An Introductory Framework for Choosing Spatiotemporal Analytical Tools in Population-Level Eco-Epidemiological Research

Kanankege, Kaushi S. T.; Álvarez, Julio; Zhang, Lin; Pérez, Andrés

doi:10.3389/fvets.2020.00339

Cited by 18 publications

(35 citation statements)

References 179 publications

(196 reference statements)

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“…The spatio-temporal analysis offers a visual output of the consequences of disease outbreaks, which can reflect the distribution and trend of disease spread in both space and time dimensions (29,34). In this study, the spatio-temporal models were employed to detect clusters of LSD outbreaks according to the spatial and temporal of the outbreaks.…”

Section: Discussionmentioning

confidence: 99%

“…Spatio-temporal analyses of the LSD outbreaks were carried out using a space-time permutation (STP) and Poisson spacetime (Poisson ST) models (32) from SaTScan v.9.6 open-source software (33,34). Both models utilize a dynamic cylindrical window, with a circular geographic base and with height corresponding to time to identify clusters (29,35).…”

Section: Spatio-temporal Analysismentioning

confidence: 99%

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The First Lumpy Skin Disease Outbreak in Thailand (2021): Epidemiological Features and Spatio-Temporal Analysis

Arjkumpa¹,

Suwannaboon²,

Boonrod³

et al. 2022

Front. Vet. Sci.

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The first outbreak of lumpy skin disease (LSD) in Thailand was reported in March 2021, but information on the epidemiological characteristics of the outbreak is very limited. The objectives of this study were to describe the epidemiological features of LSD outbreaks and to identify the outbreak spatio-temporal clusters. The LSD-affected farms located in Roi Et province were investigated by veterinary authorities under the outbreak response program. A designed questionnaire was used to obtain the data. Space-time permutation (STP) and Poisson space-time (Poisson ST) models were used to detect areas of high LSD incidence. The authorities identified 293 LSD outbreak farms located in four different districts during the period of March and the first week of April 2021. The overall morbidity and mortality of the affected cattle were 40.5 and 1.2%, respectively. The STP defined seven statistically significant clusters whereas only one cluster was identified by the Poisson ST model. Most of the clusters (n = 6) from the STP had a radius <7 km, and the number of LSD cases in those clusters varied in range of 3–51. On the other hand, the most likely cluster from the Poisson ST included LSD cases (n = 361) from 198 cattle farms with a radius of 17.07 km. This is the first report to provide an epidemiological overview and determine spatio-temporal clusters of the first LSD outbreak in cattle farms in Thailand. The findings from this study may serve as a baseline information for future epidemiological studies and support authorities to establish effective control programs for LSD in Thailand.

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Section: Discussionmentioning

confidence: 99%

Section: Spatio-temporal Analysismentioning

confidence: 99%

The First Lumpy Skin Disease Outbreak in Thailand (2021): Epidemiological Features and Spatio-Temporal Analysis

Arjkumpa¹,

Suwannaboon²,

Boonrod³

et al. 2022

Front. Vet. Sci.

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“…A SaTScan analysis seeks to identify clusters of cases in which the observed cases within a particular cluster exceed random expectation; this analysis reports the observed/expected ratio and radius of any significant clusters. In addition, we performed a global cluster analysis with Cuzick and Edward's test (global cluster detection with case-control data) in the R package smacpod [1, 3, 5, 7, 9, and 11 nearest neighbors; 999 iterations; ( 55 – 57 )]. To determine if simulated FeLV cases demonstrated spatial clustering consistent with the observed outbreak, we repeated SaTScan local cluster analysis and Cuzick and Edward's tests (at 3, 5, and 7 nearest neighbors) with FIV-based simulation results.…”

Section: Methodsmentioning

confidence: 99%

Apathogenic proxies for transmission dynamics of a fatal virus

Gilbertson

Fountain‐Jones²,

Malmberg³

et al. 2022

Front. Vet. Sci.

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Identifying drivers of transmission—especially of emerging pathogens—is a formidable challenge for proactive disease management efforts. While close social interactions can be associated with microbial sharing between individuals, and thereby imply dynamics important for transmission, such associations can be obscured by the influences of factors such as shared diets or environments. Directly-transmitted viral agents, specifically those that are rapidly evolving such as many RNA viruses, can allow for high-resolution inference of transmission, and therefore hold promise for elucidating not only which individuals transmit to each other, but also drivers of those transmission events. Here, we tested a novel approach in the Florida panther, which is affected by several directly-transmitted feline retroviruses. We first inferred the transmission network for an apathogenic, directly-transmitted retrovirus, feline immunodeficiency virus (FIV), and then used exponential random graph models to determine drivers structuring this network. We then evaluated the utility of these drivers in predicting transmission of the analogously transmitted, pathogenic agent, feline leukemia virus (FeLV), and compared FIV-based predictions of outbreak dynamics against empirical FeLV outbreak data. FIV transmission was primarily driven by panther age class and distances between panther home range centroids. FIV-based modeling predicted FeLV dynamics similarly to common modeling approaches, but with evidence that FIV-based predictions captured the spatial structuring of the observed FeLV outbreak. While FIV-based predictions of FeLV transmission performed only marginally better than standard approaches, our results highlight the value of proactively identifying drivers of transmission—even based on analogously-transmitted, apathogenic agents—in order to predict transmission of emerging infectious agents. The identification of underlying drivers of transmission, such as through our workflow here, therefore holds promise for improving predictions of pathogen transmission in novel host populations, and could provide new strategies for proactive pathogen management in human and animal systems.

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“…Point density analysis generates raster cells in which the neighborhood values are calculated using the points (farms in this case) falls within the de ned cell size. Kriging can be understood as a two-step process, where, step 1 is tting the spatial variogram or likelihood for the data observed at the sampled points; and step 2 involves the interpolation of values for unsampled points using the weights derived from this covariance structure (Isaaks and Srivastava, 1989;Kanankege et al, 2020). In co-Kriging, we used both number of hogs and the number of lagoons to calculate the distance based exposure.…”

Section: Estimation Of the Risk Of Exposure Based On Distancementioning

confidence: 99%

Importance of responsible interpretation of observational studies with an impact on public health policy

Kanankege

Traynor

Pérez

2022

Preprint

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Observational studies are often referenced when informing policy decisions in public health. However, there limitations associated with the nature of observational studies may lead to erroneous interpretations and recommendations. A peer-reviewed publication published in 2018 reported that communities living near hog Concentrated Animal Feeding Operations (CAFO) have increased negative health outcomes and mortalities, in the U.S. state of North Carolina (NC). While authors stated that associations does not imply causation, speculative interpretation of their results by media caused detrimental effects on the swine industry. The objective here was to repeat their study using updated data and to evaluate the strength of conclusions and appropriateness of methods used with the ultimate goal of alerting on the impact that study limitations may have when used as evidence for decision-making. The referenced manuscript reported results from an ecological study design that used available data from 2007–2013. They analyzed exposure to CAFOs using a logistic regression model conducted at the individual-level, while presumably correcting for six confounding factors. We used same data sources and analytical methods, with additional years of data (2007–2018). Mortality, hospital admissions, and emergency department visits related eight diseases were analyzed (anemia, kidney disease, infectious diseases, tuberculosis, low birth weight, HIV, and diabetes). The study population and their exposure was defined by categorizing NC zip codes into three by pig density; where, > 1 hogs/km2 (G1), > 232hogs/km2 (G2), and no hogs (Control). Our repeated analysis recognized that the early study approach was inadequate to assess the intended hypothesis and had shortcomings including presence of ecological fallacy, residual confounding, inconsistency of associations, and over estimation of exposure. Diseases that are not causally relatable to hog CAFOs, such as HIV and diabetes, were also prominent in the communities neighboring CAFOs (G1 and G2). These associations, in part, reflects an alternative explanation of systemic health disparities of the communities near hog CAFOs. An ecological study would not confirm or deny exposure over time nor indicate causation. Hence, we emphasize the importance of responsible interpretation of ecological studies that concerns public health, agriculture, and economy, for science to provide appropriate and accurate support to the ultimate objective of targeting policy to enhance the quality of life and health of the communities.

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An Introductory Framework for Choosing Spatiotemporal Analytical Tools in Population-Level Eco-Epidemiological Research

Cited by 18 publications

References 179 publications

The First Lumpy Skin Disease Outbreak in Thailand (2021): Epidemiological Features and Spatio-Temporal Analysis

The First Lumpy Skin Disease Outbreak in Thailand (2021): Epidemiological Features and Spatio-Temporal Analysis

Apathogenic proxies for transmission dynamics of a fatal virus

Importance of responsible interpretation of observational studies with an impact on public health policy

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