Background: Searching for the latest methods of estimating mortality rates is a major concern for researchers who are working in burden
of diseases. Child mortality is an important indicator for assessing population health care services in a country. The National and SubQDWLRQDO%XUGHQRI'LVHDVHV,QMXULHVDQG5LVN)DFWRUV
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Burden of Disease (GBD) 2010 to estimate major population health measures including child mortality rate. The need to have accurate and
valid estimation of under-5 mortality rate led to apply more powerful and reliable methods.
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summary birth history (SBH) questions from censuses and Demographic Health Survey. These datasets are gathered at national and subQDWLRQDOOHYHOV:HKDYH¿YHWLPHVHULHVRIXQGHU¿YHPRUWDOLW\UDWHVIURP6%+PHWKRGWKDWHDFKRQHFRQWDLQV\HDUWLPHSHULRG:HDOVR
calculated Child mortality rates from death registration for 5 years. The main challenge is how to combine and integrate these different time
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errors and provides uncertainty intervals. The existence of uncertainty for predicting mortality rate is one of the considerable advantages of
GPR that distinguish it from other alternative methods.
Discussion: Estimating accurate and reliable child mortality rates at national and sub-national levels is one of the important parts of NASBOD
project in Iran. Gaussian Process Regression with its special features improves achievement of this goal. GPR is a serious competitor
for other supervised mortality predictive methods. This article aims to explain the application and preferences of GPR method in estimating
child mortality rate.
Keywords: Bayesian sampling-based method, child mortality rate, gaussian process regression, NASBOD, study protocol
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