Cross-Correlation Lag Time Analysis of Meteorological Factors and Seasonal Infectious Diseases in Northern Thailand

Abstract

Seasonal infectious diseases represent a significant public health challenge in Northern Thailand. Predicting disease incidence for surveillance remains challenging due to the varying "lag times" in the relationship between climatic factors and disease outbreaks. This study aimed to identify the optimal lag times between meteorological factors and the incidence of key infectious diseases. An ecological study design was employed using weekly time-series data on the incidence of dengue fever, leptospirosis, and hand foot mouth disease (HFMD), along with data on temperature, humidity, and rainfall in Northern Thailand from 2009 to 2019. The Cross-Correlation Function (CCF) with Spearman's rank correlation was used for analyzing these relationships, comparing results from non-stationary and stationary-adjusted data. Findings revealed that dengue fever has a prolonged lag time of 25 weeks following changes in temperature (ρ=0.15, 95% CI=0.07-0.23). In contrast, leptospirosis showed a 6-week lag after increased rainfall (ρ=0.12, 95% CI=0.04-0.19). HFMD exhibited the shortest lag time of one week after a change in humidity (ρ=0.23, 95% CI=0.15-0.31). This knowledge of disease-specific lag times is crucial for developing accurate and timely early warning systems, thereby enhancing the effectiveness of regional disease prevention and control strategies.