Abstract: Objective To analyze the impact of ambient temperature on influenza incidence in Dongguan City, providing a basis for future influenza prevention and control. Methods Data on reported influenza cases and meteorological factors in Dongguan City from 2017 to 2024 were collected. A Distributed Lag Non-linear Model (DLNM) was constructed to analyze the effect of temperature on influenza incidence. Results From 2017 to 2024, the average annual reported incidence of influenza in Dongguan City was 182.01 per 100 000 population, and the median daily average temperature was 24.7 ℃. The overall cumulative effect of temperature on influenza demonstrated a unimodal distribution, with RR peaking at 3.11 (95% CI: 2.30-4.22) when the temperature reached 15.9 ℃. The relationship between single-day exposure within a 14-day lag period to extreme high temperature (P_97.5) and influenza incidence exhibited an inverted "J" shape, while the relationship with extreme low temperature (P_2.5) showed an inverted "V" shape. The detrimental effect of extreme high temperatures was significant for the total population and for males at a cumulative lag of 1-2 days, with the maximum RR observed at a 2-day cumulative lag (RR=1.36, 95% CI: 1.02-1.82 and RR=1.39, 95% CI: 1.03-1.86, respectively). For the female population, the effect was significant at a 1-day cumulative lag (RR=1.27, 95% CI: 1.00-1.61). The cumulative effect for children aged 0-<7 years was significant only on the day of exposure (RR=1.15, 95% CI: 1.01-1.32). The adverse effect of extreme low temperatures was significant for the total population, for both sexes, and for children aged 0-<7 years at a cumulative lag of 5-14 days. The greatest impact was observed in males at a cumulative lag of 11 days (RR=2.27, 95% CI: 1.64-3.15), and in females and children aged 0-<7 years at a cumulative lag of 12 days (RR=2.44, 95% CI: 1.76-3.39; and RR=2.39, 95% CI: 1.67-3.43, respectively). For individuals aged 7-<19 and ≥19 years, the cumulative effects were not statistically significant throughout the entire lag period. Conclusion Ambient temperature is a significant factor influencing influenza incidence. The risk of influenza associated with extreme low temperatures is greater than that associated with extreme high temperatures, and the effect of extreme low temperatures has a longer lag period. Children aged 0-<7 are a particularly susceptible population. It is recommended that relevant authorities strengthen influenza prevention and control measures for high-risk groups during periods of low temperature.

Key words: Influenza, Temperature, Distributed lag nonlinear model, Extreme high temperature, Extreme low temperature