Spatio-Temporal Modeling of Zika and Dengue Infections within Colombia

Martínez-Bello, Daniel Adyro; López‐Quílez, Antonio; Prieto, Alexander Torres

doi:10.3390/ijerph15071376

Cited by 19 publications

(17 citation statements)

References 46 publications

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“…Function f () represents the sub-level latent Gaussian models (LGMs) to fit the spatial and temporal nonstationary random effects for estimating the local-scale SC and TC of each covariate [ 35 , 36 ].…”

Section: Methodsmentioning

confidence: 99%

Spatial and Temporal Impacts of Socioeconomic and Environmental Factors on Healthcare Resources: A County-Level Bayesian Local Spatiotemporal Regression Modeling Study of Hospital Beds in Southwest China

Song

Wang

Yang³

et al. 2020

IJERPH

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Comprehensive investigation on understanding geographical inequalities of healthcare resources and their influencing factors in China remains scarce. This study aimed to explore both spatial and temporal heterogeneous impacts of various socioeconomic and environmental factors on healthcare resource inequalities at a fine-scale administrative county level. We collected data on county-level hospital beds per ten thousand people to represent healthcare resources, as well as data on 32 candidate socioeconomic and environmental covariates in southwest China from 2002 to 2011. We innovatively employed a cutting-edge local spatiotemporal regression, namely, a Bayesian spatiotemporally varying coefficients (STVC) model, to simultaneously detect spatial and temporal autocorrelated nonstationarity in healthcare-covariate relationships via estimating posterior space-coefficients (SC) within each county, as well as time-coefficients (TC) over ten years. Our findings reported that in addition to socioeconomic factors, environmental factors also had significant impacts on healthcare resources inequalities at both global and local space–time scales. Globally, the personal economy was identified as the most significant explanatory factor. However, the temporal impacts of personal economy demonstrated a gradual decline, while the impacts of the regional economy and government investment showed a constant growth from 2002 to 2011. Spatially, geographical clustered regions for both hospital bed distributions and various hospital bed-covariates relationships were detected. Finally, the first spatiotemporal series of complete county-level hospital bed inequality maps in southwest China was produced. This work is expected to provide evidence-based implications for future policy making procedures to improve healthcare equalities from a spatiotemporal perspective. The employed Bayesian STVC model provides frontier insights into investigating spatiotemporal heterogeneous variables relationships embedded in broader areas such as public health, environment, and earth sciences.

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Section: Methodsmentioning

confidence: 99%

Spatial and Temporal Impacts of Socioeconomic and Environmental Factors on Healthcare Resources: A County-Level Bayesian Local Spatiotemporal Regression Modeling Study of Hospital Beds in Southwest China

Song

Wang

Yang³

et al. 2020

IJERPH

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“…However these deterministic frameworks fail to capture the inherent stochasticity and spatio-temporal heterogeneities of arboviral disease, and place strong implicit assumptions on vector ecology. Although discrete-time statistical transmission models (Li et al, 2018), such as Bayesian hierarchical dynamic Poisson models (Martínez-Bello et al, 2017), spatio-temporal risk models (Lowe et al, 2014;Martínez-Bello et al, 2018), and mixed models (Lowe et al, 2017), encapsulate the stochastic dynamics of arboviruses, they fail to capture the associations between epidemiological determinants and essential transmission drivers. Individual based models are arguably better suited to capture the spatio-temporal dynamics of arboviral disease whilst allowing for an unrestricted relationship between extrinsic and intrinsic factors.…”

Section: Introductionmentioning

confidence: 99%

Inferring the ecological drivers of arboviral outbreaks

Tennant

McKinley

Recker

2019

Preprint

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The emergence and wide-spread circulation of mosquito-transmitted viral diseases, such as dengue, Zika and Chikungunya, is a global public health concern. In the absence of effective vaccines, current control measures are mostly targeted against the mosquito vector and have so far only shown limited success. The reliance on mosquitoes for transmission also imposes strong ecological constraints that can introduce significant spatial and temporal variations in disease incidence. However, the way that epidemiological and ecological factors interact and determine population-level disease dynamics is only partially understood. Here we fit a spatially-explicit individual based model defined within a Bayesian framework to Zika incidence data from Feira de Santana, allowing us to more precisely quantify the relationships between socioecological factors and arboviral outbreaks. Our results further demonstrated that the virus was likely introduced into multiple spatially segregated locations at the start of the outbreak, highlighting the benefits that spatio-temporal incidence data would bring in making modelling approaches more realistic for public health planning.where t is the individual's age in days, L is the location parameter of where infant and adult mortality coincide, {a H , b H } and {c H , d H } are the shape and scale parameters of infant and adult human mortality, respectively, and {c V , d V } are the shape and scale parameters of adult mosquito mortality, respectively.An arbovirus was introduced into the system by allowing every human individual to be either susceptible (S H ), exposed (E H ), infectious (I H ), or have recovered from (R H ) the disease. Similarly, individual mosquitoes were either susceptible (S V ), exposed (E V ) or infectious (I V ) with the virus. Mosquitoes bit at a constant rate β and infectious mosquitoes transmitted the virus with probability p H . Infected humans became infectious after 1/ H days and recovered after 1/γ days. Mosquitoes were then infected with probability p V given a bite on an infectious human, and became infectious after 1/ V days. To account for the introduction of the disease into the system, humans were also infected at an external infection rate, ι.We modelled the influence of temperature, humidity and rainfall on these transmission parameters similarly to Lourenço et al. (2017).

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“…In Colombia, Krystosik et al ( 22 ) generated city-level risk maps of chikungunya, Zika, and dengue supporting vector-control strategies; Martínez-Bello et al ( 23 ) estimated the RR for dengue and Zika by using spatiotemporal interaction effects models for 1 department and 1 city in Colombia. Riou et al ( 24 ) assessed the spatial patterns of risk for the 2013 Zika and chikungunya outbreaks in the French Polynesia islands, and Funk et al ( 25 ) jointly modeled Zika and dengue time series data from the Zika outbreak in Yap Island in the Pacific Ocean.…”

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confidence: 99%

Joint Estimation of Relative Risk for Dengue and Zika Infections, Colombia, 2015–2016

Martínez-Bello¹,

López‐Quílez²,

Prieto³

2019

Emerg. Infect. Dis.

Self Cite

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We jointly estimated relative risk for dengue and Zika virus disease (Zika) in Colombia, establishing the spatial association between them at the department and city levels for October 2015–December 2016. Cases of dengue and Zika were allocated to the 87 municipalities of 1 department and the 293 census sections of 1 city in Colombia. We fitted 8 hierarchical Bayesian Poisson joint models of relative risk for dengue and Zika, including area- and disease-specific random effects accounting for several spatial patterns of disease risk (clustered or uncorrelated heterogeneity) within and between both diseases. Most of the dengue and Zika high-risk municipalities varied in their risk distribution; those for Zika were in the northern part of the department and dengue in the southern to northeastern parts. At city level, spatially clustered patterns of dengue high-risk census sections indicated Zika high-risk areas. This information can be used to inform public health decision making.

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Spatio-Temporal Modeling of Zika and Dengue Infections within Colombia

Cited by 19 publications

References 46 publications

Spatial and Temporal Impacts of Socioeconomic and Environmental Factors on Healthcare Resources: A County-Level Bayesian Local Spatiotemporal Regression Modeling Study of Hospital Beds in Southwest China

Spatial and Temporal Impacts of Socioeconomic and Environmental Factors on Healthcare Resources: A County-Level Bayesian Local Spatiotemporal Regression Modeling Study of Hospital Beds in Southwest China

Inferring the ecological drivers of arboviral outbreaks

Joint Estimation of Relative Risk for Dengue and Zika Infections, Colombia, 2015–2016

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