Government-sponsored disinformation and the severity of respiratory infection epidemics including COVID-19: A global analysis, 2001–2020

Social Science & Medicine(2022)

Cited 11|Views2
No score
Abstract
Internet misinformation and government-sponsored disinformation campaigns have been criticized for their presumed/hypothesized role in worsening the coronavirus disease 2019 (COVID-19) pandemic. We hypothesize that these government-sponsored disinformation campaigns have been positively associated with infectious disease epidemics, including COVID-19, over the last two decades. By integrating global surveys from the Digital Society Project, Global Burden of Disease, and other data sources across 149 countries for the period 2001–2019, we examined the association between government-sponsored disinformation and the spread of respiratory infections before the COVID-19 outbreak. Then, building on those results, we applied a negative binomial regression model to estimate the associations between government-sponsored disinformation and the confirmed cases and deaths related to COVID-19 during the first 300 days of the outbreak in each country and before vaccination began. After controlling for climatic, public health, socioeconomic, and political factors, we found that government-sponsored disinformation was significantly associated with the incidence and prevalence percentages of respiratory infections in susceptible populations during the period 2001–2019. The results also show that disinformation is significantly associated with the incidence rate ratio (IRR) of cases of COVID-19. The findings imply that governments may contain the damage associated with pandemics by ending their sponsorship of disinformation campaigns.
More
Translated text
Key words
COVID-19,Pandemic,Disinformation,Government-sponsored disinformation,Respiratory infections,Internet censorship
AI Read Science
Must-Reading Tree
Example
Generate MRT to find the research sequence of this paper
Chat Paper
Summary is being generated by the instructions you defined