SPARK Insights: What is Mathematical Modelling?
COVID-19 has affected the world in so many destructive ways, but one positive we can take from this era is that knowledge of infectious diseases and epidemiology has improved as people seek answers to their questions about infectious disease outbreaks, why and how they happen, and what can be done to stop the spread of deadly diseases. The more knowledge we have, the better we are able to prepare for and respond to epidemic and pandemic diseases like influenza and novel coronaviruses like COVID-19.
As newscasters, journalists and politicians use epidemiological terminology more frequently, once nuanced jargon has become increasingly commonplace in Australia, and arguably worldwide. While people pore over the daily news to learn the Reff or case fatality rate, or how asymptomatic transmission has led to a seeding event or shake their heads in disdain as they read learn super spreaders amplifying community transmission, epidemiological terminology has crept into our lives and into our everyday conversations. The terms that were once met with disinterest or confusion have been thrust into the spotlight, making everyone an epidemiology aficionado in the span of a few months.
One area of interest for many has been mathematical modelling, which has been subjected to media scrutiny, and informed political decision making about strategic responses, quarantine measures and so much more. But how much do we really understand about modelling for infectious diseases?
Here are a few FAQs that will help you brush up on your knowledge about mathematical modelling, how it works and what it can used for.
What is modelling?
Modelling is a process in which real-life or hypothetical situations or scenarios are translated into mathematical language. These situations are analysed using well-defined rules and are guided by particular objectives. The model will produce outputs, which can then be analysed, adjusted and interpreted. Ultimately, models construct a theoretical representation of real-world situations.
There are different types of models which range in complexity. The type of model used will vary depending on the objectives and purpose of the study, the amount and quality of data available, and what is understood about the epidemiology of the disease.
Although SPECTRUM and SPARK focus on epidemiological modelling, and modelling of infectious diseases, modelling in general has a wide variety of applications across many different sectors, including finance, engineering, economics and technology.
How does modelling inform public health interventions?
Simulations and models are used to provide insights into infectious disease trends, quantify likely benefits of public health interventions and risks associated with certain actions or behaviours, and support risk assessment for emerging infectious diseases. They can provide a deeper understanding of the drivers of disease, provide frameworks for preparedness plans to mitigate negative impacts of disease outbreaks, and be used to develop strategies to respond quickly and effectively to outbreaks.
Ultimately, modelling helps to develop a scientific understanding of certain real-life or hypothetical situations, observe the effect of changes in these situations, and aid decision making. It can be utilized in across a wide range of situations and can provide critical information about infectious disease trends.
How has modelling been used during the COVID-19 pandemic?
Modelling has been used extensively to support decision making in Australia (and worldwide!) relating to the COVID-19 pandemic. Although the SPECTRUM and SPARK researchers also model other infectious disease outbreaks, COVID-19 has been a central focus in 2020 as many of the models produced by the SPECTRUM and SPARK researchers helped to inform decision- and policy-making when the situation was changing rapidly.
You can read more about how mathematical modelling gave researchers an early warning about the spread of COVID-19, how it was used to inform testing and response strategies for COVID-19 outbreaks in remote communities and how social media data allowed researchers to measure the impact of physical distancing measures on population movement during COVID-19 restrictions using mathematical models.
Does modelling predict the future?
Although they may not definitively predict the future, models can make predictions about certain outcomes in situations and scenarios. These outcomes may be affected by certain factors, such as time, population size, demographics, movement, season and more. The more information modellers have about a certain situation, the more accurate their model outputs or predictions will be. Models can use both qualitative and quantitative elements to strengthen the model.
Modellers work hard to ensure the outputs they produce have validity and accuracy, however there is always variability and randomness in real-life situations, which is important to understand when interpreting or analyzing a model – situations can change quickly, and unexpected events can occur, which means that although models are powerful tools, they should be seen as a support to decision making, rather than a decision maker in and of itself.
Who are the researchers that use these models?
Different researchers use different types of models based on their expertise. Modellers work across different fields, with different professional experiences, specialisations and skills. The modellers that contribute to SPECTRUM and SPARK’s project goals work in public health, mathematics, computer science, clinical medicine, epidemiology, project management, at universities, government departments and other institutions. The diversity within the consortium allows the researchers to engage effectively with a range of audiences, and bring their disciplinary insights and skills together to develop innovative model-based evidence to support complex decision making in public health.
(Written by Laura Bannerman, technical expertise and insights provided by Professor Jodie McVernon)