Since the 19th century, scientists and physicians aiming to control infectious diseases have relied on tools for mapping them. The most famous early practitioner of this is doubtless John Snow, who tracked a London cholera epidemic in 1854 to a single contaminated water pump, providing a vital piece of evidence to show the disease to be water-borne.
Several decades later, painstaking house-by-house mapping of smallpox epidemics in London helped Royal Commissioners determine how hospital provision should be structured. These maps, which have been preserved, are an early example of how the science of epidemiology - or the analysis of the distribution and determinants of a disease in a population - can help combat an infectious disease.
They can provide evidence of links between people's attitudes and behaviour and the spread of disease, as Heidi Larson, an anthropologist working at the London School of Hygiene and Tropical Medicine (LSHTM) - where 'that' water pump has pride of place on display - explains. 'Resistance to vaccination against smallpox was as strong then, as the so-called 'anti-vax' movement is today. It is even possible to detect patterns of vaccine compliance in the smallpox maps'.
Old methods, new tools
The principal task of epidemiologists studying outbreaks of infectious disease has hardly changed in more than 100 years, in that it involves the painstaking mapping of every case of the disease. However, whereas the tools available to even mid-20th century epidemiologists would have been vaguely recognisable to their Victorian counterparts, the last 30 years have seen dramatic changes. Today's counterparts of those smallpox mappers rely on a wide variety of powerful algorithms for statistical analysis, genome sequencing and GPS, underpinned by equally powerful, if more mundane, tools for the secure storage and rapid retrieval of vast quantities of data.
Computational molecular epidemiology - as this sub-discipline might be called - is now vitally important whenever a new outbreak of an infectious disease arises. The Ebola virus outbreak in West Africa in 2014-16 was a case in point. 'We were called on to model the transmissibility of the disease in different situations, and to find out whether any of the interventions available were able to reduce it,' says Anne Cori, a statistician and mathematical modeller based at the MRC Centre for Global Infectious Disease Analysis, at Imperial College, London.
'Our work can also be useful to medical staff on the ground, who need to estimate resources - hospital beds, units of drug or vaccine, and trained personnel - that will be needed in each area as the epidemic unfolds,' said Cori. At a higher level, the World Health Organisation and large medical charities use similar information to plan future work.
Several novel and important statistical tools and other programs to analyse epidemiological data were developed during the crisis of the 2014-16 Ebola epidemic. It was only after it ended that scientists and developers were able to pay attention to maintaining and improving their code, so it could be re-used more easily in future similar epidemics.
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