An event-based model and a map visualization approach for spatiotemporal association relations discovery of diseases diffusion

Habibi, Roya; Alesheikh, Ali Asghar; Bayat, Sayeh

doi:10.1016/j.scs.2022.104187

Cited by 4 publications

(3 citation statements)

References 39 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…These findings contribute to our understanding of the risk factors associated with FMD and offer valuable insights for developing effective control and prevention measures. Spatial analysis is a valuable tool for studying the distribution of infectious diseases [9][10][11] . Ballard & Boone (2021) 12 used geographically weighted regression (GWR) to examine the relationship between Lyme disease and land cover in the Midwestern and Northeastern states of the United States.…”

Section: Introductionmentioning

confidence: 99%

Socioeconomic and environmental determinants of Foot and Mouth Disease Incidence: An ecological, cross-sectional study across Iran using spatial modeling

Ashani,

Alesheikh,

Samani

et al. 2023

Preprint

View full text Add to dashboard Cite

Foot-and-mouth disease (FMD) is a highly contagious animal disease caused by a ribonucleic acid (RNA) virus, with significant economic costs and uneven distribution across Asia, Africa, and South America. While spatial analysis and modeling of FMD are still in their early stages, this research aimed to identify socio-environmental determinants of FMD incidence in Iran at the provincial level by studying 135 outbreaks reported between March 21, 2017, and March 21, 2018. We obtained 46 potential socio-environmental determinants and selected four variables, including percentage of population, precipitation in January, percentage of sheep, and percentage of goats, to be used in spatial regression models to estimate variation in spatial heterogeneity. In our analysis, we employed global models, namely Ordinary Least Squares (OLS), Spatial Error Model (SEM), and Spatial Lag Model (SLM), as well as local models, including Geographically Weighted Regression (GWR) and Multiscale Geographically Weighted Regression (MGWR). The MGWR model yielded the highest adjusted of 90%, outperforming the other local and global models. Using local models to map the effects of environmental determinants (such as the percentage of sheep and precipitation) on the spatial variability of FMD incidence provides decision-makers with helpful information for targeted interventions. Our findings advocate for multiscale and multidisciplinary policies to reduce FMD incidence.

show abstract

Section: Introductionmentioning

confidence: 99%

Socioeconomic and environmental determinants of Foot and Mouth Disease Incidence: An ecological, cross-sectional study across Iran using spatial modeling

Ashani,

Alesheikh,

Samani

et al. 2023

Preprint

View full text Add to dashboard Cite

show abstract

“…Spatial analysis is a valuable tool for studying the distribution of infectious diseases 9 – 11 . Ballard and Boone 12 used geographically weighted regression (GWR) to examine the relationship between Lyme disease and land cover in the Midwestern and Northeastern states of the United States.…”

Section: Introductionmentioning

confidence: 99%

Socioeconomic and environmental determinants of foot and mouth disease incidence: an ecological, cross-sectional study across Iran using spatial modeling

Nazari Ashani,

Alesheikh,

Neisani Samani

et al. 2023

Sci Rep

Self Cite

View full text Add to dashboard Cite

Foot-and-mouth disease (FMD) is a highly contagious animal disease caused by a ribonucleic acid (RNA) virus, with significant economic costs and uneven distribution across Asia, Africa, and South America. While spatial analysis and modeling of FMD are still in their early stages, this research aimed to identify socio-environmental determinants of FMD incidence in Iran at the provincial level by studying 135 outbreaks reported between March 21, 2017, and March 21, 2018. We obtained 46 potential socio-environmental determinants and selected four variables, including percentage of population, precipitation in January, percentage of sheep, and percentage of goats, to be used in spatial regression models to estimate variation in spatial heterogeneity. In our analysis, we employed global models, namely ordinary least squares (OLS), spatial error model (SEM), and spatial lag model (SLM), as well as local models, including geographically weighted regression (GWR) and multiscale geographically weighted regression (MGWR). The MGWR model yielded the highest adjusted $${R}^{2}$$ R 2 of 90%, outperforming the other local and global models. Using local models to map the effects of environmental determinants (such as the percentage of sheep and precipitation) on the spatial variability of FMD incidence provides decision-makers with helpful information for targeted interventions. Our findings advocate for multiscale and multidisciplinary policies to reduce FMD incidence.

show abstract

“…Additionally, scholars have applied various methods such as spatial stratified heterogeneity statistics [34], Kalman filtering [36][37][38], Bayesian maximum entropy [39], head/tail breaks [40], and more [41][42][43][44][45]. These methods have been employed for various applications in modelling the spatiotemporal spread of COVID-19, including identifying spatiotemporal distribution patterns [46,47], mapping spatiotemporal disease distributions [48], describing spatiotemporal characteristics of the epidemic [49][50][51], and assessing its impact on production and daily life in China [52][53][54]. These studies cover different stages of epidemic transmission.…”

Section: Introductionmentioning

confidence: 99%

A Head/Tail Breaks-Based Approach to Characterizing Space-Time Risks of COVID-19 Epidemic in China’s Cities

Wu,

Hu,

Luo

et al. 2023

IJGI

View full text Add to dashboard Cite

The novel coronavirus pneumonia (COVID-19) pandemic has caused enormous impacts around the world. Characterizing the risk dynamics for urgent epidemics such as COVID-19 is of great benefit to epidemic control and emergency management. This article presents a novel approach to characterizing the space-time risks of the COVID-19 epidemic. We analyzed the heavy-tailed distribution and spatial hierarchy of confirmed COVID-19 cases in 367 cities from 20 January to 12 April 2020, and population density data for 2019, and modelled two parameters, COVID-19 confirmed cases and population density, to measure the risk value of each city and assess the epidemic from the perspective of spatial and temporal changes. The evolution pattern of high-risk areas was assessed from a spatial and temporal perspective. The number of high-risk cities decreased from 57 in week 1 to 6 in week 12. The results show that the risk measurement model based on the head/tail breaks approach can describe the spatial and temporal evolution characteristics of the risk of COVID-19, and can better predict the risk trend of future epidemics in each city and identify the risk of future epidemics even during low incidence periods. Compared with the traditional risk assessment method model, it pays more attention to the differences in the spatial level of each city and provides a new perspective for the assessment of the risk level of epidemic transmission. It has generality and flexibility and provides a certain reference for the prevention of infectious diseases as well as a theoretical basis for government implementation strategies.

show abstract

An event-based model and a map visualization approach for spatiotemporal association relations discovery of diseases diffusion

Cited by 4 publications

References 39 publications

Socioeconomic and environmental determinants of Foot and Mouth Disease Incidence: An ecological, cross-sectional study across Iran using spatial modeling

Socioeconomic and environmental determinants of Foot and Mouth Disease Incidence: An ecological, cross-sectional study across Iran using spatial modeling

Socioeconomic and environmental determinants of foot and mouth disease incidence: an ecological, cross-sectional study across Iran using spatial modeling

A Head/Tail Breaks-Based Approach to Characterizing Space-Time Risks of COVID-19 Epidemic in China’s Cities

Contact Info

Product

Resources

About