Estimating Population Total Using Machine Learning Logistic Regression: COVID-19 Pandemic Challenges Perspective

Abstract

The estimation of the population total in undeveloped and developing countries in the recent past has attracted a lot of interest to many researchers due to the sole purpose of planning resource allocation, personnel training and infrastructure in social, health, transport, communication and education. The comprehensive census survey in many countries are conducted every ten years but the government administration changes in many counties every four to five years due to the limit of government terms as per the constitution and therefore does not coincide with the time of census survey. Further, due to the emerging COVID-19 pandemic challenges that requires ministry of health protocols of social distance, the census survey in which the methods of questionnaire and personal interview are commonly used need to be avoided and therefore there is need to search for a better and reliable estimating models for estimating the population total which is the main focus of the study. The existing and developed methods of exponential and logistic class of population total estimating modes have been considered and compared. The main problem in the logistic models in estimating the population total is the estimation of the highest possible population that can be attained for each of the administrative units. In this study a machine learning logistic regression has been proposed and incorporated to search and estimate the constant using the supervised learning process. The performance of the methods have been compared using the Root Mean Square Error (RMSE) whose values were recorded as 1.062, 1.524, 0.477, 0.819 and 0.286 for the exponential, logistic I, Logistic II, logistic III and machine learning logistic (logistic IV) in which the proposed model performed better with the least square error value of 0.286. The proposed model was then used to project the population total and projected the population total for all regions as 51.