Chapter 9 284 B.1.617.2)[93]. Recent evidence from the RIVM’s nationwide, survey-based cohort study also suggests that the risk of long COVID following infection with Omicron may be lower than for previous sub-variants[94]. Nonetheless, as we can expect to see ongoing waves of Omicron infections due to restrictions largely dissolved worldwide, and without an effective treatment for long COVID, even a small risk of long COVID will lead to an increase in the number of individuals living with the condition. It is also plausible that a future sublineage of SARS-CoV-2 may change the landscape of long COVID altogether. Thirdly, it remains unclear why women have been consistently shown to face a higher chance of developing long COVID than men. There is an urgent need for sex-specific research that takes into account specific features of women’s health that may be associated with long COVID pathogenesis[73]. Fourth, it has not been established to what extent persistent fatigue as part of long COVID (this thesis, Chapter 5) differs from other post-infectious fatigue syndromes. Given the possible overlapping pathological processes of these conditions, it is important to ensure that partnerships are established so that the underlying mechanisms and psychosocial consequences of different post-infectious illnesses can be compared and contrasted. The COFFI consortium[95] is one such platform, allowing research groups to collaborate on investigating the common features of numerous post-infectious syndromes with fatigue as a central symptom, including long COVID. Lastly, there is a paucity of data regarding the occurrence of long COVID among marginalised populations who, as outlined in Chapter 2 and Chapter 3, have been disproportionately affected by the COVID-19 pandemic. Facilitating participation in long COVID research among individuals with lower incomes, poorer health literacy, and facing language barriers, should be the rule rather than the exception. 9.3 CHALLENGES IN INTERPRETING DATA COLLECTED DURING A PANDEMIC In the final part of this thesis, I will touch upon some of the key challenges in interpreting two main data sources used – passive surveillance data and data collected via an observational study. The aim of this section is to reflect upon why optimising data collection, interpretation and relevance to guide policymaking in real-time is key to evidence-based decision-making during a crisis. 9.3.1 Surveillance data in the Netherlands Ensuring that passive surveillance data on infectious diseases are timely and comprehensive during an outbreak can be extremely challenging. This means that
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