Seasonality modeling of the distribution of Aedes albopictus in China based on climatic and environmental suitability

Forecasting and mapping dengue fever epidemics in China: a spatiotemporal analysis

BACKGROUND

Dengue fever (DF) has emerged as a significant public health concern in China. The spatiotemporal patterns and underlying influencing its spread, however, remain elusive. This study aims to identify the factors driving these variations and to assess the city-level risk of DF epidemics in China.

METHODS

We analyzed the frequency, intensity, and distribution of DF cases in China from 2003 to 2022 and evaluated 11 natural and socioeconomic factors as potential drivers. Using the random forest (RF) model, we assessed the contributions of these factors to local DF epidemics and predicted the corresponding city-level risk.

RESULTS

Between 2003 and 2022, there was a notable correlation between local and imported DF epidemics in case numbers (r = 0.41, P < 0.01) and affected cities (r = 0.79, P < 0.01). With the increase in the frequency and intensity of imported epidemics, local epidemics have become more severe. Their occurrence has increased from five to eight months per year, with case numbers spanning from 14 to 6641 per month. The spatial distribution of city-level DF epidemics aligns with the geographical divisions defined by the Huhuanyong Line (Hu Line) and Qin Mountain-Huai River Line (Q-H Line) and matched well with the city-level time windows for either mosquito vector activity (83.59%) or DF transmission (95.74%). The RF models achieved a high performance (AUC = 0.92) when considering the time windows. Importantly, they identified imported cases as the primary influencing factor, contributing significantly (24.82%) to local DF epidemics at the city level in the eastern region of the Hu Line (E-H region). Moreover, imported cases were found to have a linear promoting impact on local epidemics, while five climatic and six socioeconomic factors exhibited nonlinear effects (promoting or inhibiting) with varying inflection values. Additionally, this model demonstrated outstanding accuracy (hitting ratio = 95.56%) in predicting the city-level risks of local epidemics in China.

CONCLUSIONS

China is experiencing an increasing occurrence of sporadic local DF epidemics driven by an unavoidably higher frequency and intensity of imported DF epidemics. This research offers valuable insights for health authorities to strengthen their intervention capabilities against this disease.

Biased virus transmission following sequential coinfection of Aedes aegypti with dengue and Zika viruses

BACKGROUND

Mosquito-borne arboviruses are expanding their territory and elevating their infection prevalence due to the rapid climate change, urbanization, and increased international travel and global trade. Various significant arboviruses, including the dengue virus, Zika virus, Chikungunya virus, and yellow fever virus, are all reliant on the same primary vector, Aedes aegypti. Consequently, the occurrence of arbovirus coinfection in mosquitoes is anticipated. Arbovirus coinfection in mosquitoes has two patterns: simultaneous and sequential. Numerous studies have demonstrated that simultaneous coinfection of arboviruses in mosquitoes is unlikely to exert mutual developmental influence on these viruses. However, the viruses' interplay within a mosquito after the sequential coinfection seems intricated and not well understood.

METHODOLOGY/PRINCIPAL FINDINGS

We conducted experiments aimed at examining the phenomenon of arbovirus sequential coinfection in both mosquito cell line (C6/36) and A. aegypti, specifically focusing on dengue virus (DENV, serotype 2) and Zika virus (ZIKV). We firstly observed that DENV and ZIKV can sequentially infect mosquito C6/36 cell line, but the replication level of the subsequently infected ZIKV was significantly suppressed. Similarly, A. aegypti mosquitoes can be sequentially coinfected by these two arboviruses, regardless of the order of virus exposure. However, the replication, dissemination, and the transmission potential of the secondary virus were significantly inhibited. We preliminarily explored the underlying mechanisms, revealing that arbovirus-infected mosquitoes exhibited activated innate immunity, disrupted lipid metabolism, and enhanced RNAi pathway, leading to reduced susceptibility to the secondary arbovirus infections.

CONCLUSIONS/SIGNIFICANCE

Our findings suggest that, in contrast to simultaneous arbovirus coinfection in mosquitoes that can promote the transmission and co-circulation of these viruses, sequential coinfection appears to have limited influence on arbovirus transmission dynamics. However, it is important to note that more experimental investigations are needed to refine and expand upon this conclusion.

Mapping Potential Malaria Vector Larval Habitats for Larval Source Management in Western Kenya: Introduction to Multimodel Ensembling Approaches

Identification and mapping of larval sources are a prerequisite for effective planning and implementing mosquito larval source management (LSM). Ensemble modeling is increasingly used for prediction modeling, but it lacks standard procedures. We proposed a detailed framework to predict potential malaria vector larval habitats by using multimodel ensemble modeling, which includes selection of models, ensembling method, and predictors, evaluation of variable importance, prediction of potential larval habitats, and assessment of prediction uncertainty. The models were built and validated based on multisite, multiyear field observations and climatic/environmental variables. Model performance was tested using independent field observations. Overall, we found that the ensembled model predicted larval habitats with about 20% more accuracy than the average of the individual models ensembled. Key larval habitat predictors in western Kenya were elevation, geomorphon class, and precipitation for the 2 months prior. Additional predictors may be required to increase the predictive accuracy of the larva-positive habitats. This is the first study to provide a detailed framework for the process of multimodel ensemble modeling for malaria vector habitats. Mapping of potential habitats will be helpful in LSM planning.

The predicted potential distribution of Aedes albopictus in China under the shared socioeconomic pathway (SSP)1-2.6

Aedes albopictus (Skuse) (Diptera: Culicidae) is one of the 100 most invasive species in the world and represents a significant threat to public health. The distribution of Ae. albopictus has been expanding rapidly due to increased international trade, population movement, global warming and accelerated urbanization. Consequently, it is very important to know the potential distribution area of Ae. albopictus in advance for early warning and control of its spread and invasion. We randomly selected 282 distribution sites from 27 provincial-level administrative regions in China, and used the GARP and MaxEnt models to analyze and predict the current and future distribution areas of Ae. albopictus in China. The results showed that the current range of Ae. albopictus in China covers most provinces such as Yunnan and Guizhou Provinces, and the distribution of Ae. albopictus in border provinces such as Tibet, Gansu and Jilin Provinces tend to expand westwards. In addition, the potential distribution area of Ae. albopictus in China will continue to expand westwards due to future climate change under the SSP126 climate scenario. Furthermore, the results of environmental factor filtering showed that temperature and precipitation had a large effect on the distribution probability of Ae. albopictus.

Trends in mosquito species distribution modeling: insights for vector surveillance and disease control

Species distribution modeling (SDM) has become an increasingly common approach to explore questions about ecology, geography, outbreak risk, and global change as they relate to infectious disease vectors. Here, we conducted a systematic review of the scientific literature, screening 563 abstracts and identifying 204 studies that used SDMs to produce distribution estimates for mosquito species. While the number of studies employing SDM methods has increased markedly over the past decade, the overwhelming majority used a single method (maximum entropy modeling; MaxEnt) and focused on human infectious disease vectors or their close relatives. The majority of regional models were developed for areas in Africa and Asia, while more localized modeling efforts were most common for North America and Europe. Findings from this study highlight gaps in taxonomic, geographic, and methodological foci of current SDM literature for mosquitoes that can guide future efforts to study the geography of mosquito-borne disease risk.

Distribution areas and monthly dynamic distribution changes of three Aedes species in China: Aedes aegypti, Aedes albopictus and Aedes vexans

BACKGROUND

Mosquitoes play an absolute role in the spread of epidemic arbovirus diseases. Worldwide, Aedes aegypti and Aedes albopictus are the main vectors responsible for the spread of these mosquito-borne diseases. Aedes vexans, a mosquito species native to China, also carries mosquito-borne viruses, such as dengue fever virus and Japanese encephalitis virus, but research on this mosquito has been inadequate. Mapping the potential distribution range of and monthly change in the distribution of these three Aedes species is of particular importance for mosquito surveillance, eradication and disease control.

METHODS

Monitoring data were collected for the three Aedes species in China. Long-term temperature and precipitation data (2001-2021) and land cover data were used to represent various climate and environmental conditions. An ecological niche model was developed using a maximum entropy modeling method to predict the current optimum habitat areas for the three Aedes species and to obtain important variables influencing their monthly distribution.

RESULTS

The distribution model for the three Aedes species performed well, with an area under the receiver operating characteristic curve value of 0.991 for Ae. aegypti, 0.928 for Ae. albopictus and 0.940 for Ae. vexans. Analysis of the distribution change and mapping of the optimum habitat range for each Aedes species for each month demonstrated that temperature, precipitation and construction land were important factors influencing the distribution of these three Aedes species.

CONCLUSIONS

In China, Aedes aegypti is mainly concentrated in a few tropical regions and along the Yunnan border; Aedes albopictus is widely distributed throughout most of the country, except for the arid and semi-arid regions of northwest China; and Aedes vexans is mainly found in the northern regions. Our results provide a basis for the timing and location of surveillance efforts for high-priority mosquitoes.

The patterns and driving forces of dengue invasions in China

BACKGROUND

Global connectivity and environmental change pose continuous threats to dengue invasions from worldwide to China. However, the intrinsic relationship on introduction and outbreak risks of dengue driven by the landscape features are still unknown. This study aimed to map the patterns on source-sink relation of dengue cases and assess the driving forces for dengue invasions in China.

METHODS

We identified the local and imported cases (2006-2020) and assembled the datasets on environmental conditions. The vector auto-regression model was applied to detect the cross-relations of source-sink patterns. We selected the major environmental drivers via the Boruta algorithm to assess the driving forces in dengue outbreak dynamics by applying generalized additive models. We reconstructed the internal connections among imported cases, local cases, and external environmental drivers using the structural equation modeling.

RESULTS

From 2006 to 2020, 81,652 local dengue cases and 12,701 imported dengue cases in China were reported. The hotspots of dengue introductions and outbreaks were in southeast and southwest China, originating from South and Southeast Asia. Oversea-imported dengue cases, as the Granger-cause, were the initial driver of the dengue dynamic; the suitable local bio-socioecological environment is the fundamental factor for dengue epidemics. The Bio8 [odds ratio (OR) = 2.11, 95% confidence interval (CI): 1.67-2.68], Bio9 (OR = 291.62, 95% CI: 125.63-676.89), Bio15 (OR = 4.15, 95% CI: 3.30-5.24), normalized difference vegetation index in March (OR = 1.27, 95% CI: 1.06-1.51) and July (OR = 1.04, 95% CI: 1.00-1.07), and the imported cases are the major drivers of dengue local transmissions (OR = 4.79, 95% CI: 4.34-5.28). The intermediary effect of an index on population and economic development to local cases via the path of imported cases was detected in the dengue dynamic system.

CONCLUSIONS

Dengue outbreaks in China are triggered by introductions of imported cases and boosted by landscape features and connectivity. Our research will contribute to developing nature-based solutions for dengue surveillance, mitigation, and control from a socio-ecological perspective based on invasion ecology theories to control and prevent future dengue invasion and localization.

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.

Genome-wide SNPs reveal novel patterns of spatial genetic structure in Aedes albopictus (Diptera Culicidae) population in China

Introduction

Since the second half of the 20th century, Aedes albopictus, a vector for more than 20 arboviruses, has spread worldwide. Aedes albopictus is the main vector of infectious diseases transmitted by Aedes mosquitoes in China, and it has caused concerns regarding public health. A comprehensive understanding of the spatial genetic structure of this vector species at a genomic level is essential for effective vector control and the prevention of vector-borne diseases.

Methods

During 2016-2018, adult female Ae. albopictus mosquitoes were collected from eight different geographical locations across China. Restriction site-associated DNA sequencing (RAD-seq) was used for high-throughput identification of single nucleotide polymorphisms (SNPs) and genotyping of the Ae. albopictus population. The spatial genetic structure was analyzed and compared to those exhibited by mitochondrial cytochrome c oxidase subunit 1 (cox1) and microsatellites in the Ae. albopictus population.

Results

A total of 9,103 genome-wide SNP loci in 101 specimens and 32 haplotypes of cox1 in 231 specimens were identified in the samples from eight locations in China. Principal component analysis revealed that samples from Lingshui and Zhanjiang were more genetically different than those from the other locations. The SNPs provided a better resolution and stronger signals for novel spatial population genetic structures than those from the cox1 data and a set of previously genotyped microsatellites. The fixation indexes from the SNP dataset showed shallow but significant genetic differentiation in the population. The Mantel test indicated a positive correlation between genetic distance and geographical distance. However, the asymmetric gene flow was detected among the populations, and it was higher from south to north and west to east than in the opposite directions.

Conclusions

The genome-wide SNPs revealed seven gene pools and fine spatial genetic structure of the Ae. albopictus population in China. The RAD-seq approach has great potential to increase our understanding of the spatial dynamics of population spread and establishment, which will help us to design new strategies for controlling vectors and mosquito-borne diseases.

Human infections with neglected vector-borne pathogens in China: A systematic review

Background

Emerging vector-borne pathogens (VBPs) pose a continuous background threat to the global health. Knowledge of the occurrence, distributions and epidemiological characteristics of VBP are lacking in many countries. Outbreaks of novel VBP are of increasing global interest including those arising in China.

Methods

A systematic review of published literature was undertaken to characterize the spectrum of VBPs causing human illness in China. We searched five databases for VBP-related articles in English and Chinese published between January 1980 and June 2021, that excluded those listed in the National Notifiable Diseases Surveillance System of China. The study is registered with PROSPERO, CRD42021259540.

Findings

A total of 906 articles meeting the selection criteria were included in this study. A total of 44,809 human infections with 82 species of VBPs including 40 viruses, 33 bacteria (20 Rickettsiales bacteria, eight Spirochaetales bacteria, and five other bacteria) and nine parasites, were identified in China. Rickettsiales bacteria were the most common and widely distributed pathogens with 18,042 cases reported in 33 provinces by 347 reviewed articles, followed by Spirochaetales bacteria with 15,745 cases in 32 provinces (299 articles), viruses with 8455 cases in 30 provinces (139 articles), other bacteria with 2053 cases in 19 provinces (65 articles), parasites with 514 cases in 17 provinces (44 articles), and multiple pathogens with 3626 cases in 14 provinces (23 articles). Coxiella burnetii, Bartonella henselae and Rickettsia sibirica were the most frequently reported pathogens. A total of 18 new pathogens were reported in China during this period (these also represented their first identification globally). Based on 419 articles with clinical information, a meta-analysis revealed that flu-like illness was the most common manifestation among infections with VBPs.

Interpretation

This review helps improve the understanding of VBPs in China, demonstrating the need to consider a wider surveillance of VBPs in many different settings, thus helping to inform future research and surveillance efforts.

Funding

Natural Science Foundation of China.

Aedes albopictus life table: environment, food, and age dependence survivorship and reproduction in a tropical area

Background

Environmental conditions affect the biology of mosquito vectors. Aedes albopictus is a major vector of many important diseases including dengue, Zika, and chikungunya in China. Understanding the development, fecundity, and survivorship of Ae. albopictus mosquitoes in different environmental conditions is beneficial for the implementation of effective vector control measures.

Methods

Aedes albopictus larval and adult life-table experiments were conducted under natural conditions in indoor, half-shaded, and fully shaded settings, simulating the three major habitat types in Hainan Province, a tropical island in the South China Sea. Temperature, humidity, and light intensity were recorded daily. Larval rearing used habitat water and tap water, with and without additional artificial food. Development time, survivorship, pupation rate, and adult emergence rates were monitored. Adult mosquito survivorship and fecundity were monitored daily and reproductive rates were determined, and age-dependent survivorship and reproduction were analyzed.

Results

The pupation time and male and female emergence times were significantly shorter in indoor conditions than in shaded and half-shaded conditions for both tap water with added food and habitat water with added food groups. For habitat water with added food, the shaded environment had the lowest pupation rate among the settings. For tap water with added food group, the shaded environment had the lowest pupation rate. The mean survival time of females was 27.3 ± 0.8 days in the indoor condition, which was significantly longer than that in the half-shaded (18.4 ± 0.6 days) and shaded (13.8 ± 1.2 days) conditions. Adult mortality was age-dependent, and the rate of change in mortality with age was not significantly different among different environmental conditions. The mean net replacement rate (R₀) of female mosquitoes showed no significant difference among the three conditions, whereas the per capita intrinsic growth rate (r) in the shaded condition was 42.0% and 20.4% higher than that in the indoor and half-shaded conditions, respectively. Female daily egg mass was also age-dependent in all the settings, decaying exponentially with age.

Conclusions

Our results imply that half-shaded conditions are likely the best natural condition for adult emergence and female reproduction, and food supply is crucial for larval development and pupation. The results provide new avenues for integrated mosquito management in indoor and outdoor areas, especially in half-shaded areas.

Graphical Abstract

Supplementary Information

The online version contains supplementary material available at 10.1186/s13071-021-05081-x.

Prevalence and molecular characterization of Wolbachia in field-collected Aedes albopictus, Anopheles sinensis, Armigeres subalbatus, Culex pipiens and Cx. tritaeniorhynchus in China

Wolbachia are maternally transmitted intracellular bacteria that can naturally and artificially infect arthropods and nematodes. Recently, they were applied to control the spread of mosquito-borne pathogens by causing cytoplasmic incompatibility (CI) between germ cells of females and males. The ability of Wolbachia to induce CI is based on the prevalence and polymorphism of Wolbachia in natural populations of mosquitoes. In this study, we screened the natural infection level and diversity of Wolbachia in field-collected mosquitoes from 25 provinces of China based on partial sequence of Wolbachia surface protein (wsp) gene and multilocus sequence typing (MLST). Among the samples, 2489 mosquitoes were captured from 24 provinces between July and September, 2014 and the remaining 1025 mosquitoes were collected month-by-month in Yangzhou, Jiangsu province between September 2013 and August 2014. Our results showed that the presence of Wolbachia was observed in mosquitoes of Aedes albopictus (97.1%, 331/341), Armigeres subalbatus (95.8%, 481/502), Culex pipiens (87.0%, 1525/1752), Cx. tritaeniorhynchus (17.1%, 14/82), but not Anopheles sinensis (n = 88). Phylogenetic analysis indicated that high polymorphism of wsp and MLST loci was observed in Ae. albopictus mosquitoes, while no or low polymorphisms were in Ar. subalbatus and Cx. pipiens mosquitoes. A total of 12 unique mutations of deduced amino acid were identified in the wsp sequences obtained in this study, including four mutations in Wolbachia supergroup A and eight mutations in supergroup B. This study revealed the prevalence and polymorphism of Wolbachia in mosquitoes in large-scale regions of China and will provide some useful information when performing Wolbachia-based mosquito biocontrol strategies in China.

The mosquitoes Aedes albopictus, Anopheles sinensis, Armigeres subalbatus, Culex pipiens and Cx. tritaeniorhynchus are native to China and the major vectors in the transmission of arboviruses, protozoans and nematodes. Recently, an innovative biocontrol strategy has been developed and evaluated based on the ability of Wolbachia to induce cytoplasmic incompatibility (CI), as well as interfere with the infection and replication of pathogens. Since the ability to induce CI largely depends on the density and diversity of Wolbachia, we investigated and characterized the natural infection of Wolbachia in above-mentioned five species of field-collected mosquitoes in 25 provinces of China. The results showed that the positive rates of Wolbachia infection were high in mosquitoes of Ae. albopictus, Ar. subalbatus and Cx. pipiens in large-scale regions of China and low in Cx. tritaeniorhynchus in Guizhou province. Phylogenetic analysis based on Wolbachia surface protein (wsp) gene and five multilocus sequence typing (MLST) loci indicated the high polymorphism of Wolbachia in Ae. albopictus, and low polymorphisms in Ar. subalbatus and Cx. pipiens. This finding contributes to the understanding of the nationwide distribution of Wolbachia and the potential application of this biocontrol strategy in China.

Dengue disease dynamics are modulated by the combined influences of precipitation and landscape: A machine learning approach

BACKGROUND

Dengue is an endemic vector-borne disease influenced by environmental factors such as landscape and climate. Previous studies separately assessed the effects of landscape and climate factors on mosquito occurrence and dengue incidence. However, both factors concurrently coexist in time and space and can interact, affecting mosquito development and dengue disease transmission. For example, eggs laid in a suitable environment can hatch after being submerged in rain water. It has been difficult for conventional statistical modeling approaches to demonstrate these combined influences due to mathematical constraints.

OBJECTIVES

To investigate the combined influences of landscape and climate factors on mosquito occurrence and dengue incidence.

METHODS

Entomological, epidemiological, and landscape data from the rainy season (July-December) were obtained from respective government agencies in Metropolitan Manila, Philippines, from 2012 to 2014. Temperature, precipitation and vegetation data were obtained through remote sensing. A random forest algorithm was used to select the landscape and climate variables. Afterward, using the identified key variables, a model-based (MOB) recursive partitioning was implemented to test the combined influences of landscape and climate factors on ovitrap index (vector mosquito occurrence) and dengue incidence.

RESULTS

The MOB recursive partitioning for ovitrap index indicated a high sensitivity of vector mosquito occurrence on environmental conditions generated by a combination of high residential density areas with low precipitation. Moreover, the MOB recursive partitioning indicated high sensitivity of dengue incidence to the effects of precipitation in areas with high proportions of residential density and commercial areas.

CONCLUSIONS

Dengue dynamics are not solely influenced by individual effects of either climate or landscape, but rather by their synergistic or combined effects. The presented findings have the potential to target vector surveillance in areas identified as suitable for mosquito occurrence under specific climatic conditions and may be relevant as part of urban planning strategies to control dengue.

Analysis of the spatial distribution of Aedes albopictus in an urban area of Shanghai, China

BACKGROUND

Aedes albopictus is a vector of major arboviral diseases and a primary pest in tropical and temperate regions of China. In most cities of China, the current monitoring system for the spread of Ae. albopictus is based on the subdistrict scale and does not consider spatial distribution for analysis of species density. Thus, the system is not sufficiently accurate for epidemic investigations, especially in large cities.

METHODS

This study used an improved surveillance program, with the mosquito oviposition trap (MOT) method, integrating the actual monitoring locations to investigate the temporal and spatial distribution of Ae. albopictus abundance in an urban area of Shanghai, China from 2018 to 2019. A total of 133 monitoring units were selected for surveillance of Ae. albopictus density in the study area, which was composed of 14 subdistricts. The vector abundance and spatial structure of Ae. albopictus were predicted using a binomial areal kriging model based on eight MOTs in each unit. Results were compared to the light trap (LT) method of the traditional monitoring scheme.

RESULTS

A total of 8,192 MOTs were placed in the study area in 2018, and 7917 (96.6%) were retrieved, with a positive rate of 6.45%. In 2019, 22,715 (97.0%) of 23,408 MOTs were recovered, with a positive rate of 5.44%. Using the LT method, 273 (93.5%) and 312 (94.5%) adult female Ae. albopictus were gathered in 2018 and 2019, respectively. The Ae. albopictus populations increased slowly from May, reached a peak in July, and declined gradually from September. The MOT positivity index (MPI) showed significant positive spatial autocorrelation across the study area, whereas LT collections indicated a nonsignificant spatial autocorrelation. The MPI was suitable for spatial interpolation using the binomial areal kriging model and showed different hot spots in different years.

CONCLUSIONS

The improved surveillance system integrated with a geographical information system (GIS) can improve our understanding of the spatial and temporal distribution of Ae. albopictus in urban areas and provide a practical method for decision-makers to implement vector control and mosquito management.

Tropical Medicine in China: Bibliometric Analysis Based on Web of Science (2010-2019)

Purpose

The current study quantitatively assessed research trends in tropical medicine in China via a bibliometric method, which used the Web of Science database to analyse the research-based literature related to tropical medicine published from 2010 to 2019.

Methods

Articles were analysed according to the output and research performance of institutes and countries that collaborated with China. Distribution of keywords was applied to evaluate research trends.

Results

Our findings showed that 3372 articles in the field of tropical medicine have been indexed under the Web of Science database during the past 10 years, indicating that studies pertaining to tropical medicine have been growing from 2010 to 2019. The Chinese Center for Disease Control and Prevention (China CDC) system, which published 549 articles on tropical medicine, may be considered as the flagship of tropical medicine in China. The United States ranked first in internationally collaborative articles with China. Furthermore, Parasite & Vectors, which published 707 papers, emerged as the top journal according to the number of publications.

Conclusions

This study highlights the key institutes and topics pertaining to tropical medicine research in China. Although there has been rapid progress in research on tropical medicine in China, some gaps still remain.

Virome in adult Aedes albopictus captured during different seasons in Guangzhou City, China

Background

The mosquito Aedes albopictus is an important vector for many pathogens. Understanding the virome in Ae. albopictus is critical for assessing the risk of disease transmission, implementation of vector control measures, and health system strengthening.

Methods

In this study, viral metagenomic and PCR methods were used to reveal the virome in adult Ae. albopictus captured in different areas and during different seasons in Guangzhou, China.

Results

The viral composition of adult Ae. albopictus varied mainly between seasons. Over 50 viral families were found, which were specific to vertebrates, invertebrates, plants, fungi, bacteria, and protozoa. In rural areas, Siphoviridae (6.5%) was the most common viral family harbored by mosquitoes captured during winter and spring, while Luteoviridae (1.1%) was the most common viral family harbored by mosquitoes captured during summer and autumn. Myoviridae (7.0% and 1.3%) was the most common viral family in mosquitoes captured in urban areas during all seasons. Hepatitis B virus (HBV) was detected by PCR in a female mosquito pool. The first near full-length HBV genome from Ae. albopictus was amplified, which showed a high level of similarity with human HBV genotype B sequences. Human parechovirus (HPeV) was detected in male and female mosquito pools, and the sequences were clustered with HPeV 1 and 3 sequences.

Conclusions

Large numbers of viral species were found in adult Ae. albopictus, including viruses from vertebrates, insects, and plants. The viral composition in Ae. albopictus mainly varied between seasons. Herein, we are the first to report the detection of HPeV and HBV in mosquitoes. This study not only provides valuable information for the control and prevention of mosquito-borne diseases, but it also demonstrates the feasibility of xenosurveillance.

Graphical Abstract

Supplementary Information

The online version contains supplementary material available at 10.1186/s13071-021-04922-z.

Spatial distribution of Culex mosquito abundance and associated risk factors in Hanoi, Vietnam

Japanese encephalitis (JE) is the major cause of viral encephalitis (VE) in most Asian-Pacific countries. In Vietnam, there is no nationwide surveillance system for JE due to lack of medical facilities and diagnoses. Culex tritaeniorhynchus, Culex vishnui, and Culex quinquefasciatus have been identified as the major JE vectors in Vietnam. The main objective of this study was to forecast a risk map of Culex mosquitoes in Hanoi, which is one of the most densely populated cities in Vietnam. A total of 10,775 female adult Culex mosquitoes were collected from 513 trapping locations. We collected temperature and precipitation information during the study period and its preceding month. In addition, the other predictor variables (e.g., normalized difference vegetation index [NDVI], land use/land cover and human population density), were collected for our analysis. The final model selected for estimating the Culex mosquito abundance included centered rainfall, quadratic term rainfall, rice cover ratio, forest cover ratio, and human population density variables. The estimated spatial distribution of Culex mosquito abundance ranged from 0 to more than 150 mosquitoes per 900m2. Our model estimated that 87% of the Hanoi area had an abundance of mosquitoes from 0 to 50, whereas approximately 1.2% of the area showed more than 100 mosquitoes, which was mostly in the rural/peri-urban districts. Our findings provide better insight into understanding the spatial distribution of Culex mosquitoes and its associated environmental risk factors. Such information can assist local clinicians and public health policymakers to identify potential areas of risk for JE virus. Risk maps can be an efficient way of raising public awareness about the virus and further preventive measures need to be considered in order to prevent outbreaks and onwards transmission of JE virus.

Dengue fever and dengue virus in the People's Republic of China

Infection with dengue virus (DENV) leads to symptoms variable from dengue fever to severe dengue, which has posed a huge socioeconomic and disease burden to the world population, particularly in tropical and subtropical regions. To date, four serotypes of DENV (DENV-1 to DENV-4) have been identified to sustain the transmission cycle in humans. In the past decades, dengue incidences have become more frequent, and four serotypes and various genotypes have been identified in PR China. Several large-scale dengue outbreaks and frequent local endemics occurred in the southern and coastal provinces, and the imported dengue cases accounted primarily for the initiation of the epidemics. No antiviral drug exists for dengue, and no vaccine has been approved to use in PR China, however strategies including public awareness, national reporting system of infectious diseases and public health emergencies, vector mosquito control, personal protection, and improved environmental sanitation have greatly reduced dengue prevalence. Some new technologies in vector mosquito control are emerging and being applied for dengue control. China's territory spans tropical, subtropical, and temperate climates, hence understanding the dengue status in China will be of beneficial for the global prevention and control of dengue. Here, we review the dengue status in PR China for the past decades and the strategies emerging for dengue control.

Wide and increasing suitability for Aedes albopictus in Europe is congruent across distribution models

The Asian tiger mosquito (Aedes albopictus), a vector of dengue, Zika and other diseases, was introduced in Europe in the 1970s, where it is still widening its range. Spurred by public health concerns, several studies have delivered predictions of the current and future distribution of the species for this region, often with differing results. We provide the first joint analysis of these predictions, to identify consensus hotspots of high and low suitability, as well as areas with high uncertainty. The analysis focused on current and future climate conditions and was carried out for the whole of Europe and for 65 major urban areas. High consensus on current suitability was found for the northwest of the Iberian Peninsula, southern France, Italy and the coastline between the western Balkans and Greece. Most models also agree on a substantial future expansion of suitable areas into northern and eastern Europe. About 83% of urban areas are expected to become suitable in the future, in contrast with ~ 49% nowadays. Our findings show that previous research is congruent in identifying wide suitable areas for Aedes albopictus across Europe and in the need to effectively account for climate change in managing and preventing its future spread.

Widespread multiple insecticide resistance in the major dengue vector Aedes albopictus in Hainan Province, China

BACKGROUND

Aedes albopictus is a highly invasive mosquito and has become a potential vector of dengue, chikungunya and Zika viruses. Insecticide-based mosquito interventions are the main tools for vector-borne disease control. However, mosquito resistance to insecticides is a major threat to effective prevention and control. Five Ae. albopictus populations across Hainan Province, China were investigated for susceptibility to multiple insecticide and resistance mechanisms.

RESULTS

Larval bioassays indicated that resistance to pyrethroids was common in all larval populations. Adult bioassays revealed all populations were either resistant or highly resistant to at least four of the six synthetic insecticides (deltamethrin, permethrin, cyfluthrin, propoxur, malathion, and DDT) tested. Pre-exposure of mosquitoes to the synergistic agent piperonyl butoxide (PBO) increased mosquito mortality by 2.4-43.3% in bioassays to DDT, malathion, and permethrin and rendered mosquito sensitive to deltamethrin, cyfluthrin, and propoxur. The frequency of knockdown resistance (kdr) mutations (F1534S and F1534C) ranged from 69.8% to 89.3% and from 38.1% to 87.0% in field-resistant and sensitive populations, respectively. F1534S mutation was significantly associated with pyrethroid resistance. No mutation was detected in the acetylcholinesterase (ace-1) gene in the two examined populations.

CONCLUSION

This study provides evidence of widespread resistance to multiple insecticides in Ae. albopictus in Hainan Province, China. Both kdr mutations and metabolic detoxification were potential causes of insecticide resistance for Ae. albopictus. Our findings highlight the need for insecticide resistance management and mosquito control measures that do not entirely depend on synthetic insecticides. © 2020 The Authors. Pest Management Science published by John Wiley & Sons Ltd on behalf of Society of Chemical Industry.

Effects of Overwintering on the Survival and Vector Competence of Aedes albopictus in the Urban Life Cycle of Dengue Virus in Guangzhou, China

The Pearl River Delta, where Aedes albopictus (Ae. albopictus) is the only vector for dengue transmission, has exhibited one of the highest dengue burdens in southern China in recent decades. However, whether dengue virus (DENV) can overwinter in Ae. albopictus in the Pearl River Delta has not been determined to date. In this study, 300 field-derived Ae. albopictus mosquitoes from Guangzhou that were infected with the predominant endemic DENV-1 strain were investigated under simulated urban balcony environment from October 16, 2016, to June 16, 2017. The vertical transmission of DENV in the infected overwintering Ae. albopictus was analyzed. The DENV infected overwintering mosquitoes were evaluated for viral load at nine-time points using reverse transcription-quantitative PCR. The vector competence of the infected overwintering Ae. albopictus was also investigated by using suckling mice. Adult mosquitoes and larvae were found during the observation period. The vertical transmission of DENV-1 was documented. The DENV-1-positive rates between overwintering males and females had no difference. The proportion of DENV-1-positive overwintering mosquitoes decreased over time and had no difference beyond three months after the experiment. Overwintering mosquitoes can spread DENV-1 to hosts. No engorged mosquitoes at an ambient temperature below 15 °C were observed. The ratio of engorged mosquitoes was positively correlated with the ambient temperature ranging from 15 to 30 °C. Our results demonstrated that DENV can overwinter in Ae. albopictus in the Pearl River Delta, Ae. albopictus is the competent vector for DENV, and maintain autochthonous dengue outbreaks in the Pearl River Delta through vertical transmission.

Prediction for Global Peste des Petits Ruminants Outbreaks Based on a Combination of Random Forest Algorithms and Meteorological Data

Peste des Petits Ruminants (PPR) is an acute and highly contagious transboundary disease caused by the PPR virus (PPRV). The virus infects goats, sheep and some wild relatives of small domestic ruminants, such as antelopes. PPR is listed by the World Organization for Animal Health as an animal disease that must be reported promptly. In this paper, PPR outbreak data combined with WorldClim database meteorological data were used to build a PPR prediction model. Using feature selection methods, eight sets of features were selected: bio3, bio10, bio15, bio18, prec7, prec8, prec12, and alt for modeling. Then different machine learning algorithms were used to build models, among which the random forest (RF) algorithm was found to have the best modeling effect. The ACC value of prediction accuracy for the model on the training set can reach 99.10%, while the ACC on the test sets was 99.10%. Therefore, RF algorithms and eight features were finally selected to build the model in order to build the online prediction system. In addition, we adopt single-factor modeling and correlation analysis of modeling variables to explore the impact of each variable on modeling results. It was found that bio18 (the warmest quarterly precipitation), prec7 (the precipitation in July), and prec8 (the precipitation in August) contributed significantly to the model, and the outbreak of the epidemic may have an important relationship with precipitation. Eventually, we used the final qualitative prediction model to establish a global online prediction system for the PPR epidemic.

Dispersal patterns and population genetic structure of Aedes albopictus (Diptera: Culicidae) in three different climatic regions of China

Background

Aedes albopictus is an indigenous primary vector for dengue and Zika viruses in China. Compared with its insecticide resistance, biology and vector competence, little is known about its genetic variation, which corresponds to environmental variations. Thus, the present study examines how Ae. albopictus varies among different climatic regions in China and deciphers its potential dispersal patterns.

Methods

The genetic variation and population structure of 17 Ae. albopictus populations collected from three climatic regions of China were investigated with 11 microsatellite loci and the mitochondrial coxI gene.

Results

Of 44 isolated microsatellite markers, 11 pairs were chosen for genotyping analysis and had an average PIC value of 0.713, representing high polymorphism. The number of alleles was high in each population, with the n_e value increasing from the temperate region (3.876) to the tropical region (4.144). Twenty-five coxI haplotypes were detected, and the highest diversity was observed in the tropical region. The mean Ho value (ca. 0.557) of all the regions was significantly lower than the mean He value (ca. 0.684), with nearly all populations significantly departing from HWE and displaying significant population expansion (p value < 0.05). Two genetically isolated groups and three haplotype clades were evaluated via STRUCTURE and haplotype phylogenetic analyses, and the tropical populations were significantly isolated from those in the other regions. Most genetic variation in Ae. albopictus was detected within populations and individuals at 31.40 and 63.04%, respectively, via the AMOVA test, and a relatively significant positive correlation was observed among only the temperate populations via IBD analysis (R² = 0.6614, p = 0.048). Recent dispersions were observed among different Ae. albopictus populations, and four major migration trends with high gene flow (Nm > 0.4) were reconstructed between the tropical region and the other two regions. Environmental factors, especially temperature and rainfall, may be the leading causes of genetic diversity in different climatic regions.

Conclusions

Continuous dispersion contributes to the genetic communication of Ae. albopictus populations across different climatic regions, and environmental factors, especially temperature and rainfall, may be the leading causes of genetic variation.

Graphical abstract

Assessment of vector-host-pathogen relationships using data mining and machine learning

Infectious diseases, including vector-borne diseases transmitted by arthropods, are a leading cause of morbidity and mortality worldwide. In the era of big data, addressing broad-scale, fundamental questions regarding the complex dynamics of these diseases will increasingly require the integration of diverse datasets to produce new biological knowledge. This review provides a current snapshot of the systematic assessment of the relationships between microbial pathogens, arthropod vectors and mammalian hosts using data mining and machine learning. We employ PRISMA to identify 32 key papers relevant to this topic. Our analysis shows an increasing use of data mining and machine learning tasks and techniques, including prediction, classification, clustering, association rules mining, and deep learning, over the last decade. However, it also reveals a number of critical challenges in applying these to the study of vector-host-pathogen interactions at various systems biology levels. Here, relevant studies, current limitations and future directions are discussed. Furthermore, the quality of data in relevant papers was assessed using the FAIR (Findable, Accessible, Interoperable, Reusable) compliance criteria to evaluate and encourage reproducibility and shareability of research outcomes. Although shortcomings in their application remain, data mining and machine learning have significant potential to break new ground in understanding fundamental aspects of vector-host-pathogen relationships and their application in this field should be encouraged. In particular, while predictive modeling, feature engineering and supervised machine learning are already being used in the field, other data mining and machine learning methods such as deep learning and association rules analysis lag behind and should be implemented in combination with established methods to accelerate hypothesis and knowledge generation in the domain.