Many diseases have been linked with birth seasonality, and these fall into four main categories: mental, cardiovascular, respiratory and women's reproductive health conditions. Informatics methods are needed to uncover seasonally varying infectious diseases that may be responsible for the increased birth month-dependent disease risk observed. We have developed a method to link seasonal infectious disease data from the USA to birth month dependent disease data from humans and canines. We also include seasonal air pollution and climate data to determine the seasonal factors most likely involved in the response. We test our method with osteosarcoma, a rare bone cancer. We found the Lyme disease incidence was the most strongly correlated significant factor in explaining the birth month-osteosarcoma disease pattern (R=0.418, p=2.80X10-23), and this was true across all populations observed: canines, pediatric, and adult populations.
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