Conclusion

This paper explored the use of mathematical models to predict population growth in Tanzania. We analyzed both the exponential and logistic models, finding that the exponential model was slightly more accurate for the specific time period studied. However, we also acknowledged that the logistic model might become more important in the future as Tanzania's population approaches its carrying capacity. We then conducted a parameter sensitivity analysis to understand how changes in the birth rate could affect the population growth rate. We found that even with a higher population growth rate, using contraceptives could help keep the population growth in check.
 

We also proposed modifying the models to include migration effects. We found that incorporating migration could significantly improve the accuracy of the models, especially in the long run. This highlights the importance of considering migration when modeling population growth.

While our study provides valuable insights, it also has limitations. We used estimated values for some parameters, and we assumed constant values for others. To improve the model's accuracy, we recommend further research to quantify these parameters more precisely and to incorporate them dynamically. Additionally, we encourage collaboration among researchers to conduct more comprehensive studies on population dynamics in Tanzania.
 

By addressing these limitations and continuing to refine the models, we can gain a deeper understanding of population trends in Tanzania and develop more effective policies for managing future growth.