Chapter 9 286 throughout the pandemic, particularly following the emergence of new VoCs, was genomic surveillance. Initiatives such as the Amsterdam Regional Genomic Epidemiology & Outbreak Surveillance (ARGOS) Consortium[103], a partnership between the PHSA and the Amsterdam UMC, helped form a strong link between epidemiological and genomic data. The goal of this consortium was to quickly evaluate the impact of new VoCs on transmission dynamics, thus informing public health policy in real-time. Although each surveillance method mentioned above has its own limitations, combining the resulting sources of information allowed data interpretation to be refined, providing policymakers with a thorough understanding of the situation at hand. Indeed, current surveillance of SARS-CoV-2 in 2023 in the Netherlands utilizes multiple data sources concurrently to form an overall evaluation of COVID-19 dynamics. Taking advantage of the inter-pandemic period to further hone this methodology with respect to future outbreaks of different pathogens is imperative. A key challenge of using passive surveillance data for research purposes is highlighted was revealed in collecting the data necessary for Chapter 2 and Chapter 3 of this thesis. In these studies, we wished to match notification data to municipality records to obtain individuals’ country of birth, as these data are not collected in notification records. However, this process was time-consuming due to restrictions in privacy legislature[104]. Such legislation also hinders the matching of test data to medical records to improve completeness of hospitalisation status and investigate individual-level medical risk factors for adverse outcomes. In preparation for a future pandemic, pre-emptive integration of key data sources would allow for crucial information to be generated, including sub-group analyses of key risk populations, in a more timely and representative manner. An example of the success of this approach is in Denmark, where early linkage of several national registries allowed for rapid analyses of individual patient data within an anonymised database to guide policymakers in real-time[105, 106]. In addition to integrating existing databases of routinely collected information, utilising long-standing population-based studies may also help quickly generate crucial information. Existing cohort studies such as HELIUS allowed for seroprevalence of SARS-CoV-2 infection to be retrospectively analysed, revealing widespread transmission among the Ghanaian community during the first COVID-19 wave that had not been detected in notification data[107]. The value of the Lifelines Cohort in investigating long COVID in the Netherlands[55] has also been discussed extensively in Section 9.2.1 of the general discussion. Cross-sectional studies such as the PIENTER study can also be quickly adapted to perform sero-surveys in the Netherlands for future novel pathogens[108].
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