A Long-Term Impact Study of Mean Annual Temperature on the Incidence Rate of Chikungunya in Brazil (2014–2023)

Abstract

This study examines the long-term association between mean annual temperature and the annual incidence rate of Chikungunya in Brazil. Using national-level data from the Pan American Health Organization (PAHO) and World Bank for 2014-2023 (N=10), an Ordinary Least Squares (OLS) regression model was employed. Descriptive analysis revealed stable annual temperatures (25.20°C) alongside highly volatile Chikungunya incidence (mean=66.66 per 100,000). The regression model identified a strong positive trend (β=+33.41, 95% CI: -3.88 to 70.70), indicating that each 1°C increase in temperature is associated with an increase of 33.41 cases. While this result did not reach conventional statistical significance (p = 0.0728), likely due to the small sample size, the effect’s direction is consistent with established vector biology. The model explained 26.6% of the annual variance (Adjusted R² = 0.2662), highlighting temperature as a significant macro-climatic driver while underscoring the role of unmeasured confounding factors. These findings provide critical empirical evidence for long-term risk assessment and offer vital policy insights for developing climate-based early warning systems in vulnerable subtropical regions, such as Guangdong, China.