<?xml version="1.0" encoding="UTF-8"?><OAI-PMH xmlns="http://www.openarchives.org/OAI/2.0/" xmlns:xsi="http://www.w3.org/2001/XMLSchema-instance" xsi:schemaLocation="http://www.openarchives.org/OAI/2.0/ http://www.openarchives.org/OAI/2.0/OAI-PMH.xsd"><responseDate>2026-10-30T01:03:22Z</responseDate><request verb="GetRecord" identifier="oai:munzur-rdlopen.rawdatalibrary.net:munzur/1972" metadataPrefix="oai_dc">https://munzur-rdlopen.rawdatalibrary.net/oai/request</request><GetRecord><record><header><identifier>oai:munzur-rdlopen.rawdatalibrary.net:munzur/1972</identifier><datestamp>2026-10-28T15:29:19Z</datestamp><setSpec>openaire</setSpec><setSpec>com_munzur_1</setSpec><setSpec>col_munzur_2</setSpec></header><metadata><oai_dc:dc xmlns:oai_dc="http://www.openarchives.org/OAI/2.0/oai_dc/" xmlns:dc="http://purl.org/dc/elements/1.1/" xmlns:xsi="http://www.w3.org/2001/XMLSchema-instance" xsi:schemaLocation="http://www.openarchives.org/OAI/2.0/oai_dc/ http://www.openarchives.org/OAI/2.0/oai_dc.xsd"><dc:title>An Analysis of Dependence Relationships Between Covid-19 Pandemic and Environmental Conditions Cases Study in Indonesia, England, and Türkiye</dc:title><dc:creator>Handhayani, Teny</dc:creator><dc:creator>Caglar, Durdane Kocacoban</dc:creator><dc:contributor>Wibowo, F.W.</dc:contributor><dc:subject>Bayesian Network</dc:subject><dc:subject>COVID-19</dc:subject><dc:subject>environment</dc:subject><dc:subject>Greedy Equivalence Search</dc:subject><dc:subject>PC algorithm</dc:subject><dc:description>This study explores the causal relationships between environmental conditions and Coronavirus disease 2019 (COVID-19) using Bayesian networks in three geographically distinct cities: Jakarta (Indonesia), London (England), and Istanbul (Türkiye). The datasets collected from these cities span from January 2020 to October 2022, encompassing a variety of environmental variables (Particulate Matter (PM2.5), temperature, humidity, precipitation, wind speed, and Ultraviolet (UV) index) and COVID-19 variables (new cases, deaths, and population density). By using the PC algorithm and Greedy Equivalence Search (GES) to generate causal graphs, and applying the Intersection-Validation (InterVal) method to derive agreement graphs, this research reveals significant geographical differences in how environmental factors influence COVID-19 dynamics. The Peter-Clark (PC) algorithm identifies conditional dependencies, while GES optimizes the structure of the causal graph. The study highlights the significant impact of temperature, UV index, and humidity on COVID-19 dynamics, emphasizing the importance of localized public health strategies tailored to specific environmental conditions. These findings contribute to the understanding of environmental influences on COVID-19 and offer valuable insights for future research and policy-making aimed at mitigating the impact of pandemics in diverse geographical settings.</dc:description><dc:publisher>Institute of Electrical and Electronics Engineers Inc.</dc:publisher><dc:date>2024</dc:date><dc:type>Conference Object</dc:type><dc:identifier>https://rdlopen.rawdatalibrary.net/r/munzur/1972</dc:identifier><dc:identifier>https://doi.org/10.1109/isriti64779.2024.10963452</dc:identifier><dc:identifier>979-833151964-3</dc:identifier><dc:language>en</dc:language><dc:relation>7th International Seminar on Research of Information Technology and Intelligent Systems: Advanced Intelligent Systems in Contemporary Society, ISRITI 2024 - Proceedings</dc:relation><dc:rights>info:eu-repo/semantics/closedAccess</dc:rights><dc:type>info:eu-repo/semantics/conferenceObject</dc:type><dc:identifier>https://munzur-rdlopen.rawdatalibrary.net/tr/items/munzur/1972</dc:identifier></oai_dc:dc></metadata></record></GetRecord></OAI-PMH>