Just a month before COVID-19 emerged, the United States topped a list ranking 195 countries on their readiness to handle a viral disease.
The Global Health Security Index (GHSI), developed with input from a panel of international experts, turned out to have no predictive ability at all.
It failed because it focused on biomedical and clinical solutions while overlooking many critical aspects, such as the preparedness of people and governments to act collectively.
This demonstrates two problems in contemporary thinking about pandemic response.
First, there is a lack of recognition that social, cultural, economic and political environments are the foundation of public health. And second, there is a failure to look beyond a country’s planning and capacity to see that, unless an effective decision-making process is baked into the structure of national governance, all may still be lost.
Over the past decade researchers at the University of Melbourne have been working with policymakers to test strategies in safe, offline environments. These models have been used to better understand and predict the effect of public health responses to the pandemic.
Being able to visualise potential policy outcomes is valuable during emerging or unfolding crises. It means governments can effectively respond as circumstances change, and direct resources to where they are most needed.
Using digital data to understand human behaviours can generate real-world circumstances to represent critical structures, relationships and dynamics within a society and use these to devise realistic solutions in high-pressure situations such as pandemics.
For leaders and decision-makers, models of this kind — regardless of the issue at hand — are precious.
Models can combine multiple inputs and literally billions of policy combinations to help understand likely outcomes. They are flexible and transparent. Most importantly, they allow practice runs and training for decision-makers so they know what best to do when real crises strike.
Formal, accountable and contestable decision-making processes are crucial to pandemic emergency preparedness, yet the development of these processes is often neglected.
In the COVID-19 pandemic, national leaders who enacted decisions swiftly and in the collective interest — sometimes sacrificing short-term freedoms for longer-term benefits — have largely been better placed to manage health and economic outcomes, regardless of the technical capability available to them. Those that have passed on the responsibility to individual citizens have been less successful in limiting the impact of the pandemic.
Indexes that monitor the quality of decision-making processes might have a greater chance of predicting the trajectory of future pandemics than those that focus on technical aspects of disease monitoring and health systems alone.
If there is one thing the pandemic has made obvious, it’s that COVID-19 is as much a socially determined disease as it is a medical one.
Countries that were most successful at dealing with COVID-19 took strong, collective action to control infections before they overwhelmed health systems and economies. Collective measures included mobility restrictions, physical distancing, border closures, mask mandates, and strongly encouraged vaccinations. Officials in these countries also recognised the importance of economic packages to lessen the burden of complying with government regulations.
More than six million people are confirmed to have died from COVID-19, and some estimates suggest the real number could be three times higher.
If we equip decision-makers with the right scientific tools that create better outcomes for everyone, even during crisis situations, we will help reduce the carnage of future pandemics.
Dr Jason Thompson is an Associate Professor at the University of Melbourne. His work focuses on the translation of research into practice across the areas of transportation systems, public health, post-injury rehabilitation, and health system design.
Dr Rod McClure is an adjunct professor in the Faculty of Medicine and Health at the University of New England. He is an experienced systems scientist and practising public health physician.
The authors declare no conflict of interest.
Originally published under Creative Commons by 360info™.
