Focus: Developing and applying real-time analytical frameworks for outbreak response, from community surveillance to intervention evaluation.
COVID-19 Pandemic Response
During the COVID-19 pandemic, several major initiatives provided real-time epidemiological intelligence:
REACT Study (England)
The REal-time Assessment of Community Transmission (REACT) study was one of the largest community surveillance programmes for SARS-CoV-2. Using repeated cross-sectional surveys of random samples of the population in England, infection prevalence was tracked and resurgences were detected before they appeared in routine surveillance data.
Chadeau-Hyam, M., Wang, H., Eales, O., et al. SARS-CoV-2 infection and vaccine effectiveness in England (REACT-1): a series of cross-sectional random community surveys. The Lancet Respiratory Medicine 2022. Read paper →
Eales, O., Wang, H., Haw, D., et al. Trends in SARS-CoV-2 infection prevalence during England’s roadmap out of lockdown, January to July 2021. PLOS Computational Biology 2022. Read paper →
Eales, O., Walters, C. E., Wang, H., et al. Characterising the persistence of RT-PCR positivity and incidence in a community survey of SARS-CoV-2. Wellcome Open Research 2022. Read paper →
Elliott, P., Haw, D., Wang, H., et al. Exponential growth, high prevalence of SARS-CoV-2, and vaccine effectiveness associated with the Delta variant. Science 2021. Read paper →
Riley, S., Ainslie, K. E. C., et al. Resurgence of SARS-CoV-2: Detection by community viral surveillance. Science 2021. Read paper →
Ward, H., Atchison, C., Whitaker, M., et al. SARS-CoV-2 antibody prevalence in England following the first peak of the pandemic. Nature Communications 2021. Read paper →
Mobility Data and COVID-19 Transmission
Mobile phone mobility data provided a near-real-time proxy for population-level social distancing behaviour during the COVID-19 pandemic. Anonymised, aggregated crowd-level data showed that initial compliance with social distancing interventions in the UK was high and geographically consistent across regions. This work also examined how mobility data could be used to monitor the success of countries exiting strict social distancing measures.
Jeffrey, B., Walters, C. E., Ainslie, K. E. C., et al. Anonymised and aggregated crowd level mobility data from mobile phones suggests that initial compliance with COVID-19 social distancing interventions was high and geographically consistent across the UK. Wellcome Open Research 2020. Read paper →
Ainslie, K. E. C., et al. Evidence of initial success for China exiting COVID-19 social distancing policy. Wellcome Open Research 2020. Read paper →
Related software: pika
COVID-19 Scenario Modelling at RIVM
Scenario modelling at RIVM (the Dutch National Institute for Public Health and the Environment) to anticipate the impact of COVID-19 vaccination strategies on disease outcomes in the Netherlands, including evaluating vaccination rollout timing and booster campaigns.
See Modelling for Public Health Policy for details on this work.
ESCAPE Project
The ESCAPE (European Surveillance and Control of Antimicrobial Resistant, Pandemic, and Epidemic Diseases) project aims to build European capacity for rapid response to future pandemic threats.
Key objectives:
- Developing standardised surveillance protocols
- Building modelling capacity across European institutions
- Creating frameworks for rapid data sharing during outbreaks
- Training the next generation of outbreak responders
My contribution to ESCAPE includes developing the mitey R package for estimating key epidemiological parameters (serial interval, reproduction number), applied to scabies and other neglected diseases.
Ainslie, K. E. C., Hooiveld, M., Wallinga, J. Estimation of the epidemiological characteristics of scabies. Nature Communications 2025. Read paper →
Related software: mitey
ESCAPE resources: Project website Digital Toolkit