Mortality modelling is a practical method for the government and various fields to obtain a picture of mortality up to a specific age for a particular year. However, some information on the phenomenon may remain in the residual vector and be unrevealed from the models. We handle this issue by employing a multivariate control chart to discover substantial cohort changes in mortality behavior that the models still need to address. The Hotelling T² control chart is applied to the externally studentized deviance model, which is already optimized using a machine-learning decision tree. This study shows a mortality model with the lowest MSE, MAPE, and deviance, by accomplishing simulations in various countries. In addition, the model that is more sensitive in detecting signals on the control chart is singled out so that we can perform a decomposition to determine the attributes of death in the specific outlying age group in a particular year. The case study in the decomposition uses data from the country Saudi Arabia. The overall results demonstrate that our method of processing and producing mortality models with machine learning can be a solution for developing countries or countries with limited mortality data to produce accurate predictions through monitoring control charts.

Coronavirus Disease (COVID-19) pandemic is causing both health and economic crises. The trade-off between economic activity and health puts the authorities in a serious dilemma. Real-time data can be a solution that provides an alternate approach to make dynamic policy scenarios. One of the real-time data that can represent economic activities is the COVID-19 community mobility report. This paper aims to analyse the relationship between human mobility and economic condition during the COVID-19 pandemic, using the COVID-19 community mobility report proxy. The binary logistic regression model result by using data in Indonesia shows that human mobility has a tendency towards economic improvement during the pandemic, simultaneously with the employment rate and geographical factor.