一周），但峰值不一致，并且在初始周之后搜索并不总是呈指数增长。“冠状病毒 AND 太阳”的搜索在各国开始时间不同，但在多个国家达到峰值时间相同。
“冠状病毒 AND 5G”的开始、峰值和倍增时间模式与其他错误信息主题不同，并且在评估的国家中大多一致，这可能归因于公众对 5G 技术缺乏了解。了解错误信息的传播、不同背景下的相似性和差异，可以帮助制定适当的干预措施来限制其影响，类似于我们如何应对传染病流行。此外，阻止公众遵守公共卫生干预措施的错误信息的快速传播可能预示着未来疾病病例的增加。
