Simulation modelling of a system is the representation of the running system's model. It can be used to explore and gain new insights into new technology and to estimate the performance of systems too complex for analytical solutions, many biological systems are complex


Simulation modelling of a system reproduces the behaviour of a system using a mathematical model. Simulations modelling have become a useful tool for the mathematical modelling of many natural systems in physics (computational physics), astrophysics, climatology, chemistry and biology, human systems in economics, psychology, social science, and engineering. Further exploration of this technique in medical research, specifically on vector-borne diseases is highly valuable and demanding.

Simulation modelling involves the running of the programs that contains equations or algorithms. It is therefore the process of running a model. 

Example of a Simulation Model

Simulation Modelling of Population Dynamics of Mosquito Vectors for Rift Valley Fever Virus in a Disease Epidemic Setting. In this study, time-varying distributed delays (TVDD) and multi-way functional response equations were implemented to simulate mosquito vectors and hosts developmental stages and to establish interactions between stages and phases of mosquito vectors in relation to vertebrate hosts for infection introduction in compartmental phases. An open-source modelling platforms, Universal Simulator and Qt integrated development environment were used to develop models in C++ programming language. Results showed that Floodwater Aedines and Culicine population continued to fluctuate with temperature and water level over simulation period while controlled by availability of host for blood feeding. Infection in the system was introduced by floodwater Aedines. Culicines pick infection from infected host once to amplify disease epidemic. Simulated mosquito population show sudden unusual increase between December 1997 and January 1998 a similar period when RVF outbreak occurred in Ngorongoro district. This is an ideal approach for understanding disease transmission dynamics towards epidemics prediction, prevention and control. This approach can be used as an alternative source for generation of calibrated RVF epidemics data in different settings