Bayesian prediction of under-five mortality rates for Tanzania
Mohamed K. Mwanga,
Silas S. Mirau,
Jean M. Tchuenche,
Isambi S. Mbalawata
Affiliations
Mohamed K. Mwanga
School of Computational and Communication Science and Engineering, The Nelson Mandela African Institution of Science and Technology, P.O. Box 447, Arusha, Tanzania; Institute of Accountancy Arusha, P.O. Box 2798, Arusha, Tanzania; Corresponding author at: School of Computational and Communication Science and Engineering, The Nelson Mandela African Institution of Science and Technology, P.O. Box 447, Arusha, Tanzania.
Silas S. Mirau
School of Computational and Communication Science and Engineering, The Nelson Mandela African Institution of Science and Technology, P.O. Box 447, Arusha, Tanzania
Jean M. Tchuenche
School of Computational and Communication Science and Engineering, The Nelson Mandela African Institution of Science and Technology, P.O. Box 447, Arusha, Tanzania
Isambi S. Mbalawata
African Institute for Mathematical Sciences; Research and Innovation Centre, Rue KG590 ST, Kigali, Rwanda
Under-five mortality is a burden on health and economic systems in developing countries. This study used under-five mortality rate (U5MR) data for Tanzania from 1960 to 2020 to predict trends of under-five mortality over the period of 2021 to 2051. Using a Bayesian state space model, it is found that the model is stable in forecasting. Results show that under-five mortality will continue to decline from 48.9 in 2020 to 32.9 in 2030, a decrease of 32.7%. But despite this decrease, Tanzania will likely not meet the Sustainable Development Goal (SDG) for under-five mortality by 2030. Additional efforts by the government through evidence-based interventions should be undertaken to improve child survival by expanding access to health care, especially in rural areas, taking into account local context.
