Luyao Wang, PhD student
Research Topic
Prediction of infectious disease prevalence based on wastewater surveillance using machine learning
Research Abstract
Infectious diseases caused by various pathogens are inducing severe social and economic burden worldwide. Wastewater-based epidemiology (WBE) has been proven as a potential and viable supplementary surveillance tool for monitoring the transmission of various pathogens in communities. However, the relation between wastewater data and cumulative cases is still poorly explored. Wastewater-based epidemiology associated with machine learning techniques is currently emerging as a promising method for COVID-19 prediction. For the successful implementation of WBE, more precise estimations integrating various factors are necessary. This research aims to improve the accuracy of the COVID-19 prediction based on WBE and investigate the viability of its application to other infectious diseases.
Academic Backgrounds
2011 – 2015: Bachelor of Environmental Engineering, China University of Petroleum (East China), China
2016 – 2019 & 2021-2022: Master of Environmental Technology, Technical University of Berlin, Germany
Academic Papers
- Identification of protein biomarkers in wastewater linked to the incidence of COVID-19
Luhur Akbar Devianto, Mohan Amarasiri, Luyao Wang, Takehito Iizuka, Daisuke Sano
Science of the Total Environment, November 2024, 951, 175649. - Characterization of phage vB_EcoS-EE09 infecting E. coli DSM613 Isolated from Wastewater Treatment Plant Effluent and Comparative Proteomics of the Infected and Non-Infected Host
Jimena Barrero-Canosa, Luyao Wang, Angelah Oyugi, Simon Klaes, Pascal Fischer, Lorenz Adrian, Ulrich Szewzyk, Myriel Cooper
Microorganisms, November 2023, 11(11), 2688.
Conference Presentations
- Data-driven prediction of infectious disease cases using hybrid feature selection and multiple explanatory variables
Luyao Wang
6th Academic Forum on Environmental Studies
Tohoku University
October 25, 2024. - Prediction of COVID-19 cases based on various explanatory variables using
machine learning methods
Takehito Iizuka, Luyao Wang, Yifan Zhu, Wakana Oishi, Daisuke Sano
International Symposium for Food and Environmental Virology 2024 (ISFEV2024)
Shimadzu Tokyo Innovation Plaza
June 9-14, 2024.
Honors
- Best Research Award
Prediction of COVID-19 cases based on various explanatory variables using
machine learning methods
Takehito Iizuka, Wang Luyao, Zhu Yifan, Wakana Oishi, Daisuke Sano
International Symposium for Food and Environmental Virology 2024 (ISFEV2024)
June 9-14, 2024 - Best Oral Presentation Award
International Symposium for Food and Environmental Virology 2024 (ISFEV2024)
Shimadzu Tokyo Innovation Plaza
June 9-14, 2024