The term ‘infodemic’ coined by the WHO refers to misinformation during pandemics that can create panic, fragment social response, affect rates of transmission; encourage trade in untested treatments that put people’s lives in danger. The WHO and government agencies have to divert significant resources to combat infodemics. Their scale makes it essential to employ computational techniques for claim veracity assessment. However, existing approaches largely rely on supervised learning. Present accuracy levels fall short of that required for practical adoption as training data is small and performance tends to degrade significantly on claims/topics unseen during training: current practices are unsuitable for addressing the scale and complexity of the COVID-19 infodemic.