Comparing Weighted Markov Chain and Auto-Regressive Integrated Moving Average in the Prediction of Under-5 Mortality Annual Closing Rates in Nigeria
Bibliographic record
Abstract
In developing countries, childhood mortality rates are not only affected by socioeconomic, demographic, and health variables, but also vary across regions. Correctly predicting childhood mortality rate trends can provide a clearer understanding for health policy formulation to reduce mortality. This paper describes and compares two prediction methods: Weighted Markov Chain Model (WMC) and Autoregressive Integrated Moving Average (ARIMA) in order to establish which method can better predict the annual child mortality rate in Nigeria. The data for the study were Childhood Mortality Annual Closing Rates (CMACR) data for Nigeria from 1964-2017. The CMACR provides random values changing over time (annually), so we can analyze the mortality closing rate and predict the change range in the next state. Weighted Markov Chain (WMC), a method based on Markov theory, addresses the state and its transition procedures to describe a changing random time series. While the Autoregressive Integrated Moving Average (ARIMA) is a generalization of an Autoregressive Moving Average (ARMA) model. The findings indicate that the ARIMA model predicts CMACR for Nigeria better than WMC. The WMC entered in a loop after two iterations, and we could not use it effectively to predict the future values of CMACR.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.002 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 0.000 |
Machine scores (provisional)
The two teacher heads of the student model, read on this work. A score orders the frame for review; it never asserts a category, and the validation status ships verbatim with every row.
Baseline scores from an immature model (maturity gate not passed, 7 training rounds). Scores rank; they never assert a category.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; a candidate call from one teacher head, not a consensus.
How this classification was reached, model by model and score by score, is at the end of the page under "How this classification was reached".