Effects of meteorological factors on mosquito density in China: results from an ongoing surveillance study in Zhejiang Province

Objectives: This study aimed to investigate the effects of meteorological factors on mosquito density by using a generalized additive model (GAM), and provide a scientific basis for the surveillance and early warnings of mosquito-borne diseases in Zhejiang Province.

Methods: Data on adult mosquito and larvae density were collected through ecological surveys conducted across Zhejiang Province, China, from 2017 to 2023. Meteorological data were obtained from the National Tibetan Plateau Data Center. The Kruskal-Wallis H-test and Chi-square test were used for the descriptive analysis of mosquito distribution characteristics, and Spearman rank correlation was used for the correlation of density indices. GAMs were developed to analyze the influence of meteorological factors on mosquito density. A value of p < 0.05 was considered statistically significant.

Results: The average adult mosquito density was 10.16 per light night from 2017 to 2023 in Zhejiang Province, which exhibited a unimodal curve, peaking at 21.76 per light night in July. The average Breteau Index (BI) was 12.45. GAMs revealed that adult mosquito density and BI with or without a time lag were positively correlated with average air pressure, average monthly relative humidity, monthly sunshine hours, and average monthly temperature (all p < 0.05). Adult mosquito density and BI increased sharply below 17 °C, plateaued from 17 °C to 23 °C, and declined above 23 °C. Adult mosquito density and BI were both increased with rising humidity, and increased with monthly sunshine up to 220 h, then declined slightly above 220 h. Adult mosquito density increased with the growth of air pressure, while BI was highest when the air pressure was 970 hPa. The monthly precipitation had no contemporaneous effect on adult mosquito density or BI, yet it exerted a lagged effect on BI.

Conclusion: Certain meteorological factors significantly influenced both adult mosquito density and the larvae density, with or without a time lag. It is crucial to incorporate the impact of meteorological factors into the ongoing processes of mosquito control, monitoring, and early warning systems.

1 Introduction

Mosquitoes are vectors for hundreds of disease-causing viruses, transmitting various mosquito-borne diseases (MBDs) including dengue fever (DF), as well as Japanese encephalitis (JE), malaria, yellow fever, West Nile fever, chikungunya fever, and Sindbis virus (Gizaw et al., 2024). Previous research indicated that over 80% of the world’s population was at risk for vector-borne diseases, while mosquito-borne illnesses accounted for the greatest share of the burden (Franklinos et al., 2019). For example, studies have found that DF threatened 2.5 billion people worldwide and caused about 500 million infections each year, of which 96 million were symptomatic, resulting in roughly 0.5 million hospitalizations and more than 20,000 deaths (Bhatt et al., 2013). Accelerated urbanization and climate change have broadened the ecological niches available to mosquitoes, thereby intensifying the challenge of controlling MBDs (Knudsen and Slooff, 1992; Zhao et al., 2024). Between 2005 and 2023, mainland China totally tallied 117,892 reported dengue cases, and since 2000 the disease has steadily pushed into new prefectures, chiefly in the subtropical southwest and along the southeastern seaboard (Lai et al., 2015; Li et al., 2024). Zhejiang Province, located on the southeastern coast of China, has a warm and humid climate, which provides favorable conditions for mosquito breeding. As a result, various MBDs have frequently emerged in the region, leading to a heavy burden of vector-borne infectious diseases (Ren et al., 2023). During 2004 and 2018, Zhejiang Province has experienced 12 DF outbreaks, and in 2019 alone, the economic burden imposed by these diseases surpassed USD 0.5 million (Yu et al., 2023; Ren et al., 2022).

As neither effective medication nor vaccines are available for some of the MBDs such as DF, mosquito control has become the principal method of preventing MBDs in the past years and remained highly effective today (Huang et al., 2023; Kothari et al., 2025). Accurate data on mosquito density, seasonal patterns, and breeding sites are the cornerstone of effective vector control. These data underpin robust public-health systems and translate directly into evidence-based strategies for preventing and managing MBDs. Therefore, continuously exploring the factors associated with mosquito density holds immense significance in its efforts to mitigate and control MBDs.

The meteorological influence on the MBDs has gained increasing attention worldwide (Zhu et al., 2019). Previous studies have shown that the number of mosquitoes is affected by local climatic conditions, such as temperature, humidity, precipitation, sunshine hours, and wind speed (Costa-da-Silva et al., 2024; Ferraccioli et al., 2023; Bayoh and Lindsay, 2003). In addition to the direct influence of meteorological variables, mosquito populations often exhibit time-lagged responses to environmental changes. The life cycle of mosquitoes-from egg to adult-typically spans several days to weeks depending on temperature and humidity. Thus, changes in weather conditions may not immediately affect mosquito density but may do so after a certain lag period. Considering these lag effects is therefore crucial for accurately assessing the relationship between meteorological factors and mosquito population dynamics. Studies have shown that higher temperatures accelerate mosquito sexual maturation, increase the frequency of blood-feeding, and boost viral acquisition rates. Moreover, sustained warming could prompt mosquitoes to migrate from lower to higher latitudes, with widening the geographical range of mosquito-borne diseases (Robert et al., 2019). While variations in humidity and precipitation likewise alter mosquito abundance and hastened the dissemination of MBDs (Abu et al., 2024; Mazarire et al., 2024). Some studies suggested that temperatures above 20 °C and precipitation levels exceeding 150 mm provided the most favorable meteorological conditions for mosquito breeding (Colón-González et al., 2011). Light can influence mosquito development and reproduction via affecting the maturation of their ovaries, thereby impacting mosquito density, while relative humidity can affect the survival duration of adult mosquitoes, with high relative humidity significantly reducing their mortality rate and enhancing their activity in blood-feeding and egg-laying (Li et al., 2024). Moreover, meteorological conditions exert both short-term and long-term lagged effects on mosquito abundance and on the transmission of MBDs such as DF (Al Mobin, 2024).

The pronounced influence of meteorological variables on mosquito density underscored their potential as key predictors in early-warning and forecasting systems for both mosquito abundance and MBDs. Generalized additive models (GAMs) offer a robust framework for quantifying these relationships. Currently, it has been widely applied in these research on meteorological factors and vectors as well as related infectious diseases with favorable results (Zheng et al., 2019). For example, da Cruz Ferreira DA et al. used GAMs to investigate the relationship between meteorological variables and mosquito infestation, which found that weekly minimum temperatures above 18 °C were strongly associated with increased mosquito abundance, whereas humidity above 75% had a negative effect on mosquito abundance (da Cruz Ferreira et al., 2017). These findings furnished a robust conceptual foundation for future investigations into mosquito density and its meteorological determinants.

In this study, using data from an entomological survey in Zhejiang Province of China from 2017 to 2023, the mosquito density was analyzed, and the impact of meteorological factors on mosquito density was examined through the application of GAMs. The findings were expected to offer foundational data essential for the effective control of mosquito populations and MBDs in this region.

2 Materials and methods

2.1 Study sites and field work

The study was conducted in all 90 counties of 11 cities (Hangzhou, Huzhou, Jiaxing, Jinhua, Lishui, Ningbo, Quzhou, Shaoxing, Taizhou, Wenzhou, Zhoushan) in Zhejiang Province from 2017 to 2023. The monitoring period extended from April to November. The surveillance was conducted at fixed sampling sites that were revisited monthly throughout each monitoring season from 2017 to 2023. Therefore, the dataset represented a repeated-measures design, with temporally correlated observations collected from the same locations over time. The light trap method was used for the surveillance of the adult mosquitoes, while the Breteau Index (BI) method was used for the surveillance of the Ae. albopictus larvae. The trap was powered on 1 h before sunset, and the light was activated to attract mosquitoes in different habitats, including residential areas, parks, hospitals, farms, pasture sheds, etc. in each county. The traps continued to operate until 1 h after sunrise the following day. Trained field workers inspected and recorded the number of adult mosquitoes. Following the BI protocol, researchers inspected all the containers around the house both indoor and outdoor, tallying the number of houses surveyed and the containers found positive. No fewer than 100 households were investigated in each country per month. The water containers encompassed a variety of sources, including bonsai, cisterns, unused containers, pools, tree holes, scrap tires, greenbelts, etc. A container was considered positive if there was one Ae. albopictus larvae or pupae at least. The specimens captured were brought back to the laboratory, where they were sorted by sex and identified to species under a dissecting microscope.

The density of adult mosquito and larvae were specifically defined as follows. Adult mosquito density, the number of female adult mosquitoes trapped per light night. BI, the number of Ae. albopictus larvae or pupae positive containers per 100 houses inspected. House Index (HI), the percentage of houses with containers positive for Ae. albopictus larvae or pupae. Container Index (CI), the percentage of containers infested with Ae. albopictus larvae or pupae.

2.2 Meteorological data

Meteorological data were obtained from the National Tibetan Plateau/Third Pole Environment Data Center (TPDC, https://data.tpdc.ac.cn). These datasets were generated through the Delta spatial downscaling scheme, which combines the CRU 0.5° global climate dataset and the WorldClim high-resolution climate dataset to produce fine-scale climate surfaces across China. Each dataset has a spatial resolution of 0.008333° (~1 km) and a monthly temporal resolution. All data are provided in NetCDF (.nc) format with WGS-84 geographic coordinates, and have been validated against 496 independent ground meteorological stations distributed nationwide, showing high consistency and accuracy. The datasets were accessed via the TPDC portal to extract data for the geographic extent of Zhejiang Province (118°E–123°E, 27°N–31°N). For each county-level, gridded values were averaged to obtain monthly average ground temperature (°C), monthly average air pressure (hpa), monthly average humidity (%), monthly sunshine hours (h), monthly average wind speed (m/s), monthly average temperature (°C), etc., representing county-level meteorological conditions corresponding to the mosquito surveillance sites.

2.3 Statistical analyses

The statistical analyses were conducted with R 4.0.1 software (The R Foundation for Statistical Computing Platform). A value of p < 0.05 was considered statistically significant. All the parameters were tested for normality. The Kruskal-Wallis H-test and Chi-square were used for the descriptive analysis. Spearman’s rank correlation with or without a time lag was used to analyze the correlation between mosquito density and the meteorological factor. GAMs were used to analyze the meteorological influencing factors of mosquito density. GAMs were chosen over the generalized linear mixed model (GLMM) because GAMs offer greater flexibility in capturing complex, nonlinear relationships between mosquito density and continuous meteorological variables. This approach is particularly suitable for ecological and climatic data, where meteorological factors often exhibit threshold or saturation effects on mosquito abundance. Therefore, GAMs can provide a more accurate and interpretable representation of how temperature, humidity, and other environmental variables influence mosquito density in this study.

3 Results

3.1 The density of adult mosquitoes

From 2017 to 2023, totally, 81,765 traps were deployed and 830,874 adult female mosquitoes were captured, including 411,905 (49.57%) Culex pipiens pallens (Cx. p. pallens), 338,629 (40.76%) Culex tritaeniorhynchus (Cx. tritaeniorhynchus), 41,884(5.04%) Anopheles sinensis (An. sinensis), 17,823 (2.15%) Armigeres subalbatus (Ar. subalbatus), and 15,623 (1.88%) Ae. albopictus. Adult mosquito densities varied between different habitats (H = 473.147, p < 0.001), with the pasture sheds having an average mosquito density of 48.66 per light night, which was much higher than other habitats. In pasture sheds, Cx. tritaeniorhynchus accounted for the highest proportion of mosquito species composition at 62.12%, while Cx. p. pallens exhibited the highest composition ratio in other habitats (Table 1). The adult mosquito density exhibited an unimodal curve, with a maximum value of 21.76 per light night in July (Figure 1). The unimodal curve existed in all five habitats. The peak of mosquito density in pasture sheds occurred in July at 107.82 per light night.

Table 1

Table 1. The proportional composition of mosquito species in different habitats in Zhejiang Province from 2017 to 2023.

Figure 1

Figure 1. The seasonal distribution of adult mosquito density in Zhejiang Province from 2017 to 2023.

3.2 The density of larvae mosquitoes

Totally, 1,196,285 households were investigated in the study, of which 114,662 were found to be positive, with the HI of 9.58. 699,491 containers were monitored, of which 148,950 were found to be positive, with the CI being 21.29. The average value of BI was 12.45, with the lowest recorded value being 9.30 in 2022. There were statistically significant differences in BI, HI, and CI in different years (H = 39.500, p < 0.001; χ² = 1890.597, p < 0.001; χ² = 3206.389, p < 0.001) (Table 2). Specifically, the BI, CI, and HI values in 2018, 2019, and 2020 were significantly higher than the multi-year average (p < 0.001), indicating increased breeding intensity of larvae during these years, while 2022 exhibited the lowest indices, suggesting effective vector control or adverse climatic conditions. The temporal patterns of the three indices were generally consistent, with higher CI corresponding to greater HI and consequently elevated BI. This internal coherence among indices supports the reliability of larval density surveillance across years. Significant differences were observed in the positive breeding of larvae among the various water-holding containers (χ² = 5804.061, p < 0.001). The container with the highest positive rate was scrap tires (32.09%), followed by unused containers (24.14%) and cisterns (21.62%).

Table 2

Table 2. Results of Ae. albopictus larvae monitoring in Zhejiang Province from 2017 to 2023.

As for regional distribution, Hangzhou was the only city with an annual average BI less than 10.0. The BI in Ningbo, Jinhua, and Taizhou have consistently exceeded 15.0 (Figure 2). In terms of different years, Ningbo had BI above 20.0 in 2018 (20.74) and 2019 (26.18), whereas the BI of Quzhou reached 20.91 in 2019 and Taizhou reached 21.39 in 2018.

Figure 2

Figure 2. The BI in 11 cities of Zhejiang Province from 2017 to 2023.

3.3 Correlations between mosquito density and meteorological factors

Time-series curves were plotted to examine the temporal relationships between key meteorological factors and mosquito density. A strong synergistic relationship between adult mosquito density and temperature was observed (Figure 3a). Adult mosquito density was also influenced by precipitation and relative humidity (Figures 3b,c). However, the association with sunshine duration was less apparent in the visualization (Figure 3d). Similarly, the relationships between larvae mosquito density and major meteorological variables were analyzed. Compared with adult mosquito density, larvae density exhibited a clearer and more consistent trend with temperature (Figure 4a). Larvae density increased with rising temperatures but declined when temperatures became excessively high. Moreover, larvae density showed a strong positive correspondence with precipitation (Figure 4b) and a moderate consistency with relative humidity (Figure 4c). In contrast, its association with sunshine duration was relatively weak and less easily discernible (Figure 4d).

Figure 3

Figure 3. Time series curves of adult mosquitoes density and primary climatic factors from 2017 to 2023. Climatic factors: (a) average temperature (b) average precipitation (c) average relative humidity (d) average sunshine hours.

Figure 4

Figure 4. Time series curves of larvae density and primary climatic factors from 2017 to 2023. Climatic factors: (a) average temperature (b) average precipitation (c) average relative humidity (d) average sunshine hours.

A correlation analysis was conducted between meteorological factors and mosquito density. Results showed that the adult mosquito density and larvae density were affected by various meteorological factors, including average monthly ground temperature, average monthly air pressure, average monthly relative humidity, average monthly sunshine hours, average monthly precipitation, average monthly minimum temperature, average monthly temperature, and average monthly maximum temperature, etc. (p < 0.001). Besides, there was a statistical correlation between adult mosquito density and average monthly wind speed (p < 0.001) (Table 3).

Table 3

Table 3. The correlations between the mosquito density and the meteorological factors.

3.4 The results of the GAMs

GAMs was used to analyze the influence of meteorological factors related to mosquito density. After exploratory analysis, the significant variables in the correlation analysis were included in the GAMs in a non-linear form, and the time factor (month) was included to control the long-term and seasonal trends, while the administrative planning of counties in Zhejiang Province was included in the model as a random effect to control for the potential influence of geographical differences on mosquito density. The effect of meteorological factors on mosquito density were analyzed firstly in the current month. There were statistical associations between both adult mosquito density or larvae density and some meteorological factors, including average monthly air pressure, average monthly relative humidity, monthly sunshine hours, and average monthly temperature (all p < 0.05).

The adult mosquito density and larvae density were positively correlated to humidity, which were both increased with rising humidity (Figure 5). Adult and larvae mosquito densities rose with monthly sunshine hours up to approximately 220 h, while beyond this threshold, further increases in sunshine were associated with a modest decline in both densities (Figure 6). Adult mosquito density and larvae density increased rapidly with the growth of temperature when the temperature was below 17 °C, had a plateau when the temperature was between 17 °C and 23 °C, and began to decrease when the temperature was above 23 °C (Figure 7). The adult mosquito density continued to increase with the growth of air pressure, while the larvae density was highest when the air pressure was 970 hPa (Figure 8).

Figure 5

Figure 5. Relationship between mosquito density and average relative humidity in Zhejiang Province from 2017 to 2023.

Figure 6

Figure 6. Relationship between mosquito density and sunshine hours in Zhejiang Province from 2017 to 2023.

Figure 7

Figure 7. Relationship between mosquito density and average temperature in Zhejiang Province from 2017 to 2023.

Figure 8

Figure 8. Relationship between mosquito density and average air pressure in Zhejiang Province from 2017 to 2023.

The lag effect analysis had been conducted and statistically significant relationships were found between some meteorological factors (average monthly air pressure, monthly sunshine hours, average monthly air temperature) and adult mosquito density with one-month or two-month lag (all p < 0.001). Besides, there was a statistically significant relationship between adult mosquito density with one-month lag and average monthly wind speed. Likewise, statistically significant relationships were identified between some meteorological factors (average monthly air pressure, monthly sunshine hours, average monthly wind speed, average monthly air temperature, average monthly precipitation) and the larvae density with one-month or two-month lag (all p < 0.001) (Table 4).

Table 4

Table 4. The lag effects of mosquito density and meteorological factors in Zhejiang Province from 2017 to 2023.

4 Discussion

This study aimed to investigate the influence of meteorological factors on mosquito density in Zhejiang Province from 2017 to 2023. After analysis, we found that the average monthly air pressure, average monthly relative humidity, monthly sunshine hours, and average monthly temperature were significantly associated with adult mosquito density and BI. In addition, some meteorological factors such as average monthly air pressure, monthly sunshine hours, average monthly wind speed, average monthly air temperature, and average monthly precipitation showed lagged effects, suggesting delayed ecological impacts on vector breeding and development cycles.

The Cx. p. pallens and Cx. tritaeniorhynchus were the major species in Zhejiang Province, accounting for 90% of the total mosquito population captured by the light trap method, consistent with the surveillance results reported by Guo et al. (2014) during 2008 and 2012. Notably, certain mosquito species, particularly the Cx. tritaeniorhynchus exhibited a strong zoophilic preference, which explained the significantly higher mosquito densities observed in pasture sheds compared to other habitats across Zhejiang Province (Guo et al., 2014). Nevertheless, the number of Ae. albopictus was underestimated during the light trap method because they exhibit a preference for daytime activity (Wang et al., 2025). As a supplement, the BI served as a primary indicator for evaluating the Ae. albopictus larvae and risk of DF transmission. The guidelines further emphasized the imperative for the implementation of preventive and control measures when the BI surpasses a threshold of 5, with a heightened emphasis on strengthening these measures when the BI exceeds 20 (WHO, 2009). Three different risks of HI, with <0.1% as low, 0.1–5% as medium, and >5% as high, were suggested by the Pan American Health Organization to prevent dengue transmission (Sanchez et al., 2006). The CI demonstrated 11.7 as the optimal outbreak threshold (Luo et al., 2015). Our surveillance data revealed average indices (BI = 12.45, HI = 9.58, CI = 21.29) all exceeding these critical values, indicating substantial DF transmission risk in Zhejiang Province. Notably, scrap tires harbored the highest infestation rates of Ae. albopictus, most likely because their intricate structure traps water that was difficult to drain, allowing prolonged stagnation and creating an ideal microhabitat for mosquito breeding. Thus, the removal of water-holding containers, particularly discarded tires, was essential for reducing mosquito density and preventing dengue transmission.

Meteorological factors significantly influenced mosquito population dynamics through both direct and indirect mechanisms (Ferraccioli et al., 2023; Hwang et al., 2020). Previous studies using Structural Equation Modelling (SEM) have demonstrated that seasonal regulation exerts both direct and indirect effects on mosquito larvae, thereby elucidating the causal relationships between environmental variables and mosquito density (Arcos et al., 2021). In contrast, we employed the GAMs to further capture the nonlinear associations between mosquito density and meteorological factors. It has been demonstrated that the optimal growth temperature for adult mosquitoes was 21–23 °C, and increased temperatures accelerated the growth of mosquitoes, and this effect was greater at temperatures below 24 °C (Hwang et al., 2020). When the temperature drops below 17 °C, mosquitoes cease feeding; at temperatures below 16.5 °C, their egg-laying capacity decreases; when the temperature falls below 16 °C, the duration of the mosquito larval stage is prolonged; and at temperatures below 14.8 °C, they stop laying eggs altogether (Colón-González et al., 2011). Our study showed that adult and larvae mosquito densities increased sharply as temperatures increased up to 17 °C, remained stable between 17 °C and 23 °C, and declined once temperatures exceeded 23 °C, which lended support to the aforementioned point. Mosquitoes shortened their breeding period as temperature got warmer, accelerating the hatching of eggs and reducing the duration of the plumage period, thereby increasing mosquito densities (Moser et al., 2023). However, extreme summer temperatures produced a countervailing effect through thermal stress, which significantly reduced adult mosquito longevity and consequently decreased population densities (Agyekum et al., 2021; Liu et al., 2023). Larvae development was also temperature-dependent. The optimal water temperature for the larval stage of mosquitoes was approximately 28 °C, with slow growth occurring at temperatures below 25 °C and complete cessation of development at temperatures below 10 °C (Bayoh and Lindsay, 2003). The average temperature could exert its influence on larvae density indirectly by regulating the thermal regime of the breeding water.

Sunshine duration also exerted significant influence on mosquito abundance. Our results showed that both adult and larvae densities reached their maximum at around 220 h of sunshine per month and declined steadily as sunshine hours either increased or decreased from this threshold. The mechanisms by which sunshine duration affects mosquito density remain contentious, with studies reporting divergent patterns across varying ecological settings. Recent studies had unveiled a significant positive correlation between the hatching rate of Ae. albopictus larvae and two key environmental factors: elevated temperatures and increased sunshine hours (Ciota et al., 2014). Conversely, several studies had reported that larvae preferred shaded habitats and show markedly higher survival in low-light conditions (Sukiato et al., 2019; Barrera et al., 2006). These findings demonstrated that mosquito density exhibited a unimodal response to sunshine hours, while both insufficient and excessive sunlight exposure imposed physiological constraints that ultimately limited mosquito density.

A significant positive correlation between mosquito density and humidity was observed in our study. Previous research demonstrated that humidity played a critical role in modulating mosquito density and the transmission of MBDs (Abu et al., 2024; Brown et al., 2023). High humidity supported mosquito survival and activity by creating favorable microclimatic conditions (Mazarire et al., 2024). However, excessive humidity (>75%) has been shown to suppress mosquito density, possibly due to adverse effects on adult mosquito survival or oviposition behavior (da Cruz Ferreira et al., 2017). The precipitation had a one-month and two-month lag effect on larvae density. However, no significant association was found between precipitation and adult mosquito density in the studied period. A previous study showed that regions with higher rainfall exhibited higher mosquito density year-round (Baafi and Hurford, 2025). Specifically, increased rainfall generated additional stagnant water bodies, which served as critical breeding sites for immature mosquito stages (Ferraccioli et al., 2023). Meanwhile, some studies had demonstrated that the precipitation over 1–4 weeks before sampling negatively correlated with the mosquito density as events such as flooding and excessive rainfall could flush out breeding sites, thus reducing the vector population (Benedum et al., 2018; Seidahmed and Eltahir, 2016).

Our analysis has several strengths. This study benefits from its comprehensive data coverage across Zhejiang Province from 2017 to 2023, offering robust spatial and temporal resolution that markedly strengthens the reliability of our findings. Moreover, we analyzed both adult mosquito density and the larvae density to make the results more comprehensive. However, some limitations should also be acknowledged. This analysis focused exclusively on meteorological variables, while some confounding effects caused by other critical environmental determinants were ignored, such as population density, the Normalized Difference Vegetation Index, socio-economic deprivation, and road density (Wang et al., 2023), which needs to be further explored in future studies.

5 Conclusion

Our findings indicated that adult mosquito density and larvae density were statistically correlated with average monthly air pressure, relative humidity, sunshine duration, and temperature. The average monthly air pressure, monthly sunshine duration, average monthly wind speed, average monthly air temperature and average monthly precipitation exhibited certain lag effect. To prevent MBDs, the impact of meteorological factors should be incorporated into existing surveillance systems.

Data availability statement

The original contributions presented in the study are included in the article/supplementary material, further inquiries can be directed to the corresponding authors.

Ethics statement

The manuscript presents research on animals that do not require ethical approval for their study.

Author contributions

JW: Writing – review & editing, Writing – original draft. ZS: Writing – original draft. ZGo: Writing – review & editing. ML: Data curation, Investigation, Writing – review & editing. QL: Data curation, Investigation, Writing – review & editing. TL: Data curation, Investigation, Writing – review & editing. ZGu: Investigation, Writing – review & editing, Data curation. JS: Writing – review & editing. JJ: Writing – review & editing.

Funding

The author(s) declared that financial support was received for this work and/or its publication. This work was supported by the Zhejiang Medical and Health Technology Project (No. 2024KY889& No. 2025KY773), Zhejiang Provincial Science and Technology Program for Disease Prevention and Control (2026JKZ016), the Key Program of Health Commission of Zhejiang Province/Science Foundation of National Health Commission (WKJ-ZJ-2523), key grants of Department of Science and Technology of Zhejiang Province (2024C03216, 2025C02186), the National Key Research and Development Project by the Ministry of Science and the Technology of China (2023YFC2308705), the National Natural Science Foundation of China (U23A20496, 82574164), Disease Prevention and Control Innovation Team of Zhejiang Province (2026JKC-04).

Acknowledgments

We thank all colleagues from Zhejiang CDC, local CDCs and Zhejiang University School of Public Health participated in the study for their important contributions.

Conflict of interest

The author(s) declared that this work was conducted in the absence of any commercial or financial relationships that could be construed as a potential conflict of interest.The authors declare that the research was conducted in the absence of any commercial or financial relationships that could be construed as a potential conflict of interest.

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The author(s) declared that Generative AI was not used in the creation of this manuscript.

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