ADVANCING EPIDEMIOLOGY THROUGH COMPUTATION

Center for Computational Epidemiology and Infectious Diseases

Leveraging advanced computational methods to understand, predict, and combat infectious diseases on a global scale.

High-Impact Research

Leading innovations in epidemiology

Innovative Solutions

Transforming global health

About Our Center

Scientist working in a lab

Our Mission

The Center for Computational Epidemiology and Infectious Diseases (CCEID) is dedicated to advancing the field of epidemiology through innovative computational methods and cutting-edge research.

Our interdisciplinary team of researchers combines expertise in epidemiology, data science, and computer science to develop novel approaches for understanding and combating infectious diseases.

01.

Advanced Modeling

Developing sophisticated computational models for disease spread.

02.

Data Analysis

Leveraging big data to extract meaningful epidemiological insights.

WHAT WE OFFER

Advanced research capabilities

Computational Modeling

Computational Modeling

Advanced simulations for predicting disease spread and evaluating intervention strategies.

Data Analytics

Data Analytics

Cutting-edge analysis of large-scale epidemiological datasets to uncover patterns and trends.

Machine Learning

Machine Learning

Applying AI and machine learning algorithms to improve disease detection and prediction.

Featured Publications

  • Preventing antimalarial drug resistance with triple artemisinin-based combination therapies

    Nguyen TD *, Gao B*, Amaratunga C, Dhorda M, Tran TN-A, White NJ, Dondorp AM, Boni MF*, Aguas R*

    Published in Nature Communications, 2023

    * indicates equal contribution

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  • Antimalarial mass drug administration in large populations and the evolution of drug resistance

    Nguyen TD *, Tran TN-A* , Parker DM, White NJ, Boni MF

    Published in PLoS Global Health, 2023

    * indicates equal contribution

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  • Pre-existing partner-drug resistance facilitates the emergence and spread of artemisinin resistance: a consensus modelling

    Watson OJ*, Gao B*, Nguyen TD*, Tran TN-A, Penny MA, Smith DL, Okell L, Aguas R, Boni MF

    Published in The Lancet Microbe, 2022

    * indicates equal contribution

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  • Optimal population-level deployment of artemisinin combination therapies.

    Nguyen TD, Olliaro P, Dondorp AM, Baird JK, Lam HM, Farrar J, Thwaites GE, White NJ, Boni MF

    Published in Lancet Global Health, 2015

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