Australia has relied heavily on computer models during the COVID-19 outbreak and a team of researchers, including those from The University of Western Australia, has developed a new process to harness multiple disease models for outbreak management.
The international research team will immediately implement the process to help inform policy decisions for the COVID-19 outbreak.
Professor David Pannell from UWA's Centre for Environmental Economics and Policy joined a team of six modellers and epidemiologists from the United States, the United Kingdom and China to design a process that could support policymakers worldwide.
The process has been published in Science and was awarded a Grant for Rapid Response Research (RAPID) from the National Science Foundation in the United States to deliver the approach.
During a disease outbreak, many research groups independently generate models to help inform public health policy for managing the outbreak.
These models are used to make predictions on how the disease will spread, which groups will be impacted most severely, or how implementing a particular management action would affect these dynamics.
Professor Pannell said that COVID-19 being a new disease meant epidemiological modelling groups had to operate with a high degree of uncertainty.
"At the onset of an outbreak, particularly for a new disease, a large amount of information is often unavailable or unknown, and researchers must make decisions about how to incorporate this uncertainty into their models, leading to differing projections and differing policy recommendations," Professor Pannell said.
"For the COVID-19 outbreak, for example, uncertainty is present in a wide range of areas, from infection rate to spread mechanisms to people's level of compliance with a lock-down."
The researchers' three-part modelling process is designed to make the most of the expertise across multiple modelling groups, to avoid the human biases that can emerge when a group of people interprets and uses information, and to provide the information most needed by decision makers.
Once policy-makers have confirmed their objective for management and the management options to be assessed, the research groups initially work independently to encourage a wide range of ideas without prematurely conforming to a certain way of thinking.
Then, the groups formally discuss their models and their results with each other to allow them to examine why their models might disagree. This allows each group to modify and re-run its model to come up with the best predictions. Policy-makers examine a suite of results, rather than a single average number, so that they can account for the uncertainties that remain.
Professor Pannell said the process allowed for the synthesis and evaluation of input from multiple modelling groups in an efficient and timely manner.
"Even after initial decisions are made, the process can continue as new information about the outbreak and management becomes available," Professor Pannell said.
"This 'adaptive management' strategy can allow researchers to refine their models and make new predictions as the outbreak progresses.
"For COVID-19, this process might inform how and when isolation and travel bans are lifted, and the circumstances in which these or other measures might be necessary again in the future."
The research team will share results with the US Centers for Disease Control and Prevention as they are generated.
