STATISTICAL ANALYSIS OF FACTORS ASSOCIATED WITH MALARIA INFECTIONS

ABSTRACT Malaria remains a persistent public health challenge in Nigeria, with asymptomatic infections significantly contributing to ongoing transmission despite existing control measures. This study investigates the demographic, environmental, and clinical factors associated with malaria diagnosis, particularly among asymptomatic carriers, using statistical methods. Data analysis was conducted using descriptive statistics, chi-square tests of independence, and binary logistic regression to identify significant predictors of malaria prevalence. The results indicated no statistically significant association between malaria diagnosis and variables such as age, gender, region, fever, chills, fatigue, and parasite detection. However, the presence of sweats showed a statistically significant relationship (p = 0.047), with an odds ratio of 1.612, suggesting individuals with sweats are more likely to test positive for malaria. The logistic regression model demonstrated limited predictive power, with a Nagelkerke R² of 0.04 and an overall classification accuracy of 60%, highlighting the complexity of malaria transmission and diagnosis. These findings underscore the limitations of relying solely on self-reported symptoms for malaria detection and emphasize the need for more comprehensive diagnostic approaches. The study provides valuable insights for improving malaria control strategies in Nigeria by focusing on asymptomatic carriers and integrating laboratory-confirmed diagnostics with statistical modeling.