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Simulated datasets for population dynamics of sickle cell anaemia
S O Edeki1, O O Akanbi1,2
1Department of Mathematics, Covenant University, Ota, Nigeria.
Data in Brief
|March 16, 2018
Summary
This study simulated Sickle Cell Anaemia (SCA) inheritance patterns using Monte Carlo Simulation. The generated datasets can serve as benchmarks for health and marital counseling.
Area of Science:
- Genetics
- Mathematical Biology
- Computational Simulation
Background:
- Sickle Cell Anaemia (SCA) is a genetic disorder with complex inheritance patterns.
- Understanding SCA inheritance is crucial for genetic counseling and public health.
- Existing simulation methods may require enhancement for detailed genetic analysis.
Purpose of the Study:
- To examine the mathematical inheritance formation of Sickle Cell Anaemia (SCA).
- To generate simulated datasets for SCA inheritance analysis.
- To provide benchmark data for health and marital counseling institutions.
Main Methods:
- Utilized Monte Carlo Simulation (MCS) technique.
- Employed the Physical Simulation Smith's Statistical (PSSS) package as a random number generator.
- Simulated 156 births across seven generations, considering reproductive adults and excluding migration.
Main Results:
- Generated simulated datasets reflecting SCA inheritance patterns.
- The simulation successfully modeled genetic transmission over multiple generations.
- The datasets provide a robust foundation for further computational and statistical analysis.
Conclusions:
- The simulated datasets effectively represent SCA inheritance.
- These datasets can serve as valuable benchmarks for health and marital counseling.
- The MCS approach offers a viable method for studying genetic disease transmission.
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