Dengue and Dengue Virus in Guangdong, China, 1978-2017: Epidemiology, Seroprevalence, Evolution, and Policies

Genomic variants and molecular epidemiological characteristics of dengue virus in China revealed by genome-wide analysis

Since its first academic record in 1978, dengue epidemics have occurred in all provinces of China, except Xizang. The epidemiological and molecular features of the whole genome of dengue virus (DENV) have not yet been completely elucidated, interfering with prevention and control strategies for dengue fever in China. Here, we obtained 553 complete genomes of the four serotypes of DENV (DENV1-4) isolated in China from the GenBank database to analyze the phylogeny, recombination, genomic variants, and selection pressure and to estimate the substitution rates of DENV genomes. Phylogenetic analyses indicated that DENV sequences from China did not cluster together and were genetically closer to those from Southeast Asian countries in the maximum likelihood trees, indicating that DENV was not endemic in China. Thirty intra-serotype recombinant sequences were identified for DENV1-4, with the highest frequency in DENV4. Selection pressure analyses revealed that 13 codons under positive selection were located in the C, NS1, NS2A, NS3, and NS5 proteins. For DENV1 to DENV3, the substitution rates evaluated in this study were 9.23 × 10-4, 7.59 × 10-4, and 7.06 × 10-4 substitutions per site per year, respectively. These findings improve our understanding of the evolution of DENV in China.

Advancing knowledge of One Health in China: lessons for One Health from China's dengue control and prevention programs

Background

The emergence of dengue fever has prompted significant public health responses, highlighting the need for a comprehensive understanding of One Health in addressing vector-borne diseases. China's experience in dengue control and prevention programs offers valuable insights into the successful integration of multidisciplinary strategies.

Aims

The review aims to: (1) systematically analyze lessons from China's dengue control and prevention programs, focusing on the integration of these efforts with the One Health approach; (2) underscore the reasons of optimizing the dengue control and prevention program; (3) highlight the alignment of China's dengue control strategies with the One Health framework; (4) contribute to global efforts in combating dengue, providing scientific evidence and strategic recommendations for other regions facing similar challenges.

Results

Through a comprehensive literature review and expert interviews, this study found China's approach to dengue control and prevention implemented through a hierarchical system led by the government, with collaborative efforts across multiple departments. This multi-sectoral collaboration mechanism enables the technical interventions well executed by health and disease control institutions, optimizing the integration of multiple cost-effeteness approaches, such as case management, early detection and outbreak response, reducing local transmission, and minimizing severe cases and fatalities. It was found that community participation and public health education have played a vital role in raising awareness, promoting personal protective measures, and enhancing the overall effectiveness of control efforts. The implementation of these integrated interventions has resulted in reduced dengue cases and improved capacity of outbreak response. China's dengue control strategies under the One Health framework, with focus on interdisciplinary collaboration, incorporated environmental and ecological interventions, which reduced mosquito breeding sites and improved sanitation. The findings of the review underscore the need for continuous improvement in early warning systems, scientific research, and the adoption of the One Health approach to address emerging challenges posed by climate change and the cross-border spread of infectious diseases.

Conclusion

China's dengue control and prevention programs provide a compelling case study for the effective application of the One Health approach. By systematically analyzing the integration of multidisciplinary strategies, this review reveals valuable lessons on optimizing public health responses to vector-borne diseases. The alignment of these strategies with One Health principles not only enhances the effectiveness of dengue control efforts in China but also offers a framework that can be adapted by other regions facing similar challenges. Ultimately, the insights gained from this analysis contribute to the global fight against dengue, emphasizing the need for collaborative and holistic approaches in public health initiatives.

Dengue virus surveillance in Nepal yields the first on-site whole genome sequences of isolates from the 2022 outbreak

BACKGROUND

The 4 serotypes of dengue virus (DENV1-4) can each cause potentially deadly dengue disease, and are spreading globally from tropical and subtropical areas to more temperate ones. Nepal provides a microcosm of this global phenomenon, having met each of these grim benchmarks. To better understand DENV transmission dynamics and spread into new areas, we chose to study dengue in Nepal and, in so doing, to build the onsite infrastructure needed to manage future, larger studies.

METHODS AND RESULTS

During the 2022 dengue season, we enrolled 384 patients presenting at a hospital in Kathmandu with dengue-like symptoms; 79% of the study participants had active or recent DENV infection (NS1 antigen and IgM). To identify circulating serotypes, we screened serum from 50 of the NS1+ participants by RT-PCR and identified DENV1, 2, and 3 - with DENV1 and 3 codominant. We also performed whole-genome sequencing of DENV, for the first time in Nepal, using our new on-site capacity. Sequencing analysis demonstrated the DENV1 and 3 genomes clustered with sequences reported from India in 2019, and the DENV2 genome clustered with a sequence reported from China in 2018.

CONCLUSION

These findings highlight DENV's geographic expansion from neighboring countries, identify China and India as the likely origin of the 2022 DENV cases in Nepal, and demonstrate the feasibility of building onsite capacity for more rapid genomic surveillance of circulating DENV. These ongoing efforts promise to protect populations in Nepal and beyond by informing the development and deployment of DENV drugs and vaccines in real time.

Preparing the developing world for the next pandemic: Evidence from China's R&D blueprint for emerging infectious diseases

BACKGROUND

With double pressures of endemic and imported emerging infectious diseases (EIDs), China's ability to detect, prevent and control the unknown virus is of regional and global interest. This study aimed to establish an R&D Blueprint for EIDs in China by identifying the list of prioritized diseases and medical countermeasures (MCMs) that need proactive actions for the next pandemic.

METHODS

The process mainly referred to the World Health Organization's prioritization methodology, supplemented by pipeline landscape, rapid risk assessment and multi-dimensional analysis. The study included five steps: 1) identifying potential pathogens, 2) screening into the long list, 3) prioritizing the long list, 4) identifying the final list and 5) generating an R&D Blueprint.

RESULTS

China's R&D Blueprint identified 14 viral pathogens and two virus groups (i.e., Influenza HxNy and Coronavirus X) for proactive and representative MCM development. At least one diagnostic candidate in preclinical study, and one therapeutic and one vaccine candidate in Phase I/II clinical trials for each prioritized pathogen were suggested to be developed as strategic national stockpiles. Various generalized and innovative platform technologies were also highlighted for enhancing overall capacities of EID preparedness and response, covering basic research, experiment, detection, prevention and control, surveillance and information sharing.

CONCLUSIONS

This is the first study in developing countries that established an R&D Blueprint of prioritized diseases, countermeasures and technologies. Our findings could help to drive pre-emptive scientific and technological actions toward emerging pathogens that may cause the next epidemic and could provide evidence-based strategies for developing countries to establish their national health research agenda tailored to health and research context under resource-limited settings.

Fitting the return period of dengue fever epidemic in Guangdong province of China

Objectives

The prevention and control of dengue fever (DF) has been a major public health issue in Guangdong (GD) province, China. This study aims to analyze the return period (RP) and the return level (RL) of DF epidemic in GD, to help the formulation of prevention and control plan.

Methods

Three models, namely Lognormal distribution (Lognor D.), normal distribution (Norm D.), and generalized logistic distribution (GLD) were selected to fit the annual number of indigenous DF cases in GD from 1978 to 2021. The coefficient of determination (R2), the root mean squared error (RMSE), and the Akaike information criterion (AIC) were used to evaluate the goodness of fit. We predicted the RP of 45130 historical maximum cases that occurred in 2014 and the RP of 4884 peak cases that occurred in 2019 over the 5 years up to 2021.

Results

Fitting through the three models, the R2 was 0.98, 0.98, and 0.96, respectively. The predicted RLs of the annual DF case number were between 297 and 43234, 297 and 43233, 362 and 41868 for the RPs of 2-45 years. The predicted RPs of DF outbreaks exceeding the historical maximum were 43, 43, and 44 years, and the RPs of DF epidemic exceeding the peak in 2019 were 7, 7, and 8 years, respectively. Therefore, we predicted that GD would experience a DF outbreak beyond the historical maximum in the next 35 or 36 years from 2022. And in the next 4 or 5 years from 2022, there would be a DF epidemic exceeding the peak in 2019.

Conclusions

The study discloses a temporal periodicity inherent to the DF epidemic in GD. The three models are applicable for forecasting and evaluating the RP and RL of DF epidemic in GD, separately.

Dengue Virus Surveillance in Nepal Yields the First On-Site Whole Genome Sequences of Isolates from the 2022 Outbreak

Background

The 4 serotypes of dengue virus (DENV1-4) can each cause potentially deadly dengue disease, and are spreading globally from tropical and subtropical areas to more temperate ones. Nepal provides a microcosm of this global phenomenon, having met each of these grim benchmarks. To better understand DENV transmission dynamics and spread into new areas, we chose to study dengue in Nepal and, in so doing, to build the onsite infrastructure needed to manage future, larger studies.

Methods and Results

During the 2022 dengue season, we enrolled 384 patients presenting at a hospital in Kathmandu with dengue-like symptoms; 79% of the study participants had active or recent DENV infection (NS1 antigen and IgM). To identify circulating serotypes, we screened serum from 50 of the NS1 + participants by RT-PCR and identified DENV1, 2, and 3 - with DENV1 and 3 codominant. We also performed whole-genome sequencing of DENV, for the first time in Nepal, using our new on-site capacity. Sequencing analysis demonstrated the DENV1 and 3 genomes clustered with sequences reported from India in 2019, and the DENV2 genome clustered with a sequence reported from China in 2018.

Conclusion

These findings highlight DENV's geographic expansion from neighboring countries, identify China and India as the likely origin of the 2022 DENV cases in Nepal, and demonstrate the feasibility of building onsite capacity for more rapid genomic surveillance of circulating DENV. These ongoing efforts promise to protect populations in Nepal and beyond by informing the development and deployment of DENV drugs and vaccines in real time.

Genomic epidemiology of dengue in Shantou, China, 2019

Objectives

Dengue has been endemic in Southeast Asian countries for decades. There are few reports tracing the dynamics of dengue in real time. In this study, we generated hundreds of pathogen genomes to understand the genomic epidemiology of an outbreak in a hyper-endemic area of dengue.

Methods

We leveraged whole-genome short-read sequencing (PE150) to generate genomes of the dengue virus and investigated the genomic epidemiology of a dengue virus transmission in a mesoscale outbreak in Shantou, China, in 2019.

Results

The outbreak was sustained from July to December 2019. The total accumulated number of laboratory-confirmed cases was 944. No gender bias or fatalities were recorded. Cambodia and Singapore were the main sources of imported dengue cases (74.07%, n = 20). A total of 284 dengue virus strains were isolated, including 259 DENV-1, 24 DENV-2, and 1 DENV-3 isolates. We generated the entire genome of 252 DENV isolates (229 DENV-1, 22 DENV-2, and 1 DENV-3), which represented 26.7% of the total cases. Combined epidemiological and phylogenetic analyses indicated multiple independent introductions. The internal transmission evaluations and transmission network reconstruction supported the inference of phylodynamic analysis, with high Bayes factor support in BSSVS analysis. Two expansion founders and transmission chains were detected in CCH and LG of Shantou.

Conclusions

We observed the instant effects of genomic epidemiology in monitoring the dynamics of DENV and highlighted its prospects for real-time tracing of outbreaks of other novel agents in the future.

Mosquito densovirus significantly reduces the vector susceptibility to dengue virus serotype 2 in Aedes albopictus mosquitoes (Diptera: Culicidae)

BACKGROUND

Dengue virus (DENV) is a major public health threat, with Aedes albopictus being the confirmed vector responsible for dengue epidemics in Guangzhou, China. Mosquito densoviruses (MDVs) are pathogenic mosquito-specific viruses, and a novel MDV was previously isolated from Ae. albopictus in Guangzhou. This study aims to determine the prevalence of MDVs in wild Ae. albopictus populations and investigate their potential interactions with DENV and impact on vector susceptibility for DENV.

METHODS

The prevalence of MDV in wild mosquitoes in China was investigated using open access sequencing data and PCR detection in Ae. albopictus in Guangzhou. The viral infection rate and titers in MDV-persistent C6/36 cells were evaluated at 12, 24, 48, 72, 96, and 120 h post infection (hpi) by indirect immunofluorescence assay (IFA) and real time quantitative PCR (RT-qPCR). The midgut infection rate (MIR), dissemination rate (DR), and salivary gland infection rate (SGIR) in various tissues of MDV-infected mosquitoes were detected and quantified at 0, 5, 10, and 15 days post infection (dpi) by RT-PCR and RT-qPCR. The chi-square test evaluated dengue virus serotype 2 (DENV-2) and Aedes aegypti densovirus (AaeDV) infection rates and related indices in mosquitoes, while Tukey's LSD and t-tests compared viral titers in C6/36 cells and tissues over time.

RESULTS

The results revealed a relatively wide distribution of MDVs in Aedes, Culex, and Anopheles mosquitoes in China and an over 68% positive rate. In vitro, significant reductions in DENV-2 titers in supernatant at 120 hpi, and an apparent decrease in DENV-2-positive cells at 96 and 120 hpi were observed. In vivo, DENV-2 in the ovaries and salivary glands was first detected at 10 dpi in both monoinfected and superinfected Ae. albopictus females, while MDV superinfection with DENV-2 suppressed the salivary gland infection rate at 15 dpi. DENV-2 titer in the ovary and salivary glands of Ae. albopictus was reduced in superinfected mosquitoes at 15 dpi.

CONCLUSIONS

MDVs is widespread in natural mosquito populations, and replication of DENV-2 is suppressed in MDV-infected Ae. albopictus, thus reducing vector susceptibility to DENV-2. Our study supports the hypothesis that MDVs may contribute to reducing transmission of DENV and provides an alternative strategy for mosquito-transmitted disease control.

Climate change and Aedes albopictus risks in China: current impact and future projection

BACKGROUND

Future distribution of dengue risk is usually predicted based on predicted climate changes using general circulation models (GCMs). However, it is difficult to validate the GCM results and assess the uncertainty of the predictions. The observed changes in climate may be very different from the GCM results. We aim to utilize trends in observed climate dynamics to predict future risks of Aedes albopictus in China.

METHODS

We collected Ae. albopictus surveillance data and observed climate records from 80 meteorological stations from 1970 to 2021. We analyzed the trends in climate change in China and made predictions on future climate for the years 2050 and 2080 based on trend analyses. We analyzed the relationship between climatic variables and the prevalence of Ae. albopictus in different months/seasons. We built a classification tree model (based on the average of 999 runs of classification and regression tree analyses) to predict the monthly/seasonal Ae. albopictus distribution based on the average climate from 1970 to 2000 and assessed the contributions of different climatic variables to the Ae. albopictus distribution. Using these models, we projected the future distributions of Ae. albopictus for 2050 and 2080.

RESULTS

The study included Ae. albopictus surveillance from 259 sites in China found that winter to early spring (November-February) temperatures were strongly correlated with Ae. albopictus prevalence (prediction accuracy ranges 93.0-98.8%)-the higher the temperature the higher the prevalence, while precipitation in summer (June-September) was important predictor for Ae. albopictus prevalence. The machine learning tree models predicted the current prevalence of Ae. albopictus with high levels of agreement (accuracy > 90% and Kappa agreement > 80% for all 12 months). Overall, winter temperature contributed the most to Ae. albopictus distribution, followed by summer precipitation. An increase in temperature was observed from 1970 to 2021 in most places in China, and annual change rates varied substantially from -0.22 ºC/year to 0.58 ºC/year among sites, with the largest increase in temperature occurring from February to April (an annual increase of 1.4-4.7 ºC in monthly mean, 0.6-4.0 ºC in monthly minimum, and 1.3-4.3 ºC in monthly maximum temperature) and the smallest in November and December. Temperature increases were lower in the tropics/subtropics (1.5-2.3 ºC from February-April) compared to the high-latitude areas (2.6-4.6 ºC from February-April). The projected temperatures in 2050 and 2080 by this study were approximately 1-1.5 °C higher than those projected by GCMs. The estimated current Ae. albopictus risk distribution had a northern boundary of north-central China and the southern edge of northeastern China, with a risk period of June-September. The projected future Ae. albopictus risks in 2050 and 2080 cover nearly all of China, with an expanded risk period of April-October. The current at-risk population was estimated to be 960 million and the future at-risk population was projected to be 1.2 billion.

CONCLUSIONS

The magnitude of climate change in China is likely to surpass GCM predictions. Future dengue risks will expand to cover nearly all of China if current climate trends continue.