Modelling: Understanding pandemics and how to control them.

New disease challenges, societal demands and better or novel types of data, drive innovations in the structure, formulation and analysis of epidemic models. Innovations in modelling can lead to new insights into epidemic processes and better use of available data, yielding improved disease control and stimulating collection of better data and new data types. Here we identify key challenges for the structure, formulation, analysis and use of mathematical models of pathogen transmission relevant to current and future pandemics.

Modelling varicella vaccination - What does a lack of surge in herpes zoster incidence tell us about exogenous boosting?

Background: For decades, assessments of the impact of universal varicella vaccination on the epidemiology of varicella and herpes zoster (HZ) have been made using mathematical modelling. Decreased virus circulation and the resulting diminished exogenous boosting have been predicted to lead to a surge in HZ incidence. Lately, the exogenous boosting hypothesis has been challenged due to a lack of an extensive surge in HZ incidence in countries with, by now long-standing universal varicella vaccination.

Key questions for modelling COVID-19 exit strategies.

Combinations of intense non-pharmaceutical interventions (lockdowns) were introduced worldwide to reduce SARS-CoV-2 transmission. Many governments have begun to implement exit strategies that relax restrictions while attempting to control the risk of a surge in cases. Mathematical modelling has played a central role in guiding interventions, but the challenge of designing optimal exit strategies in the face of ongoing transmission is unprecedented.

"How do online and offline sampling compare in a multinational study of drug use and nightlife behaviour?".

Background: Online sampling is widely used to recruit hard to reach samples such as drug users at nightlife events. We conducted the first study comparing differences in demographics, drug use and nightlife behaviour between an online sample of young adults engaging with the European nightlife scene, and an offline sample recruited at nightclubs and festivals in Europe.

Methods: Online participants who attended at least six nightlife events in the past 12 months were recruited using social media advertising (May-November 2017).