Data_Sheet_1_The epidemiology and evolutionary dynamics of massive dengue outbreak in China, 2019.ZIP

IntroductionIn 2019, China experienced massive dengue outbreaks with high incidence and expanded outbreak areas. The study aims to depict dengue’s epidemiology and evolutionary dynamics in China and explore the possible origin of these outbreaks.MethodsRecords of confirmed dengue cases in 2019 were obtained from the China Notifiable Disease Surveillance System. The sequences of complete envelope gene detected from the outbreak provinces in China in 2019 were retrieved from GenBank. Maximum Likelihood trees were constructed to genotype the viruses. The median-joining network was used to visualize fine-scale genetic relationships. Four methods were used to estimate the selective pressure.ResultsA total of 22,688 dengue cases were reported, 71.4% of which were indigenous cases and 28.6% were imported cases (including from abroad and from other domestic provinces). The abroad cases were predominantly imported from Southeast Asia countries (94.6%), with Cambodia (3,234 cases, 58.9%), and Myanmar (1,097 cases, 20.0%) ranked as the top two. A total of 11 provinces with dengue outbreaks were identified in the central-south of China, of which Yunnan and Guangdong provinces had the highest number of imported and indigenous cases. The primary source of imported cases in Yunnan was from Myanmar, while in the other ten provinces, the majority of imported cases were from Cambodia. Guangdong, Yunnan and Guangxi provinces were China’s primary sources of domestically imported cases. Phylogenetic analysis of the viruses in outbreak provinces revealed three genotypes: (I, IV, and V) in DENV 1, Cosmopolitan and Asian I genotypes in DENV 2, and two genotypes (I and III) in DENV 3. Some genotypes concurrently circulated in different outbreak provinces. Most of the viruses were clustered with those from Southeast Asia. Haplotype network analysis showed that Southeast Asia, possibly Cambodia and Thailand, was the respective origin of the viruses in clade 1 and 4 for DENV 1. Positive selection was detected at codon 386 in clade 1.ConclusionDengue importation from abroad, especially from Southeast Asia, resulted in the dengue epidemic in China in 2019. Domestic transmission between provinces and positive selection on virus evolution may contribute to the massive dengue outbreaks.</p

Spatial autocorrelation analyses.

A. Scrub typhus morbidity in twelve years. B. GISA. C. LISA. (The vector data of Chinese administrative divisions were provided by CNNDS. Figure 5 was created for this manuscript using ArcGIS.).</p

Scrub typhus distribution in 2006–2017.

A. in twelve years; B. in 2006; C. in 2007; D. in 2008; E. in 2009; F. in 2010; G. in 2011; H. in 2012; I. in 2013; J. in 2014; K. in 2015; L. in 2016; M. in 2017. (The vector data of Chinese administrative divisions were provided by CNNDS. Figure 2 was created for this manuscript using ArcGIS.).</p

The yearly rainfall zone and the climate zone.

A. the yearly rainfall zone. B. the climate zone. (Data of the yearly rainfall zone and the climate zone were obtained from Research Center for Eco-Environmental Sciences, Chinese Academy of Sciences. Figure 9 was created for this manuscript using ArcGIS.).</p

The onset characteristics of scrub typhus in the key sub regions.

The onset characteristics of scrub typhus in the key sub regions.</p

Time-series analyses of scrub typhus in key regions in 2006–2017.

A. North region. B. Southwest region B. C. South region.</p

Spatial diffusion analyses of scrub typhus in 2006–2017.

A. Spatial diffusion mapping of scrub typhus in 2006–2017. B. Spatial diffusion of scrub typhus by Trend. C. Newly emerging Counties of scrub typhus along the years. (The vector data of Chinese administrative divisions were provided by CNNDS. Figure 3 was created for this manuscript using ArcGIS.).</p

Time-series analyses of scrub typhus in mainland China, 2006–2017.

A. Time-series mapping of monthly scrub typhus. B. Time-series mapping of yearly scrub typhus.</p

Additional file 1 of The patterns and driving forces of dengue invasions in China

Additional file 1: Figure S1 The distributions of China at provincial-level administrative divisions. Table S1. The list of the environmental drivers. Table S2. Augmented Dickey-Fuller test (unit root test) for local cases. Table S3. Critical values for test statistics of local cases. Table S4. Augmented Dickey-Fuller test (unit root test) for imported cases. Table S5. Critical values for test statistics of imported cases. Table S6. Lag selections for VAR. Table S7. Granger causality test. Table S8. VAR estimation results for local cases. Table S9. VAR estimation results for imported cases. Table S10. Roots of the characteristic polynomial for VAR. Table S11. Box-Ljung test for residuals. Table S12. ARCH test for Heteroscedasticity. Table S13. Variance decomposition for VAR. Figure S2. Grid search for random forest. Table S14. Estimation results in GAM. Table S15. PCA results for bioclimate variables. Table S16. Contributions of the BIO variables. Table S17. PCA results for NDVI variables. Table S18. Contributions of the NDVI variables. Table S19. PCA results for HFI and HMI variables. Table S20. Contributions of the Soc_Eco and HF_HM variables. Table S21. Model selections in SEM

Space-time scan statistic analyses for scrub typhus.

A. the purely spatial analysis scanning in 12 years. B. the retrospective space-time analysis scanning from 2006 to 2017. (The vector data of Chinese administrative divisions were provided by CNNDS. Figure 6 was created for this manuscript using ArcGIS.).</p