https://doi.org/10.4081/gh.2021.1004
Spatio-temporal modelling of tick life-stage count data with spatially varying coefficients
Submitted: 6 April 2021
Accepted: 20 May 2021
Published: 20 October 2021
Accepted: 20 May 2021
Abstract Views: 1074
PDF: 408
HTML: 79
HTML: 79
Publisher's note
All claims expressed in this article are solely those of the authors and do not necessarily represent those of their affiliated organizations, or those of the publisher, the editors and the reviewers. Any product that may be evaluated in this article or claim that may be made by its manufacturer is not guaranteed or endorsed by the publisher.
All claims expressed in this article are solely those of the authors and do not necessarily represent those of their affiliated organizations, or those of the publisher, the editors and the reviewers. Any product that may be evaluated in this article or claim that may be made by its manufacturer is not guaranteed or endorsed by the publisher.
Authors
There is a vast amount of geo-referenced data in many fields of study including ecological studies. Geo-referencing is usually by point referencing; that is, latitudes and longitudes or by areal referencing, which includes districts, counties, states, provinces and other administrative units. The availability of large geo-referenced datasets for modelling has necessitated the development and application of spatial statistical methods. However, spatial varying coefficients models exploring the abundance of tick counts remain limited. In this study we used data that was collected and prepared by researchers in the Department of Biological Sciences from the Old Dominion University, Virginia, USA. We modelled tick life-stage counts and abundance variability from 12 sampling locations, with 5 different habitats (numbered 1-5), three habitat types; namely: woods, edges and grass; collected monthly from May 2009 through December 2018. Spatio-temporal Poisson and spatio-temporal negative binomial (NB) count data models were fitted to the data and compared using the deviance information criteria (DIC). The NB model outperformed the Poisson models with all its DIC values being smaller than those of the Poisson model. Results showed that the covariates varied spatially across counties. There was a decreasing time (in years) effect over the study period. However, even though the time effect was decreasing over the study period, space-time interaction effects were seen to be increasing over time in York County.
Anderson JF, 2002. The natural history of ticks. Med Clin North Am 86:205-18.
DOI: https://doi.org/10.1016/S0025-7125(03)00083-X
Assunçao J, Gamerman D, Assunçao R, 1999. Regional differences in factor productivities of Brazilian agriculture: a space varying parameter approach. In Proceedings of the XV Latin American Meeting of the Econometric Society.
Bernardinelli L, Clayton D, Montomoli C, 1995. Bayesian estimates of disease maps: how important are priors? Stat Med 14:2411-31.
DOI: https://doi.org/10.1002/sim.4780142111
Besag J, York J, Mollié A, 1991. Bayesian image restoration, with two applications in spatial statistics. Ann Inst Stat Math 43:1-20.
DOI: https://doi.org/10.1007/BF00116466
Bowman AS, Nuttall PA, Chappell L, 2004. Ticks: biology, disease and control. Parasitol 129:S1-S1.
DOI: https://doi.org/10.1017/S0031182004006560
Bratton RL, Corey GR, 2005. Tick-borne disease. Am Fam Physician 71:2323-30.
CDC, 2020. Guide to the surveillance of metastriate ticks (Acari: Ixodidae) and their pathogens in the U.S. Division of Vector-Borne Diseases, CDC, Atlanta & Ft. Collings.
Childs JE, Paddock CD, 2003. The ascendancy of Amblyomma americanum as a vector of pathogens affecting humans in the United States. Annu Rev Entomol 48:307-37.
DOI: https://doi.org/10.1146/annurev.ento.48.091801.112728
Cliff AD, Ord JK, 1981. Spatial processes: models & applications. Taylor & Francis, Boca Raton, FL, USA.
Fritzen CM, Huang J, Westby K, Freye JD, Dunlap B, Yabsley MJ, Schardein M, Dunn JR, Jones TF, Moncayo AC, 2011. Infection prevalences of common tick-borne pathogens in adult lone star ticks (Amblyomma americanum) and American dog ticks (Dermacentor variabilis) in Kentucky. Am J Trop Med Hyg 85:718-23.
DOI: https://doi.org/10.4269/ajtmh.2011.10-0583
Gamerman D, Moreira AR, Rue H, 2003. Space-varying regression models: specifications and simulation. Comput Stat Data Anal 42:513-33.
DOI: https://doi.org/10.1016/S0167-9473(02)00211-6
Goddard J, Varela-Stokes AS, 2009. Role of the lone star tick, Amblyomma americanum (L.), in human and animal diseases. Vet Parasitol 160:1-12.
DOI: https://doi.org/10.1016/j.vetpar.2008.10.089
Hendricks B, Mark-Carew M, Conley J, 2017. Evaluating the utility of companion animal tick surveillance practices for monitoring spread and occurrence of human Lyme disease in West Virginia, 2014-2016. Geospat Health 12:582.
DOI: https://doi.org/10.4081/gh.2017.582
Kollars TM, 1993. Ticks (Acari: Ixodidae) infesting medium-sized wild mammals in southwestern Tennessee. J Med Entomol 30:896-900.
DOI: https://doi.org/10.1093/jmedent/30.5.896
Lambert D, 1992. Zero-inflated Poisson regression, with an application to defects in manufacturing. Technometrics 34:1-14.
DOI: https://doi.org/10.2307/1269547
Lantos PM, Nigrovic LE, Auwaerter PG, Fowler Jr VG, Ruffin F, Brinkerhoff RJ, Reber J, Williams C, Broyhill J, Pan WK, 2015. Geographic expansion of Lyme disease in the southeastern United States, 2000-2014. In Open forum infectious diseases, pp. ofv143. Oxford University Press.
DOI: https://doi.org/10.1093/ofid/ofv143
Linske MA, Williams SC, Stafford KC, Lubelczyk CB, Henderson EF, Welch M, Teel PD, 2020. Determining effects of winter weather conditions on adult Amblyomma americanum (Acari: Ixodidae) survival in Connecticut and Maine, USA. Insects 11:13.
DOI: https://doi.org/10.3390/insects11010013
Manda SO, Leyland A, 2007. An empirical comparison of maximum likelihood and Bayesian estimation methods for multivariate disease mapping: theory and methods. S Afr Stat J 41:1-21.
Mixson TR, Campbell SR, Gill JS, Ginsberg HS, Reichard MV, Schulze TL, Dasch GA, 2006. Prevalence of Ehrlichia, Borrelia, and Rickettsial agents in Amblyomma americanum (Acari: Ixodidae) collected from nine states. J Med Entomol 43:1261-8.
DOI: https://doi.org/10.1603/0022-2585(2006)43[1261:POEBAR]2.0.CO;2
Nathavitharana RR, Mitty JA, 2015. Diseases from North America: focus on tick-borne infections. Clin Med (Northfield Il) 15:74.
DOI: https://doi.org/10.7861/clinmedicine.14-6-74
Ngesa O, Mwambi H, Achia T, 2014. Bayesian spatial semi-parametric modeling of HIV variation in Kenya. PLoS One 9:e103299.
DOI: https://doi.org/10.1371/journal.pone.0103299
Okango E, Mwambi H, Ngesa O, Achia T, 2015. Semi-parametric spatial joint modeling of HIV and HSV-2 among women in Kenya. PLoS One 10:e0135212.
DOI: https://doi.org/10.1371/journal.pone.0135212
QGIS DT, 2009. QGIS Geographic Information System, Open Source Geospatial Foundation.
Salman MD, 2012. Ticks and tick-borne diseases: geographical distribution and control strategies in the Euro-Asia region; CABI. Available from: https://www.cabi.org/bookshop/book/9781845938536/
DOI: https://doi.org/10.1079/9781845938536.0000
Sayler K, Rowland J, Boyce C, Weeks E, 2017. Borrelia burgdorferi DNA absent, multiple Rickettsia spp. DNA present in ticks collected from a teaching forest in North Central Florida. Ticks Tick Borne Dis 8:53-9.
DOI: https://doi.org/10.1016/j.ttbdis.2016.09.016
Sonenshine, D. E. (1979) Insects of Virginia: No. 13: Ticks of virginia (Acari, Metastigmata). Research Division Bulletin 139, Department of Entomology, Virginia Polytechnic Institute and State University, Blacksburg, VA, USA.
Sonenshine DE, Stout IJ, 1971. Ticks infesting medium-sized wild mammals in two forest localities in Virginia (Acarina: Ixodidae). J Med Entomol 8:217-27.
DOI: https://doi.org/10.1093/jmedent/8.3.217
Spiegelhalter DJ, Best NG, Carlin BP, Van Der Linde A, 2002. Bayesian measures of model complexity and fit. J Roy Stat Soc Ser B (Stat Method) 64:583-639.
DOI: https://doi.org/10.1111/1467-9868.00353
Stein KJ, Waterman M, Waldon JL, 2008. The effects of vegetation density and habitat disturbance on the spatial distribution of ixodid ticks (Acari: Ixodidae). Geospat Health 2:241-52.
DOI: https://doi.org/10.4081/gh.2008.247
Stromdahl E, Hickling G, 2012. Beyond Lyme: aetiology of tickâ€borne human diseases with emphasis on the Southâ€Eastern United States. Zoonoses Public Health 59:48-64.
DOI: https://doi.org/10.1111/j.1863-2378.2012.01475.x
Tälleklint L, Jaenson TG, 1998. Increasing geographical distribution and density of Ixodes ricinus (Acari: Ixodidae) in central and northern Sweden. J Med Entomol 35:521-6.
DOI: https://doi.org/10.1093/jmedent/35.4.521
Willis D, Carter R, Murdock C, Blair B, 2012. Relationship between habitat type, fire frequency, and Amblyomma americanum populations in eastâ€central Alabama. J Vector Ecol 37:373-81.
DOI: https://doi.org/10.1111/j.1948-7134.2012.00241.x
Zeileis A, Kleiber C, Jackman S, 2008. Regression models for count data in R. J Stat Softw 27:1-25.
DOI: https://doi.org/10.1007/978-0-387-77318-6_4
How to Cite
Lephoto, T., Mwambi, H., Bodhlyera, O., & Gaff, H. (2021). Spatio-temporal modelling of tick life-stage count data with spatially varying coefficients. Geospatial Health, 16(2). https://doi.org/10.4081/gh.2021.1004
PAGEPress has chosen to apply the Creative Commons Attribution NonCommercial 4.0 International License (CC BY-NC 4.0) to all manuscripts to be published.
