Abstract
(1) Background: Calibration of Simpact Cyan can help to improve estimates related to the transmission dynamics of the Human Immunodeficiency Virus (HIV). Age-mixing patterns in sexual partnerships, onward transmissions, and temporal trends of HIV incidence are determinants which can inform the design of efficient prevention, and linkage-to-care programs. Using an agent-based model (ABM) simulation tool, we investigated, through a simulation study, if estimates of these determinants can be obtained with high accuracy by combining summary features from different data sources. (2) Methods: With specific parameters, we generated the benchmark data, and calibrated the default model in three scenarios based on summary features for comparison. For calibration, we used Latin Hypercube Sampling approach to generate parameter values, and Approximation Bayesian Computation to choose the best fitting ones. In all calibration scenarios the mean square root error was used as a measure to depict the estimates accuracy. (3) Results: The accuracy measure showed relatively no difference between the three scenarios. Moreover, we found that in all scenarios, age and gender strata incidence trends were poorly estimated. (4) Conclusions: Using synthetic benchmarks, we showed that it is possible to infer HIV transmission dynamics using an ABM of HIV transmission. Our results suggest that any type of summary feature provides adequate information to estimate HIV transmission network determinants. However, it is advisable to check the level of accuracy of the estimates of interest using benchmark data.
Highlights
The Human Immunodeficiency Virus (HIV) infection remains a major public health concern in sub-Saharan Africa (SSA)
Simpact Cyan [30], an integrated modelling framework which is an agent-based model (ABM) used to run a simulation study from which we explore its calibration with regard to estimation of determinants of the HIV transmission network
The following results are the mean values for age-mixing patterns in sexual partnerships, temporal trends in HIV incidence, and the distribution of onward transmissions
Summary
The Human Immunodeficiency Virus (HIV) infection remains a major public health concern in sub-Saharan Africa (SSA). The vast majority of new HIV infections are caused by unprotected sexual intercourse [1]. High-risk sexual behaviour of individuals, such as having a large number of concurrent and lifetime sexual partners, and a high frequency of unprotected sex increases the risk of acquiring HIV infection [2,4,5,6,7]. For HIV and other sexually transmitted diseases, individual behaviours affect the transmission dynamics [8]. Our understanding of patterns of age-related sexual partner choices at population level known as age-mixing patterns [9] can help to undertake behaviour change interventions to prevent HIV transmission
Sign-in/Register to view highlights & summary Sign-in/Register to access full text options 
Free) 
Talk to us
Join us for a 30 min session where you can share your feedback and ask us any queries you have
Schedule a call
