tetano
Editor, Senior Moderator
BMC Infect Dis
. 2024 Oct 2;24(1):1093.
doi: 10.1186/s12879-024-09996-5. Study of the driving factors of the abnormal influenza A (H3N2) epidemic in 2022 and early predictions in Xiamen, China
Hansong Zhu[SUP] #[/SUP][SUP] 1 2 [/SUP], Feifei Qi[SUP] #[/SUP][SUP] 3 4 [/SUP], Xiaoying Wang[SUP] #[/SUP][SUP] 5 [/SUP], Yanhua Zhang[SUP] #[/SUP][SUP] 6 [/SUP], Fangjingwei Chen[SUP] #[/SUP][SUP] 7 [/SUP], Zhikun Cai[SUP] #[/SUP][SUP] 6 [/SUP], Yuyan Chen[SUP] 8 [/SUP], Kaizhi Chen[SUP] 9 [/SUP], Hongbin Chen[SUP] 6 [/SUP], Zhonghang Xie[SUP] 6 10 [/SUP], Guangmin Chen[SUP] 6 10 [/SUP], Yiyang Zhu[SUP] 11 [/SUP], Xiaoyuan Zhang[SUP] 12 [/SUP], Xu Han[SUP] 12 [/SUP], Shenggen Wu[SUP] 13 14 [/SUP], Si Chen[SUP] 15 16 17 [/SUP], Yuying Fu[SUP] 18 [/SUP], Fei He[SUP] 19 [/SUP], Yuwei Weng[SUP] 20 21 [/SUP], Jianming Ou[SUP] 22 23 [/SUP]
Affiliations
Background: Influenza outbreaks have occurred frequently these years, especially in the summer of 2022 when the number of influenza cases in southern provinces of China increased abnormally. However, the exact evidence of the driving factors involved in the prodrome period is unclear, posing great difficulties for early and accurate prediction in practical work.
Methods: In order to avoid the serious interference of strict prevention and control measures on the analysis of influenza influencing factors during the COVID-19 epidemic period, only the impact of meteorological and air quality factors on influenza A (H3N2) in Xiamen during the non coronavirus disease 2019 (COVID-19) period (2013/01/01-202/01/24) was analyzed using the distribution lag non-linear model. Phylogenetic analysis of influenza A (H3N2) during 2013-2022 was also performed. Influenza A (H3N2) was predicted through a random forest and long short-term memory (RF-LSTM) model via actual and forecasted meteorological and influenza A (H3N2) values.
Results: Twenty nine thousand four hundred thirty five influenza cases were reported in 2022, accounting for 58.54% of the total cases during 2013-2022. A (H3N2) dominated the 2022 summer epidemic season, accounting for 95.60%. The influenza cases in the summer of 2022 accounted for 83.72% of the year and 49.02% of all influenza reported from 2013 to 2022. Among them, the A (H3N2) cases in the summer of 2022 accounted for 83.90% of all A (H3N2) reported from 2013 to 2022. Daily precipitation(20-50 mm), relative humidity (70-78%), low (≤ 3 h) and high (≥ 7 h) sunshine duration, air temperature (≤ 21 °C) and O[SUB]3[/SUB] concentration (≤ 30 µg/m[SUP]3[/SUP], > 85 µg/m[SUP]3[/SUP]) had significant cumulative effects on influenza A (H3N2) during the non-COVID-19 period. The daily values of PRE, RHU, SSD, and TEM in the prodrome period of the abnormal influenza A (H3N2) epidemic (19-22 weeks) in the summer of 2022 were significantly different from the average values of the same period from 2013 to 2019 (P < 0.05). The minimum RHU value was 70.5%, the lowest TEM value was 16.0 °C, and there was no sunlight exposure for 9 consecutive days. The highest O[SUB]3[/SUB] concentration reached 164 µg/m[SUP]3[/SUP]. The range of these factors were consistent with the risk factor range of A (H3N2). The common influenza A (H3N2) variant genotype in 2022 was 3 C.2a1b.2a.1a. It was more accurate to predict influenza A (H3N2) with meteorological forecast values than with actual values only.
Conclusion: The extreme weather conditions of sustained low temperature and wet rain may have been important driving factors for the abnormal influenza A (H3N2) epidemic. A low vaccination rate, new mutated strains, and insufficient immune barriers formed by natural infections may have exacerbated this epidemic. Meteorological forecast values can aid in the early prediction of influenza outbreaks. This study can help relevant departments prepare for influenza outbreaks during extreme weather, provide a scientific basis for prevention strategies and risk warnings, better adapt to climate change, and improve public health.
Keywords: Air quality; Influenza; LSTM; Meteorological factors; Phylogenetic analysis; Random forest (RF).
. 2024 Oct 2;24(1):1093.
doi: 10.1186/s12879-024-09996-5. Study of the driving factors of the abnormal influenza A (H3N2) epidemic in 2022 and early predictions in Xiamen, China
Hansong Zhu[SUP] #[/SUP][SUP] 1 2 [/SUP], Feifei Qi[SUP] #[/SUP][SUP] 3 4 [/SUP], Xiaoying Wang[SUP] #[/SUP][SUP] 5 [/SUP], Yanhua Zhang[SUP] #[/SUP][SUP] 6 [/SUP], Fangjingwei Chen[SUP] #[/SUP][SUP] 7 [/SUP], Zhikun Cai[SUP] #[/SUP][SUP] 6 [/SUP], Yuyan Chen[SUP] 8 [/SUP], Kaizhi Chen[SUP] 9 [/SUP], Hongbin Chen[SUP] 6 [/SUP], Zhonghang Xie[SUP] 6 10 [/SUP], Guangmin Chen[SUP] 6 10 [/SUP], Yiyang Zhu[SUP] 11 [/SUP], Xiaoyuan Zhang[SUP] 12 [/SUP], Xu Han[SUP] 12 [/SUP], Shenggen Wu[SUP] 13 14 [/SUP], Si Chen[SUP] 15 16 17 [/SUP], Yuying Fu[SUP] 18 [/SUP], Fei He[SUP] 19 [/SUP], Yuwei Weng[SUP] 20 21 [/SUP], Jianming Ou[SUP] 22 23 [/SUP]
Affiliations
- PMID: 39358703
- PMCID: PMC11446044
- DOI: 10.1186/s12879-024-09996-5
Background: Influenza outbreaks have occurred frequently these years, especially in the summer of 2022 when the number of influenza cases in southern provinces of China increased abnormally. However, the exact evidence of the driving factors involved in the prodrome period is unclear, posing great difficulties for early and accurate prediction in practical work.
Methods: In order to avoid the serious interference of strict prevention and control measures on the analysis of influenza influencing factors during the COVID-19 epidemic period, only the impact of meteorological and air quality factors on influenza A (H3N2) in Xiamen during the non coronavirus disease 2019 (COVID-19) period (2013/01/01-202/01/24) was analyzed using the distribution lag non-linear model. Phylogenetic analysis of influenza A (H3N2) during 2013-2022 was also performed. Influenza A (H3N2) was predicted through a random forest and long short-term memory (RF-LSTM) model via actual and forecasted meteorological and influenza A (H3N2) values.
Results: Twenty nine thousand four hundred thirty five influenza cases were reported in 2022, accounting for 58.54% of the total cases during 2013-2022. A (H3N2) dominated the 2022 summer epidemic season, accounting for 95.60%. The influenza cases in the summer of 2022 accounted for 83.72% of the year and 49.02% of all influenza reported from 2013 to 2022. Among them, the A (H3N2) cases in the summer of 2022 accounted for 83.90% of all A (H3N2) reported from 2013 to 2022. Daily precipitation(20-50 mm), relative humidity (70-78%), low (≤ 3 h) and high (≥ 7 h) sunshine duration, air temperature (≤ 21 °C) and O[SUB]3[/SUB] concentration (≤ 30 µg/m[SUP]3[/SUP], > 85 µg/m[SUP]3[/SUP]) had significant cumulative effects on influenza A (H3N2) during the non-COVID-19 period. The daily values of PRE, RHU, SSD, and TEM in the prodrome period of the abnormal influenza A (H3N2) epidemic (19-22 weeks) in the summer of 2022 were significantly different from the average values of the same period from 2013 to 2019 (P < 0.05). The minimum RHU value was 70.5%, the lowest TEM value was 16.0 °C, and there was no sunlight exposure for 9 consecutive days. The highest O[SUB]3[/SUB] concentration reached 164 µg/m[SUP]3[/SUP]. The range of these factors were consistent with the risk factor range of A (H3N2). The common influenza A (H3N2) variant genotype in 2022 was 3 C.2a1b.2a.1a. It was more accurate to predict influenza A (H3N2) with meteorological forecast values than with actual values only.
Conclusion: The extreme weather conditions of sustained low temperature and wet rain may have been important driving factors for the abnormal influenza A (H3N2) epidemic. A low vaccination rate, new mutated strains, and insufficient immune barriers formed by natural infections may have exacerbated this epidemic. Meteorological forecast values can aid in the early prediction of influenza outbreaks. This study can help relevant departments prepare for influenza outbreaks during extreme weather, provide a scientific basis for prevention strategies and risk warnings, better adapt to climate change, and improve public health.
Keywords: Air quality; Influenza; LSTM; Meteorological factors; Phylogenetic analysis; Random forest (RF).