This study applies Box-Jenkins time series methodology to monthly confirmed malaria cases collected in Togo (2022-2024). ARIMA, SARIMA, and ARIMAX models were compared to evaluate temporal dynamics and forecasting performance. Results demonstrate strong annual seasonality, with SARIMA providing the most accurate and parsimonious model for predicting malaria incidence. The analysis highlights the relevance of routine surveillance data for epidemiological forecasting and public health planning in resource-constrained settings.

This notebook presents a concise exploratory data analysis of three NamUs datasets: Missing Persons, Unclaimed Persons, and Unidentified Persons—with the goal of understanding their structure, distributions, and analytic affordances as they relate to visulization.

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