Modelling Suicide in Southern Africa

Hassan Ngala

2024-07-09

Introduction

Background

Putting this to perspective its like losing Tana River (315,941) and Taita Taveta (340,664) counties at the Coast of Kenya.

Global Statistics

Methods

Analysis

Stationarity assessment of Time series

The results below are from the differenced time series to ensure stationarity in first and second-order moments of the data.

KPSS test

At \(\alpha=0.05\) level of significance, results from the KPSS test indicates stationarity in the series p-value=0.1.

ACF plot of suicide rates

PACF plot of suicide rates

Auto Arima model

Findings

Implications

Policy and intervention recommendations based on findings

References

  1. World Health Organization (WHO). Preventing suicide: A global imperative. 2014.
  2. Ministry of Health Kenya. Mental Health Policy 2015-2030.
  3. https://africa.businessinsider.com/local/lifestyle/10-african-countries-with-the-highest-suicide-rate/vd6sept 4.https://rpubs.com/cordelljones/suicide