Modelling Infant Mortality Rate using Time Series Models

Tayo P. Ogundunmade (1) , Akintola O. Daniel (2) , Abdulazeez M. Awwal (3)
(1) Department of Statistics, University of Ibadan, Ibadan, Nigeria
(2) Department of Statistics, University of Ibadan, Ibadan, Nigeria
(3) Department of Statistics, University of Ibadan, Ibadan, Nigeria
Fulltext View | Download


The world’s main indicator of children’s health and general development is the infant mortality rate for infant under the age of five. Infant mortality is the term used to describe the death of a child before their first birthday. The infant mortality rate (IMR), which is the number of deaths of infants under one year of age per 1,000 live births, can be used to describe the prevalence of infant mortality in a population. Comparing the death rate of children under the age of five is the child mortality rate, commonly referred to as the under-five mortality rate. Nigeria, one of the nations with a high under-five mortality rate of 117 per 1,000 live births in 2019, is among those nations. The nation is among the top five nations with the highest mortality rate for children under five in 2019. This study aims to model infant mortality(Live birth and Still birth) rate using time series models and to predict the mortality rate using these models. Adeoyo Maternity Hospital Yemetu in Ibadan provided the data for this study. The data set is a monthly data and also a secondary data span for a period of 12 years (2009 to 2020). The time plot showed visual inspection and non-stationarity. Differencing was done and the unit root test performed for the purpose of comparison thereafter. Augmented-Dickey Fuller test and Phillip Perron unit root test was further tested for the establishment of stationarity in order to the main objectives. Three time series methods are the Autoregressive Integrated Moving Average Model(ARIMA), Exponential Smoothing and the Holt-Winters Method were used to model and predict the infant mortality rate data. The result shows that ARIMA order=c(0,0,1) with zero (0) mean for stillbirth and ARIMA order=c(1,0, 2) for live birth with the smallest AIC = (9.102 and 13.991). Akaike Information Criterion(AIC) values of (9.289, 14.139) and (9.102, 13.991) for live birth and still birth, respectively, were derived by exponential smoothing and Holtwinters technique. This means that Holtwinters' technique, which yielded the lowest AIC when compared to ARIMA and exponential smoothing, is the most accurate predictor of both stillbirth and live birth data. Given the high mortality rate for children under the age of five, it is crucial for the government to place more of an emphasis on health issues and to solve the problems plaguing Nigeria's child health care system.

Article Details

How to Cite
T. P. Ogundunmade, A. O. Daniel, and A. M. Awwal, “Modelling Infant Mortality Rate using Time Series Models”, Int. J. Data. Science., vol. 4, no. 2, pp. 107-115, Dec. 2023.


Adepoju AA, Oludunni AA, Ogundunmade TP. Pettitt and Bayesian Change Point Detection in the Price of Kerosene in The Southwestern Region of Nigeria. Int J Data Sci, 2022; 3: 33-44. DOI: 10.18517/ijods.3.1.33-44, 2022.

Graffam, J. T., La, A., Afon, I., Idahor, U., Oladimeji, T., Mitchell, K., & Keku, E. O. (2023). Particulate matter and infant mortality: A narrative review. International Public Health Journal, 15(1).

Ogundunmade TP, Adepoju AA. The performance of artificial neural network using heterogeneous transfer functions. Int J Data Sci, 2021; 2: 92-103. DOI: 10.18517/ ijods.2.2.92-103.2021.

Ogundunmade TP, Adepoju AA, Allam A. Predicting crude oil price in Nigeria with machine learning models. Mod Econ Manag, 2022; 1: 4. DOI: 10.53964/mem.2022004.

Ogundunmade TP, Adepoju AA, Allam A. Stock price forecasting: Machine learning models with K-fold and repeated cross validation approaches. Mod Econ Manag, 2022; 1: 2. DOI: 10.53964/mem.2022001.

Ayansola OA, Ogundunmade TP, Adedamola AO. Modelling Willingness to Pay of Electricity Supply Using Machine Learning Approach. Mod Econ Manag, 2022; 1: 9. DOI: 10.53964/mem.2022009.

Zilidis, C., & Hadjichristodoulou, C. (2020). Economic crisis impact and social determinants of perinatal outcomes and infant mortality in Greece. International journal of environmental research and public health, 17(18), 6606.

Singh, G. K., & Stella, M. Y. (2019). Infant mortality in the United States, 1915-2017: large social inequalities have persisted for over a century. International Journal of Maternal and Child Health and AIDS, 8(1), 19.

Reed, J., Case, S., & Rijhsinghani, A. (2023). Maternal obesity: Perinatal implications. SAGE Open Medicine, 11, 20503121231176128.

Holm, I. A., Poduri, A., & Goldstein, R. D. (2022). Re: Technical Report for Updated 2022 Recommendations for Reducing Infant Deaths in the Sleep Environment. Pediatrics, 150(6), e2022059737.

Gonçalves, B. P., Procter, S. R., Paul, P., Chandna, J., Lewin, A., Seedat, F., ... & Mahtab, S. (2022). Group B streptococcus infection during pregnancy and infancy: estimates of regional and global burden. The Lancet Global Health, 10(6), e807-e819.

Wojcik, M. H., Schwartz, T. S., Thiele, K. E., Paterson, H., Stadelmaier, R., Mullen, T. E., ... & Agrawal, P. B. (2019). Infant mortality: the contribution of genetic disorders. Journal of Perinatology, 39(12), 1611-1619.

Muhe, L. M., McClure, E. M., Nigussie, A. K., Mekasha, A., Worku, B., Worku, A., ... & Goldenberg, R. L. (2019). Major causes of death in preterm infants in selected hospitals in Ethiopia (SIP): a prospective, cross-sectional, observational study. The Lancet Global Health, 7(8), e1130-e1138.

Kulkarni, V. G., Sunilkumar, K. B., Nagaraj, T. S., Uddin, Z., Ahmed, I., Hwang, K., ... & Goldenberg, R. L. (2021). Maternal and fetal vascular lesions of malperfusion in the placentas associated with fetal and neonatal death: results of a prospective observational study. American journal of obstetrics and gynecology, 225(6), 660-e1.