ASSESSING MALARIA AND TYPHOID FEVER TRENDS USING CORRELATION AND COVARIANCE: CASE STUDY OF ADAMAWA REGION (CAMEROON)

Malaria and Typhoid Fever are two diseases classified as potentially epidemiological in Cameroon, and where cases of coinfection are often reported in Health Facilities. To assess the degree and direction of this interdependence, correlation and covariance are specifically used in this work. A set of statistical approaches is applied using the Python programming language to a dataset of weekly cases for both diseases in the Adamawa Region of Cameroon, spanning from January 2021 to December 2024 (four years). The proposed analytical framework encompasses graphs and algebraic approaches to correlation, including cross-correlation, cross-covariance, and their corresponding time lags, as well as rolling window functions. First and foremost, the stationarity of each series is examined. The values obtained for the correlation coefficients are 0.73 for Pearson and 0.63 for Spearman, both of which exceed 0.5, indicating strong correlations. There is a strong peak at lag 0 for cross-correlation, suggesting a significant contemporaneous relationship. The time lag cross-correlation consistently shows high values (between 0.8 and 1) for all lags. At lag zero, the series vary together and the time lag cross-covariance remains above zero. Overall, the two diseases exhibit the same directionality with an immediate correlation, and peaks are explicitly observed in mid-2023 and the beginning of 2024. This work provides statistical knowledge for both the population and stakeholders, helps predict disease trends, and informs strategies for the joint management of the diseases. It opens up ways for examining causalities and multivariate analysis.