Quantitative Risk Modeling Interdisciplinary Instructional Training

Published: 22 Dec 2016 Source:

Quantitative Risk Modeling

Interdisciplinary Instructional Training

6th8th February, 2017: Kwame Nkrumah University of Science and Technology

Sponsored by

Safe Water for Food (SaWaFo) Project

Participant of Quantitative Risk Modeling Interdisciplinary Instructional Training will learn the underlying concepts how to use scientific data to perform risk modeling and other quantitative modeling techniques. The Instructional Training is design for graduate students, doctoral and post-doctoral fellows and early –career professionals/Scientist. Participants will gain hands-on experience with host of quantitative risk modeling software options and other tools while they participate in realistic case studies.

Participants will:

  1. Participate in discussion forum and instructional lectures
  2. Engage in specific hands on exercises

We assume no prior knowledge in quantitative modeling experience on the part of participants; therefore we will include tutorial models and applications for quantitative risk modeling.

No registration fee is required. However, only limited seats are available.

Accepting applications until January 31st, 2017, acceptance notification will be released by February 2nd, 2017

Registration Information




Motivation (Max: 150 words)

Rank e.g PG Student, PG Doctoral student, early career professional/scientist

Should be sent to the following email:



Program Contact

Dr. Emmanuel de-Graft Johnson Owusu-Ansah or Rejoice Ametepeh


Training Includes:

Microbial Hazards Identification

Chemical Hazards Identification

Quantification and Fitting of probabilistic distributions on Data

Quantitative Modeling Techniques

Understanding and Selection of Probabilistic Distributions

Expert Judgment Elicitation

Modeling with Hardly any Data

Chemical Risk Assessment and Hazard Quotient

Microbial Risk Assessment

Uncertainty Quantification in Risk Models and Sensitivity Analysis.

NB: Participants with physical science and engineering backgrounds can also apply because the quantitative modeling approach can be applied in all other fields of science.





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