A Systematic Review of Joint Spatial and Spatiotemporal Models in Health Research

Tesema, Getayeneh Antehunegn; Tessema, Zemenu Tadesse; Héritier, Stéphane; Stirling, Rob G.; Earnest, Arul

doi:10.3390/ijerph20075295

Cited by 9 publications

(9 citation statements)

References 102 publications

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“…Scale and Temporal Resolution: The weakness of current DHF risk maps originating from scale refers to limitations in the spatial resolution or granularity at which the predictions are made [97]. DHF is a vector-borne disease transmitted by Aedes aegypti, and its incidence can vary signi cantly at different geographic scales, such as district, municipality, regency/city, province, country, region, and continental [7].…”

Section: B) Predictorsmentioning

confidence: 99%

Spatial Model of Dengue Hemorrhagic Fever (DHF) Risk: Scoping Review

Pakaya

Daniel

Widayani

2023

Preprint

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This review aims to provide a comprehensive overview of the important predictors, and additionally spatial modeling tools capable of producing Dengue Hemorrhagic Fever (DHF) risk maps. A literature search was conducted in PubMed, Scopus, Science Direct, and Google Scholar for studies reporting DHF risk factors. The Preferred Reporting Items for Systematic Reviews (PRISMA) 2020 statement is used to report this scoping review. It lasted from January 2011 to August of 2022. Initially 1329 articles were found, after inclusion and exclusion criteria, 45 manuscripts were selected. A variety of models and techniques were used to identify DHF risk areas with an arrangement of various multiple-criteria decision-making, statistical, and Machine Learning technique. We found that There was no pattern of predictor use associated with particular approaches; instead, a wide range of predictors was used to create DHF risk maps. Predictors are various variables or factors that are considered when assessing the likelihood or intensity of DHF outbreaks in a specific area in the context of DHF risk mapping. These predictors can include climatology factors (e.g., temperature, rainfall, humidity), socio-economic indicators (e.g., population density, urbanization level), environmental factors (land-use, elevation) and other relevant factors (e.g., mosquito abundance, previous DHF cases). The spatial model of DHF risk is a valuable tool for public health authorities, policymakers, and communities to identify areas at higher risk of dengue transmission, but its limitations underscore the importance of complementing it with other approaches and considering contextual factors for a more holistic assessment of DHF outbreaks. It enables targeted interventions, such as vector control measures and public awareness campaigns, to be implemented in high-risk areas, ultimately helping to mitigate the impact of dengue outbreaks and protect public health.

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Section: B) Predictorsmentioning

confidence: 99%

Spatial Model of Dengue Hemorrhagic Fever (DHF) Risk: Scoping Review

Pakaya

Daniel

Widayani

2023

Preprint

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“…In recent years, there have been several reviews on Bayesian spatiotemporal models, most of which followed the Preferred Reporting Items for Systematic Reviews and Metaanalysis (PRISMA) framework [12,13]. These reviews have covered various applications, including spatial and spatiotemporal methods for mapping malaria risk [14], modelling dengue fever [15], analysing COVID-19 epidemiology [16], and exploring joint spatial and spatiotemporal methods in health research [17]. However, it is noted that only a limited number of these reviews have delved specifically into spatiotemporal models or Bayesian inference methods.…”

Section: Introductionmentioning

confidence: 99%

A Review of Bayesian Spatiotemporal Models in Spatial Epidemiology

Wang,

Chen,

Xue

2024

IJGI

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Spatial epidemiology investigates the patterns and determinants of health outcomes over both space and time. Within this field, Bayesian spatiotemporal models have gained popularity due to their capacity to incorporate spatial and temporal dependencies, uncertainties, and intricate interactions. However, the complexity of modelling and computations associated with Bayesian spatiotemporal models vary across different diseases. Presently, there is a limited comprehensive overview of Bayesian spatiotemporal models and their applications in epidemiology. This article aims to address this gap through a thorough review. The review commences by delving into the historical development of Bayesian spatiotemporal models concerning disease mapping, prediction, and regression analysis. Subsequently, the article compares these models in terms of spatiotemporal data distribution, general spatiotemporal data models, environmental covariates, parameter estimation methods, and model fitting standards. Following this, essential preparatory processes are outlined, encompassing data acquisition, data preprocessing, and available statistical software. The article further categorizes and summarizes the application of Bayesian spatiotemporal models in spatial epidemiology. Lastly, a critical examination of the advantages and disadvantages of these models, along with considerations for their application, is provided. This comprehensive review aims to enhance comprehension of the dynamic spatiotemporal distribution and prediction of epidemics. By facilitating effective disease scrutiny, especially in the context of the global COVID-19 pandemic, the review holds significant academic merit and practical value. It also aims to contribute to the development of improved ecological and epidemiological prevention and control strategies.

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“…Spatiotemporal analysis, a multidisciplinary approach that examines the distribution of diseases, events, or phenomena across both geographical space and time, has emerged as a powerful tool in the field of public health and healthcare management [ 3 ]. This analytical framework facilitates a deeper understanding of the complex interplay between geographic locations, temporal trends, and health-related outcomes.…”

Section: Introductionmentioning

confidence: 99%

Spatiotemporal distribution of migraine in China: analyses based on baidu index

Lin,

Zhu,

Qiu

et al. 2023

BMC Public Health

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Background In recent years, innovative approaches utilizing Internet data have emerged in the field of syndromic surveillance. These novel methods aim to aid in the early prediction of epidemics across various scenarios and diseases. It has been observed that these systems demonstrate remarkable accuracy in monitoring outbreaks even before they become apparent in the general population. Therefore, they serve as valuable complementary tools to augment existing methodologies. In this study, we aimed to investigate the spatiotemporal distribution of migraine in China by leveraging Baidu Index (BI) data. Methods Migraine-related BI data from January 2014 to December 2022 were leveraged, covering 301 city-level areas from 31 provincial-level regions by using the keyword “migraine (偏头痛)”. Prevalence data from the Global Burden of Disease study (GBD) were attracted to ensure the reliability of utilizing migraine-related BI data for research. Comprehensive analytical methods were then followed to investigate migraine’s spatiotemporal distribution. The Seasonal-Trend decomposition procedure based on Loess (STL) was used to identify the temporal distribution. Spatial distribution was explored using the Getis-Ord Gi* statistic, standard deviation ellipse analysis, Moran’s Index, and Ordinary Kriging. The top eight migraine-related search terms were analyzed through the Demand Graph feature in the Baidu Index platform to understand the public’s concerns related to migraine. Results A strong association was observed between migraine-related BI and the prevalence data of migraine from GBD with a Spearman correlation coefficient of 0.983 (P = 4.96 × 10− 5). The overall trend of migraine-related BI showed a gradual upward trend over the years with a sharp increase from 2017 to 2019. Seasonality was observed and the peak period occurred in spring nationwide. The middle-lower reaches of the Yangtze River were found to be hotspots, while the eastern coastal areas had the highest concentration of migraine-related BI, with a gradual decrease towards the west. The most common search term related to migraine was “How to treat migraine quickly and effectively (偏头痛怎么办最快最有效的方法)”. Conclusions This study reveals important findings on migraine distribution in China, underscoring the urgent need for effective prevention and management strategies.

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A Systematic Review of Joint Spatial and Spatiotemporal Models in Health Research

Cited by 9 publications

References 102 publications

Spatial Model of Dengue Hemorrhagic Fever (DHF) Risk: Scoping Review

Spatial Model of Dengue Hemorrhagic Fever (DHF) Risk: Scoping Review

A Review of Bayesian Spatiotemporal Models in Spatial Epidemiology

Spatiotemporal distribution of migraine in China: analyses based on baidu index

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