Anna C. Nilsson

Anna C. Nilsson serves as Associate Professor in Clinical Infection Medicine at Lund University's Faculty of Medicine and is affiliated with the Infect@LU research group. Her work spans infectious disease research with focus on Staphylococcus aureus, antibiotic resistance, and pandemic-related critical care outcomes.

Her research interests center on infectious disease pathogenesis, particularly Methicillin-Resistant Staphylococcus Aureus (MRSA) dynamics (68% of her work), COVID-19 long-term effects (60%), and emergency department infection management (40%). She investigates symptom patterns (61%), drug resistance mechanisms (54%), and therapeutic interventions across diverse patient populations including injection drug users and critically ill patients.

Recent publication trends reveal strong emphasis on post-pandemic health outcomes and antibiotic resistance evolution. Her 2024-2025 work shows increasing focus on mental health sequelae in critical illness survivors and molecular mechanisms of Staphylococcus aureus resistance. The RifRSA project (2021-2026) represents her major ongoing investigation into rifampicin resistance during tuberculosis treatment.

Her scientific contributions align with UN Sustainable Development Goals related to health and well-being. Current collaborations span multiple European institutions with active networks in emergency medicine, microbiology, and critical care research.

As Principal Investigator on the RifRSA study funded by Maggie Stephens Foundation, she leads research on Staphylococcus aureus resistance patterns during latent tuberculosis treatment. Her work demonstrates significant media engagement with coverage in 2 news outlets, 12 X platform discussions, and 13 Mendeley readers for key publications.

She maintains active laboratory work through Infect@LU with focus on bacterial genomics and clinical outcomes research. Current projects investigate longitudinal infection patterns in vulnerable populations and biomarker development for emergency department risk stratification.