Do we understand our decision making data?

Thanks to COVID 19, modelling has become a familiar term in everyday life. Unfortunately, the unknown factors of the disease and the unpredictability of the new emerging variants at the beginning of the pandemic led some to ask ‘What 's the point of the modelling if is it so often ‘wrong’?”  The COVID lesson is modelling is not enough, we also need modelling literacy.

One common myth, reinforced by terms such as ‘prediction modelling’ and ‘forecast’, is that modelling predicts the future. It does not.

Modelling uses information available at a point in time to understand how a disease might behave in the future given a certain set of factors, or assumptions. This might include amount of disease already in the community, transmissibility, public compliance with advice on reducing the spread and availability and effectiveness of vaccines. Usually several ‘scenarios’ (including best and worst case) are presented where some of these factors are varied.

When governments use modelling to decide on the interventions necessary to restrict disease transmission, or to calculate the number of people that might require hospital treatment, they are doing so with uncertain and constantly changing information. Modelling literacy is the ability to understand what a model can and cannot do and how it should be used to inform decisions. A degree of modelling literacy is also needed to produce better models. Data collectors need to understand how good quality data will improve the accuracy of modelling results – and therefore how critical it is they do a good job of this.  Improved accuracy will also give the population confidence in modelling so they adhere to interventions aimed at reducing transmission and improve our chances of realising the ‘best case scenario’.

SPARK (Strengthening Preparedness in the Asia-Pacific Region with Knowledge)is an initiative by the Indo-Pacific Centre for Health Security to improve knowledge of infectious disease transmission and the value of using evidence for policy. The project provides training for health informatics students and researchers to build capacity for infectious disease surveillance, modelling and policy in the Indo Pacific region.  Programs offered through SPARK partner institutions in Australia, Thailand, Vietnam and Indonesia will generate a cohort of students and researchers in the region who can collect, analyse and use data on infectious diseases to inform decision making.

Improving skills to apply models in the country context is essential as disease transmission will differ according to factors such as demographics, levels of poverty and the underlying health of the population within a country. Participants will be able to apply their skills to infectious disease priorities in their countries and utilise a network of experts in the region.

SPARK is led by the Peter Doherty Institute and works with partners and collaborators globally, with a central focus on the Indo -Pacific region.

Sonja Firth, a technical associate at the Nossal Institute, is working with SPARK to deliver an education program across the Indo-Pacific Region to improve knowledge on infectious disease preparedness, analysis and response.

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Sonja Firth