Comparison and validation of two computational models of Chagas disease: A thirty year perspective from Venezuela

• Main Authors: Sarah M. Bartsch, Jennifer K. Peterson, Daniel L. Hertenstein, Laura Skrip, Martial Ndeffo-Mbah, Alison P. Galvani, Andrew P. Dobson, Bruce Y. Lee
• Format: Article
• Language: English
• Published: Elsevier 2017-03-01
• Series: Epidemics
• Subjects: Chagas disease; Trypanosoma cruzi; Model; Simulation; Model comparison
• Online Access: http://www.sciencedirect.com/science/article/pii/S1755436516300743

Background: Mathematical models can help aid public health responses to Chagas disease. Models are typically developed to fulfill a particular need, and comparing outputs from different models addressing the same question can help identify the strengths and weaknesses of the models in answering particular questions, such as those for achieving the 2020 goals for Chagas disease. Methods: Using two separately developed models (PHICOR/CIDMA model and Princeton model), we simulated dynamics for domestic transmission of Trypanosoma cruzi (T. cruzi). We compared how well the models targeted the last 9 years and last 19 years of the 1968–1998 historical seroprevalence data from Venezuela. Results: Both models were able to generate the T. cruzi seroprevalence for the next time period within reason to the historical data. The PHICOR/CIDMA model estimates of the total population seroprevalence more closely followed the trends seen in the historic data, while the Princeton model estimates of the age-specific seroprevalence more closely followed historic trends when simulating over 9 years. Additionally, results from both models overestimated T. cruzi seroprevalence among younger age groups, while underestimating the seroprevalence of T. cruzi in older age groups. Conclusion: The PHICOR/CIDMA and Princeton models differ in level of detail and included features, yet both were able to generate the historical changes in T. cruzi seroprevalence in Venezuela over 9 and 19-year time periods. Our model comparison has demonstrated that different model structures can be useful in evaluating disease transmission dynamics and intervention strategies.