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1. A Bayesian spatio-temporal model for forecasting Anaplasma species seroprevalence in domestic dogs within the contiguous United States.

Author

Liu, Yan, Watson, Stella C., Gettings, Jenna R., Lund, Robert B., Nordone, Shila K., Yabsley, Michael J., and McMahan, Christopher S.

Subjects
ANAPLASMA, SEROPREVALENCE, BAYESIAN analysis, BACTERIA classification, LABORATORY dogs
Abstract

This paper forecasts the 2016 canine Anaplasma spp. seroprevalence in the United States from eight climate, geographic and societal factors. The forecast’s construction and an assessment of its performance are described. The forecast is based on a spatial-temporal conditional autoregressive model fitted to over 11 million Anaplasma spp. seroprevalence test results for dogs conducted in the 48 contiguous United States during 2011–2015. The forecast uses county-level data on eight predictive factors, including annual temperature, precipitation, relative humidity, county elevation, forestation coverage, surface water coverage, population density and median household income. 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. The correlation between the observed and model-estimated county-by-county Anaplasma spp. seroprevalence for the five-year period 2011–2015 is 0.902, demonstrating reasonable model accuracy. The weighted correlation (accounting for different sample sizes) between 2015 observed and forecasted county-by-county Anaplasma spp. seroprevalence is 0.987, exhibiting that the proposed approach can be used to accurately forecast Anaplasma spp. seroprevalence. The forecast presented herein can a priori alert veterinarians to areas expected to see Anaplasma spp. seroprevalence beyond the accepted endemic range. The proposed methods may prove useful for forecasting other diseases. [ABSTRACT FROM AUTHOR]

Published
2017
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2. A Bayesian spatio-temporal model for forecasting Anaplasma species seroprevalence in domestic dogs within the contiguous United States

Author

Michael J. Yabsley, Yan Liu, Jenna R. Gettings, Shila K. Nordone, Robert Lund, Stella C. Watson, and Christopher S. McMahan

Subjects
Veterinary medicine, Anaplasmosis, Range (biology), Climate, Prevalence, Rickettsiales, lcsh:Medicine, Pathology and Laboratory Medicine, Population density, Geographical locations, 0403 veterinary science, 0302 clinical medicine, Mathematical and Statistical Techniques, Seroepidemiologic Studies, Statistics, Medicine and Health Sciences, Dog Diseases, lcsh:Science, Mammals, Multidisciplinary, biology, 04 agricultural and veterinary sciences, Antibodies, Bacterial, Bacterial Pathogens, Geography, Veterinary Diseases, Medical Microbiology, Vertebrates, Physical Sciences, Pathogens, Statistics (Mathematics), Research Article, Anaplasma, Arthropoda, 040301 veterinary sciences, 030231 tropical medicine, Research and Analysis Methods, Microbiology, 03 medical and health sciences, Dogs, Population Metrics, Arachnida, Parasitic Diseases, Seroprevalence, Animals, Statistical Methods, Microbial Pathogens, Population Density, Bacteria, Population Biology, Ixodes, lcsh:R, Organisms, Biology and Life Sciences, Bayes Theorem, biology.organism_classification, Invertebrates, United States, Sample size determination, Amniotes, North America, Household income, Veterinary Science, lcsh:Q, People and places, Mathematics, Forecasting
Abstract

This paper forecasts the 2016 canine Anaplasma spp. seroprevalence in the United States from eight climate, geographic and societal factors. The forecast’s construction and an assessment of its performance are described. The forecast is based on a spatial-temporal conditional autoregressive model fitted to over 11 million Anaplasma spp. seroprevalence test results for dogs conducted in the 48 contiguous United States during 2011–2015. The forecast uses county-level data on eight predictive factors, including annual temperature, precipitation, relative humidity, county elevation, forestation coverage, surface water coverage, population density and median household income. 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. The correlation between the observed and model-estimated county-by-county Anaplasma spp. seroprevalence for the five-year period 2011–2015 is 0.902, demonstrating reasonable model accuracy. The weighted correlation (accounting for different sample sizes) between 2015 observed and forecasted county-by-county Anaplasma spp. seroprevalence is 0.987, exhibiting that the proposed approach can be used to accurately forecast Anaplasma spp. seroprevalence. The forecast presented herein can a priori alert veterinarians to areas expected to see Anaplasma spp. seroprevalence beyond the accepted endemic range. The proposed methods may prove useful for forecasting other diseases.

Published
2017