Temperature and Influenza in New York City: A Distributed Lag Nonlinear Modeling Study

Influenza continues to be a major seasonal public health issue, especially in large urban areas such as New York City. While prior studies have highlighted temperature as a crucial factor influencing influenza transmission, the detailed relationship between temperature variations and influenza incidence in NYC, including both immediate and delayed effects, remains insufficiently understood. This study uses the Distributed Lag Nonlinear Model (DLNM), which analyzes weekly laboratory-confirmed influenza cases from New York State Department of Healths open data portal, together with meteorological data obtained from John F. Kennedy Airport from January 2009 to May 2025. Results indicate a distinct nonlinear relationship between temperature and influenza risk, with sharply elevated risks at extremely low temperatures (e.g, -8 C) and significantly reduced risks at warmer temperatures (e.g, around 16C). Lag analysis further demonstrates that cold temperatures exert delayed effects on influenza incidence, with peak risks observed two weeks following exposure to low temperatures. These findings underscore the critical role of temperature in influenza transmission dynamics and suggest the need for targeted public health interventions during periods of cold weather.