COVID-19 Spatiotemporal Hotspots and Prediction Based on Wavelet and Neural Network

Charandabi, Neda Kaffash; Gholami, Amir

doi:10.1201/9781003181590-19

Cited by 3 publications

(1 citation statement)

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“…In this context, a spatial 'hotspot' is a distinct spatial domain or cluster with an exceptionally high transmission and infection rates while a temporal 'hot moment' is a temporal domain or cluster characterized by high transmission or infection rates. A number of analytical tools are available for assessing hotspots and hot moments, including; (i) geospatial tools such as geoinformatics and geostatistics, (ii) wavelet and spectral techniques, and (iii) artificial intelligence and network analysis (Charandabi and Gholami, 2021;Bwire et al, 2017;Pinheiro et al, 2021). Artificial intelligence, including the use of data mining techniques, artificial neural networks, and in silico techniques and networks analysis have capabilities to reveal trends and patterns in data that are not obvious using conventional statistical tools.…”

Section: Www-be As a Novel Decision-support Toolmentioning

confidence: 99%

Wastewater, waste, and water-based epidemiology (WWW-BE): A novel hypothesis and decision-support tool to unravel COVID-19 in low-income settings?

Gwenzi

2022

Science of The Total Environment

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Traditional wastewater-based epidemiology (W-BE) relying on SARS-CoV-2 RNA detection in wastewater is attractive for understanding COVID-19. Yet traditional W-BE based on centralized wastewaters excludes putative SARS-CoV-2 reservoirs such as: (i) wastewaters from shared on-site sanitation facilities, (ii) solid waste including faecal sludge from non-flushing on-site sanitation systems and COVID-19 personal protective equipment (PPE), (iii) raw/untreated water, and (iv) drinking water supply systems in low-income countries (LICs). A novel hypothesis and decision-support tool based on Wastewater (on-site sanitation, municipal sewer systems), solid Waste, and raw/untreated and drinking Water-based epidemiology (WWW-BE) is proposed for understanding COVID-19 in LICs. The WWW-BE conceptual framework, including components and principles is presented. Evidence on the presence of SARS-CoV-2 and its proxies in wastewaters, solid materials/waste (papers, metals, fabric, plastics), and raw/untreated surface water, groundwater and drinking water is discussed. Taken together, wastewaters from municipal sewer and on-site sanitation systems, solid waste such as faecal sludge and COVID-19 PPE, raw/untreated surface water and groundwater, and drinking water systems in LICs act as reservoirs that receive and harbour SARS-CoV-2, and then transmit it to humans. Hence, WWW-BE could serve a dual function in estimating the prevalence and potential transmission of COVID-19. Several applications of WWW-BE as a hypothesis and decision support tool in LICs are discussed. WWW-BE aggregates data from various infected persons in a spatial unit, hence, putatively requires less resources (analytical kits, personnel) than individual diagnostic testing, making it an ideal decision-support tool for LICs. The novelty, and a critique of WWW-BE versus traditional W-BE are presented. Potential challenges of WWW-BE include: (i) biohazards and biosafety risks, (ii) lack of expertise, analytical equipment, and accredited laboratories, and (iii) high uncertainties in estimates of COVID-19 cases. Future perspectives and research directions including key knowledge gaps and the application of novel and emerging technologies in WWW-BE are discussed.

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Section: Www-be As a Novel Decision-support Toolmentioning

confidence: 99%

Wastewater, waste, and water-based epidemiology (WWW-BE): A novel hypothesis and decision-support tool to unravel COVID-19 in low-income settings?

Gwenzi

2022

Science of The Total Environment

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A novel architecture of Web-GIS for mapping and analysis of echinococcosis in Poland

Kulawiak

Sulima

et al. 2022

Appl Geomat

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Echinococcosis is an infectious disease transferred through ingestion of food or water which has been contaminated with eggs of the Echinococcus tapeworm, which are spread by intermediate parasite hosts. Because the latter are primarily territorial, research related to diagnosis and prevention of echinococcosis requires investigation of environmental factors, which can be supported with the use of a Geographical Information System (GIS). In particular, since this type of research is usually performed by a team of medical researchers working individually with many patients, it can greatly benefit from the use of a Web-GIS, which provides remote access to data integration, storage and analysis tools. However, most existing Web-GIS solutions dedicated for disease mapping either exhibit limited functionality and interactivity (often due to the use of a thin-client approach) or are tied to a particular technological platform through a thick client based on a browser plugin. Moreover, few of the existing solutions provide user-friendly tools for data input, and none is directly integrated into the workflow of clinicians. This paper proposes a novel system architecture for Web-GIS dedicated to disease mapping, which attempts to optimize the use of client and server hardware resources while providing the user with a variety of analytic functionalities. The proposed architecture has been implemented in the form of a system for remote mapping and analysis of echinococcosis cases in Poland, which has been designed in cooperation with a major Polish hospital centre.

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Spatial-temporal prediction of high-risk areas of Covid-19 disease using Geographically Weighted Regression and Multi Layer Perceptron neural network

Karimi,

Ilkhani khosrowshahi,

Kaffash Charandabi

2023

JGST

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COVID-19 Spatiotemporal Hotspots and Prediction Based on Wavelet and Neural Network

Cited by 3 publications

References 0 publications

Wastewater, waste, and water-based epidemiology (WWW-BE): A novel hypothesis and decision-support tool to unravel COVID-19 in low-income settings?

Wastewater, waste, and water-based epidemiology (WWW-BE): A novel hypothesis and decision-support tool to unravel COVID-19 in low-income settings?

A novel architecture of Web-GIS for mapping and analysis of echinococcosis in Poland

Spatial-temporal prediction of high-risk areas of Covid-19 disease using Geographically Weighted Regression and Multi Layer Perceptron neural network

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