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Bayesian spatial and spatio-temporal approaches to modelling dengue fever: a systematic review

  • A. Aswi (a1), S. M. Cramb (a1) (a2), P. Moraga (a3) and K. Mengersen (a1)
Abstract

Dengue fever (DF) is one of the world's most disabling mosquito-borne diseases, with a variety of approaches available to model its spatial and temporal dynamics. This paper aims to identify and compare the different spatial and spatio-temporal Bayesian modelling methods that have been applied to DF and examine influential covariates that have been reportedly associated with the risk of DF. A systematic search was performed in December 2017, using Web of Science, Scopus, ScienceDirect, PubMed, ProQuest and Medline (via Ebscohost) electronic databases. The search was restricted to refereed journal articles published in English from January 2000 to November 2017. Thirty-one articles met the inclusion criteria. Using a modified quality assessment tool, the median quality score across studies was 14/16. The most popular Bayesian statistical approach to dengue modelling was a generalised linear mixed model with spatial random effects described by a conditional autoregressive prior. A limited number of studies included spatio-temporal random effects. Temperature and precipitation were shown to often influence the risk of dengue. Developing spatio-temporal random-effect models, considering other priors, using a dataset that covers an extended time period, and investigating other covariates would help to better understand and control DF transmission.

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Copyright
This is an Open Access article, distributed under the terms of the Creative Commons Attribution licence (http://creativecommons.org/licenses/by/4.0/), which permits unrestricted re-use, distribution, and reproduction in any medium, provided the original work is properly cited.
Corresponding author
Author for correspondence: A. Aswi, E-mail: aswi@hdr.qut.edu.au
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References
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1.Vanessa, R et al. (2012) Surveillance of dengue fever virus: a review of epidemiological models and early warning systems. PLoS Neglected Tropical Diseases 6, e1648.
2.Gubler, DJ (1998) Dengue and dengue hemorrhagic fever. Clinical Microbiology Reviews 11, 480496.
3.Cabrera, M (2013) Spatio-Temporal Modelling of Dengue Fever in Zulia State, Venezuela (Dissertation). University of Bath, Bath, UK, 250 pp.
4.Gibbons, RV and Vaughn, DW (2002) Dengue: an escalating problem. British Medical Journal 324, 15631566.
5.World Health Organization (2009) Dengue: Guidelines for Diagnosis, Treatment, Prevention and Control. Geneva: WHO.
6.Chen, J-Y (2009) Spatial Analysis of Dengue Incidence in Taiwan (thesis). University of Pittsburgh, Taiwan, 37 pp.
7.Raja, NS and Devi, S (2006) The incidence of dengue disease in a university teaching hospital in Malaysia in 2002, 2003 and 2004. Infectious Diseases Journal of Pakistan 15, 99102.
8.Austin, PC et al. (2002) Bayeswatch: an overview of Bayesian statistics. Journal of Evaluation in Clinical Practice 8, 277286.
9.Blangiardo, M et al. (2013) Spatial and spatio-temporal models with R-INLA. Spatial and Spatio-temporal Epidemiology 7, 3955.
10.Gelman, A (2013) Bayesian Data Analysis, 3rd edn. Hoboken: CRC Press, p. 663.
11.Blangiardo, M (2015) Spatial and Spatio-Temporal Bayesian Models with R-INLA, 1st edn. Chichester, West Sussex: John Wiley and Sons, Inc., p. 308.
12.Dunson, DB (2001) Commentary: practical advantages of Bayesian analysis of epidemiologic data. American Journal of Epidemiology 153, 12221226.
13.Naish, S et al. (2014) Climate change and dengue: a critical and systematic review of quantitative modelling approaches. BMC Infectious Diseases 14, 167.
14.Morin, CW, Comrie, AC and Ernst, K (2013) Climate and dengue transmission: evidence and implications. Environmental Health Perspectives (Online) 121, 12641272.
15.Fan, JC et al. (2015) A systematic review and meta-analysis of dengue risk with temperature change. International Journal of Environmental Research and Public Health 12, 115.
16.Limkittikul, K, Brett, J and L'Azou, M (2014) Epidemiological trends of dengue disease in Thailand (2000–2011): a systematic literature review. PLoS Neglected Tropical Diseases 8, e3241.
17.Messina, JP et al. (2015) The many projected futures of dengue. Nature Reviews Microbiology 13, 230239.
18.Viennet, E et al. (2016) Public health responses to and challenges for the control of dengue transmission in high-income countries: four case studies. PLoS Neglected Tropical Diseases 10, e0004943.
19.Oliveira, M, Ribeiro, H and Castillo-Salgado, C (2013) Geospatial analysis applied to epidemiological studies of dengue: a systematic review. Revista Brasileira de Epidemiologia 16, 907917.
20.Louis, VR et al. (2014) Modeling tools for dengue risk mapping – a systematic review. International Journal of Health Geographics 13, 50.
21.Liberati, A et al. (2009) The PRISMA statement for reporting systematic reviews and meta-analyses of studies that evaluate health care interventions: explanation and elaboration. PLoS Medicine 6, e1000100.
22.Moher, D et al. (2009) Preferred reporting items for systematic reviews and meta-analyses: the PRISMA statement. PLoS Medicine 6, e1000097.
23.Costa, ACC et al. (2015) Surveillance of dengue vectors using spatio-temporal Bayesian modeling. BMC Medical Informatics and Decision Making 15, 93.
24.Harris, RC et al. (2016) Systematic review of mathematical models exploring the epidemiological impact of future TB vaccines. Human Vaccines & Immunotherapeutics 12, 28132832.
25.Fone, D et al. (2003) Systematic review of the use and value of computer simulation modelling in population health and health care delivery. Journal of Public Health 25, 325335.
26.Lowe, R et al. (2011) Spatio-temporal modelling of climate-sensitive disease risk: towards an early warning system for dengue in Brazil. Computers & Geosciences 37, 371381.
27.Lowe, R et al. (2013) The development of an early warning system for climate-sensitive disease risk with a focus on dengue epidemics in Southeast Brazil. Statistics in Medicine 32, 864883.
28.Lowe, R et al. (2014) Dengook for the World Cup in Brazil: an early warning model framework driven by real-time seasonal climate forecastsue outl. The Lancet Infectious Diseases 14, 619626.
29.Wijayanti, SP et al. (2016) The importance of socio-economic versus environmental risk factors for reported dengue cases in Java, Indonesia. PLoS Neglected Tropical Diseases 10, e0004964.
30.Yu, HL et al. (2011) A spatio-temporal climate-based model of early dengue fever warning in southern Taiwan. Stochastic Environmental Research and Risk Assessment 25, 485494.
31.Vazquez-Prokopec, GM et al. (2010) Quantifying the spatial dimension of dengue virus epidemic spread within a tropical urban environment. PLoS Neglected Tropical Diseases 4, e920.
32.Johansson, MA, Dominici, F and Glass, GE (2009) Local and global effects of climate on dengue transmission in Puerto Rico. PLoS Neglected Tropical Diseases 3, e382.
33.Fernandes, MVM, Schmidt, AM and Migon, HS (2009) Modelling zero-inflated spatio-temporal processes. Statistical Modelling 9, 325.
34.Astutik, S et al. (2013) Bayesian spatial-temporal autologistic regression model on dengue hemorrhagic fever in East Java, Indonesia. Applied Mathematical Sciences 7, 435443.
35.Sani, A et al. (2015) Relative risk analysis of dengue cases using convolution extended into spatio-temporal model. Journal of Applied Statistics 42, 25092519.
36.Jaya, IGNM et al. (2016) Bayesian spatial modeling and mapping of dengue fever: a case study of dengue fever in the city of Bandung, Indonesia. International Journal of Applied Mathematics and Statistics 54, 94103.
37.Mukhsar et al. (2016) Extended convolution model to Bayesian spatio-temporal for diagnosing the DHF endemic locations. Journal of Interdisciplinary Mathematics 19, 233244.
38.Kikuti, M et al. (2015) Spatial distribution of dengue in a Brazilian urban slum setting: role of socioeconomic gradient in disease risk. PLoS Neglected Tropical Diseases 9, e0003937.
39.Honorato, T et al. (2014) Spatial analysis of distribution of dengue cases in Espírito Santo, Brazil, in 2010: use of Bayesian model. Revista Brasileira de Epidemiologia 17, 150159.
40.Hu, W et al. (2012) Spatial patterns and socioecological drivers of dengue fever transmission in Queensland, Australia. Environmental Health Perspectives 120, 260266.
41.Costa, JV, Donalisio, MR and Silveira, LVD (2013) Spatial distribution of dengue incidence and socio-environmental conditions in Campinas, Sao Paulo State, Brazil, 2007. Cadernos De Saude Publica 29, 15221532.
42.Vargas, WP et al. (2015) Association among house infestation index, dengue incidence, and sociodemographic indicators: surveillance using geographic information system. BMC Public Health 15, 746.
43.Ferreira, GS and Schmidt, AM (2006) Spatial modelling of the relative risk of dengue fever in Rio de Janeiro for the epidemic period between 2001 and 2002. Brazilian Journal of Probability and Statistics 20, 2947.
44.Pepin, KM et al. (2015) Utility of mosquito surveillance data for spatial prioritization of vector control against dengue viruses in three Brazilian cities. Parasites & Vectors 8, 98.
45.Zhu, G et al. (2016) Inferring the spatio-temporal patterns of dengue transmission from surveillance data in Guangzhou, China. PLoS Neglected Tropical Diseases 10, e0004633.
46.Martínez-Bello, D, Lopez-Quilez, A and Alexander Torres, P (2017) Relative risk estimation of dengue disease at small spatial scale. International Journal of Health Geographics 16, 31.
47.Martínez-Bello, D, López-Quílez, A and Prieto, AT (2018) Spatiotemporal modeling of relative risk of dengue disease in Colombia. Stochastic Environmental Research and Risk Assessment 32, 15871601.
48.Lowe, R et al. (2016) Quantifying the added value of climate information in a spatio-temporal dengue model. Stochastic Environmental Research and Risk Assessment 30, 20672078.
49.Chien, L-C and Yu, H-L (2014) Impact of meteorological factors on the spatiotemporal patterns of dengue fever incidence. Environment International 73, 4656.
50.Restrepo, AC, Baker, P and Clements, AC (2014) National spatial and temporal patterns of notified dengue cases, Colombia 2007–2010. Tropical Medicine & International Health 19, 863871.
51.Hu, W et al. (2011) Spatial analysis of notified dengue fever infections. Epidemiology and Infection 139, 391399.
52.Lekdee, K and Ingsrisawang, L (2013) Generalized linear mixed models with spatial random effects for spatio-temporal data: an application to dengue fever mapping. Journal of Mathematics and Statistics 9, 137143.
53.Mukhsar et al. (2016) Construction posterior distribution for Bayesian mixed ZIP spatio-temporal model. International Journal of Biology and Biomedicine 1, 3239.
54.Samat, N and Percy, D (2012) Vector-borne infectious disease mapping with stochastic difference equations: an analysis of dengue disease in Malaysia. Journal of Applied Statistics 39, 20292046.
55.Yu, HL et al. (2014) An online spatiotemporal prediction model for dengue fever epidemic in Kaohsiung (Taiwan). Biometrical Journal 56, 428440.
56.Yu, H-L, Lee, C-H and Chien, L-C (2016) A spatiotemporal dengue fever early warning model accounting for nonlinear associations with hydrological factors: a Bayesian maximum entropy approach. Stochastic Environmental Research and Risk Assessment 30, 21272141.
57.Kumar, VS et al. (2016) Spatial mapping of acute diarrheal disease using GIS and estimation of relative risk using empirical Bayes approach. Clinical Epidemiology and Global Health 5, 8796.
58.Riebler, A et al. (2016) An intuitive Bayesian spatial model for disease mapping that accounts for scaling. Statistical Methods in Medical Research 25, 11451165.
59.Lawson, A (2003) Disease Mapping with WinBUGS and MLwiN. Hoboken, NJ: J. Wiley, p. 277.
60.Botella-Rocamora, P, López-Quílez, A and Martinez-Beneito, M. (2013) Spatial moving average risk smoothing. Statistics in Medicine 32, 25952612.
61.Lawson, AB and MacNab, YC (2011) On Gaussian Markov random fields and Bayesian disease mapping. Statistical Methods in Medical Research 20, 4968.
62.Besag, J, York, J and Mollié, A (1991) Bayesian image restoration, with two applications in spatial statistics. Annals of the Institute of Statistical Mathematics 43, 120.
63.Leroux, BG, Lei, X and Breslow, N (2000) Estimation of disease rates in small areas: a new mixed model for spatial dependence. In Halloran, ME and Berry, D (eds), The IMA Volumes in Mathematics and Its Applications. New York, NY: Springer New York, pp. 179191.
64.Kandhasamy, C and Ghosh, K (2017) Relative risk for HIV in India – an estimate using conditional auto-regressive models with Bayesian approach. Spatial and Spatio-temporal Epidemiology 20, 2734.
65.Eckert, N et al. (2007) Hierarchical Bayesian modelling for spatial analysis of the number of avalanche occurrences at the scale of the township. Cold Regions Science and Technology 50, 97112.
66.Kanevski, M. (2008) Advanced Mapping of Environmental Data/Geostatistics, Machine Learning and Bayesian Maximum Entropy, 1st edn. Hoboken: John Wiley & Sons.
67.Christakos, G (2005) Interdisciplinary Public Health Reasoning and Epidemic Modelling: The Case of Black Death. Berlin, Heidelberg: Springer, p. 331.
68.Christakos, G (2002) On the assimilation of uncertain physical knowledge bases: Bayesian and non-Bayesian techniques. Advances in Water Resources 25, 12571274.
69.Zheng, Y and Zhu, J (2012) Markov chain Monte Carlo for a spatial-temporal autologistic regression model. Journal of Computational and Graphical Statistics 17, 123137.
70.Paz-Soldan, VA et al. (2014) Strengths and weaknesses of Global Positioning System (GPS) data-loggers and semi-structured interviews for capturing fine-scale human mobility: findings from Iquitos, Peru. PLoS Neglected Tropical Diseases 8, e2888.
71.Steven, TS et al. (2013) House-to-house human movement drives dengue virus transmission. Proceedings of the National Academy of Sciences 110, 994999.
72.Carroll, R et al. (2016) Spatio-temporal Bayesian model selection for disease mapping. Environmetrics 27, 466478.
73.Knorr-Held, L (1999) Bayesian modelling of inseparable space-time variation in disease risk. Statistics in Medicine 19, 25552567.
74.Banerjee, S and Carlin, BP (2003) Semiparametric spatio-temporal frailty modeling. Environmetrics 14, 523535.
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