For over a century scientists have struggled to prove viral contagion. There are gaping holes in the current model of viral transmission but these holes are not a reason to throw out the concept of a virus. We need a new model that fits the evidence. Here I set out the evidence for and against the current model and suggest a way to reconcile the apparent contradictions.

Evidence for viral contagion

Viral genetic material turns up in clusters of sick people. The sequences match. They change over time with new mutations in consistent ways without reverting. Even though testing is not perfect, people with positive tests are far more likely to be sick than not.
Viruses have been well described. Even if isolation methods are not flawless, electron microscopy and crystallography have shown fine-grained details including the shape of structures like the surface of the spike protein.
At high doses, transmission works. Human challenge trials demonstrate that viral exposure can cause illness when the dose is high enough.
Genetic tracing of viruses during outbreaks shows that distinct lineages spread from person to person in predictable clusters, confirming person-to-person transmission.
This evidence explains the mechanism of viral infection. But it does not explain the timing of the waves of infection that are characteristic of many viruses like influenza and COVID-19.

Where the traditional model fails

Normal-dose challenge trials often fail. The evidence here is strong: under experimental conditions, exposure frequently does not result in illness. A recent study confirmed this again.
Hospital-acquired infections peak at the same time as cases in the community. If spread were primarily driven by close contact, we would expect a lag, as community infections peak then admissions then within hospital infections. But the expected lag does not occur. In fact, hospital-acquired infections peak before the admissions to the hospital.
Waves occur with seasonal regularity. Epidemic peaks in the UK often occur with peak deaths at predictable times of year before falling away for a time:
Every early January
Often early April
Sometimes in July
Every late October
In the USA, the southern states saw more of a summer wave during Covid and wastewater analysis for viral load shows a repeating pattern that continues to this day. The January 2022 Omicron wave (which occurred after a huge Christmas vaccine booster campaign) was exceptionally sharp in its rise and fall and followed by a deficit. However, the timing of peaks and troughs is predictable, with a low every spring and a peak every summer and winter.

Read More: The Unsolved Mystery of How Viruses Spread – and Why Germ Theory Isn’t the Whole Answer