Of Lyme disease and machine learning in a One Health world.

Abstract

Objective: Lyme disease is a vector-borne emerging zoonosis in Ontario driven by human population growth and climate change. Lyme disease is also a prime example of the One Health concept. While little can be done to immediately reverse climate change and population growth, public health must resort to health communication as its best option for disease control until an effective vaccine becomes available. Disease surveillance enabling precision public health has an important role in this respect: one of the goals of disease surveillance is to forecast the future burden of disease to inform those who need to know. The goal of this study was to forecast the burden of Lyme disease using automated machine learning and statistical learning approaches.

Methods: Lyme disease reports were retrieved from Ontario's integrated Public Health Information System surveillance system from January 2005 to December 2023. The reports from January 2005 to December 2021 were used as training data, and reports from January 2022 to December 2023 served as validation data. Forecasts from a seasonal autoregressive integrated moving-average model were used as a benchmark for forecasts from a feed-forward single-layer neural network machine learning algorithm.

Results: The Lyme disease burden in Ontario is predicted to increase dramatically. Neither the neural network nor the seasonal autoregressive integrated moving-average model proved to be generally more accurate.

Conclusions: The increasing burden of human Lyme disease is concerning to public health, further indicating ecosystem changes and challenges for canine health.

Clinical relevance: Human Lyme disease surveillance provides useful information to veterinarians.