Using Model-based Evidence to Optimise Medical Intervention Profiles and Disease Management Strategies for COVID-19 Control
This project’s multidisciplinary team of researchers combined expertise in mathematical modelling and epidemiology to develop and put into operation a model-based decision framework called OpenCOVID. This was used to inform the optimal properties of strategies for the delivery of disease prevention and therapeutic approaches.
Decision Making During a Global Pandemic
During the COVID-19 pandemic, countries’ governments were required to make rapid decisions about the control measures that would be imposed to prevent the spread of the virus and reduce the associated burden of the disease on healthcare services. At the beginning of the pandemic, there was no proven medical or pharmaceutical tool to prevent or treat the disease and so the measures taken primarily included spatial distancing and lockdown. Over time, as vaccines and effective treatment options were developed and discovered, the scope of decision making and the number of variables that were relevant to this decision making, grew to consider these new developments.
Guidance was needed which took account of the relevant variables to enable the design of optimal control and intervention strategies. It was essential that these control strategies suppressed transmission and averted mortality, while providing a route out of the lockdown measures.
Mathematical Modelling to Support Decision Making
Very early in the pandemic, the multidisciplinary team of epidemiologists and mathematical modellers involved in this project recognised the potential for the use of mathematical modelling to support decision-makers. They observed that models of COVID-19 transmission dynamics could be developed efficiently and effectively to estimate the quantitative impact of COVID-19 interventions using the available information about disease progression, disease transmission, host immunity and health system interactions.
Using state-of-the-art epidemiological modelling and analysis, the team developed a model-based decision framework called OpenCOVID. This framework was designed to assist decision makers in optimising diagnostics, testing response strategies and deploying new treatments and vaccines. It enabled the estimation and comparison of individual and population health consequences of alternative diagnostic and pharmaceutical strategies.
The Enduring Utility and Impact of OpenCOVID
The work carried out by this team was impactful across scientific, policy, clinical and public health spheres. Notably, during the pandemic, the team, led by Prof Melissa Penny contributed to the activities of the European COVID-19 Scenario Hub led by the European Centre for Disease Prevention and Control (ECDC). Additionally, the team’s work continues to inform future European pandemic response as a result of contributions to a longer-term forecast initiative led by the ECDC COVID-19 Forecast Hub. Furthermore, the model is evolving to incorporate more nuanced considerations of immunity, accounting for natural infection or vaccination.
The adaptable tool developed in response to the COVID-19 pandemic is proving to have broad and enduring benefit. The team is working to extend OpenCOVID’s scope to encompass general respiratory virus dynamics, with a primary focus on the respiratory syncytial virus (RSV), a disease which is particularly concerning for paediatric and older adult populations.
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