Research team digitizes more than 100 years of Canadian infectious disease data

Summary

Twenty-five years ago, in a neglected storage area at the Ontario Ministry of Health, David Earn happened upon epidemiological gold: two boxes of hand-written documents accounting for 50 years of weekly infectious disease incidence reports, spanning 1939-1989. The buried treasure was exactly the sort of thing that the McMaster University professor hoped to unearth during his visit — historical public health data that could help contextualize current and future infectious disease outbreaks. “Initially, the Ministry said that they couldn’t provide the data — that they didn’t have the time to search through their archives for us,” recalls Earn, a professor in McMaster’s Department of Mathematics and Statistics. “So, I offered to come to Toronto and look through their files myself, if they would let me. I basically begged, insisting on the value of the historical records, and I wouldn’t let it go. Eventually, I guess I became too much of a nuisance and they relented.” The documents uncovered that day catalyzed a massive retrospective research project that has culminated in a complete, province-by-province inventory of Canadian infectious disease records. The result, published today in PLOS Global Public Health, is what Earn describes as a “genuinely beautiful dataset” that strings together more than 100 years of historical epidemiological information. Altogether, the new database — the Canadian Notifiable Disease Incidence Dataset, or “CANDID” — contains more than a million infectious disease incidence counts that date back as far as 1903. The dataset, which is now publicly accessible, captures weekly, monthly, and quarterly case numbers for diseases like poliomyelitis, hepatitis, tuberculosis, whooping cough, influenza, rubella, mumps, measles, and many others, and tracks their spread in each province and territory across time. “Data like these reveal the speed and shape of outbreaks and recurrent epidemics of the past, and allow us to test models that predict patterns...