The past couple of years has been a roller coaster of shock, despair, hope and marvel, as the COVID pandemic has ravaged the world and changed the way we live – in some ways, forever. Throughout the pandemic, most governments have done their best to “follow the science”. Teams of scientists have worked not just to discover treatments and vaccines for this new global enemy, but also to develop public health plans and strategies that might help to slow its spread.
The science of epidemiology has become increasingly prominent in running our countries and our lives, but where do those data come from?
What is Epidemiology?
The science of epidemiology has come a long way since the term was first used around 200 years ago. Known as the father of modern epidemiology, John Snow is most famous for his investigations into 19th-century cholera outbreaks in London (England), where his identification of a dirty water pump, and subsequent chlorination of the water, ended an epidemic. Although Snow’s methods were not widely applied until after his death, this was the first clear example of applying epidemiology to a public health situation, to avoid further outbreaks of disease.
The methods used in these historical times are not difficult to imagine – they would have involved mapping the outbreaks, looking for common causative factors, evaluating them, and finding a suitable intervention to correct them. To a certain extent, the same approach applies today, but now we are aided by a range of technologies that make our epidemiological studies faster, better and more reliable.
Most markedly, the need to achieve independent samples large enough to provide adequate statistical power led to the development of large research teams, collaborative studies, medical and surgical equipment and the introduction of analytical tools. By the end of the 20thcentury, new technologies had already transformed the science of epidemiology, which was particularly affected by advances in data technology, modelling and genetics.
Data Technology
While multicentre biobanks and collaborative databases are greatly impressive, perhaps one of the most fascinating development of our century has been the use of smart devices to collect data from individuals in real time. Smartphones, wearables and such other mobile connected devices already provide powerful real-time epidemiology tools at scale [1].
As well as providing a multitude of data for continuous measurement of critical biomarkers for medical diagnostics, physiological health monitoring and evaluation, these technologies have been implemented during the COVID pandemic in various ‘track and trace’ programmes intended to remove infected individuals (or those at risk of infection) from the general population to reduce spread. If used correctly and routinely, these tools have the potential to dramatically reduce the spread of disease.
Modelling
Epidemiological modelling can be a powerful tool to assist in health policy development, and disease prevention and control. Models can vary from simple deterministic mathematical models through to complex spatially-explicit simulations and decision support systems.
Throughout the COVID pandemic, mathematical models have been instrumental in understanding how the virus might impact populations, helping to inform government policies around the world [2]. Some of these models were freely available on-line, and I can’t be the only person who was utterly smitten with many of these, explaining with little red and white dots how changes in behaviour or viral transmission rates might affect spread of the disease.
Genetic Epidemiology
Another important development is the use of genetic epidemiology, which brings together genetics, epidemiology and biostatistics to identify genes controlling risk for complex and heterogeneous diseases [3].
In the past two decades, the available tools for genetic epidemiology have expanded from a genetic focus (considering one gene at a time) to a genomic focus (considering the entire genome of an organism). The use of isoenzymes as genetic markers, the direct analysis of DNAs, and the production of highly specific monoclona...
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