Data-driven computational modeling of vector-borne macroparasitic disease transmission and elimination
Our goal with this project is develop and apply data-driven modelling approaches combining mathematical, statistical, and computational techniques to discover local disease transmission models for the major vector-borne macroparasitic diseases, lymphatic filariasis and onchocerciasis, in order to better understand between-site heterogeneities in their transmission, predict the impacts of interventions, and determine prospects for their elimination in endemic countries. These models are also used during the development stages of novel intervention tools to evaluate their potential efficacy and cost-effectiveness compared to standard strategies. We use Bayesian data-model assimilation and updating methods to discover the respective local models and improve scientific knowledge of localized transmission dynamics, estimate probabilities of disease freedom given surveillance data, and couple model predictions with economic analysis and spatial mapping, so that more effective evidence-based decision making support is provided to policy makers at different levels, ranging from global to local administrative scales.
Computational science is a multidisciplinary field that utilizes advanced computing to understand and solve complex problems. Researchers at the EIGH develop computational models, hardware, software, data management, and more to tackle a variety of global health concerns.
At the EIGH, our researchers use epidemiology to understand the distribution and determinants of the health and disease conditions in specific populations, and to identify risk factors for certain diseases. This allows them to develop, implement, and measure the impact of targeted, preventative healthcare methods.
At the EIGH, our researchers work to combat a number of various illnesses, including infectious diseases caused by organisms like bacteria, viruses, fungi, and parasites. These diseases can also be spread from one person to another and may be transmitted from animals to humans.
Vector-borne disease research is a historic strength of the EIGH. Our researchers study multiple parts of the vector-borne disease lifecycle, such as how the parasites, viruses, and bacteria cause these kinds of diseases, how the vectors spread these diseases, and how to improve prevention methods in tropical and subtropical areas, which have the highest burden of vector-borne illnesses.
- Gates Foundation and the NTD Modelling Consortium
- University of Warwick, UK
- Erasmus Medical Center, Netherlands
- The Carter Center, Atlanta
- Haiti project ,University of Notre Dame
- Ministries of Health of Ghana, Tanzania, Kenya, Nigeria, and Uganda
- University of South Florida
NOTRE DAME PARTNERSHIPS
- Center for Research Computing
Michael, E., Smith, M., Katabawa, M., et al. (accepted). Substantiating freedom from parasitic infection: combining transmission model predictions with disease surveys.
Michael, E., Sharma, S., Smith, M., et al. (accepted). Quantifying the value of surveillance data for improving model predictions of lymphatic filariasis elimination.
Michael, E., Singh, B., Mayala, B., Smith, M., Hampton, S., & Nabrzyski, J. Continental-scale, data-driven predictive assessment of eliminating the vector-borne disease, lymphatic filariasis, in sub-Saharan Africa by 2020.
Smith, M., Singh, B., & Michael, E. Assessing endgame strategies for the elimination of lymphatic filariasis: A model-based evaluation of the impact of DEC-medicated salt.
Smith, M., Singh, B., Irvine, M., Stolk, W., Subramanian, S., Hollingsworth, D., & Michael, E. Predicting lymphatic filariasis transmission and elimination dynamics using a multi-model ensemble framework.
Michael, E., & Singh, B. Heterogeneous dynamics, robustness/fragility trade-offs, and the eradication of the macroparasitic disease, lymphatic filariasis.
Singh, B., & Michael, E. Bayesian calibration of simulation models for supporting management of the elimination of the macroparasitic disease, lymphatic filariasis.