Dynamic spatiotemporal modeling of the infected rate of visceral leishmaniasis in human in an endemic area of Amhara regional state, Ethiopia

Godana, Anteneh Asmare; Mwalili, Samuel; Orwa, George Otieno

doi:10.1371/journal.pone.0212934

Cited by 10 publications

(12 citation statements)

References 25 publications

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“…Paixão-Seva et al (2017) [15] simultaneously model the infected human, vector and dog populations in relation to landscape, climatic and economic factors, and in particular use proximity to a highway and gas pipeline as indicators of human movement. Where aetiology is not the focus, analyses often incorporate GPS locations of cases to identify hotspots and predict disease spread at a local village or household level [16], or across health facilities [17].…”

Section: Plos Neglected Tropical Diseasesmentioning

confidence: 99%

A spatio-temporal approach to short-term prediction of visceral leishmaniasis diagnoses in India

et al. 2020

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Background The elimination programme for visceral leishmaniasis (VL) in India has seen great progress, with total cases decreasing by over 80% since 2010 and many blocks now reporting zero cases from year to year. Prompt diagnosis and treatment is critical to continue progress and avoid epidemics in the increasingly susceptible population. Short-term forecasts could be used to highlight anomalies in incidence and support health service logistics. The model which best fits the data is not necessarily most useful for prediction, yet little empirical work has been done to investigate the balance between fit and predictive performance. Methodology/Principal findings We developed statistical models of monthly VL case counts at block level. By evaluating a set of randomly-generated models, we found that fit and one-month-ahead prediction were strongly correlated and that rolling updates to model parameters as data accrued were not crucial for accurate prediction. The final model incorporated auto-regression over four months, spatial correlation between neighbouring blocks, and seasonality. Ninety-four percent of 10-90% prediction intervals from this model captured the observed count during a 24-month test period. Comparison of one-, three-and four-month-ahead predictions from the final model fit demonstrated that a longer time horizon yielded only a small sacrifice in predictive power for the vast majority of blocks. Conclusions/Significance The model developed is informed by routinely-collected surveillance data as it accumulates, and predictions are sufficiently accurate and precise to be useful. Such forecasts could, for example, be used to guide stock requirements for rapid diagnostic tests and drugs. More PLOS NEGLECTED TROPICAL DISEASES

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Section: Plos Neglected Tropical Diseasesmentioning

confidence: 99%

A spatio-temporal approach to short-term prediction of visceral leishmaniasis diagnoses in India

et al. 2020

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“…For our knowledge, this is the first study to propose a spatio-temporal method for modeling the variability of VL and the effects of control measure covariates. Other studies related to VL used spatio-temporal modeling in a Bayesian approach, however, evaluating other effects, such as demographic, socioeconomic, climatic and environmental [12,13].…”

Section: Introductionmentioning

confidence: 99%

Canine serological survey and dog culling and its relationship with human visceral leishmaniasis in an endemic urban area

Bermudi

Costa

Nunes³

et al. 2020

BMC Infect Dis

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Background: Visceral leishmaniasis is an important but neglected disease that is spreading and is highly lethal when left untreated. This study sought to measure the Leishmania infantum seroprevalence in dogs, the coverage of its control activities (identification of the canine reservoir by serological survey, dog culling and insecticide spraying) and to evaluate its relationship with the occurrence of the disease in humans in the municipalities of Araçatuba and Birigui, state of São Paulo, Brazil. Methods: Information from 2006 to 2015 was georeferenced for each municipality and modeling was performed for the two municipalities together. To do this, latent Gaussian Bayesian models with the incorporation of a spatiotemporal structure and Poisson distribution were used. The Besag-York-Mollie models were applied for random spatial effects, as also were autoregressive models of order 1 for random temporal effects. The modeling was performed using the INLA (Integrated Nested Laplace Approximations) deterministic approach, considering both the numbers of cases as well as the coverage paired year by year and lagged at one and two years. Results: Control activity coverage was observed to be generally low. The behavior of the temporal tendency in the human disease presented distinct patterns in the two municipalities, however, in both the tendency was to decline. The canine serological survey presented as a protective factor only in the two-year lag model. Conclusions: The canine serological coverage, even at low intensity, carried out jointly with the culling of the positive dogs, suggested a decreasing effect on the occurrence of the disease in humans, whose effects would be seen two years after it was carried out.

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“…( 3a ) denotes the area average of across the cells within . Following Cameletti et al ( 2013 ) and Godana et al ( 2019 ), we assume that changes over time following a first-order autoregressive (AR1) process with coefficient : with defined as a mean square differentiable process 10 (Stein 1999 ) with the temporally independent but spatially correlated innovations following a zero-mean Gaussian distribution with spatiotemporal covariance function: for is the homogeneous variance of , i.e., for every and , and the spatial autocorrelation matrix as a function of the distance between and at time (e.g., the Euclidean distance). Under the assumption that the covariance function only depends on , it is a Matérn covariance function satisfying the second-order stationarity and isotropy assumptions.…”

Section: The Spatiotemporal Generalized Geoadditive-gaussian Field Modelmentioning

confidence: 97%

“…The final component, in Eq. ( 3b ) is the spatiotemporal GF in cell at time , indicating the true but unobserved relative risk (Cameletti et al 2013 ; Godana et al 2019 ). Hence, is the “own” spatiotemporal interaction effect of cell .…”

Section: The Spatiotemporal Generalized Geoadditive-gaussian Field Modelmentioning

confidence: 99%

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Spatiotemporal high-resolution prediction and mapping: methodology and application to dengue disease

Jaya

Folmer

2022

J Geogr Syst

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Dengue disease has become a major public health problem. Accurate and precise identification, prediction and mapping of high-risk areas are crucial elements of an effective and efficient early warning system in countering the spread of dengue disease. In this paper, we present the fusion area-cell spatiotemporal generalized geoadditive-Gaussian Markov random field (FGG-GMRF) framework for joint estimation of an area-cell model, involving temporally varying coefficients, spatially and temporally structured and unstructured random effects, and spatiotemporal interaction of the random effects. The spatiotemporal Gaussian field is applied to determine the unobserved relative risk at cell level. It is transformed to a Gaussian Markov random field using the finite element method and the linear stochastic partial differential equation approach to solve the “big n” problem. Sub-area relative risk estimates are obtained as block averages of the cell outcomes within each sub-area boundary. The FGG-GMRF model is estimated by applying Bayesian Integrated Nested Laplace Approximation. In the application to Bandung city, Indonesia, we combine low-resolution area level (district) spatiotemporal data on population at risk and incidence and high-resolution cell level data on weather variables to obtain predictions of relative risk at subdistrict level. The predicted dengue relative risk at subdistrict level suggests significant fine-scale heterogeneities which are not apparent when examining the area level. The relative risk varies considerably across subdistricts and time, with the latter showing an increase in the period January–July and a decrease in the period August–December.

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Dynamic spatiotemporal modeling of the infected rate of visceral leishmaniasis in human in an endemic area of Amhara regional state, Ethiopia

Cited by 10 publications

References 25 publications

A spatio-temporal approach to short-term prediction of visceral leishmaniasis diagnoses in India

A spatio-temporal approach to short-term prediction of visceral leishmaniasis diagnoses in India

Canine serological survey and dog culling and its relationship with human visceral leishmaniasis in an endemic urban area

Spatiotemporal high-resolution prediction and mapping: methodology and application to dengue disease

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