Investigating the Causal Effects of Climate on Lyme Disease Incidence

Abstract

Climate is a factor that influences the spread of many infectious diseases, especially vector-borne diseases. For instance, temperature and humidity are crucial in understanding transmission patterns of malaria due to their influence on the abundance of mosquitoes. Lyme disease is an infection transmitted by ticks that is the most commonly reported vector-borne disease in North America. It has been rapidly spreading out from 2 major focal points in New England and north-central United States since it was first discovered in the late 1970s. The Center for Disease Control estimates over 300,000 people are treated for Lyme disease each year in the United States, with the total cost of Lyme disease testing alone estimated at $492 million. To understand how climatic conditions affect the spread of Lyme disease, we performed causal analysis on time series data on weather, tick populations in Maine, and Lyme disease incidence in New England states using a recently-developed method called convergent cross mapping (CCM). CCM is based on nonlinear state space reconstruction, and tests if X is causally influencing Y by determining how well the historical record of Y values can reliably estimate X. CCM analysis supports the usage of some predictors drawn from previous works, such as annual minimum temperature, while casting doubt on others, such as the Palmer Hydrological Drought Index. Surprisingly, CCM suggests the value of climatic variables never before considered in modelling, such as wind speed.