A Spatio-GraphNet Model for Real-time Contact Tracing of CoVID-19 Infection in Resource Limited Settings

Ekpenyong, Moses E.; Udo, Ifiok J.; Uzoka, Faith-Michael E.; Attai, Kingsley

doi:10.1145/3418094.3418141

Cited by 3 publications

(2 citation statements)

References 27 publications

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“… Ekpenyong et al (2020) , presented a model called Spatio-GraphNet for tracing the people infected by COVID-19 using graph theory. The results were optimal, as the Spatio-GraphNet model generated real-time spatial information on sources of contagion and the predictive behavior of the spread of COVID-19.…”

Section: Related Workmentioning

confidence: 99%

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COVID-19 spread algorithm in the international airport network-DetArpds

Guevara

Coronel

Maldonado

et al. 2023

PeerJ Computer Science

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Due to COVID-19, the spread of diseases through air transport has become an important issue for public health in countries globally. Moreover, mass transportation (such as air travel) was a fundamental reason why infections spread to all countries within weeks. In the last 2 years in this research area, many studies have applied machine learning methods to predict the spread of COVID-19 in different environments with optimal results. These studies have implemented algorithms, methods, techniques, and other statistical models to analyze the information in accuracy form. Accordingly, this study focuses on analyzing the spread of COVID-19 in the international airport network. Initially, we conducted a review of the technical literature on algorithms, techniques, and theorems for generating routes between two points, comprising an analysis of 80 scientific papers that were published in indexed journals between 2017 and 2021. Subsequently, we analyzed the international airport database and information on the spread of COVID-19 from 2020 to 2022 to develop an algorithm for determining airport routes and the prevention of disease spread (DetARPDS). The main objective of this computational algorithm is to generate the routes taken by people infected with COVID-19 who transited the international airport network. The DetARPDS algorithm uses graph theory to map the international airport network using geographic allocations to position each terminal (vertex), while the distance between terminals was calculated with the Euclidian distance. Additionally, the proposed algorithm employs the Dijkstra algorithm to generate route simulations from a starting point to a destination air terminal. The generated routes are then compared with chronological contagion information to determine whether they meet the temporality in the spread of the virus. Finally, the obtained results are presented achieving a high probability of 93.46% accuracy for determining the entire route of how the disease spreads. Above all, the results of the algorithm proposed improved different computational aspects, such as time processing and detection of airports with a high rate of infection concentration, in comparison with other similar studies shown in the literature review.

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Section: Related Workmentioning

confidence: 99%

“…This section describes the creation of an interconnected network of international air terminals (airports) by applying graph theory ( Ekpenyong et al, 2020 ). The air terminal graph is based on the information obtained from the IATA database (described in section), as follows ( Eq.…”

Section: Covid-19 Spread Analysismentioning

confidence: 99%

COVID-19 spread algorithm in the international airport network-DetArpds

Guevara

Coronel

Maldonado

et al. 2023

PeerJ Computer Science

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A Context, Location and Preference-Aware System for Safe Pedestrian Mobility

Costa

Nixon

Bhattacharjee

et al. 2021

2021 22nd IEEE International Conference on Mobile Data Management (MDM)

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Applications of Technological Solutions in Primary Ways of Preventing Transmission of Respiratory Infectious Diseases—A Systematic Literature Review

Leite

Albuquerque

Pinheiro

2021

IJERPH

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With the growing concern about the spread of new respiratory infectious diseases, several studies involving the application of technology in the prevention of these diseases have been carried out. Among these studies, it is worth highlighting the importance of those focused on the primary forms of prevention, such as social distancing, mask usage, quarantine, among others. This importance arises because, from the emergence of a new disease to the production of immunizers, preventive actions must be taken to reduce contamination and fatalities rates. Despite the considerable number of studies, no records of works aimed at the identification, registration, selection, and rigorous analysis and synthesis of the literature were found. For this purpose, this paper presents a systematic review of the literature on the application of technological solutions in the primary ways of respiratory infectious diseases transmission prevention. From the 1139 initially retrieved, 219 papers were selected for data extraction, analysis, and synthesis according to predefined inclusion and exclusion criteria. Results enabled the identification of a general categorization of application domains, as well as mapping of the adopted support mechanisms. Findings showed a greater trend in studies related to pandemic planning and, among the support mechanisms adopted, data and mathematical application-related solutions received greater attention. Topics for further research and improvement were also identified such as the need for a better description of data analysis and evidence.

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A Spatio-GraphNet Model for Real-time Contact Tracing of CoVID-19 Infection in Resource Limited Settings

Cited by 3 publications

References 27 publications

COVID-19 spread algorithm in the international airport network-DetArpds

COVID-19 spread algorithm in the international airport network-DetArpds

A Context, Location and Preference-Aware System for Safe Pedestrian Mobility

Applications of Technological Solutions in Primary Ways of Preventing Transmission of Respiratory Infectious Diseases—A Systematic Literature Review

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