With respect to AIM1, we developed and applied a disease-spread model to compare how well different testing strategies (including large-scale testing) could detect and control a pandemic. Combining with epidemiological outputs, such as infections, hospitalizations, and deaths avoided, we also developed a decision-analytic economic model with direct and indirect cost estimates, allowing for the calculation of Incremental Cost-Effectiveness Ratios (ICERs) across alternative testing and intervention strategies for assessing cost-effectiveness. Related to AIM2, we further advanced our user-friendly saliva sample collector by improving its design and made it compatible with downstream automated laboratory equipment, followed by both technical and analytical validations. Moreover, we have developed a strategy to combine thousands of samples by transforming them into microscopic droplets and processing them together with automated laboratory equipment. Also, we have assessed that our test will be effective in detecting the latest SARS-CoV-2 variants, as well as Influenza A subtypes of public health relevance, next to optimizing the conditions to ensure reliable virus detection in saliva samples collected in sampling cards. Regarding AIM3, we set up and ran a digital cohort study over two seasons to assess which self-testing and sampling approaches best identify acute respiratory infections and how user-friendly the used digital tool is. Moreover, we identified the key requirements for large-scale testing during future pandemics, including how to collect and transport samples efficiently, how to integrate them into automated labs, and how to ensure the whole process is fast, reliable, and ready to scale when needed.