Bayesian Spatiotemporal Modeling and Mapping of Infectious Diseases: Methodology and Applications to Dengue Disease in Bandung City and Covid-19 in West Java, Indonesia

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    Dengue disease is among the biggest health hazards in the twenty-first century. It is endemic to more than one hundred tropical and subtropical countries and affects hundreds of millions of people in the Asia-Pacific region, the Americas, the Middle East, and Africa every year. South Asia and Southeast Asia have the highest levels of vulnerability to Dengue disease. However, it continues to spread and increase worldwide.
    In late December 2019, the COVID-19 pandemic, caused by the new coronavirus (SARS-CoV-2), broke out. It emerged in Wuhan, China, and spread to more than 200 countries worldwide. Since December 2019, COVID-19 has infected over 172.2 million people, and more than 3.67 million deaths have been reported as of July 2021.
    Dengue and COVID-19 have serious public health and socioeconomic implications. To prevent disease transmission and mitigate health and socioeconomic consequences, an effective and efficient early warning system (EWS) is required. To this end, adequate understanding of the spatiotemporal development of diseases is needed. The main objective of this thesis is to develop methodologies for the analysis, prediction, and mapping of the spatiotemporal distribution of infectious diseases at various spatiotemporal scales, with applications to Dengue disease in Bandung and COVID-19 in West Java.
    Originele taal-2English
    KwalificatieDoctor of Philosophy
    Toekennende instantie
    • Rijksuniversiteit Groningen
    Begeleider(s)/adviseur
    • van Wissen, Leonardus, Supervisor
    • Folmer, Henk, Supervisor
    Datum van toekenning20-jun.-2022
    Plaats van publicatie[Groningen]
    Uitgever
    DOI's
    StatusPublished - 2022

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