  Many governments have managed to control their COVID-19 outbreak with a
simple message: keep the effective '$R$ number' $R<1$ to prevent widespread
contagion and flatten the curve. This raises the question whether a similar
policy could control dangerous online 'infodemics' of information,
misinformation and disinformation. Here we show, using multi-platform data from
the COVID-19 infodemic, that its online spreading instead encompasses a
different dynamical regime where communities and users within and across
independent platforms, sporadically form temporary active links on similar
timescales to the viral spreading. This allows material that might have died
out, to evolve and even mutate. This has enabled niche networks that were
already successfully spreading hate and anti-vaccination material, to rapidly
become global super-spreaders of narratives featuring fake COVID-19 treatments,
anti-Asian sentiment and conspiracy theories. We derive new tools that
incorporate these coupled social-viral dynamics, including an online $R$, to
help prevent infodemic spreading at all scales: from spreading across platforms
(e.g. Facebook, 4Chan) to spreading within a given subpopulation, or community,
or topic. By accounting for similar social and viral timescales, the same
mathematical theory also offers a quantitative description of other
unconventional infection profiles such as rumors spreading in financial markets
and colds spreading in schools.
