Epidemiology, transmission, vector and factors analysis of chikungunya outbreak in 2025, Quanzhou City of China

Epidemiology, transmission, vector and factors analysis of chikungunya outbreak in 2025, Quanzhou City of China | Research Square window.SnipcartSettings = { analytics: { enabled: false } }; (function() { var accessVector = localStorage.getItem('access_vector') || ''; window.dataLayer = window.dataLayer || []; if (accessVector) { window.dataLayer.push({ user: { profile: { profileInfo: { snid: accessVector } } } }); } })(); (function(w,d,s,l,i){w[l]=w[l]||[];w[l].push({'gtm.start':new Date().getTime(),event:'gtm.js'});var f=d.getElementsByTagName(s)[0],j=d.createElement(s),dl=l!='dataLayer'?'&l='+l:'';j.async=true;j.src='https://www.googletagmanager.com/gtm.js?id='+i+dl;f.parentNode.insertBefore(j,f);})(window,document,'script','dataLayer','GTM-K279D39R'); Browse Preprints In Review Journals COVID-19 Preprints AJE Video Bytes Research Tools Research Promotion AJE Professional Editing AJE Rubriq About Preprint Platform In Review Editorial Policies Our Team Advisory Board Help Center Sign In Submit a Preprint Cite Share Download PDF Short Report Epidemiology, transmission, vector and factors analysis of chikungunya outbreak in 2025, Quanzhou City of China Shenggen Wu, Jiayuan Xie, Hongfeng Zhao, Jiangyi Liu, Ruyi Guo, and 13 more This is a preprint; it has not been peer reviewed by a journal. https://doi.org/ 10.21203/rs.3.rs-8507244/v1 This work is licensed under a CC BY 4.0 License Status: Posted Version 1 posted You are reading this latest preprint version Abstract Background: Following a major chikungunya outbreak in Foshan City, China, locally transmitted cases have been reported across the country. Research remains limited to epidemiology, transmission dynamics, vector characteristics, and risk factors of chikungunya fever. Methods: We analyzed 164 chikungunya cases reported in Quanzhou City, Fujian Province, between August 18 and September 9, 2025, along with mosquito surveillance data collected from August 25 to September 13, 2025. We examined case characteristics, symptom patterns, Cycle threshold (Ct) values trends, and vector distributions. Logistic regression was used to identify demographic factors and mosquito density indices associated with infection risk. Results: Among 164 cases, 50.0% were male and 38.4% were aged ≥ 60 years. Ct values were significantly influenced by age ( P = 0.0044) and the onset-to-diagnosis intervals ( P < 0.0001), but not by symptom patterns. Advanced age was a major risk factor, particularly for individuals aged 70 – 85 years (OR = 6.28, 95% CI = 4.22 – 9.24) and those 85 years or older (OR = 2.93, 95% CI = 1.06 – 8.17). Higher mosquito larval and adult densities also increased infection risk. Following implementation of emergency response protocols and intensive mosquito control measures, zero incident cases were reported within 21 days. Conclusions: Chikungunya disproportionately affects older adults, especially those aged ≥ 70 years. Viral load increases with age and decreases with delayed diagnosis. The lack of correlation between symptoms and viral load suggests that asymptomatic individuals may be equally infectious, providing guidelines for screening and management of high-risk groups . Chikungunya fever Epidemiology Outbreak Cycle threshold value Risk factors Figures Figure 1 Figure 2 Figure 3 Figure 4 Figure 5 Figure 6 Figure 7 Figure 8 Figure 9 Figure 10 Background Chikungunya fever is an acute vector-borne infectious disease caused by the chikungunya virus (CHIKV), which has shown rapid spread and outbreak epidemics worldwide in recent years. Local transmission has been reported in 119 nations and areas[ 1 , 2 ]. Climate change and increased international travel have heightened the risk of transmission in non-endemic areas, establishing chikungunya fever as a major global public health threat[ 3 , 4 ]. In China, most cases have historically been imported, though the risk of local transmission has increased substantially in recent years. The first locally transmitted outbreak in China occurred in Guangdong province in 2010[ 5 ]. Imported cases rose steadily from 2015 to 2019 but declined sharply between 2020 and 2022 due to COVID-19 prevention measures[ 6 ]. In July 2025, Foshan City in Guangdong Province experienced the largest local outbreak since 2010[ 7 ]. Although this outbreak has been contained, China's extensive Aedes mosquito distribution and general lack of population immunity create vector circumstances that are favorable for local CHIKV transmission. Future local epidemics brought on by imported patients are still a possibility. Epidemiological evidence on chikungunya fever outbreak risk remains limited. Reviews have identified key knowledge gaps, including understanding of high-risk populations, outbreak scale, and the duration of natural immunity[ 8 , 9 ]. While several studies suggest that infection risk may vary according to individual characteristics[ 10 – 12 ], more evidence is needed to better understand these patterns and inform targeted prevention strategies. Few studies have examined the relationship between cycle threshold (Ct) values and clinical symptoms or patient characteristics. A study of the 2016 outbreak in Delhi, India, discovered correlations between various clinical characteristics and CHIKV burden[ 13 ]. Research on the 2025 Foshan, China outbreak demonstrated that viral loads varied according to peak infection periods and symptom onset-to-diagnosis intervals[ 14 ]. However, the complex relationships among viral load, patient age, time to diagnosis, and clinical presentation remain unclear. Additionally, results on the effect of interventions on epidemic curves and the influence of vector surveillance measures like the Adult Density Index (ADI) and Breteau Index (BI) on chikungunya epidemics are still conflicting. Compared to dengue, research on vector control for chikungunya is far less common. While reviews have shown that environmental interventions can reduce vector indices, the quality of evidence linking these reductions to decreased disease incidence is poor[ 15 – 17 ]. Though chikungunya and dengue share the same Aedes mosquito vectors and control strategies may apply to both diseases, further research is needed to confirm the effectiveness of mosquito control measures specifically for Chikungunya. Thus, this study described the epidemiological characteristics of the 2025 chikungunya outbreak in Quanzhou City, Fujian Province. We aim to examine how Ct values relate to age, onset-to-diagnosis interval and clinical presentation, and identify risk factors associated with infection to inform evidence-based prevention and control strategies. Methods 1.Data sources 164 case data in Quanzhou from August 18 to September 9 were obtained. Sex, age, occupation, current address (district/street), date of onset of disease, date of diagnosis, Ct value of the first positive test, and symptoms were among the dataset. Detailed symptom information was available from hospital records for 159 cases. Mosquito surveillance data from 328 sampling sites of Quanzhou between August 25 and September 13 was acquired. These data were obtained through special entomological surveys and routine vector monitoring systems implemented during the outbreak. At the community (administrative village) level, daily emergency surveillance was conducted using the BI method for larval monitoring and the double-layered tent trapping method for adult mosquito monitoring, with the intensity and coverage adjusted based on outbreak dynamics and manpower. The larval density index is: BI = (number of positive containers ÷ number of households surveyed) × 100, where positive containers refer to waterlogged containers where Aedes aegypti larvae (tsetse) or pupae are detected by inspection. One household is defined as every 30 square meters of area in residential homes, collective dormitories (with two rooms counted as one household), or public spaces. The BI values below 5 represents the threshold for controlling mosquito-borne disease transmission, 5 to 10 indicates transmission risk, 10 to 20 indicates risk of clustered outbreaks, and above 20 indicates risk of localized outbreaks. ADI: Tent Lure Index (only/top-hour = number of female Aedes aegypti mosquitoes captured (only)/duration (minutes) × 60 minutes. 2.Case definition According to the Diagnosis of Chikungunya Fever (WS/T 590–2018) issued by the National Health Commission of the People's Republic of China [ 18 ], confirmed cases were defined as suspected or clinically diagnosed cases meeting at least one of the following laboratory criteria: (1) Detection of chikungunya virus RNA by nucleic acid amplification methods such as RT-PCR; (2) Isolation of chikungunya virus from the patient's serum; (3)Either a four-fold or greater increase in serum chikungunya virus-specific IgG antibody titer from the acute to the recovery phase, or detection of chikungunya virus-specific IgM antibody in acute-phase serum. 3.Statistical analysis We analyzed the epidemiological characteristics of the outbreak by examining demographic characteristics and tempo-spatial distributions of reported cases. Demographic variables (age, sex, occupation) and region were compared between groups using Fisher's exact test. Cases were further characterized by onset-to-diagnosis intervals, clinical symptoms, Ct values, and BI and ADI distributions. Data visualization was performed using Python 3.7, and statistical analyses were conducted using SPSS 24.0. All tests were two-sided, with P < 0.05 considered statistically significant. Chi-square tests were used to compare categorical variables. We applied the Kruskal-Wallis test for general comparisons and Bonferroni-corrected Dunn post-hoc tests for pairwise comparisons to evaluate the association between Ct values and several parameters. Multiple linear regression analyses were performed, with Ct values as the dependent variable and sex, age, occupation, district, onset-to-diagnosis interval, and symptoms as the independent variables. Variables were coded as stated in Table 1 . Table 1 Variable Coding Scheme for Multiple Linear Regression Analysis Variable Coding Sex 0 = Male, 1 = Female Age(years) 0 = 0–9, 1 = 10–29, 2 = 30–49, 3 = 50–69, 4 = 70 − 89, 5 = ≥ 90 Occupation 0 = Student, 1 = Commercial/service, 2 = Farmer, 3 = Retiree, 4 = Teacher, 5 = Homemaker/unemployed, 6 = Civil servant, 7 = Office worker, 8 = Manual worker, 9 = Self-employed District 0 = Licheng, 1 = Luojiang, 2 = Fengze, 3 = Jinjiang, 4 = Nan'an Symptom onset-to-diagnosis interval (days) 0 = 0, 1 = 1, 2 = 2, 3 = 3, 4 = 4, 5 = 5, 6 = 6, 7 = 7 Symptoms 1 = Fever, 2 = Fever and arthralgia, 3 = Fever and rash, 4 = Fever, rash, and arthralgia, 5 = Arthralgia, 6 = Arthralgia and fever, 7 = Arthralgia and rash, 8 = Rash, 9 = Asymptomatic To further investigate the relationship between Ct values and onset-to-diagnosis interval, we fitted Ct values using nonlinear least squares regression with the following function[ 19 ]: $$\:y={b}_{1}-{b}_{2}{x}^{-0.5}+{b}_{3}{x}^{-1}\:\:\:\:\:\:\:\:\:\:\:\:\:\:\:\:\:\:\:\:\:\:\:\:\:\:\:\:\:\:\:\:\:\:\:\:\:\:\:\:\:\:\:\:（1）$$ To identify factors associated with chikungunya fever risk, we conducted logistic regression analysis using the LogisticRegression module of the scikit-learn package in Python 3.7. The model assessed associations between demographic factors (age, sex), environmental indices (BI, ADI), and infection risk. The regression model was specified as follows: $$\:logit\left(P\right)=\text{ln}\left(\frac{P}{1-P}\right)={\beta\:}_{0}+{\beta\:}_{1}{X}_{1}+{\beta\:}_{2}{X}_{2}+\dots\:+{\beta\:}_{n}{X}_{n}$$ 2 P represents the probability of infection in the study population; X₁, X₂, ..., Xₙ are the independent variables (age, sex, BI, ADI); β₀ is the intercept representing the log-odds of infection when all independent variables equal zero; and β₁ , β₂ , ..., βₙ are the regression coefficients for the respective variables. Results 1.Epidemiological characteristics of chikungunya fever Demographic, temporal and spatial distribution A total of 164 chikungunya cases were reported across five districts in Quanzhou, with Licheng and Fengze Districts accounting for 71.3% of all cases (Fig. 1A). The male-to-female ratio of cases was 1:1, with an average age of 46.5 ± 24.3 years (median: 49.0 years). Homemakers/unemployed individuals, students, and retirees accounted for 68.3% of all instances (Table 2; Fig. 1B and C). The peak reporting period was between August 29 and September 8. Case counts decreased after intensive vector control operations in Licheng District (August 28 – 30) and both Licheng and Fengze Districts (September 4 – 7). Daily confirmed cases reduced to less than five after the completion of vector control measures in both districts (Fig. 1D). Table 2 Characteristics of Chikungunya Cases in Quanzhou, 2025 Characteristic n（%） Sex Male 82 (50.0) Female 82 (50.0) Age group (years) 0 – 9 12(7.3) 10 – 29 34(20.7) 30 – 49 37(22.6) 50 – 69 44(26.8) 70 – 89 36(22.0) ≥ 90 1(0.6) Occupation Student 37(22.6) Teacher 5(3.0) Commercial/service 8(4.9) Civil servant 2(1.2) Office worker 15(9.1) Manual worker 4(2.4) Self-employed 17(10.4) Farmer 1(0.6) Homemaker/unemployed 55(33.5) Retiree 20(12.2) Clinical characteristics Joint pain was the most frequently reported symptom among both male and female patients, affecting 45.1% and 46.3% respectively. Only one female patient (1.2%) was asymptomatic (Fig. 2). Statistical testing indicated no significant difference in symptom presentation between genders ( χ² = 8.204, P = 0.414). All age groups had a significant prevalence of joint pain except patients aged 10 – 29 years. The highest occurrence was observed among those aged 50 – 69 years (65.9%). The single asymptomatic case was a patient in the 30 – 49 years age group. No statistically significant difference in symptom distribution was found across age groups ( χ² = 0.040, P = 0.841). Symptom presentation varied according to the time interval between symptom onset and diagnosis. Patients diagnosed within 0 – 2 days of symptom onset most commonly presented with fever (Fig. 3). As the interval extended to 3 – 5 days, symptom patterns became increasingly diverse with a growing proportion of mixed presentations, notably fever and arthralgia. Beyond this period, symptom composition remained relatively stable. Differences in demographic characteristics Chi-square analysis of demographic factors revealed no statistically significant variations in case distribution across age groups ( c 2 = 3.612， P = 0.620 ＞ 0.05), occupations ( c 2 = 12.357， P = 0.154 ＞ 0.05), regions ( c 2 = 4.565， P = 0.295 ＞ 0.05) and sex (Table 3). Table3 Demographic Characteristics of Chikungunya Fever Cases in Quanzhou, 2025 Characteristic Male (n，%) Female (n，%) c 2 P Age group (years) 3.612 0.620 0 – 9 5(6.1) 7(8.5) 10 – 29 15(18.3) 19(23.2) 30 – 49 22(26.8) 15(18.3) 50 – 69 23(28.0) 21(25.6) 70 – 89 16(19.5) 20(24.4) ≥ 90 1(1.2) 0(0.0) Occupation Student 12(14.6) 5(6.1) 12.357 0.154 Teacher 3(3.7) 1(1.2) Commercial service 10(12.2) 5(6.1) Government employee 2(2.4) 0(0.0) Other worker 22(26.8) 33(40.2) Manual worker 3(3.7) 2(2.4) Self-employed 8(9.8) 12(14.6) Farmer 1(1.2) 0(0.0) Domestic/unemployed 5(6.1) 3(3.7) Retired 16(19.5) 21(25.6) District Licheng 58(70.7) 58(70.7) 4.565 0.295 Luojiang 1(1.2) 0(0.0) Fengze 18(22.0) 23 (28.0) Jinjiang 3(3.7) 0(0.0) Nan'an 2(2.4) 1(1.2) 2.Onset-to-diagnosis characteristics The symptom onset-to-diagnosis interval decreased progressively throughout the outbreak period (Fig. 4A). Though some intervals extended to 6 – 7 days, most patients were diagnosed within 0 – 2 days of symptom onset. Jinjiang City experiencing notably longer diagnostic delays than other districts, reaching a median interval of 3 days. In contrast, both Licheng and Fengze districts achieved shorter median intervals of 1 day (Fig. 4B) Most chikungunya fever cases occurred in Licheng and Fengze districts. Within Licheng District, Kaiyuan, Lizhong, Changtai, Fuqiao, and Linjiang streets maintained consistent median intervals of approximately 1 day. Despite sharing the same median interval, Haibin Street recorded several outlier cases with periods of 4 – 7 days or longer. Jiangnan Street exhibited the longest median interval at 2 days (Fig. 4C). In Fengze District, Fengze Street achieved a median diagnostic interval of nearly 0 days, while Chengdong Street maintained a stable median of 1 day. Beifeng Street recorded the most extreme case with a 6-day delay, whereas both Qingyuan and Donghu streets documented cases with delays extending to 5 days. Quanxiu Street exhibited the longest median interval within Fengze District at 3.5 days (IQR: 2 – 5 days) (Fig. 4D). 3.Ct values and infection risk Univariate analysis of age, onset-to-diagnosis interval, symptoms and Ct values Cases showed a statistically significant decreasing trend in Ct values with increasing age (Kruskal-Wallis test， c 2 = 16.475， P = 0.006). Dunn's post-hoc test with Bonferroni correction revealed significant differences in Ct values between the 10–29 years and 70–89 years age groups ( P = 0.007) , as well as between the 30 – 49 years and 70 – 89 years age groups ( P = 0.018) (Fig. 5A). The relationship between Ct values and onset-to-diagnosis interval is presented in Fig. 5B. Ct values remained below 30 during the first three days following symptom onset, after which the median values increased sharply and progressively (Kruskal-Wallis test, χ² = 27.311, P = 0.001). Dunn's post-hoc test with Bonferroni correction found significant changes in Ct values between groups at 1 and 5 days ( P = 0.031) and 2 and 5 days ( P = 0.049). Most individuals with common symptoms such as fever, arthralgia, or both, had Ct values in the comparatively low range (23 – 27). Patients with arthralgia and rash or rash had significantly higher median Ct values of around 30. Asymptomatic individuals demonstrated Ct values comparable to those with other symptoms. Overall, Ct values differed significantly across symptom combinations (Kruskal-Wallis test， c 2 =25.823， P = 0.001). Dunn's post-hoc test with Bonferroni correction identified a significant difference in Ct values between the arthralgia and rash groups ( P = 0.046) (Fig. 5C). Fitting case Ct values to the onset-to-diagnosis interval resulted in the functional expression . All three fitted parameters (b 1 、b 2 、b 3 ) were statistically significant ( P ＜ 0.01). Ct values were lowest in the early stage of illness, reaching a minimum value of 15 near symptom onset, and subsequently increased to peak at day 4 (Fig. 6). This aligns with the previously observed distribution. Multivariate analysis of factors associated with Ct Values Multiple linear regression analysis identified age ( P = 0.0044) and onset-to-diagnosis interval ( P < 0.0001) as the only significant predictors of Ct values. Both variables exhibited positive correlations with Ct values, indicating that viral load decreased with increasing age and with longer onset-to-diagnosis intervals (Table 4). Table 4 Factors Associated with Ct Values Variable Partial Regression Coefficient Standard Error Standardized Regression Coefficient t P Sex 0.093 - 0.010 0.146 0.8840 Age -0.811 0.281 -0.225 -2.887 0.0044 Occupation 0.098 - 0.063 0.801 0.4242 District 0.102 - 0.023 0.335 0.7382 Onset-to-diagnosis interval 1.412 0.229 0.434 6.176 ＜0.0001 Symptoms 0.100 - 0.048 0.680 0.4977 Factors influencing individual infection risk The association between demographic characteristics and infection risk was evaluated for sex and age. Age emerged as a main risk factor for chikungunya fever infection (70 – 85 years group: OR = 6.28，95%CI = 4.22 – 9.24；≥ 85 years group: OR = 2.93，95%CI = 1.06 – 8.17). Conversely, sex was not significantly associated with infection risk (Fig. 7). Impact of vectors on disease transmission BI distribution BI followed a rise-and-fall pattern throughout the outbreak period (Fig. 8). Two daily case counts peaked on August 31 and September 4, reaching a maximum of nearly 30 cases. Correlation analysis found no significant association between BI and daily case numbers ( r = 0.210， P = 0.375). ADI distribution The overall trend of ADI paralleled that of BI throughout the outbreak period (Fig. 9). Correlation analysis revealed no significant association between ADI and daily case numbers ( r = 0.209， P = 0.376). BI and ADI in relation to infection risk The association between infection risk and both BI and ADI were investigated. Both BI (Q2: OR = 1.68，95%CI = 1.51 – 1.84；Q3: OR = 1.26，95%CI = 1.19 – 1.34) and ADI (Q3: OR = 5.60，95%CI = 3.74 – 7.70) were identified as significant risk factors for chikungunya fever infection. The results persisted after adjusting for potential confounding variables including age and sex (Fig. 10). Discussion Based on data from the 2025 chikungunya outbreak in Quanzhou, Fujian Province, China, this study detailed and examined the epidemiological characteristics, symptom distribution, Ct value trends, and vector patterns of the outbreak. Vector control measures implemented during the outbreak effectively reduced daily case incidence, as proved by corresponding declines in mosquito surveillance indicators. Currently, chikungunya fever has not established endemic transmission in China, with cases mainly consisting of imported infections and associated risk of local spread. Following the detection of an imported case in Shunde District, Foshan City, Guangdong Province, a total of 4754 confirmed cases were reported through July 26, corresponding to an incidence rate of 49.44 per 100,000 population[ 20 ]. While the Quanzhou outbreak shared epidemiological similarities with the Foshan outbreak, the two differed in case burden. Several factors account for this variation. First, Fujian Province reported fewer imported cases than Guangdong Province. As a major center for foreign trade and a primary destination for Southeast Asian labor migration, Guangdong faces elevated risks associated with population mobility and international case importation[ 21 ]. The Foshan outbreak showed distinct clustering patterns related to age, occupation, and household transmission[ 22 ]. While the Quanzhou outbreak demonstrated some occupational clustering with homemakers and unemployed individuals accounting for 33.5% of cases, other clustering patterns were not observed. Delayed case detection and diagnosis have been identified as critical factors driving community transmission and regional spread of chikungunya fever[ 23 , 24 ]. Fujian Province conducts routine vector surveillance and imported case management for mosquito-borne diseases, immediately implementing health alerts and personnel measures following outbreak emergence in neighboring provinces[ 21 , 25 ].After activation of the emergency response on August 25, Quanzhou rapidly deployed case isolation and vector control interventions, effectively limiting local transmission risk. Analysis of Ct value distribution discovered that viral load increased gradually with advancing age, consistent with previous studies suggesting an association between older age and increased viral loads[ 26 , 27 ]. High viral loads were detected in patients immediately following symptom onset and declined after day 3 post-onset. The Diagnosis of Chikungunya Fever (WS/T 590–2018)[ 28 ], and studies from other endemic regions[ 29 – 31 ] support this temporal pattern, which indicate that chikungunya fever is characterized by peak viremia during days 1–2 after symptom onset, with subsequent decline on days 3–4. Patients with fever, fever and arthralgia, or other combinations of these symptoms exhibited relatively low Ct values. This result has been confirmed in recent studies on clinical characteristics and viral loads of chikungunya cases, suggesting that fever and arthralgia represent the most characteristic symptom complex associated with high viral load[ 32 – 34 ]. However, symptoms did not emerge as a significant predictor of Ct values in multivariate analysis. Based on these results, infectiousness may remain comparable across asymptomatic, symptomatic, and severe cases. Limited evidence currently supports this hypothesis[ 35 , 36 ] and further investigation is needed. Infection risk varies by age, with individuals aged 70 years and above being at highest risk. Previous studies and official guidelines identify individuals aged 60 or 65 years and above as high-risk populations for chikungunya fever, broadly consistent with our findings[ 10 , 37 , 38 ]. Timely intervention measures demonstrated clear effectiveness. The epidemic curve showed a marked decline following intervention implementation, with all vector surveillance indicators decreasing to levels within acceptable control thresholds. This conclusion is consistent with outcomes observed during the 2019 imported outbreak in Ruili, Yunnan[ 39 ] and the 2025 outbreak in Foshan, Guangdong[ 22 ], emphasizing the importance of timely case detection and isolation combined with mosquito surveillance and control. This study has several limitations. First, the small sample size may have affected our ability to detect meaningful differences in Ct values across various factors such as age. Vector surveillance was conducted only in selected outbreak locations within Quanzhou rather than across all districts and counties in the region. This incomplete coverage may explain the lack of observed correlations between temporal changes in case numbers and vector surveillance indices (BI and ADI). Meanwhile, comparable studies have indicated that reductions in vector indices following targeted interventions correspond with decreasing case trends during outbreaks[ 40 , 41 ]. These findings suggest that future investigations should employ transmission dynamic modeling approaches to fully capture and characterize complex interactions. Conclusions Age and onset-to-diagnosis interval considerably influenced Ct values in Chikungunya fever cases, while symptoms showed no significant effect. Age was identified as an important risk factor for infection, with individuals aged ≥ 70 years constituting a high-risk group. Implementation of intervention measures effectively reduced vector indices below critical thresholds, thus preventing further disease transmission. Abbreviations CHIKV: chikungunya virus Ct ： Cycle threshold BI ： Breteau Index ADI ：Adult Density Index Declarations Ethics approval and consent to participate The data for this study were obtained from the public health surveillance system, hospital medical records, and routine vector monitoring programs. The study was exempt from ethical review, and the Medical Ethics Committee of Xiamen University waived the requirement for informed consent because (1) all analyzed data were anonymized, (2) no medical interventions or biological samples were involved, and (3) the study procedures and results did not affect patient clinical management. Consent for publication Not applicable. Availability of data and materials The datasets used and/or analyzed during the current study are available from the corresponding author on reasonable request. Competing interests The authors declare no competing interests. Clinical trial number Not applicable. Funding This research was supported by fund for Fujian Province Health Young and Middle-aged Leading Talents Training Program (No. 2839), National Science and Technology Major Project for the Prevention and Control of Emerging, Sudden and Major Infectious Diseases (2026ZD01908800) and the National Key Research and Development Program of China (No. 2024YFC2311404). The funders of the study had no role in study design, data collection, data analysis, data interpretation, or writing of the report. Authors' contributions YW-W, TM-C, ZY-Z, J-R, WM-W, JM-O conceived and designed the study. SG-W, JY-X, HF-Z, JY-L, RY-G acquired, analyzed and interpreted the data. SG-W, JY-X, HF-Z, JY-L, RY-G, SS-Z, JW-L, JJ-C, WJ-Y, YH-S drafted the manuscript. SG-W, JY-X, HF-Z, JY-L, RY-G, QP-C, R-F participated in the revision and discussion of the manuscript. 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Haider N, Vairo F, Ippolito G, Zumla A, Kock RA. Basic Reproduction Number of Chikungunya Virus Transmitted by Aedes Mosquitoes. Emerg Infect Dis. 2020;26(10):2429–31. Additional Declarations No competing interests reported. Cite Share Download PDF Status: Posted Version 1 posted You are reading this latest preprint version Research Square lets you share your work early, gain feedback from the community, and start making changes to your manuscript prior to peer review in a journal. As a division of Research Square Company, we’re committed to making research communication faster, fairer, and more useful. We do this by developing innovative software and high quality services for the global research community. Our growing team is made up of researchers and industry professionals working together to solve the most critical problems facing scientific publishing. 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1","display":"","copyAsset":false,"role":"figure","size":315904,"visible":true,"origin":"","legend":"\u003cp\u003e\u003cstrong\u003eSpatiotemporal and Demographic Distribution of Chikungunya Fever Cases in Quanzhou, 2025\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003e(A)Spatial distribution of cases in Quanzhou City. \u003c/strong\u003eThe darker shading indicates higher case density. \u003cstrong\u003e(B)Age and sex distribution. (C)Occupational distribution. \u003c/strong\u003einner ring displays major occupational groups, while the outer ring shows subcategories. \u003cstrong\u003e(D)Temporal distribution of confirmed cases. \u003c/strong\u003eshaded areas indicate periods of vector control interventions (light orange and light blue)\u003c/p\u003e","description":"","filename":"floatimage1.png","url":"https://assets-eu.researchsquare.com/files/rs-8507244/v1/2acf2df3b6588ec34ac0aa57.png"},{"id":100426903,"identity":"7de8813e-664b-48ec-9d90-be404f241e9f","added_by":"auto","created_at":"2026-01-16 14:20:03","extension":"png","order_by":2,"title":"Figure 2","display":"","copyAsset":false,"role":"figure","size":219058,"visible":true,"origin":"","legend":"\u003cp\u003e\u003cstrong\u003eSymptom Distribution of Chikungunya Fever Cases by Sex and Age in Quanzhou, 2025\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003e(A)\u003c/strong\u003eStacked bar graph showing the percentage distribution of symptoms by age group in male patients. \u003cstrong\u003e(B)\u003c/strong\u003eStacked bar graph showing the percentage distribution of symptoms by age group in female patients.\u003c/p\u003e","description":"","filename":"floatimage2.png","url":"https://assets-eu.researchsquare.com/files/rs-8507244/v1/c2551b28024a1d5911bc4f6f.png"},{"id":100426863,"identity":"8c976a64-3df7-45b8-b964-19f2c5bdcb67","added_by":"auto","created_at":"2026-01-16 14:20:00","extension":"png","order_by":3,"title":"Figure 3","display":"","copyAsset":false,"role":"figure","size":145881,"visible":true,"origin":"","legend":"\u003cp\u003e\u003cstrong\u003eDistribution of Chikungunya Fever Cases by Onset-to-Diagnosis Interval in Quanzhou, 2025\u003c/strong\u003e\u003c/p\u003e","description":"","filename":"floatimage3.png","url":"https://assets-eu.researchsquare.com/files/rs-8507244/v1/72045feb508dcc5a99650eff.png"},{"id":100426981,"identity":"64ca1826-b150-4c63-a9df-12f86a75c3a3","added_by":"auto","created_at":"2026-01-16 14:20:14","extension":"png","order_by":4,"title":"Figure 4","display":"","copyAsset":false,"role":"figure","size":188622,"visible":true,"origin":"","legend":"\u003cp\u003e\u003cstrong\u003eDistribution of Onset-to-diagnosis Intervals for Chikungunya Fever Cases by district, Quanzhou, 2025\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003e(A)\u003c/strong\u003eExponential decay curve of onset-to-diagnosis intervals over time. The blue line represents the trend in onset-to-diagnosis intervals, and the light blue shaded area represents the 95% confidence interval. \u003cstrong\u003e(B)\u003c/strong\u003eComparison of onset-to-diagnosis interval distributions across districts of Quanzhou (Licheng, Luojiang, Fengze, Jinjiang, and Nan'an). Box plots show median, IQR and data range. Colored transparent areas represent kernel density estimation distributions, and scatter points show individual data values. \u003cstrong\u003e(C)\u003c/strong\u003eDistribution of onset-to-diagnosis intervals across subdistricts of Licheng District. \u003cstrong\u003e(D)\u003c/strong\u003eDistribution of onset-to-diagnosis intervals across subdistricts of Fengze District.\u003c/p\u003e","description":"","filename":"floatimage4.png","url":"https://assets-eu.researchsquare.com/files/rs-8507244/v1/ec8541f866678af80dbb3b94.png"},{"id":100426902,"identity":"3c098c4e-300e-456a-99b6-7098503e3b5f","added_by":"auto","created_at":"2026-01-16 14:20:03","extension":"png","order_by":5,"title":"Figure 5","display":"","copyAsset":false,"role":"figure","size":182041,"visible":true,"origin":"","legend":"\u003cp\u003e\u003cstrong\u003eDistribution of Ct values of Chikungunya Fever Cases in Quanzhou, 2025\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003e(A)\u003c/strong\u003eTrends in Ct values across different age groups. The dark blue solid line represents the mean Ct value for each group, and the error bars and blue shaded area indicate the 95% confidence interval. \u003cstrong\u003e(B)\u003c/strong\u003eTrends in Ct values across different onset-to-diagnosis interval groups.\u003cstrong\u003e (C)\u003c/strong\u003eTrends in Ct values across different symptom groups.\u003c/p\u003e","description":"","filename":"floatimage5.png","url":"https://assets-eu.researchsquare.com/files/rs-8507244/v1/b50b2fd10744632aeececcf1.png"},{"id":100426785,"identity":"77e60092-9d06-4d32-96bf-3e612055e7e5","added_by":"auto","created_at":"2026-01-16 14:19:54","extension":"png","order_by":6,"title":"Figure 6","display":"","copyAsset":false,"role":"figure","size":105405,"visible":true,"origin":"","legend":"\u003cp\u003e\u003cstrong\u003eTrends in Ct values by Onset-to-diagnosis Interval\u003c/strong\u003e\u003c/p\u003e","description":"","filename":"floatimage6.png","url":"https://assets-eu.researchsquare.com/files/rs-8507244/v1/e7e6132e78906a9fdc167edf.png"},{"id":100546177,"identity":"1edb5be9-e74d-45b0-a129-ed35349117ca","added_by":"auto","created_at":"2026-01-19 08:00:16","extension":"png","order_by":7,"title":"Figure 7","display":"","copyAsset":false,"role":"figure","size":146433,"visible":true,"origin":"","legend":"\u003cp\u003e\u003cstrong\u003eAssociation between Demographic Factors (sex and age) and Individual Infection Risk\u003c/strong\u003e\u003c/p\u003e","description":"","filename":"floatimage7.png","url":"https://assets-eu.researchsquare.com/files/rs-8507244/v1/ed1c7b9d7ff97899067a52f9.png"},{"id":100426843,"identity":"98ef8051-5548-40f6-a211-38234e37898a","added_by":"auto","created_at":"2026-01-16 14:19:58","extension":"png","order_by":8,"title":"Figure 8","display":"","copyAsset":false,"role":"figure","size":195272,"visible":true,"origin":"","legend":"\u003cp\u003e\u003cstrong\u003eDistribution of BI of Chikungunya Fever Cases in Quanzhou, 2025\u003c/strong\u003e\u003c/p\u003e","description":"","filename":"floatimage8.png","url":"https://assets-eu.researchsquare.com/files/rs-8507244/v1/6a963ef7ead9f83e101fc578.png"},{"id":100426861,"identity":"0deefc34-72f6-42fa-8da7-cdf759f5f50a","added_by":"auto","created_at":"2026-01-16 14:20:00","extension":"png","order_by":9,"title":"Figure 9","display":"","copyAsset":false,"role":"figure","size":193411,"visible":true,"origin":"","legend":"\u003cp\u003e\u003cstrong\u003eDistribution of ADI of Chikungunya Fever Cases in Quanzhou, 2025\u003c/strong\u003e\u003c/p\u003e","description":"","filename":"floatimage9.png","url":"https://assets-eu.researchsquare.com/files/rs-8507244/v1/a022616d08e54a1df5175cff.png"},{"id":100426834,"identity":"2cee0792-b12c-4314-808a-ffc1d63ef937","added_by":"auto","created_at":"2026-01-16 14:19:57","extension":"png","order_by":10,"title":"Figure 10","display":"","copyAsset":false,"role":"figure","size":243875,"visible":true,"origin":"","legend":"\u003cp\u003e\u003cstrong\u003eCorrelation Analysis between BI, ADI and Daily Incidence Levels\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eDaily case counts were categorized into four groups (Q1 to Q4) based on the 25th percentile, median, and 75th percentile, with Q1 serving as the reference group.\u003c/p\u003e","description":"","filename":"floatimage10.png","url":"https://assets-eu.researchsquare.com/files/rs-8507244/v1/28c3ef08e32c6aa179061f18.png"},{"id":100594695,"identity":"dd295bfb-c781-4776-8beb-4d4af00c95f3","added_by":"auto","created_at":"2026-01-19 13:43:55","extension":"pdf","order_by":0,"title":"","display":"","copyAsset":false,"role":"manuscript-pdf","size":3248271,"visible":true,"origin":"","legend":"","description":"","filename":"manuscript.pdf","url":"https://assets-eu.researchsquare.com/files/rs-8507244/v1/42b310de-1959-4f7a-8c09-7817cdf43116.pdf"}],"financialInterests":"No competing interests reported.","formattedTitle":"Epidemiology, transmission, vector and factors analysis of chikungunya outbreak in 2025, Quanzhou City of China","fulltext":[{"header":"Background","content":"\u003cp\u003eChikungunya fever is an acute vector-borne infectious disease caused by the chikungunya virus (CHIKV), which has shown rapid spread and outbreak epidemics worldwide in recent years. Local transmission has been reported in 119 nations and areas[\u003cspan citationid=\"CR1\" class=\"CitationRef\"\u003e1\u003c/span\u003e, \u003cspan citationid=\"CR2\" class=\"CitationRef\"\u003e2\u003c/span\u003e]. Climate change and increased international travel have heightened the risk of transmission in non-endemic areas, establishing chikungunya fever as a major global public health threat[\u003cspan citationid=\"CR3\" class=\"CitationRef\"\u003e3\u003c/span\u003e, \u003cspan citationid=\"CR4\" class=\"CitationRef\"\u003e4\u003c/span\u003e]. In China, most cases have historically been imported, though the risk of local transmission has increased substantially in recent years. The first locally transmitted outbreak in China occurred in Guangdong province in 2010[\u003cspan citationid=\"CR5\" class=\"CitationRef\"\u003e5\u003c/span\u003e]. Imported cases rose steadily from 2015 to 2019 but declined sharply between 2020 and 2022 due to COVID-19 prevention measures[\u003cspan citationid=\"CR6\" class=\"CitationRef\"\u003e6\u003c/span\u003e]. In July 2025, Foshan City in Guangdong Province experienced the largest local outbreak since 2010[\u003cspan citationid=\"CR7\" class=\"CitationRef\"\u003e7\u003c/span\u003e]. Although this outbreak has been contained, China's extensive Aedes mosquito distribution and general lack of population immunity create vector circumstances that are favorable for local CHIKV transmission. Future local epidemics brought on by imported patients are still a possibility.\u003c/p\u003e \u003cp\u003eEpidemiological evidence on chikungunya fever outbreak risk remains limited. Reviews have identified key knowledge gaps, including understanding of high-risk populations, outbreak scale, and the duration of natural immunity[\u003cspan citationid=\"CR8\" class=\"CitationRef\"\u003e8\u003c/span\u003e, \u003cspan citationid=\"CR9\" class=\"CitationRef\"\u003e9\u003c/span\u003e]. While several studies suggest that infection risk may vary according to individual characteristics[\u003cspan additionalcitationids=\"CR11\" citationid=\"CR10\" class=\"CitationRef\"\u003e10\u003c/span\u003e\u0026ndash;\u003cspan citationid=\"CR12\" class=\"CitationRef\"\u003e12\u003c/span\u003e], more evidence is needed to better understand these patterns and inform targeted prevention strategies.\u003c/p\u003e \u003cp\u003eFew studies have examined the relationship between cycle threshold (Ct) values and clinical symptoms or patient characteristics. A study of the 2016 outbreak in Delhi, India, discovered correlations between various clinical characteristics and CHIKV burden[\u003cspan citationid=\"CR13\" class=\"CitationRef\"\u003e13\u003c/span\u003e]. Research on the 2025 Foshan, China outbreak demonstrated that viral loads varied according to peak infection periods and symptom onset-to-diagnosis intervals[\u003cspan citationid=\"CR14\" class=\"CitationRef\"\u003e14\u003c/span\u003e]. However, the complex relationships among viral load, patient age, time to diagnosis, and clinical presentation remain unclear.\u003c/p\u003e \u003cp\u003eAdditionally, results on the effect of interventions on epidemic curves and the influence of vector surveillance measures like the Adult Density Index (ADI) and Breteau Index (BI) on chikungunya epidemics are still conflicting. Compared to dengue, research on vector control for chikungunya is far less common. While reviews have shown that environmental interventions can reduce vector indices, the quality of evidence linking these reductions to decreased disease incidence is poor[\u003cspan additionalcitationids=\"CR16\" citationid=\"CR15\" class=\"CitationRef\"\u003e15\u003c/span\u003e\u0026ndash;\u003cspan citationid=\"CR17\" class=\"CitationRef\"\u003e17\u003c/span\u003e]. Though chikungunya and dengue share the same Aedes mosquito vectors and control strategies may apply to both diseases, further research is needed to confirm the effectiveness of mosquito control measures specifically for Chikungunya.\u003c/p\u003e \u003cp\u003eThus, this study described the epidemiological characteristics of the 2025 chikungunya outbreak in Quanzhou City, Fujian Province. We aim to examine how Ct values relate to age, onset-to-diagnosis interval and clinical presentation, and identify risk factors associated with infection to inform evidence-based prevention and control strategies.\u003c/p\u003e"},{"header":"Methods","content":"\u003cp\u003e \u003cb\u003e1.Data sources\u003c/b\u003e \u003c/p\u003e \u003cp\u003e164 case data in Quanzhou from August 18 to September 9 were obtained. Sex, age, occupation, current address (district/street), date of onset of disease, date of diagnosis, Ct value of the first positive test, and symptoms were among the dataset. Detailed symptom information was available from hospital records for 159 cases.\u003c/p\u003e \u003cp\u003eMosquito surveillance data from 328 sampling sites of Quanzhou between August 25 and September 13 was acquired. These data were obtained through special entomological surveys and routine vector monitoring systems implemented during the outbreak. At the community (administrative village) level, daily emergency surveillance was conducted using the BI method for larval monitoring and the double-layered tent trapping method for adult mosquito monitoring, with the intensity and coverage adjusted based on outbreak dynamics and manpower. The larval density index is: BI = (number of positive containers\u0026thinsp;\u0026divide;\u0026thinsp;number of households surveyed) \u0026times; 100, where positive containers refer to waterlogged containers where Aedes aegypti larvae (tsetse) or pupae are detected by inspection. One household is defined as every 30 square meters of area in residential homes, collective dormitories (with two rooms counted as one household), or public spaces. The BI values below 5 represents the threshold for controlling mosquito-borne disease transmission, 5 to 10 indicates transmission risk, 10 to 20 indicates risk of clustered outbreaks, and above 20 indicates risk of localized outbreaks. ADI: Tent Lure Index (only/top-hour\u0026thinsp;=\u0026thinsp;number of female Aedes aegypti mosquitoes captured (only)/duration (minutes) \u0026times; 60 minutes.\u003c/p\u003e \u003cp\u003e \u003cb\u003e2.Case definition\u003c/b\u003e \u003c/p\u003e \u003cp\u003eAccording to the \u003cem\u003eDiagnosis of Chikungunya Fever\u003c/em\u003e (WS/T 590\u0026ndash;2018) issued by the National Health Commission of the People's Republic of China [\u003cspan citationid=\"CR18\" class=\"CitationRef\"\u003e18\u003c/span\u003e], confirmed cases were defined as suspected or clinically diagnosed cases meeting at least one of the following laboratory criteria: (1) Detection of chikungunya virus RNA by nucleic acid amplification methods such as RT-PCR; (2) Isolation of chikungunya virus from the patient's serum; (3)Either a four-fold or greater increase in serum chikungunya virus-specific IgG antibody titer from the acute to the recovery phase, or detection of chikungunya virus-specific IgM antibody in acute-phase serum.\u003c/p\u003e \u003cp\u003e \u003cb\u003e3.Statistical analysis\u003c/b\u003e \u003c/p\u003e \u003cp\u003eWe analyzed the epidemiological characteristics of the outbreak by examining demographic characteristics and tempo-spatial distributions of reported cases. Demographic variables (age, sex, occupation) and region were compared between groups using Fisher's exact test. Cases were further characterized by onset-to-diagnosis intervals, clinical symptoms, Ct values, and BI and ADI distributions. Data visualization was performed using Python 3.7, and statistical analyses were conducted using SPSS 24.0. All tests were two-sided, with P\u0026thinsp;\u0026lt;\u0026thinsp;0.05 considered statistically significant.\u003c/p\u003e \u003cp\u003eChi-square tests were used to compare categorical variables. We applied the Kruskal-Wallis test for general comparisons and Bonferroni-corrected Dunn post-hoc tests for pairwise comparisons to evaluate the association between Ct values and several parameters. Multiple linear regression analyses were performed, with Ct values as the dependent variable and sex, age, occupation, district, onset-to-diagnosis interval, and symptoms as the independent variables. Variables were coded as stated in Table\u0026nbsp;\u003cspan refid=\"Tab1\" class=\"InternalRef\"\u003e1\u003c/span\u003e.\u003c/p\u003e \u003cp\u003e \u003cdiv class=\"gridtable\"\u003e\u003ctable float=\"Yes\" id=\"Tab1\" border=\"1\"\u003e \u003ccaption language=\"En\"\u003e \u003cdiv class=\"CaptionNumber\"\u003eTable 1\u003c/div\u003e \u003cdiv class=\"CaptionContent\"\u003e \u003cp\u003eVariable Coding Scheme for Multiple Linear Regression Analysis\u003c/p\u003e \u003c/div\u003e \u003c/caption\u003e \u003ccolgroup cols=\"3\"\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c1\" colnum=\"1\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c2\" colnum=\"2\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c3\" colnum=\"3\"\u003e\u003c/div\u003e \u003cthead\u003e \u003ctr\u003e \u003cth align=\"left\" colname=\"c1\"\u003e \u003cp\u003eVariable\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/th\u003e \u003cth align=\"left\" colname=\"c3\"\u003e \u003cp\u003eCoding\u003c/p\u003e \u003c/th\u003e \u003c/tr\u003e \u003c/thead\u003e \u003ctbody\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eSex\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e0\u0026thinsp;=\u0026thinsp;Male, 1\u0026thinsp;=\u0026thinsp;Female\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eAge(years)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e0\u0026thinsp;=\u0026thinsp;0\u0026ndash;9, 1\u0026thinsp;=\u0026thinsp;10\u0026ndash;29, 2\u0026thinsp;=\u0026thinsp;30\u0026ndash;49, 3\u0026thinsp;=\u0026thinsp;50\u0026ndash;69, 4\u0026thinsp;=\u0026thinsp;70 \u0026minus;\u0026thinsp;89, 5\u0026thinsp;=\u0026thinsp;\u0026ge;\u0026thinsp;90\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eOccupation\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e0\u0026thinsp;=\u0026thinsp;Student, 1\u0026thinsp;=\u0026thinsp;Commercial/service, 2\u0026thinsp;=\u0026thinsp;Farmer, 3\u0026thinsp;=\u0026thinsp;Retiree, 4\u0026thinsp;=\u0026thinsp;Teacher, 5\u0026thinsp;=\u0026thinsp;Homemaker/unemployed, 6\u0026thinsp;=\u0026thinsp;Civil servant, 7\u0026thinsp;=\u0026thinsp;Office worker, 8\u0026thinsp;=\u0026thinsp;Manual worker, 9\u0026thinsp;=\u0026thinsp;Self-employed\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eDistrict\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e0\u0026thinsp;=\u0026thinsp;Licheng, 1\u0026thinsp;=\u0026thinsp;Luojiang, 2\u0026thinsp;=\u0026thinsp;Fengze, 3\u0026thinsp;=\u0026thinsp;Jinjiang, 4\u0026thinsp;=\u0026thinsp;Nan'an\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eSymptom onset-to-diagnosis interval (days)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e0\u0026thinsp;=\u0026thinsp;0, 1\u0026thinsp;=\u0026thinsp;1, 2\u0026thinsp;=\u0026thinsp;2, 3\u0026thinsp;=\u0026thinsp;3, 4\u0026thinsp;=\u0026thinsp;4, 5\u0026thinsp;=\u0026thinsp;5, 6\u0026thinsp;=\u0026thinsp;6, 7\u0026thinsp;=\u0026thinsp;7\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eSymptoms\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e1\u0026thinsp;=\u0026thinsp;Fever, 2\u0026thinsp;=\u0026thinsp;Fever and arthralgia, 3\u0026thinsp;=\u0026thinsp;Fever and rash, 4\u0026thinsp;=\u0026thinsp;Fever, rash, and arthralgia, 5\u0026thinsp;=\u0026thinsp;Arthralgia, 6\u0026thinsp;=\u0026thinsp;Arthralgia and fever, 7\u0026thinsp;=\u0026thinsp;Arthralgia and rash, 8\u0026thinsp;=\u0026thinsp;Rash, 9\u0026thinsp;=\u0026thinsp;Asymptomatic\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003c/tbody\u003e \u003c/colgroup\u003e \u003c/table\u003e\u003c/div\u003e \u003c/p\u003e \u003cp\u003eTo further investigate the relationship between Ct values and onset-to-diagnosis interval, we fitted Ct values using nonlinear least squares regression with the following function[\u003cspan citationid=\"CR19\" class=\"CitationRef\"\u003e19\u003c/span\u003e]:\u003cdiv id=\"Equa\" class=\"Equation\"\u003e\u003cdiv format=\"TEX\" class=\"mathdisplay\" id=\"FileID_Equa\" name=\"EquationSource\"\u003e\n$$\\:y={b}_{1}-{b}_{2}{x}^{-0.5}+{b}_{3}{x}^{-1}\\:\\:\\:\\:\\:\\:\\:\\:\\:\\:\\:\\:\\:\\:\\:\\:\\:\\:\\:\\:\\:\\:\\:\\:\\:\\:\\:\\:\\:\\:\\:\\:\\:\\:\\:\\:\\:\\:\\:\\:\\:\\:\\:\\:（1）$$\u003c/div\u003e\u003c/div\u003e\u003c/p\u003e \u003cp\u003eTo identify factors associated with chikungunya fever risk, we conducted logistic regression analysis using the LogisticRegression module of the scikit-learn package in Python 3.7. The model assessed associations between demographic factors (age, sex), environmental indices (BI, ADI), and infection risk. The regression model was specified as follows:\u003cdiv id=\"Equ1\" class=\"Equation\"\u003e\u003cdiv format=\"TEX\" class=\"mathdisplay\" id=\"FileID_Equ1\" name=\"EquationSource\"\u003e\n$$\\:logit\\left(P\\right)=\\text{ln}\\left(\\frac{P}{1-P}\\right)={\\beta\\:}_{0}+{\\beta\\:}_{1}{X}_{1}+{\\beta\\:}_{2}{X}_{2}+\\dots\\:+{\\beta\\:}_{n}{X}_{n}$$\u003c/div\u003e\u003cdiv class=\"EquationNumber\"\u003e2\u003c/div\u003e\u003c/div\u003e\u003c/p\u003e \u003cp\u003e \u003cem\u003eP\u003c/em\u003e represents the probability of infection in the study population; X₁, X₂, ..., Xₙ are the independent variables (age, sex, BI, ADI); \u003cem\u003eβ₀\u003c/em\u003e is the intercept representing the log-odds of infection when all independent variables equal zero; and \u003cem\u003eβ₁\u003c/em\u003e, \u003cem\u003eβ₂\u003c/em\u003e, ..., \u003cem\u003eβₙ\u003c/em\u003e are the regression coefficients for the respective variables.\u003c/p\u003e"},{"header":"Results","content":"\u003cp\u003e\u003cstrong\u003e1.Epidemiological characteristics of chikungunya fever\u0026nbsp;\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003e\u003cem\u003eDemographic,\u0026nbsp;\u003c/em\u003e\u003cem\u003etemporal and spatial distribution\u0026nbsp;\u003c/em\u003e\u003c/p\u003e\n\u003cp\u003eA total of 164 chikungunya cases were reported across five districts in Quanzhou, with Licheng and Fengze Districts accounting for 71.3% of all cases (Fig. 1A). The male-to-female ratio of cases was 1:1, with an average age of 46.5 \u0026plusmn; 24.3 years (median: 49.0 years). Homemakers/unemployed individuals, students, and retirees accounted for 68.3% of all instances (Table 2; Fig. 1B and C). The peak reporting period was between August 29 and September 8. Case counts decreased after intensive vector control operations in Licheng District (August 28 \u0026ndash; 30) and both Licheng and Fengze Districts (September 4 \u0026ndash; 7). Daily confirmed cases reduced to less than five after the completion of vector control measures in both districts (Fig. 1D).\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eTable 2 Characteristics of Chikungunya Cases in Quanzhou, 2025\u003c/strong\u003e\u003c/p\u003e\n\u003cdiv align=\"\"\u003e\n \u003ctable border=\"1\" cellspacing=\"0\" cellpadding=\"0\" width=\"501\"\u003e\n \u003ctbody\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 24.5509%;\"\u003e\n \u003cp\u003eCharacteristic\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 26.3473%;\"\u003e\n \u003cp\u003e \u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 22.5549%;\"\u003e\n \u003cp\u003e \u003c/p\u003e\n \u003c/td\u003e\n \u003ctd colspan=\"2\" valign=\"top\" style=\"width: 26.5469%;\"\u003e\n \u003cp\u003en（%）\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 24.5509%;\"\u003e\n \u003cp\u003eSex\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 26.3473%;\"\u003e\n \u003cp\u003eMale\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 22.5549%;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 22.7545%;\"\u003e\n \u003cp\u003e82 (50.0)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 3.79242%;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 24.5509%;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 26.3473%;\"\u003e\n \u003cp\u003eFemale\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 22.5549%;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 22.7545%;\"\u003e\n \u003cp\u003e82 (50.0)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 3.79242%;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 24.5509%;\"\u003e\n \u003cp\u003eAge group (years)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 26.3473%;\"\u003e\n \u003cp\u003e0 \u0026ndash; 9\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 22.5549%;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 22.7545%;\"\u003e\n \u003cp\u003e12(7.3)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 3.79242%;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 24.5509%;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 26.3473%;\"\u003e\n \u003cp\u003e10 \u0026ndash; 29\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 22.5549%;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 22.7545%;\"\u003e\n \u003cp\u003e34(20.7)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 3.79242%;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 24.5509%;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 26.3473%;\"\u003e\n \u003cp\u003e30 \u0026ndash; 49\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 22.5549%;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 22.7545%;\"\u003e\n \u003cp\u003e37(22.6)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 3.79242%;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 24.5509%;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 26.3473%;\"\u003e\n \u003cp\u003e50 \u0026ndash; 69\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 22.5549%;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 22.7545%;\"\u003e\n \u003cp\u003e44(26.8)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 3.79242%;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 24.5509%;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 26.3473%;\"\u003e\n \u003cp\u003e70 \u0026ndash; 89\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 22.5549%;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 22.7545%;\"\u003e\n \u003cp\u003e36(22.0)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 3.79242%;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 24.5509%;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 26.3473%;\"\u003e\n \u003cp\u003e\u0026ge;\u0026nbsp;90\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 22.5549%;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 22.7545%;\"\u003e\n \u003cp\u003e1(0.6)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 3.79242%;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 24.5509%;\"\u003e\n \u003cp\u003eOccupation\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 26.3473%;\"\u003e\n \u003cp\u003eStudent\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 22.5549%;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 22.7545%;\"\u003e\n \u003cp\u003e37(22.6)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 3.79242%;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 24.5509%;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 26.3473%;\"\u003e\n \u003cp\u003eTeacher\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 22.5549%;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 22.7545%;\"\u003e\n \u003cp\u003e5(3.0)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 3.79242%;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 24.5509%;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 26.3473%;\"\u003e\n \u003cp\u003eCommercial/service\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 22.5549%;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 22.7545%;\"\u003e\n \u003cp\u003e8(4.9)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 3.79242%;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 24.5509%;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 26.3473%;\"\u003e\n \u003cp\u003eCivil servant\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 22.5549%;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 22.7545%;\"\u003e\n \u003cp\u003e2(1.2)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 3.79242%;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 24.5509%;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 26.3473%;\"\u003e\n \u003cp\u003eOffice worker\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 22.5549%;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 22.7545%;\"\u003e\n \u003cp\u003e15(9.1)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 3.79242%;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 24.5509%;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 26.3473%;\"\u003e\n \u003cp\u003eManual worker\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 22.5549%;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 22.7545%;\"\u003e\n \u003cp\u003e4(2.4)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 3.79242%;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 24.5509%;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 26.3473%;\"\u003e\n \u003cp\u003eSelf-employed\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 22.5549%;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 22.7545%;\"\u003e\n \u003cp\u003e17(10.4)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 3.79242%;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 24.5509%;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 26.3473%;\"\u003e\n \u003cp\u003eFarmer\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 22.5549%;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 22.7545%;\"\u003e\n \u003cp\u003e1(0.6)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 3.79242%;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 24.5509%;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 26.3473%;\"\u003e\n \u003cp\u003eHomemaker/unemployed\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 22.5549%;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 22.7545%;\"\u003e\n \u003cp\u003e55(33.5)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 3.79242%;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 24.5509%;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 26.3473%;\"\u003e\n \u003cp\u003eRetiree\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 22.5549%;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 22.7545%;\"\u003e\n \u003cp\u003e20(12.2)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 3.79242%;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003c/tbody\u003e\n \u003c/table\u003e\n\u003c/div\u003e\n\u003cp\u003e\u003cem\u003eClinical characteristics\u003c/em\u003e\u003c/p\u003e\n\u003cp\u003eJoint pain was the most frequently reported symptom among both male and female patients, affecting 45.1% and 46.3% respectively. Only one female patient (1.2%) was asymptomatic (Fig. 2).\u0026nbsp;Statistical testing indicated no significant difference in symptom presentation between genders (\u003cem\u003e\u0026chi;\u0026sup2;\u003c/em\u003e = 8.204, \u003cem\u003eP\u0026nbsp;\u003c/em\u003e= 0.414). All age groups had a significant prevalence of joint pain except patients aged 10 \u0026ndash; 29 years. The highest occurrence was observed among those aged 50 \u0026ndash; 69 years (65.9%). The single asymptomatic case was a patient in the 30 \u0026ndash; 49 years age group. No statistically significant difference in symptom distribution was found across age groups (\u003cem\u003e\u0026chi;\u0026sup2;\u003c/em\u003e = 0.040, \u003cem\u003eP\u003c/em\u003e = 0.841).\u003c/p\u003e\n\u003cp\u003eSymptom presentation varied according to the time interval between symptom onset and diagnosis. Patients diagnosed within 0 \u0026ndash; 2 days of symptom onset most commonly presented with fever (Fig. 3). As the interval extended to 3 \u0026ndash; 5 days, symptom patterns became increasingly diverse with a growing proportion of mixed presentations, notably fever and arthralgia. Beyond this period, symptom composition remained relatively stable.\u003c/p\u003e\n\u003cp\u003e\u003cem\u003eDifferences in demographic characteristics\u003c/em\u003e\u003c/p\u003e\n\u003cp\u003eChi-square analysis of demographic factors revealed no statistically significant variations in case distribution across age groups (\u003cem\u003ec\u003c/em\u003e\u003csup\u003e2\u0026nbsp;\u003c/sup\u003e= 3.612，\u003cem\u003eP\u0026nbsp;\u003c/em\u003e= 0.620 ＞ 0.05), occupations (\u003cem\u003ec\u003c/em\u003e\u003csup\u003e2\u0026nbsp;\u003c/sup\u003e= 12.357，\u003cem\u003eP\u0026nbsp;\u003c/em\u003e= 0.154 ＞ 0.05), regions (\u003cem\u003ec\u003c/em\u003e\u003cem\u003e\u0026nbsp;\u003c/em\u003e\u003csup\u003e2\u003c/sup\u003e= 4.565，\u003cem\u003eP\u0026nbsp;\u003c/em\u003e= 0.295 ＞ 0.05) and sex (Table 3).\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eTable3 Demographic Characteristics of Chikungunya Fever Cases in Quanzhou, 2025\u003c/strong\u003e\u003c/p\u003e\n\u003cdiv align=\"\"\u003e\n \u003ctable border=\"1\" cellspacing=\"0\" cellpadding=\"0\" width=\"554\"\u003e\n \u003ctbody\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 161px;\"\u003e\n \u003cp\u003eCharacteristic\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 94px;\"\u003e\n \u003cp\u003eMale\u003c/p\u003e\n \u003cp\u003e(n，%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 113px;\"\u003e\n \u003cp\u003eFemale\u003c/p\u003e\n \u003cp\u003e(n，%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 94px;\"\u003e\n \u003cp\u003e\u003cem\u003ec\u003c/em\u003e\u003csup\u003e2\u003c/sup\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 91px;\"\u003e\n \u003cp\u003e\u003cem\u003eP\u003c/em\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 161px;\"\u003e\n \u003cp\u003e\u003cstrong\u003eAge group (years)\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 94px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 113px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd rowspan=\"7\" valign=\"top\" style=\"width: 94px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003cp\u003e3.612\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd rowspan=\"7\" valign=\"top\" style=\"width: 91px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003cp\u003e0.620\u003c/p\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 161px;\"\u003e\n \u003cp\u003e0 \u0026ndash; 9\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 94px;\"\u003e\n \u003cp\u003e5(6.1)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 113px;\"\u003e\n \u003cp\u003e7(8.5)\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 161px;\"\u003e\n \u003cp\u003e10 \u0026ndash; 29\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 94px;\"\u003e\n \u003cp\u003e15(18.3)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 113px;\"\u003e\n \u003cp\u003e19(23.2)\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 161px;\"\u003e\n \u003cp\u003e30 \u0026ndash; 49\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 94px;\"\u003e\n \u003cp\u003e22(26.8)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 113px;\"\u003e\n \u003cp\u003e15(18.3)\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 161px;\"\u003e\n \u003cp\u003e50 \u0026ndash; 69\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 94px;\"\u003e\n \u003cp\u003e23(28.0)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 113px;\"\u003e\n \u003cp\u003e21(25.6)\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 161px;\"\u003e\n \u003cp\u003e70 \u0026ndash; 89\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 94px;\"\u003e\n \u003cp\u003e16(19.5)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 113px;\"\u003e\n \u003cp\u003e20(24.4)\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 161px;\"\u003e\n \u003cp\u003e\u0026ge; 90\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 94px;\"\u003e\n \u003cp\u003e1(1.2)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 113px;\"\u003e\n \u003cp\u003e0(0.0)\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 161px;\"\u003e\n \u003cp\u003e\u003cstrong\u003eOccupation\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 94px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 113px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 94px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 91px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 161px;\"\u003e\n \u003cp\u003eStudent\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 94px;\"\u003e\n \u003cp\u003e12(14.6)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 113px;\"\u003e\n \u003cp\u003e5(6.1)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd rowspan=\"10\" valign=\"top\" style=\"width: 94px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003cp\u003e12.357\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd rowspan=\"10\" valign=\"top\" style=\"width: 91px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003cp\u003e0.154\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 161px;\"\u003e\n \u003cp\u003eTeacher\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 94px;\"\u003e\n \u003cp\u003e3(3.7)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 113px;\"\u003e\n \u003cp\u003e1(1.2)\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 161px;\"\u003e\n \u003cp\u003eCommercial service\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 94px;\"\u003e\n \u003cp\u003e10(12.2)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 113px;\"\u003e\n \u003cp\u003e5(6.1)\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 161px;\"\u003e\n \u003cp\u003eGovernment employee\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 94px;\"\u003e\n \u003cp\u003e2(2.4)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 113px;\"\u003e\n \u003cp\u003e0(0.0)\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 161px;\"\u003e\n \u003cp\u003eOther worker\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 94px;\"\u003e\n \u003cp\u003e22(26.8)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 113px;\"\u003e\n \u003cp\u003e33(40.2)\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 161px;\"\u003e\n \u003cp\u003eManual worker\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 94px;\"\u003e\n \u003cp\u003e3(3.7)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 113px;\"\u003e\n \u003cp\u003e2(2.4)\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 161px;\"\u003e\n \u003cp\u003eSelf-employed\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 94px;\"\u003e\n \u003cp\u003e8(9.8)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 113px;\"\u003e\n \u003cp\u003e12(14.6)\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 161px;\"\u003e\n \u003cp\u003eFarmer\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 94px;\"\u003e\n \u003cp\u003e1(1.2)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 113px;\"\u003e\n \u003cp\u003e0(0.0)\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 161px;\"\u003e\n \u003cp\u003eDomestic/unemployed\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 94px;\"\u003e\n \u003cp\u003e5(6.1)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 113px;\"\u003e\n \u003cp\u003e3(3.7)\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 161px;\"\u003e\n \u003cp\u003eRetired\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 94px;\"\u003e\n \u003cp\u003e16(19.5)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 113px;\"\u003e\n \u003cp\u003e21(25.6)\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 161px;\"\u003e\n \u003cp\u003e\u003cstrong\u003eDistrict\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 94px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 113px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 94px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 91px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 161px;\"\u003e\n \u003cp\u003eLicheng\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 94px;\"\u003e\n \u003cp\u003e58(70.7)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 113px;\"\u003e\n \u003cp\u003e58(70.7)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd rowspan=\"5\" valign=\"top\" style=\"width: 94px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003cp\u003e4.565\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd rowspan=\"5\" valign=\"top\" style=\"width: 91px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003cp\u003e0.295\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 161px;\"\u003e\n \u003cp\u003eLuojiang\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 94px;\"\u003e\n \u003cp\u003e1(1.2)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 113px;\"\u003e\n \u003cp\u003e0(0.0)\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 161px;\"\u003e\n \u003cp\u003eFengze\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 94px;\"\u003e\n \u003cp\u003e18(22.0)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 113px;\"\u003e\n \u003cp\u003e23 (28.0)\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 161px;\"\u003e\n \u003cp\u003eJinjiang\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 94px;\"\u003e\n \u003cp\u003e3(3.7)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 113px;\"\u003e\n \u003cp\u003e0(0.0)\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 161px;\"\u003e\n \u003cp\u003eNan\u0026apos;an\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 94px;\"\u003e\n \u003cp\u003e2(2.4)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 113px;\"\u003e\n \u003cp\u003e1(1.2)\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003c/tbody\u003e\n \u003c/table\u003e\n\u003c/div\u003e\n\u003cp\u003e\u0026nbsp;\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003e2.Onset-to-diagnosis characteristics\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eThe symptom onset-to-diagnosis interval decreased progressively throughout the outbreak period (Fig. 4A).\u0026nbsp;Though some intervals extended to 6 \u0026ndash; 7 days, most patients were diagnosed within 0 \u0026ndash; 2 days of symptom onset. Jinjiang City experiencing notably longer diagnostic delays than other districts, reaching a median interval of 3 days. In contrast, both Licheng and Fengze districts achieved shorter median intervals of 1 day (Fig. 4B)\u003c/p\u003e\n\u003cp\u003eMost chikungunya fever cases occurred in Licheng and Fengze districts. Within Licheng District, Kaiyuan, Lizhong, Changtai, Fuqiao, and Linjiang streets maintained consistent median intervals of approximately 1 day. Despite sharing the same median interval, Haibin Street recorded several outlier cases with periods of 4 \u0026ndash; 7 days or longer. Jiangnan Street exhibited the longest median interval at 2 days (Fig. 4C).\u003c/p\u003e\n\u003cp\u003eIn Fengze District, Fengze Street achieved a median diagnostic interval of nearly 0 days, while Chengdong Street maintained a stable median of 1 day. Beifeng Street recorded the most extreme case with a 6-day delay, whereas both Qingyuan and Donghu streets documented cases with delays extending to 5 days. Quanxiu Street exhibited the longest median interval within Fengze District at 3.5 days (IQR: 2 \u0026ndash; 5 days) (Fig. 4D).\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003e3.Ct values and infection risk\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003e\u003cem\u003eUnivariate analysis of age, onset-to-diagnosis interval, symptoms and Ct values\u003c/em\u003e\u003c/p\u003e\n\u003cp\u003eCases showed a statistically significant decreasing trend in Ct values with increasing age (Kruskal-Wallis test，\u003cem\u003ec\u003c/em\u003e\u003csup\u003e2\u0026nbsp;\u003c/sup\u003e= 16.475，\u003cem\u003eP\u0026nbsp;\u003c/em\u003e= 0.006). Dunn\u0026apos;s post-hoc test with Bonferroni correction revealed significant differences in Ct values between the 10\u0026ndash;29 years and 70\u0026ndash;89 years age groups (\u003cem\u003eP\u003c/em\u003e = 0.007) , as well as between the 30 \u0026ndash; 49 years and 70 \u0026ndash; 89 years age groups (\u003cem\u003eP\u0026nbsp;\u003c/em\u003e= 0.018) (Fig. 5A).\u003c/p\u003e\n\u003cp\u003eThe relationship between Ct values and onset-to-diagnosis interval is presented in Fig. 5B. Ct values remained below 30 during the first three days following symptom onset, after which the median values increased sharply and progressively (Kruskal-Wallis test, \u003cem\u003e\u0026chi;\u0026sup2;\u0026nbsp;\u003c/em\u003e= 27.311, \u003cem\u003eP\u003c/em\u003e = 0.001). Dunn\u0026apos;s post-hoc test with Bonferroni correction found significant changes in Ct values between groups at 1 and 5 days (\u003cem\u003eP\u003c/em\u003e = 0.031) and 2 and 5 days (\u003cem\u003eP\u003c/em\u003e = 0.049).\u003c/p\u003e\n\u003cp\u003eMost individuals with common symptoms such as fever, arthralgia, or both, had Ct values in the comparatively low range (23 \u0026ndash; 27). Patients with arthralgia and rash or rash had significantly higher median Ct values of around 30. Asymptomatic individuals demonstrated Ct values comparable to those with other symptoms. Overall, Ct values differed significantly across symptom combinations (Kruskal-Wallis test，\u003cem\u003ec\u003c/em\u003e\u003csup\u003e2\u003c/sup\u003e=25.823，\u003cem\u003eP\u0026nbsp;\u003c/em\u003e= 0.001). Dunn\u0026apos;s post-hoc test with Bonferroni correction identified a significant difference in Ct values between the arthralgia and rash groups (\u003cem\u003eP\u0026nbsp;\u003c/em\u003e= 0.046) (Fig. 5C).\u003c/p\u003e\n\u003cp\u003eFitting case Ct values to the onset-to-diagnosis interval resulted in the functional expression \u003cimg width=\"243\" height=\"22\" src=\"data:image/png;base64,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\" alt=\"image\"\u003e. All three fitted parameters (b\u003csub\u003e1\u003c/sub\u003e、b\u003csub\u003e2\u003c/sub\u003e、b\u003csub\u003e3\u003c/sub\u003e) were statistically significant (\u003cem\u003eP\u0026nbsp;\u003c/em\u003e＜ 0.01). Ct values were lowest in the early stage of illness, reaching a minimum value of 15 near symptom onset, and subsequently increased to peak at day 4 (Fig. 6). This aligns with the previously observed distribution.\u003c/p\u003e\n\u003cp\u003e\u003cem\u003eMultivariate analysis of factors associated with Ct Values\u003c/em\u003e\u003c/p\u003e\n\u003cp\u003eMultiple linear regression analysis identified age (\u003cem\u003eP\u003c/em\u003e = 0.0044) and onset-to-diagnosis interval (\u003cem\u003eP\u0026nbsp;\u003c/em\u003e\u0026lt; 0.0001) as the only significant predictors of Ct values. Both variables exhibited positive correlations with Ct values, indicating that viral load decreased with increasing age and with longer onset-to-diagnosis intervals (Table 4).\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eTable 4 Factors Associated with Ct Values\u003c/strong\u003e\u003c/p\u003e\n\u003cdiv align=\"\"\u003e\n \u003ctable border=\"1\" cellspacing=\"0\" cellpadding=\"0\"\u003e\n \u003ctbody\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003eVariable\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003ePartial Regression\u0026nbsp;\u003c/p\u003e\n \u003cp\u003eCoefficient\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003eStandard Error\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003eStandardized Regression Coefficient\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e\u003cem\u003et\u003c/em\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e\u003cem\u003eP\u003c/em\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003eSex\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e0.093\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e-\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e0.010\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e0.146\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e0.8840\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003eAge\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e-0.811\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e0.281\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e-0.225\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e-2.887\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e0.0044\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003eOccupation\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e0.098\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e-\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e0.063\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e0.801\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e0.4242\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003eDistrict\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e0.102\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e-\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e0.023\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e0.335\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e0.7382\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003eOnset-to-diagnosis\u003c/p\u003e\n \u003cp\u003einterval\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e1.412\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e0.229\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e0.434\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e6.176\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e＜0.0001\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003eSymptoms\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e0.100\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e-\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e0.048\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e0.680\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e0.4977\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003c/tbody\u003e\n \u003c/table\u003e\n\u003c/div\u003e\n\u003cp\u003e\u003cem\u003eFactors influencing individual infection risk\u003c/em\u003e\u003c/p\u003e\n\u003cp\u003eThe association between demographic characteristics and infection risk was evaluated for sex and age. Age emerged as a main risk factor for chikungunya fever infection (70 \u0026ndash; 85 years group: OR = 6.28，95%CI = 4.22 \u0026ndash; 9.24；\u0026ge;\u0026nbsp;85 years group: OR = 2.93，95%CI = 1.06 \u0026ndash; 8.17). Conversely, sex was not significantly associated with infection risk\u0026nbsp;(Fig. 7).\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eImpact of vectors on disease transmission\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003e\u003cem\u003eBI distribution\u003c/em\u003e\u003c/p\u003e\n\u003cp\u003eBI followed a rise-and-fall pattern throughout the outbreak period (Fig. 8).\u0026nbsp;Two daily case counts peaked on August 31 and September 4, reaching a maximum of nearly 30 cases. Correlation analysis found no significant association between BI and daily case numbers (\u003cem\u003er\u003c/em\u003e = 0.210，\u003cem\u003eP\u0026nbsp;\u003c/em\u003e= 0.375).\u003c/p\u003e\n\u003cp\u003e\u003cem\u003eADI distribution\u003c/em\u003e\u003c/p\u003e\n\u003cp\u003eThe overall trend of ADI paralleled that of BI throughout the outbreak period (Fig. 9). Correlation analysis revealed no significant association between ADI and daily case numbers (\u003cem\u003er\u0026nbsp;\u003c/em\u003e= 0.209，\u003cem\u003eP\u0026nbsp;\u003c/em\u003e= 0.376).\u003c/p\u003e\n\u003cp\u003e\u003cem\u003eBI and ADI in relation to infection risk\u003c/em\u003e\u003c/p\u003e\n\u003cp\u003eThe association between infection risk and both BI and ADI were investigated. Both BI (Q2: OR = 1.68，95%CI = 1.51 \u0026ndash; 1.84；Q3: OR = 1.26，95%CI = 1.19 \u0026ndash; 1.34) and ADI (Q3: OR = 5.60，95%CI = 3.74 \u0026ndash; 7.70) were identified as significant risk factors for chikungunya fever infection. The results persisted after adjusting for potential confounding variables including age and sex (Fig. 10).\u003c/p\u003e"},{"header":"Discussion","content":"\u003cp\u003eBased on data from the 2025 chikungunya outbreak in Quanzhou, Fujian Province, China, this study detailed and examined the epidemiological characteristics, symptom distribution, Ct value trends, and vector patterns of the outbreak. Vector control measures implemented during the outbreak effectively reduced daily case incidence, as proved by corresponding declines in mosquito surveillance indicators.\u003c/p\u003e \u003cp\u003eCurrently, chikungunya fever has not established endemic transmission in China, with cases mainly consisting of imported infections and associated risk of local spread. Following the detection of an imported case in Shunde District, Foshan City, Guangdong Province, a total of 4754 confirmed cases were reported through July 26, corresponding to an incidence rate of 49.44 per 100,000 population[\u003cspan citationid=\"CR20\" class=\"CitationRef\"\u003e20\u003c/span\u003e]. While the Quanzhou outbreak shared epidemiological similarities with the Foshan outbreak, the two differed in case burden. Several factors account for this variation. First, Fujian Province reported fewer imported cases than Guangdong Province. As a major center for foreign trade and a primary destination for Southeast Asian labor migration, Guangdong faces elevated risks associated with population mobility and international case importation[\u003cspan citationid=\"CR21\" class=\"CitationRef\"\u003e21\u003c/span\u003e]. The Foshan outbreak showed distinct clustering patterns related to age, occupation, and household transmission[\u003cspan citationid=\"CR22\" class=\"CitationRef\"\u003e22\u003c/span\u003e]. While the Quanzhou outbreak demonstrated some occupational clustering with homemakers and unemployed individuals accounting for 33.5% of cases, other clustering patterns were not observed. Delayed case detection and diagnosis have been identified as critical factors driving community transmission and regional spread of chikungunya fever[\u003cspan citationid=\"CR23\" class=\"CitationRef\"\u003e23\u003c/span\u003e, \u003cspan citationid=\"CR24\" class=\"CitationRef\"\u003e24\u003c/span\u003e]. Fujian Province conducts routine vector surveillance and imported case management for mosquito-borne diseases, immediately implementing health alerts and personnel measures following outbreak emergence in neighboring provinces[\u003cspan citationid=\"CR21\" class=\"CitationRef\"\u003e21\u003c/span\u003e, \u003cspan citationid=\"CR25\" class=\"CitationRef\"\u003e25\u003c/span\u003e].After activation of the emergency response on August 25, Quanzhou rapidly deployed case isolation and vector control interventions, effectively limiting local transmission risk.\u003c/p\u003e \u003cp\u003eAnalysis of Ct value distribution discovered that viral load increased gradually with advancing age, consistent with previous studies suggesting an association between older age and increased viral loads[\u003cspan citationid=\"CR26\" class=\"CitationRef\"\u003e26\u003c/span\u003e, \u003cspan citationid=\"CR27\" class=\"CitationRef\"\u003e27\u003c/span\u003e]. High viral loads were detected in patients immediately following symptom onset and declined after day 3 post-onset. The \u003cem\u003eDiagnosis of Chikungunya Fever\u003c/em\u003e (WS/T 590\u0026ndash;2018)[\u003cspan citationid=\"CR28\" class=\"CitationRef\"\u003e28\u003c/span\u003e], and studies from other endemic regions[\u003cspan additionalcitationids=\"CR30\" citationid=\"CR29\" class=\"CitationRef\"\u003e29\u003c/span\u003e\u0026ndash;\u003cspan citationid=\"CR31\" class=\"CitationRef\"\u003e31\u003c/span\u003e] support this temporal pattern, which indicate that chikungunya fever is characterized by peak viremia during days 1\u0026ndash;2 after symptom onset, with subsequent decline on days 3\u0026ndash;4. Patients with fever, fever and arthralgia, or other combinations of these symptoms exhibited relatively low Ct values. This result has been confirmed in recent studies on clinical characteristics and viral loads of chikungunya cases, suggesting that fever and arthralgia represent the most characteristic symptom complex associated with high viral load[\u003cspan additionalcitationids=\"CR33\" citationid=\"CR32\" class=\"CitationRef\"\u003e32\u003c/span\u003e\u0026ndash;\u003cspan citationid=\"CR34\" class=\"CitationRef\"\u003e34\u003c/span\u003e]. However, symptoms did not emerge as a significant predictor of Ct values in multivariate analysis. Based on these results, infectiousness may remain comparable across asymptomatic, symptomatic, and severe cases. Limited evidence currently supports this hypothesis[\u003cspan citationid=\"CR35\" class=\"CitationRef\"\u003e35\u003c/span\u003e, \u003cspan citationid=\"CR36\" class=\"CitationRef\"\u003e36\u003c/span\u003e] and further investigation is needed.\u003c/p\u003e \u003cp\u003eInfection risk varies by age, with individuals aged 70 years and above being at highest risk. Previous studies and official guidelines identify individuals aged 60 or 65 years and above as high-risk populations for chikungunya fever, broadly consistent with our findings[\u003cspan citationid=\"CR10\" class=\"CitationRef\"\u003e10\u003c/span\u003e, \u003cspan citationid=\"CR37\" class=\"CitationRef\"\u003e37\u003c/span\u003e, \u003cspan citationid=\"CR38\" class=\"CitationRef\"\u003e38\u003c/span\u003e]. Timely intervention measures demonstrated clear effectiveness. The epidemic curve showed a marked decline following intervention implementation, with all vector surveillance indicators decreasing to levels within acceptable control thresholds. This conclusion is consistent with outcomes observed during the 2019 imported outbreak in Ruili, Yunnan[\u003cspan citationid=\"CR39\" class=\"CitationRef\"\u003e39\u003c/span\u003e] and the 2025 outbreak in Foshan, Guangdong[\u003cspan citationid=\"CR22\" class=\"CitationRef\"\u003e22\u003c/span\u003e], emphasizing the importance of timely case detection and isolation combined with mosquito surveillance and control.\u003c/p\u003e \u003cp\u003eThis study has several limitations. First, the small sample size may have affected our ability to detect meaningful differences in Ct values across various factors such as age. Vector surveillance was conducted only in selected outbreak locations within Quanzhou rather than across all districts and counties in the region. This incomplete coverage may explain the lack of observed correlations between temporal changes in case numbers and vector surveillance indices (BI and ADI). Meanwhile, comparable studies have indicated that reductions in vector indices following targeted interventions correspond with decreasing case trends during outbreaks[\u003cspan citationid=\"CR40\" class=\"CitationRef\"\u003e40\u003c/span\u003e, \u003cspan citationid=\"CR41\" class=\"CitationRef\"\u003e41\u003c/span\u003e]. These findings suggest that future investigations should employ transmission dynamic modeling approaches to fully capture and characterize complex interactions.\u003c/p\u003e"},{"header":"Conclusions","content":"\u003cp\u003eAge and onset-to-diagnosis interval considerably influenced Ct values in Chikungunya fever cases, while symptoms showed no significant effect. Age was identified as an important risk factor for infection, with individuals aged\u0026thinsp;\u0026ge;\u0026thinsp;70 years constituting a high-risk group. Implementation of intervention measures effectively reduced vector indices below critical thresholds, thus preventing further disease transmission.\u003c/p\u003e"},{"header":"Abbreviations","content":"\u003cp\u003e\u003cstrong\u003eCHIKV:\u003c/strong\u003e chikungunya virus\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eCt\u003c/strong\u003e\u003cstrong\u003e：\u003c/strong\u003eCycle threshold\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eBI\u003c/strong\u003e\u003cstrong\u003e：\u003c/strong\u003eBreteau Index\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eADI\u003c/strong\u003e：Adult Density Index\u003c/p\u003e"},{"header":"Declarations","content":"\u003cp\u003e\u003cstrong\u003eEthics approval and consent to participate\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eThe data for this study were obtained from the public health surveillance system, hospital medical records, and routine vector monitoring programs. The study was exempt from ethical review, and the Medical Ethics Committee of Xiamen University waived the requirement for informed consent because (1) all analyzed data were anonymized, (2) no medical interventions or biological samples were involved, and (3) the study procedures and results did not affect patient clinical management.\u0026nbsp;\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eConsent for publication\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eNot applicable.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eAvailability of data and materials\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eThe datasets used and/or analyzed during the current study are available from the corresponding author on reasonable request.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eCompeting interests\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eThe authors declare no competing interests.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eClinical trial number\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eNot applicable.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eFunding\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eThis research was supported by fund for Fujian Province Health Young and Middle-aged Leading Talents Training Program (No. 2839), National Science and Technology Major Project for the Prevention and Control of Emerging, Sudden and Major Infectious Diseases (2026ZD01908800) and the National Key Research and Development Program of China (No. 2024YFC2311404). The funders of the study had no role in study design, data collection, data analysis, data interpretation, or writing of the report.\u0026nbsp;\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eAuthors\u0026apos; contributions\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eYW-W, TM-C, ZY-Z, J-R, WM-W, JM-O conceived and designed the study. SG-W, JY-X, HF-Z, JY-L, RY-G acquired, analyzed and interpreted the data. SG-W, JY-X, HF-Z, JY-L, RY-G, SS-Z, JW-L, JJ-C, WJ-Y, YH-S drafted the manuscript. SG-W, JY-X, HF-Z, JY-L, RY-G, QP-C, R-F participated in the revision and discussion of the manuscript. All authors have read and approved the final version for submission.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eAcknowledgements\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eThis research has been conducted using data reported by local health institutions in Quanzhou. We would like to express our gratitude to the Fujian Center for Disease Control and Prevention for their technical guidance, and the medical institutions and disease control staff at all levels in Quanzhou for their diligent efforts in case reporting and outbreak response.\u003c/p\u003e"},{"header":"References","content":"\u003col\u003e\n\u003cli\u003eWeaver SC, Lecuit M. Chikungunya virus and the global spread of a mosquito-borne disease. N Engl J Med. 2015;372(13):1231\u0026ndash;9.\u003c/li\u003e\n\u003cli\u003eBurt FJ, Chen W, Miner JJ, Lenschow DJ, Merits A, Schnettler E, et al. Chikungunya virus: an update on the biology and pathogenesis of this emerging pathogen. Lancet Infect Dis. 2017;17(4):e107\u0026ndash;e17.\u003c/li\u003e\n\u003cli\u003eRockl\u0026ouml;v J, Dubrow R. Climate change: an enduring challenge for vector-borne disease prevention and control. Nat Immunol. 2020;21(5):479\u0026ndash;83.\u003c/li\u003e\n\u003cli\u003eRyan SJ, Carlson CJ, Mordecai EA, Johnson LR. 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Epidemiological and clinical characteristics of the chikungunya outbreak in Ruili City, Yunnan Province, China. J Med Virol. 2022;94(2):499\u0026ndash;506.\u003c/li\u003e\n\u003cli\u003eJourdain F, de Valk H, No\u0026euml;l H, Paty MC, L\u0026apos;Ambert G, Franke F, et al. Estimating chikungunya virus transmission parameters and vector control effectiveness highlights key factors to mitigate arboviral disease outbreaks. PLoS Negl Trop Dis. 2022;16(3):e0010244.\u003c/li\u003e\n\u003cli\u003eHaider N, Vairo F, Ippolito G, Zumla A, Kock RA. Basic Reproduction Number of Chikungunya Virus Transmitted by Aedes Mosquitoes. Emerg Infect Dis. 2020;26(10):2429\u0026ndash;31.\u003c/li\u003e\n\u003c/ol\u003e"}],"fulltextSource":"","fullText":"","funders":[],"hasAdminPriorityOnWorkflow":false,"hasManuscriptDocX":true,"hasOptedInToPreprint":true,"hasPassedJournalQc":"","hasAnyPriority":true,"hideJournal":true,"highlight":"","institution":"","isAcceptedByJournal":false,"isAuthorSuppliedPdf":false,"isDeskRejected":"","isHiddenFromSearch":false,"isInQc":false,"isInWorkflow":false,"isPdf":false,"isPdfUpToDate":true,"isWithdrawnOrRetracted":false,"journal":{"display":true,"email":"[email protected]","identity":"researchsquare","isNatureJournal":false,"hasQc":true,"allowDirectSubmit":true,"externalIdentity":"","sideBox":"","snPcode":"","submissionUrl":"/submission","title":"Research Square","twitterHandle":"researchsquare","acdcEnabled":true,"dfaEnabled":false,"editorialSystem":"","reportingPortfolio":"","inReviewEnabled":false,"inReviewRevisionsEnabled":true},"keywords":"Chikungunya fever, Epidemiology, Outbreak, Cycle threshold value, Risk factors","lastPublishedDoi":"10.21203/rs.3.rs-8507244/v1","lastPublishedDoiUrl":"https://doi.org/10.21203/rs.3.rs-8507244/v1","license":{"name":"CC BY 4.0","url":"https://creativecommons.org/licenses/by/4.0/"},"manuscriptAbstract":"\u003cp\u003e\u003cstrong\u003eBackground: \u003c/strong\u003eFollowing a major chikungunya outbreak in Foshan City, China, locally transmitted cases have been reported across the country. Research remains limited to epidemiology, transmission dynamics, vector characteristics, and risk factors of chikungunya fever.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eMethods: \u003c/strong\u003eWe analyzed 164 chikungunya cases reported in Quanzhou City, Fujian Province, between August 18 and September 9, 2025, along with mosquito surveillance data collected from August 25 to September 13, 2025. We examined case characteristics, symptom patterns, Cycle threshold (Ct) values trends, and vector distributions. Logistic regression was used to identify demographic factors and mosquito density indices associated with infection risk.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eResults:\u003c/strong\u003e Among 164 cases, 50.0% were male and 38.4% were aged ≥ 60 years. Ct values were significantly influenced by age (\u003cem\u003eP\u003c/em\u003e = 0.0044) and the onset-to-diagnosis intervals (\u003cem\u003eP \u003c/em\u003e\u0026lt; 0.0001), but not by symptom patterns. Advanced age was a major risk factor, particularly for individuals aged 70 – 85 years (OR = 6.28, 95% CI = 4.22 – 9.24) and those 85 years or older (OR = 2.93, 95% CI = 1.06 – 8.17). Higher mosquito larval and adult densities also increased infection risk. Following implementation of emergency response protocols and intensive mosquito control measures, zero incident cases were reported within 21 days.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eConclusions:\u003c/strong\u003e Chikungunya disproportionately affects older adults, especially those aged ≥ 70 years. Viral load increases with age and decreases with delayed diagnosis. The lack of correlation between symptoms and viral load suggests that asymptomatic individuals may be equally infectious, providing guidelines for screening and management of high-risk groups\u003cstrong\u003e.\u003c/strong\u003e\u003c/p\u003e","manuscriptTitle":"Epidemiology, transmission, vector and factors analysis of chikungunya outbreak in 2025, Quanzhou City of China","msid":"","msnumber":"","nonDraftVersions":[{"code":1,"date":"2026-01-16 14:16:03","doi":"10.21203/rs.3.rs-8507244/v1","editorialEvents":[{"type":"communityComments","content":0}],"status":"published","journal":{"display":true,"email":"[email protected]","identity":"researchsquare","isNatureJournal":false,"hasQc":true,"allowDirectSubmit":true,"externalIdentity":"","sideBox":"","snPcode":"","submissionUrl":"/submission","title":"Research Square","twitterHandle":"researchsquare","acdcEnabled":true,"dfaEnabled":false,"editorialSystem":"","reportingPortfolio":"","inReviewEnabled":false,"inReviewRevisionsEnabled":true}}],"origin":"","ownerIdentity":"d5b3338d-1833-4dce-b040-70e09545053a","owner":[],"postedDate":"January 16th, 2026","published":true,"recentEditorialEvents":[],"rejectedJournal":[],"revision":"","amendment":"","status":"posted","subjectAreas":[],"tags":[],"updatedAt":"2026-01-16T14:19:09+00:00","versionOfRecord":[],"versionCreatedAt":"2026-01-16 14:16:03","video":"","vorDoi":"","vorDoiUrl":"","workflowStages":[]},"version":"v1","identity":"rs-8507244","journalConfig":"researchsquare"},"__N_SSP":true},"page":"/article/[identity]/[[...version]]","query":{"redirect":"/article/rs-8507244","identity":"rs-8507244","version":["v1"]},"buildId":"XKTyCvWXoU3ODBz1xrDgd","isFallback":false,"isExperimentalCompile":false,"dynamicIds":[84888],"gssp":true,"scriptLoader":[]}