Bangladesh is an overpopulated and the most densely populated country. It is the world's eighth-most populous country in south Asia with over 160 million people. Population problem in Bangladesh is one of the most burning issues in the recent years. So the increasing trend in population is a great threat to the nation and for this reason, the projection of the population of Bangladesh is essential. The purpose of this paper is to model and design the population growth in Bangladesh to predict the future population size. The exponential and the logistic growth models are applied to predict the population of Bangladesh during 1980 to 2080 using the actual data from 1980 to 2016. By using the exponential growth model, the predicted growth rate has been estimated approximately 2.67% and the population of Bangladesh has been predicted to be 1191 million in 2080. We have determined the carrying capacity (K) and vital coefficients a and b for the population prediction in vein of logistic growth model. Thus, the population growth rate of Bangladesh according to the logistic model has been estimated approximately 4.03% and the total population of Bangladesh has been predicted to be 245 million in 2080.
exponential growth model, logistic population model, carrying capacity, population growth, vital coefficient
 Akçakaya HR, Gulve PS. (2000). Population viability analysis in conservation planning: an overview. Ecological Bulletins 48: 9-21. https://doi.org/10.2307/20113245
 Biswas MHA, Ara M, Haque MN, Rahman MA. (2011). Application of control theory in the efficient and sustainable forest management. International Journal of Scientific & Engineering Research 2(3): 26-33.
 Biswas MHA, Paiva LT, Pinho MD. (2014). A SEIR model for control of infectious diseases with constraints. Mathematical Biosciences and Engineering 11(4): 761-784. https://doi.org/10.3934/mbe.2014.11.761
 Biswas HA. (2014). Optimal control of Nipah Virus (NiV) infections: A Bangladesh scenario. Journal of Pure and Applied Mathematics: Advances and Applications 12(1): 77-104.
 Biswas HA. (2012). Model and control strategy of the deadly Nipah Virus (NiV) infections in Bangladesh. Research & Reviews in BioSciences 6(12): 370-377.
 Cohen JE. (1995). Population growth and earth’s human carrying capacity. American Association for the Advancement of Science 269(5222): 341-346. https://doi.org/10.1126/science.7618100
 Deshotel D. (2013). Modeling World Population. Available at http://home2.fvcc.edu/~dhicketh/DiffEqns/spring13projects/Population%20Model%20Project%202013/PopulationModels2013.pdf.
 Edwards CH, Penney DE. (2004). Differential equations and boundary value problems computing and modeling. 3rd edition, Pearson Education, Inc.
 Farid KS, Ahamed JU, Sharma PK, Begum S. (2011). Population dynamics in Bangladesh: Data sources, current facts and past trends. J Bangladesh Agriculture University 9(1): 121-130. https://doi.org/10.3329/jbau.v9i1.8754
 Haque MM, Ahamed F, Anam S, Kabir MR. (2012). Future population projection of Bangladesh by growth rate modeling using logistic population model. Annals of Pure and Applied Mathematics 1(2): 192-202.
 Islam MR. (2011). Modeling and predicting cumulative population of Bangladesh. American Journal of Computational and Applied Mathematics 1(2): 98-100.
 Islam T, Fiebig DG, Meade N. (2002). Modelling multinational telecommunications demand with limited data. International Journal of Forecasting 18: 605-624. https://doi.org/10.1016/S0169-2070(02)00073-0
 Koya PR, Goshu AT. (2013). Generalized mathematical model for biological growths. Open Journal of Modelling
and Simulation 1: 42-53. https://doi.org/10.4236/ojmsi.2013.14008
 Malthus TR. (1893). An Essay on the Principle of Population. (1st edition, plus excepts 1893 2nd edition), Introduction by Philip Appeman, and assorted commentary on Malthus edited by Appleman, Norton Critical Edition, ISBN 0-393-09202-X.
 Mahsin M, Hossain SS. (2012). Population forecasts for Bangladesh, using a Bayesian Methodology. Journal of Health, Population and Nutrition 30(4): 456-463. https://doi.org/10.3329/jhpn.v30i4.13331
 Murray JD. (1989). Mathematical Biology. 2nd edition, Springer–Verlag Berlin.
 Ofori T, Ephraim L, Nyarko F. (2013). Mathematical modeling of Ghana’s population growth. International Journal of Modern Management Sciences 2(2): 57–66.
 Pozzi F, Small C, Yetman G. (2002). Modeling the distribution of human population with night-time satellite imagery and gridded population of the world. Proceedings of Pecora 15/Land Satellite Information IV/ISPRS Commission I/FIEOS Conference.
 Roy B, Roy SK. (2015). Analysis of prey-predator three species models with vertebral and invertebral predators. International Journal Dynamics and Control 3: 306-312. https://doi.org/10.1007/s40435-015-0153-6
 Roy SK, Roy B. (2016). Analysis of prey-predator three species fishery model with harvesting including prey refuge and migration. International Journal of Bifurcation and Chaos 26(1650022). https://doi.org/10.1142/S021812741650022X
 Sardar AK, Hanif M, Asaduzzaman M, Biswas MHA. (2016). Mathematical analysis of the two species Lotka-Volterra predator-prey inter-specific game theoretic competition model. Advanced Modeling and Optimization 18(2): 231-242.
 Tsoularis A, Wallace J. (2001). Analysis of logistic growth models. Res. Lett. Inf. Math. Sci (2): 23-46. https://doi.org/10.1016/S0025-5564(02)00096-2
 The World Bank Population Report. Available at http://data.worldbank.org/indicator/SP.POP.TOTL.
 Wali A, Ntubabare D, Mboniragira V. (2011). Mathematical modeling of Rwanda’s population growth. Applied Mathematical Science 5(53): 2617-2628.
 Wali A, Kagoyire E, Icyingeneye P. (2012). Mathematical modeling of Uganda’s population growth. Applied Mathematical Science 6(84): 4155-4168.