“5G technology causes or spreads COVID-19”

• Claim: 5G technology causes or spreads COVID-19
• Verdict: FALSE — scientific evidence completely refutes any causal connection between 5G and COVID-19
• Evidence: L1 (highest level) — multiple independent studies, social network analysis, epidemiological data
• Key anomaly: The 5G-COVID conspiracy theory spread faster than the virus itself, despite having no scientific basis. Only 7.3-14.3% of surveyed populations believed in this connection, yet the misinformation led to real-world acts of vandalism against telecommunications infrastructure (S004, S007)
• 30-second check: COVID-19 is a viral disease caused by SARS-CoV-2, which spreads through respiratory droplets. 5G is a radiofrequency technology. Radio waves cannot create or transmit viruses. The pandemic began in Wuhan in late 2019, where 5G was not widely deployed, and spread to regions without 5G coverage (S012, S015)

Steelman — what proponents claim

Proponents of the 5G-COVID connection advance several main arguments that must be examined in their most compelling form:

Temporal coincidence argument: The rollout of 5G networks in some cities coincided with the beginning of the COVID-19 pandemic in late 2019 to early 2020. Proponents point to this temporal overlap as potential evidence of a connection (S004, S016).

Radiofrequency exposure argument: Some claim that 5G millimeter waves can be absorbed by skin cells acting like antennas, potentially affecting biological processes. There even exists a retracted scientific paper that attempted to establish such a link (S018, S015).

Immune suppression argument: The theory suggests that 5G radiation may weaken the human immune system, making people more susceptible to viral infections, including COVID-19 (S009, S005).

Geographic correlation: Proponents point to the fact that some cities with early 5G deployment also became COVID-19 epicenters, such as Wuhan in China and some European cities (S012).

Social network analysis revealed that 34.8% of a sample of 233 tweets contained views linking 5G and COVID-19, demonstrating significant presence of this theory in public discourse (S004, S014).

What the evidence actually shows

Absence of biological mechanism: The fundamental problem with the theory is the lack of any plausible biological mechanism. COVID-19 is caused by the SARS-CoV-2 virus, which transmits through respiratory droplets from person to person. Radiofrequency radiation, including 5G, cannot create viruses, alter their genetic material, or facilitate their transmission (S015, S005).

Epidemiological data refutes the connection: Systematic analysis shows that COVID-19 spread in regions without any 5G coverage, including rural areas, developing countries, and places where 5G had not yet been deployed. Spatial analysis revealed logical fallacies in attempts to establish geographic correlation between 5G and COVID-19 (S012).

Low belief levels in the theory: A large-scale study in sub-Saharan African countries showed that only 7.3% of respondents believed in the 5G-COVID connection, with regional variations from 5.4% to 14.4%. This indicates that despite widespread dissemination of the theory on social media (78% obtain information about 5G from online sources), the majority of people do not accept this misinformation (S007).

Risk factors for misinformation belief: Research identified specific demographic and psychological factors associated with belief in the conspiracy theory. Females had 1.86 times higher odds of believing the myth, unemployed individuals had 1.91 times higher odds, and Central African populations showed 2.12 times higher odds. Importantly, those who believed the pandemic would not continue had 1.59 times higher odds of believing the conspiracy theory, indicating the role of optimism bias (S007).

COVID-19 misinformation in context: Contrary to the "infodemic" narrative, large-scale analysis of approximately 325 million social media posts showed that COVID-19 posts from March to May 2020 were 0.37 times as likely to link to "not credible" sources compared to the previous year. This suggests that health misinformation is a systemic feature of online communication, not unique to COVID-19 (S005).

Detection and analysis methods: Researchers developed sophisticated methods to identify and analyze 5G-COVID misinformation. Natural Language Processing (NLP), Social Network Analysis (SNA), and Graph Neural Networks effectively identify misinformation dissemination patterns. The CoMID model, combining content analysis with user propensity data, showed a 5% performance improvement (F1 score) compared to baseline methods (S009, S008).

Real-world consequences: The conspiracy theory led to destructive attacks on 5G towers in the UK and other countries, demonstrating how misinformation can result in real physical damage to infrastructure (S004, S007).

Conflicts and uncertainties

Retracted research problem: One significant issue in the scientific literature is the existence of a retracted paper that attempted to establish a link between 5G and COVID-19. Research on the COVID-19 retraction process revealed inconsistency and incompleteness in retraction procedures, creating risks for evidence-based decision-making. Discredited research may continue to inform clinical trials, policy, and practice (S006, S015).

Bias in peer review process: The paper claiming a link between 5G and COVID-19 symptoms underwent a biased peer review process conducted by vocal critics of 5G. The authors cherry-picked studies supporting their claim and acknowledged that their findings do not prove a link between 5G and COVID-19 symptoms (S015).

Role of social media platforms: While social media facilitates rapid misinformation spread, it also serves as a platform for debunking false claims. Content analysis showed that 32.2% of tweets from the sample denounced the conspiracy theory, while 33.0% were general tweets without expressing personal views (S004, S014).

Absence of authority figures: Social network analysis revealed a lack of authority figures actively combating such misinformation in the early pandemic period. This leadership vacuum allowed the conspiracy theory to spread relatively unchecked (S004).

Complexity of correcting misinformation: An experimental study with 502 participants showed that repeated exposure to myths within corrections increased misinformation familiarity and ultimately enhanced its credibility, even among those with low or moderate prior beliefs. This "backfire effect" presents a significant challenge for correction strategies (S010).

Interpretation risks

Correlation-causation fallacy: The primary logical error in the 5G-COVID theory is conflating temporal or geographic coincidence with causation. The fact that two events occur simultaneously or in the same location does not mean one causes the other. Spatial analysis revealed multiple logical fallacies in attempts to establish connections based on geographic data (S012).

Selective data use: Theory proponents often selectively present data, ignoring numerous counterexamples. For instance, they may point to cities with 5G and high COVID-19 rates while ignoring numerous regions with high COVID-19 rates without 5G, or regions with 5G and low COVID-19 rates (S012, S015).

Misunderstanding of radiofrequency radiation: There is a fundamental misunderstanding of the nature of radiofrequency radiation. 5G uses non-ionizing radiation, which lacks sufficient energy to damage DNA or create viruses. This is qualitatively different from ionizing radiation (such as X-rays or gamma rays) that can cause biological damage (S005).

Risk of repeating misinformation in corrections: Research shows that corrections should avoid dominant framing of original false claims and minimize unnecessary repetitions. Repeated exposure to myths, even in the context of debunking, can increase their perceived credibility through the "illusory truth effect" (S010).

Trust in science and scientists: Public trust in scientists is crucial for adherence to health recommendations. Studies often rely on assumptions about scientists and neglect scientific uncertainty, which can undermine trust when reality proves more complex (S002).

Recommendations for information evaluation

When evaluating claims about the 5G-COVID connection or any other health misinformation, consider the following:

• Verify sources: Posts from "not credible" sources are 3.67 times more likely to contain misinformation (S005)
• Look for biological plausibility: Is there a known mechanism by which the alleged cause could lead to the alleged effect?
• Consider alternative explanations: Can observed correlations be explained by other factors?
• Check scientific consensus: What does the majority of experts in the relevant field say?
• Beware of selective data: Is the complete picture presented or only supporting evidence?
• Consider motivation: Who benefits from spreading this information?

Conclusion: The claim that 5G technology causes or spreads COVID-19 is completely false and has no scientific basis. This is a classic example of a conspiracy theory that emerged from temporal coincidence, misunderstanding of technology and biology, and spread through social media. While only a minority of people believe this theory, it has led to real damage in the form of vandalism against telecommunications infrastructure. Understanding the factors that make people susceptible to such misinformation and developing effective correction strategies remain important public health challenges.