Background: Post COVID-19 Condition (PCC, also known as long COVID and post-acute sequelae of COVID-19) is a major public health concern with severe and pervasive impacts on physical and mental health. PCC is highly heterogeneous and may manifest as different clusters of symptoms of varying intensity and duration. The etiology of PCC remains uncertain, though several underlying pathophysiological mechanisms, such as cellular damage, inflammatory cytokines, and a hypercoagulable state, are thought to contribute to PCC inception and trajectory. Examination of potential serological markers of PCC, accounting for clinical covariates, may yield emergent pathophysiological insights. Objectives: Primary objectives of this thesis are to 1) Identify key clinical and potential serological predictors of PCC; 2) Acquire clinical and serological data in a large-scale prospective observational study; 3) Assess relationships between PCC and serological markers, accounting for clinical covariates; 4) Systematically review evidence to date on primary observational studies comparing serological response between people with and without persistent symptoms post COVID-19 recovery; 5) Discuss persisting gaps in knowledge and data quality, and propose strategies for resolve. Methods: This thesis is framed around three core efforts: 1) The design of survey questions and study materials, recruitment of participants, and data collection in a large-scale prospective cohort study launched in 2020; 2) The assessment of relationships between pre-defined serological predictors and PCC, accounting for clinical covariates; and 3) A robust rapid review of PCC onset and phenotype as functions of serological markers. Expert opinion was sought to define serological predictors. Clinical predictors were defined a priori based on systematic reviews meeting AMSTAR 2 guidelines. Conclusions: To address objectives, we described efforts to collect clinical and serological data from a large-scale prospective cohort study; identify PCC-cases and infected-controls; assess associations between pre-defined serological predictors (IgG titres targeting SARS-CoV-2 spike (S), nucleocapsid (N), and receiver binding domain (RBD) antigens, and efficient neutralization) and PCC; and synthesized findings from an extensive rapid review on PCC as a function of serological markers. Our multivariate analysis using Stop the Spread Ottawa data is, to our knowledge, the first Canadian study to report the direction and magnitude of association between selected serological predictors (anti-IgG response to S, N, and RBD SARS-CoV-2 antigens, and neutralizing efficiency) and PCC status and impact on quality of life. Finally, we described five potential strategies which may improve the accessibility, quality, and amalgamation of data pertaining to PCC: 1) Fostering comparability between studies to enable synthesis of multiple datasets; 2) Advancing the characterization and consensus on PCC phenotypes; 3) Employing innovative modelling strategies that could potentially yield novel insights; 4) Promoting robust collaboration and knowledge sharing among research teams; and 5) Engaging people with lived experience at all stages of research.

Two years after the first recorded outbreak of Covid-19 its long-term effects are still not completely understood. An unknown proportion of all covid patients go on to develop post-acute covid syndrome and suffer long-term symptoms and health effects long after the initial infection subsides. Project Crowdsourcing Långtidscovid-Sverige sent out in summer of 2021 an open online survey and gathered respondents through crowdsourcing to gather info about people in Sweden with prolonged health effects lasting at least three months after confirmed or suspected Covid-19 infection. In this thesis an explorative analysis of the aforementioned survey is conducted with its initial focus placed onthe progression of symptoms. Descriptive statistics are provided for the survey sample; hierarchical clusteringon principal components is performed; and association rule mining as well as sequence rule mining is used toextract frequently co-occurring symptoms. Women stand for 85.2% of all respondents, possibly indicating a skewed gender distribution in the sample. The average age of a respondent is 50 years old, but ranges between 18 and 80 years of age. The number of reported symptoms tend to diminish over time and symptoms within the 'air passages' category diminish on average quicker than other categories. Hierarchical clustering with Ward’s criterion revealed 4 clusters with an average silhouette coefficient of 0.246. The resulting clusters are not well-separated from each other and have some overlap in their bordering regions, and should therefore be interpreted with caution. Broadly speaking, individuals from cluster 1, 3 and 4 are distinguished primarily by their total number of symptoms reported, meanwhile cluster 2 is characterized by individuals that experience many symptoms early on and fewer symptoms later on. The most prevalent symptom over the entire period is fatigue (90.2%), closely followed by worsening symptomsafter physical activity (87.1%), problems with concentration (82.3%), headaches (79.5%), and brain fog (77.9%). There are several strong associations between various symptoms, especially for symptoms within the same category. Most symptoms have a sequential correlation with themselves and have an increased tendency to occur several times.