A Multicriteria Spatiotemporal System for Influenza Epidemic Surveillance

Younsi, Fatima-Zohra; Hamdadou, Djamila; Chakhar, Salem

doi:10.4018/978-1-5225-6164-4.ch008

Cited by 4 publications

(5 citation statements)

References 24 publications

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“…In what follows, we summarize the SIR within SW process without vaccination and with vaccination in a detail pseudo algorithm. The pseudo-algorithms are inspired by the pseudo algorithms used in Younsi et al(2019b): BEGIN 1. Initially, generation of SW network and determination of the list of neighbors of each agent.…”

Section: Sir Compartmental Epidemic Model Within Social Networkmentioning

confidence: 99%

Dynamic Contact Network Simulation Model Based on Multi-Agent Systems

Younsi

Hamdadou

2021

International Journal of Healthcare Information Systems and Informatics

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Epidemic spread poses a new challenge to the public health community. Given its very rapid spread, public health decision makers are mobilized to fight and stop it by setting disposal several tools. This ongoing research aims to design and develop a new system based on Multi-Agent System, Suscpetible-Infected-Removed (SIR) model and Geographic Information System (GIS) for public health officials. The proposed system aimed to find out the real and responsible factors for the epidemic spread and explaining its emergence in human population. Moreover, it allows to monitor the disease spread in space and time and provides rapid early warning alert of disease outbreaks. In this paper, a multi-agent epidemic spread simulation system is proposed, discussed and implemented. Simulation result shows that the proposed multi-agent disease spread system performs well in reflecting the evolution of dynamic disease spread system's behavior

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Section: Sir Compartmental Epidemic Model Within Social Networkmentioning

confidence: 99%

Dynamic Contact Network Simulation Model Based on Multi-Agent Systems

Younsi

Hamdadou

2021

International Journal of Healthcare Information Systems and Informatics

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“…As shown in Table II, with the exception of Younsi et al (2018), none of the discussed approaches can jointly deal with all the characteristics of seasonal influenza disease, namely (i) the complex nature of seasonal influenza dynamics, (ii) the social structure and activities of individuals, and (iii) the diversity of criteria affecting the seasonal influenza risk assessment. There is also a lack of appropriate decision tools permitting one to monitor and control the spatial and temporal spread of seasonal influenza disease while accounting for the medical, socio-economic, and environmental criteria.…”

Section: Table I Characteristics Of Discussed Approachesmentioning

confidence: 99%

“…Boutkhoum et al (2015Boutkhoum et al ( , 2016 argue that the combination of multicriteria decision analysis with fuzzy sets theory offers an efficient approach to solve complex decision problems and to extract relevant information from S-OLAP results. In a related study, Younsi et al (2018) extended SYDSEP by incorporating the PROMETHEE II (Brans and Mareschal, 2005) ranking method, which improved its analytical capabilities. However, the use of PROMETHEE II in practice necessitates the specification of several preference parameters.…”

Section: Table II Comparative Studymentioning

confidence: 99%

“…Outranking relation-based aggregation where a preference degree is assigned to every ordered pair of districts with respect to each criterion, and then a global preference degree is obtained by aggregating all the partial preference degrees. Examples of using outranking relationbased aggregation methods are found in Mercat-Rommens et al (2015) and Younsi et al (2018).…”

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confidence: 99%

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A Dominance‐Based Rough Set Approach for an Enhanced Assessment of Seasonal Influenza Risk

et al. 2020

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Accounting for about 290,000–650,000 deaths across the globe, seasonal influenza is estimated by the World Health Organization to be a major cause of mortality. Hence, there is a need for a reliable and robust epidemiological surveillance decision‐making system to understand and combat this epidemic disease. In a previous study, the authors proposed a decision support system to fight against seasonal influenza. This system is composed of three subsystems: (i) modeling and simulation, (ii) data warehousing, and (iii) analysis. The analysis subsystem relies on spatial online analytical processing (S‐OLAP) technology. Although the S‐OLAP technology is useful in analyzing multidimensional spatial data sets, it cannot take into account the inherent multicriteria nature of seasonal influenza risk assessment by itself. Therefore, the objective of this article is to extend the existing decision support system by adding advanced multicriteria analysis capabilities for enhanced seasonal influenza risk assessment and monitoring. Bearing in mind the characteristics of the decision problem considered in this article, a well‐known multicriteria classification method, the dominance‐based rough set approach (DRSA), was selected to boost the existing decision support system. Combining the S‐OLAP technology and the multicriteria classification method DRSA in the same decision support system will largely improve and extend the scope of analysis capabilities. The extended decision support system has been validated by its application to assess seasonal influenza risk in the northwest region of Algeria.

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“…On the other hand, Gulyaeva et al (2020) reported a model for AI along the Pacific Rim using geospatial methods. In addition, the use of multiple-criteria decision analysis (MCDA) identified potential high-risk areas by monitoring specific factors (Egli et al, 2019;Younsi et al, 2019;Stenkamp-Strahm et al, 2020). All these contribute to a better understanding of the transmission of influenza viruses that could be estimated in great detail by combining geographic, epidemiological and immunological data.…”

Section: Introductionmentioning

confidence: 99%

Geospatial modelling to estimate the territory at risk of establishment of influenza type A in Mexico - An ecological study

et al. 2021

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The aim of this study was to estimate the territory at risk of establishment of influenza type A (EOITA) in Mexico, using geospatial models. A spatial database of 1973 outbreaks of influenza worldwide was used to develop risk models accounting for natural (natural threat), anthropic (man-made) and environmental (combination of the above) transmission. Then, a virus establishment risk model; an introduction model of influenza A developed in another study; and the three models mentioned were utilized using multi-criteria spatial evaluation supported by geographically weighted regression (GWR), receiver operating characteristic analysis and Moran’s I. The results show that environmental risk was concentrated along the Gulf and Pacific coasts, the Yucatan Peninsula and southern Baja California. The identified risk for EOITA in Mexico were: 15.6% and 4.8%, by natural and anthropic risk, respectively, while 18.5% presented simultaneous environmental, natural and anthropic risk. Overall, 28.1% of localities in Mexico presented a High/High risk for the establishment of influenza type A (area under the curve=0.923, P<0.001; GWR, r2=0.840, P<0.001; Moran’s I =0.79, P<0.001). Hence, these geospatial models were able to robustly estimate those areas susceptible to EOITA, where the results obtained show the relation between the geographical area and the different effects on health. The information obtained should help devising and directing strategies leading to efficient prevention and sound administration of both human and financial resources.

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A Multicriteria Spatiotemporal System for Influenza Epidemic Surveillance

Cited by 4 publications

References 24 publications

Dynamic Contact Network Simulation Model Based on Multi-Agent Systems

Dynamic Contact Network Simulation Model Based on Multi-Agent Systems

A Dominance‐Based Rough Set Approach for an Enhanced Assessment of Seasonal Influenza Risk

Geospatial modelling to estimate the territory at risk of establishment of influenza type A in Mexico - An ecological study

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