Gabriel Pedersen

Vaccines are important to protect the population against viral diseases, including SARS-CoV-2, which is currently causing the COVID-19 pandemic. When developing a vaccine, it is necessary to administer virus to animals to elucidate whether vaccine candidates are protective. E.g. for testing vaccines against COVID-19, candidate vaccines are administered to animals and these are then challenged with the virus. The viral challenge is a fine tuned balance, since administering too much virus will cause unnecessary suffering and is not physiologically relevant (not reflecting the virus exposure normally encountered), whilst administering too little virus will give only few symptoms and thus requires more animals to assess if the vaccine prevents disease. Monitoring disease in itself presents some challenges. Animals are monitored by animal caretakers scoring for activity, however this is not done continuously and is based on a subjective assessment.

In this project, we will use automated monitoring to continuously asses animals following viral infection. In this way, the data quality should be improved, allowing us to use lower viral challenge doses and thus improving animal welfare. Furthermore, we expect that automated monitoring, by giving less data variation, will allow us to reduce the number of animals used to test protective efficacy of vaccines.