198 Chapter 9 Throughout human history, viral respiratory infections have been a threat to public health1 causing significant morbidity and mortality every year2,3. Their ability to rapidly spread through populations and cause illness has led to continuous efforts to increase our understanding of these viral diseases. This thesis focuses on the use of large observational data sets, consisting of often routinely collected data to understand various aspects of (emerging) viral respiratory infections. Part I: Infectious disease surveillance To significantly increase the speed at which data are collected, processed and analysed, and to decrease costs it is essential to make a switch from manual to automated data collection for infectious disease surveillance4. In the first part of this thesis, different sources of passively collected data were studied for their use in infectious disease surveillance. In Chapter 2 three different proxies for Severe Acute Respiratory Infection (SARI) were compared as surveillance indicators. At present, there is no robust sentinel or universal SARI surveillance system in the Netherlands, but SARI surveillance is essential for disease control and prevention, pandemic preparedness and capacity management. Ideally, a surveillance system should be (near) real-time, combine syndromic surveillance with pathogen testing and be automated where possible, to decrease the administrative burden. In this proof-of-concept study, three different surveillance indicators were compared: International Classification of Disease (ICD)- 10 codes indicative of a SARI diagnosis, the number of Reverse Transcription Polymerase Chain Reaction (RT-PCR) tests, and the number of patients with contact and droplet isolation precaution labels registered. All three of these proxies could easily be extracted from electronic health records. The aim of this study was to assess whether these proxies were indicative of SARI and could be pragmatically used for monitoring of trends and capacity management in SARI surveillance A total of 117,404 admissions were included in our analysis, including 11,959 RTPCR tests, 4,683 contact and droplet precaution labels and 3,908 ICD-10 diagnostic codes. The three surveillance indicators followed roughly the same pattern, with differences in the absolute values. The average absolute count in the first time-period (pre-COVID-19) was lower than the average absolute count during and after the pandemic. The correlation between the different surveillance indicators was highest between contact and droplet precautions and ICD-10 diagnostic codes in the third time period (0.84). Positive RT-PCR virological test results were also collected. The total number of RT-PCR tests increased over time, especially since the COVID-19 pandemic, accompanied by a lower proportion of positive test results.
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