Background: This paper forecasts next year’s canine heartworm prevalence in the United States from 16 climate, geographic and societal factors. The forecast’s construction and an assessment of its performance are described.
Methods: The forecast is based on a spatial-temporal conditional autoregressive model fitted to over 31 million antigen heartworm tests conducted in the 48 contiguous United States during 2011–2015. The forecast uses county-level data on 16 predictive factors, including temperature, precipitation, median household income, local forest and surface water coverage, and presence/absence of eight mosquito species. Non-static factors are extrapolated into the forthcoming year with various statistical methods. The fitted model and factor extrapolations are used to estimate next year’s regional prevalence.
Results: The correlation between the observed and model-estimated county-by-county heartworm prevalence for the 5-year period 2011–2015 is 0.727, demonstrating reasonable model accuracy. The correlation between 2015 observed and forecasted county-by-county heartworm prevalence is 0.940, demonstrating significant skill and showing that heartworm prevalence can be forecasted reasonably accurately.
Conclusions: The forecast presented herein can a priori alert veterinarians to areas expected to see higher than normal heartworm activity. The proposed methods may prove useful for forecasting other diseases.
Keywords: Autoregression, CAR Model, Head-banging, Heartworm, Kriging, Prevalence, Spatio-temporal correlation
Bowman et al. Parasites & Vectors (2016) 9:540