Associate Project Scientist, Population Health and Disease Prevention
Public Health

Malaria, Computer Modeling, Geographic Information Sciences, Remote Sensing, Remote Sensing, Hydrology, Watershed Modeling, Data Science and Data Management

Spatial and Temporal Heterogeneity in Malaria Epidemiology and Surveillance. My research in malaria epidemiology and surveillance has provided critical insights into the spatial and temporal dynamics of disease transmission, with a focus on vector ecology, environmental determinants, and surveillance strategies. By integrating field-based entomological and epidemiological investigations with advanced geospatial techniques—including remote sensing, GIS, and computational modeling—I have identified key ecological and climatic factors driving malaria risk. Notably, my work has documented the emergence of Anopheles stephensi in the Horn of Africa, posing a significant challenge to malaria elimination efforts (Hawaria et al., 2023). I have also examined vector habitat diversity in irrigated and non-irrigated ecosystems, demonstrating how land use changes impact malaria vector populations (Orondo et al., 2023). Additionally, my research has advanced serological surveillance approaches to assess human exposure to Plasmodium falciparum and Plasmodium vivax, refining our ability to monitor transmission dynamics (Jeang et al., 2023). My studies on malaria transmission heterogeneity across eco-epidemiological zones in Kenya provide a foundation for adaptive, risk-based malaria interventions (Zhou et al., 2024). Collectively, my research informs national malaria control policies, bridging entomology, epidemiology, and geospatial science to enhance evidence-based surveillance and targeted intervention strategies in the fight against malaria in Africa.

Epidemiological Modeling of Malaria Vector and Disease Transmission. My research examines the impact of human socio-economic activities, such as migration, agriculture, and water management, on malaria transmission dynamics in regions like the East African highlands and the Greater Mekong Subregion. By modeling environmental modifications, including irrigation and land use changes, I explore how these factors influence vector breeding sites, transmission patterns, and seasonal variability. My work, which integrates hydrology-based models with remotely sensed data, has improved predictions of vector habitat distribution, particularly in Ethiopia (Jiang et al., 2021). Additionally, I have modeled the efficacy of long-lasting microbial larviciding (LLML) as an intervention strategy, demonstrating its potential for reducing transmission, especially in sub-Saharan Africa (Zhou et al., 2024). My research also examines the socio-economic determinants of malaria health-seeking behaviors, informing malaria prevention strategies and policy development globally (Dixit et al., 2016).

Digital Data Collection and Management for Epidemic Surveillance. I have transformed epidemic surveillance by leading the adoption of digital data collection and management platforms, such as Open Data Kit (ODK) and REDCap, and utilizing robust database systems like MySQL and PostgreSQL to enable real-time, accurate data acquisition. As Data Core Director for ICEMRs, I’ve ensured compliance with NIH data-sharing requirements by implementing CDISC standards, promoting standardized data exchange, and fostering collaboration. My work integrates Geographic Information Systems (GIS) and spatial analysis to inform comprehensive surveillance plans, particularly for malaria in Africa (Githure et al., 2022). Additionally, I have contributed to adaptive intervention strategies, optimizing malaria control through data-driven approaches, including block-cluster randomized trials and simulation studies for integrated malaria control in sub-Saharan Africa (Zhou et al., 2020; Zhou et al., 2021). These innovations have revolutionized the efficiency and accuracy of epidemic surveillance, equipping researchers with tools to monitor disease dynamics and optimize interventions in real-time.

Dr. Ming-Chieh Lee has a background in biological and agricultural engineering with over 15 years of experience integrating hydrology, Geographic Information Systems (GIS), computer modeling, and data management to study malaria’s epidemiological and ecological dimensions. As a research scientist at the University of California, Irvine, I have applied advanced geospatial analytics to identify malaria transmission hotspots and develop predictive models for outbreak forecasting. I have played a key role in multiple International Centers of Excellence for Malaria Research (ICEMR) projects, including the Southeast Asia ICEMR (2010–2017, 2024–2029) and the Sub-Saharan Africa ICEMR (2017–2029). As a co-investigator on several R01 and R21 malaria research projects, I have led data management and geospatial analytics efforts to support field operations and intervention planning. With over 100 peer-reviewed publications, my work has advanced malaria research and informed strategic disease control efforts. My multidisciplinary expertise and leadership position me to contribute meaningfully to the proposed project, aligning with broader public health initiatives.

Public Health

Link to this profile
https://faculty.uci.edu/profile/?facultyId=7302

Last updated
02/10/2025