Association between influenza incidence and meteorological factors in Anhui Province based on a distributed lag non-linear model

doi:10.20199/j.issn.1672-2302.2026.01.007

Abstract

Abstract:

Objective To analyze the effects of meteorological factors on influenza incidence in Anhui Province, and to provide evidence for the development of influenza control strategies. Methods Influenza surveillance data in Anhui Province from 2016 to 2019 were retrieved from the Chinese Center for Disease Control and Prevention Information System. Meteorological data for the corresponding period were collected from the Anhui Meteorological Bureau. A distributed lag non-linear model (DLNM) was applied to evaluate the exposure-lag effects of meteorological factors on weekly influenza incidence. Results From 2016 to 2019, a total of 152 284 influenza cases were reported in Anhui Province, with an average annual incidence of 60.95 per 100 000 population, showing an increasing trend over the study period. The exposure-response relationship between influenza risk and weekly mean temperature, weekly mean relative humidity, and weekly precipitation presented approximately in inverted U-shape, U-shape, and U-shape for those years. Influenza risk increased when weekly mean temperature ranged from 10.40 to 17.20 ℃, yet decreased at 17.40-26.40 ℃ (both P<0.05). Relatively high temperature (P₇₅=24.27 ℃) exhibited a protective effect over lag 0-3 weeks (RR=0.75, 95%CI: 0.58-0.98). Weekly mean relative humidity was associated with increased influenza risk at 55.50%-68.40% and 83.20%-92.00% (both P<0.05). Both relatively low humidity (P₅=62.77%) and high relatively humidity (P₉₅=88.34%) showed significantly elevated cumulative effects over lag 0-3 weeks (RR=1.48, 95%CI: 1.14-1.92; RR=1.51, 95%CI: 1.16-1.96). Influenza risk increased upon weekly precipitation ranging from 242.20 to 1 254.60 mm (P<0.05), and relatively high precipitation (P₉₅=1 090.65 mm) exhibited significantly elevated cumulative effects over lag 0-3 weeks (RR=1.46, 95%CI: 1.02-2.10). Conclusion Relatively low temperature, low and high relative humidity, and high precipitation may add the risk of influenza incidence in Anhui Province, whereas relatively high temperature may reduce the risks. These effects exhibit certain lagged and sustained characteristics. Incorporating meteorological indicators into influenza prediction models may help improve early warning accuracy.

Key words: Influenza, Meteorological factors, Distributed lag non-linear model, Anhui Province

CLC Number:

R511.7，R181.3

LIU Yaqian, GONG Lei, DAI Yanni, GENG Haoxiang, ZHANG Liqin, MENG Meng, ZHU Meng, ZHU Biao, WU Jiabing. Association between influenza incidence and meteorological factors in Anhui Province based on a distributed lag non-linear model[J]. Journal of Tropical Diseases and Parasitology, 2026, 24(1): 36-40.

Figures/Tables 4

Table 1

Figure 1

Figure 2

Table 2

References 24

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变量	最低值	M（Q₁, Q₃）	最高值
周发病数（例）	86	351（205，617）	12 252
周平均气温（℃）	-1.92	17.26（8.59，24.27）	32.14
周平均相对湿度（%）	55.41	77.69（70.83，83.03）	92.08
周平均风速（m/s）	1.43	2.08（1.88，2.33）	3.31
周总降水量（mm）	0	229.03（71.65，552.98）	3 509.63

气象因素	RR（95%CI）值
气象因素	滞后0周	滞后1周	滞后2周	滞后3周	滞后0~3周
周平均气温（℃）
P₅（2.48）	1.32（0.91~1.92）	1.19（0.90~1.56）	1.00（0.75~1.32）	0.78（0.54~1.13）	1.23（0.75~2.03）
P₂₅（8.59）	1.30（0.96~1.75）	1.11（0.90~1.37）	0.99（0.80~1.23）	0.93（0.69~1.24）	1.32（0.96~1.82）
P₇₅（24.27）	0.86（0.66~1.12）	0.93（0.78~1.10）	0.97（0.82~1.15）	0.98（0.76~1.26）	0.75（0.58~0.98）*
P₉₅（29.59）	0.92（0.60~1.41）	0.89（0.68~1.16）	0.90（0.69~1.18）	0.96（0.64~1.45）	0.71（0.44~1.14）
周平均相对湿度（%）
P₅（62.77）	1.11（0.98~1.26）	1.06（0.96~1.17）	1.08（0.98~1.19）	1.16（1.03~1.31）*	1.48（1.14~1.92）*
P₂₅（70.83）	1.05（0.97~1.13）	1.00（0.94~1.06）	1.00（0.94~1.06）	1.07（0.99~1.15）	1.12（0.94~1.33）
P₇₅（83.03）	1.04（0.99~1.10）	1.04（1.00~1.09）	1.03（0.99~1.07）	0.99（0.94~1.05）	1.11（0.99~1.24）
P₉₅（88.34）	1.23（1.09~1.39）*	1.11（1.01~1.22）*	1.06（0.96~1.16）	1.04（0.92~1.18）	1.51（1.16~1.96）*
周总降水量（mm）
P₅（0.05）	1.02（0.90~1.16）	0.95（0.87~1.05）	0.98（0.89~1.07）	1.09（0.96~1.23）	1.03（0.80~1.34）
P₂₅（71.65）	1.01（0.94~1.08）	0.97（0.92~1.02）	0.98（0.93~1.03）	1.04（0.98~1.11）	1.00（0.87~1.15）
P₇₅（552.98）	1.02（0.96~1.09）	1.04（0.99~1.10）	1.06（1.00~1.12）	1.07（1.00~1.14）	1.20（1.03~1.41）*
P₉₅（1 090.65）	1.05（0.91~1.22）	1.06（0.94~1.20）	1.11（0.98~1.25）	1.18（1.02~1.37）*	1.46（1.02~2.10）*