Spatial prediction of human brucellosis (HB) using a GIS-based adaptive neuro-fuzzy inference system (ANFIS)

Babaie, Elnaz; Alesheikh, Ali Asghar; Tabasi, Mohammad

doi:10.1016/j.actatropica.2021.105951

Cited by 8 publications

(6 citation statements)

References 39 publications

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“…In addition, a GIS-based adaptive neuro-fuzzy inference system was employed to explore the spatial distribution patterns of human brucellosis (HB) in Mazandaran Province, Iran [ 101 ]. In 2012 and 2013, most of the hotspots were in the western region of the province, whereas in 2018, they were predominantly concentrated in the eastern region.…”

Section: Modern Technologies To Enhance Zoonotic Disease Surveillancementioning

confidence: 99%

Modern technologies and solutions to enhance surveillance and response systems for emerging zoonotic diseases

Zhang,

Guo,

2024

Science in One Health

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Section: Modern Technologies To Enhance Zoonotic Disease Surveillancementioning

confidence: 99%

Modern technologies and solutions to enhance surveillance and response systems for emerging zoonotic diseases

Zhang,

Guo,

2024

Science in One Health

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“…The coefficient of determination (R 2 ) indicator was used to examine the model's predictability [3]. The closer R 2 is to 1, the closer the predicted values by the model are to the observed values.…”

Section: Model Validationmentioning

confidence: 99%

“…Previous studies have demonstrated that combining a geographical information system (GIS) with spatial statistics or machine learning algorithms can provide a robust framework to explore the spatial patterns of infectious diseases [3,4]. For instance, Tabasi et al [5] identified the spatio-temporal clusters of COVID-19 based on epidemiological features in Golestan province, Iran.…”

Section: Introductionmentioning

confidence: 99%

Spatio-Temporal Modeling of COVID-19 Spread in Relation to Urban Land Uses: An Agent-Based Approach

Tabasi,

Alesheikh,

Kalantari

et al. 2023

Sustainability

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This study aims to address the existing gaps in evidence regarding spatio-temporal modeling of COVID-19 spread, specifically focusing on the impact of different urban land uses in a geospatial information system framework. It employs an agent-based model at the individual level in Gorgan, northeast Iran, characterized by diverse spatial and demographic features. The interactions between human agents and their environment were considered by incorporating social activities based on different urban land uses. The proposed model was integrated with the susceptible–asymptomatic–symptomatic–on treatment–aggravated infection–recovered–dead epidemic model to better understand the disease transmission at the micro-level. The effect of various intervention scenarios, such as social distancing, complete and partial lockdowns, restriction of social gatherings, and vaccination was investigated. The model was evaluated in three modes of cases, deaths, and the spatial distribution of COVID-19. The results show that the disease was more concentrated in central areas with a high population density and dense urban land use. The proposed model predicted the distribution of disease cases and mortality for different age groups, achieving 72% and 71% accuracy, respectively. Additionally, the model was able to predict the spatial distribution of disease cases at the neighborhood level with 86% accuracy. Moreover, findings demonstrated that early implementation of control scenarios, such as social distancing and vaccination, can effectively reduce the transmission of COVID-19 spread and control the epidemic. In conclusion, the proposed model can serve as a valuable tool for health policymakers and urban planners. This spatio-temporal model not only advances our understanding of COVID-19 dynamics but also provides practical tools for addressing future pandemics and urban health challenges.

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“…Geographical Information System (GIS) is a valuable tool in medical geography, which sheds light on the spatiotemporal behavior of diseases [4][5][6][7]. Investigating only one aspect of the spatial [8,9] and temporal [10,11] trend of COVID-19 cannot present the spatial and temporal variations of the infection simultaneously.…”

Section: Introductionmentioning

confidence: 99%

Spatiotemporal Surveillance of COVID-19 Based on Epidemiological Features: Evidence from Northeast Iran

et al. 2022

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Spatiotemporal analysis of COVID-19 cases based on epidemiological characteristics leads to more refined findings about health inequalities and better allocation of medical resources in a spatially and timely fashion. While existing literature has explored the spatiotemporal clusters of COVID-19 worldwide, little attention has been paid to investigate the space-time clusters based on epidemiological features. This study aims to identify COVID-19 clusters by epidemiological factors in Golestan province, one of the highly affected areas in Iran. This cross-sectional study used GIS techniques, including local spatial autocorrelations, directional distribution statistics, and retrospective space-time Poisson scan statistics. The results demonstrated that Golestan has been facing an upward trend of epidemic waves, so the case fatality rate (CFR) of the province was roughly 2.5 times the CFR in Iran. Areas with a more proportion of young adults were more likely to generate space-time clusters. Most high-risk clusters have emerged since early June 2020. The infection first appeared in the west and southwest of the province and gradually spread to the center, east, and northeast regions. The results also indicated that the detected clusters based on epidemiological features varied across the province. This study provides an opportunity for health decision-makers to prioritize disease-prone areas and more vulnerable populations when allocating medical resources.

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Spatial prediction of human brucellosis (HB) using a GIS-based adaptive neuro-fuzzy inference system (ANFIS)

Cited by 8 publications

References 39 publications

Modern technologies and solutions to enhance surveillance and response systems for emerging zoonotic diseases

Modern technologies and solutions to enhance surveillance and response systems for emerging zoonotic diseases

Spatio-Temporal Modeling of COVID-19 Spread in Relation to Urban Land Uses: An Agent-Based Approach

Spatiotemporal Surveillance of COVID-19 Based on Epidemiological Features: Evidence from Northeast Iran

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