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Abstract Background One Health approach is crucial to tackling complex global public health threats at the interface of humans, animals, and the environment. As outlined in the One Health Joint Plan of Action, the international One Health community includes stakeholders from different sectors. Supported by the Bill & Melinda Gates Foundation, an academic community for One Health action has been proposed with the aim of promoting the understanding and real-world implementation of One Health approach and contribution towards the Sustainable Development Goals for a healthy planet. Main text The proposed academic community would contribute to generating high-quality scientific evidence, distilling local experiences as well as fostering an interconnected One Health culture and mindset, among various stakeholders on different levels and in all sectors. The major scope of the community covers One Health governance, zoonotic diseases, food security, antimicrobial resistance, and climate change along with the research agenda to be developed. The academic community will be supported by two committees, including a strategic consultancy committee and a scientific steering committee, composed of influential scientists selected from the One Health information database. A workplan containing activities under six objectives is proposed to provide research support, strengthen local capacity, and enhance global participation. Conclusions The proposed academic community for One Health action is a crucial step towards enhancing communication, coordination, collaboration, and capacity building for the implementation of One Health. By bringing eminent global experts together, the academic community possesses the potential to generate scientific evidence and provide advice to local governments and international organizations, enabling the pursuit of common goals, collaborative policies, and solutions to misaligned interests. Graphical Abstract

Abstract Background Chongming Island in China serves as a breeding and shelter point on the East Asian–Australasian Flyway. The resting frequency of migratory birds, abundance of mosquito populations, and the popular domestic poultry industry pose a potential risk of mosquito-borne zoonotic diseases. The aim of this study is to explore the role of migratory birds in the spread of mosquito-borne pathogens and their prevalent status on the island. Methods We conducted a mosquito-borne pathogen surveillance in 2021, in Chongming, Shanghai, China. Approximately 67,800 adult mosquitoes belonging to ten species were collected to investigate the presence of flaviviruses, alphaviruses, and orthobunyaviruses by RT-PCR. Genetic and phylogenetic analyses were conducted to explore the virus genotype and potential nature source. Serological survey was performed by ELISA to characterize Tembusu virus (TMUV) infection among domestic poultry. Results Two strains of TMUV and Chaoyang virus (CHAOV) and 47 strains of Quang Binh virus (QBV) were detected in 412 mosquito pools, with the infection rate of 0.16, 0.16, and 3.92 per 1000 Culex tritaeniorhynchus, respectively. Furthermore, TMUVs viral RNA was found in serum samples of domestic chickens and faecal samples of migratory birds. Antibodies against TMUV were detected in domestic avian serum samples, generally ranging from 44.07% in pigeons to 55.71% in ducks. Phylogenetic analyses indicated that the TMUV detected in Chongming belonged to Cluster 3, Southeast Asia origin, and most closely related to the CTLN strain, which caused a TMUV outbreak in chickens in Guangdong Province in 2020, but distant from strains obtained previously in Shanghai, which were involved in the 2010 TMUV outbreak in China. Conclusions We speculate that the TMUV was imported to Chongming Island through long-distance spreading by migratory birds from Southeast Asia, followed by spill over and transmission in mosquitoes and domestic avian species, threatening the local domestic poultry. In addition, the expansion and prevalence of insect-specific flaviviruses and its simultaneous circulation with mosquito-borne virus are worthy of close attention and further study.

Abstract Background Data-driven research is a very important component of One Health. As the core part of the global One Health index (GOHI), the global One Health Intrinsic Drivers index (IDI) is a framework for evaluating the baseline conditions of human-animal-environment health. This study aims to assess the global performance in terms of GOH-IDI, compare it across different World Bank regions, and analyze the relationships between GOH-IDI and national economic levels. Methods The raw data among 146 countries were collected from authoritative databases and official reports in November 2021. Descriptive statistical analysis, data visualization and manipulation, Shapiro normality test and ridge maps were used to evaluate and identify the spatial and classificatory distribution of GOH-IDI. This paper uses the World Bank regional classification and the World Bank income groups to analyse the relationship between GOH-IDI and regional economic levels, and completes the case studies of representative countries. Results The performance of One Health Intrinsic Driver in 146 countries was evaluated. The mean (standard deviation, SD) score of GOH-IDI is 54.05 (4.95). The values (mean SD) of different regions are North America (60.44, 2.36), Europe and Central Asia (57.73, 3.29), Middle East and North Africa (57.02, 2.56), East Asia and Pacific (53.87, 5.22), Latin America and the Caribbean (53.75, 2.20), South Asia (52.45, 2.61) and sub-Saharan Africa (48.27, 2.48). Gross national income per capita was moderately correlated with GOH-IDI (R 2 = 0.651, Deviance explained = 66.6%, P

Abstract Background China is progressing towards the goal of schistosomiasis elimination, but there are still some problems, such as difficult management of infection source and snail control. This study aimed to develop deep learning models with high-resolution remote sensing images for recognizing and monitoring livestock bovine, which is an intermediate source of Schistosoma japonicum infection, and to evaluate the effectiveness of the models for real-world application. Methods The dataset of livestock bovine’s spatial distribution was collected from the Chinese National Platform for Common Geospatial Information Services. The high-resolution remote sensing images were further divided into training data, test data, and validation data for model development. Two recognition models based on deep learning methods (ENVINet5 and Mask R-CNN) were developed with reference to the training datasets. The performance of the developed models was evaluated by the performance metrics of precision, recall, and F1-score. Results A total of 50 typical image areas were selected, 1125 bovine objectives were labeled by the ENVINet5 model and 1277 bovine objectives were labeled by the Mask R-CNN model. For the ENVINet5 model, a total of 1598 records of bovine distribution were recognized. The model precision and recall were 81.9% and 80.2%, respectively. The F1 score was 0.81. For the Mask R-CNN mode, 1679 records of bovine objectives were identified. The model precision and recall were 87.3% and 85.2%, respectively. The F1 score was 0.87. When applying the developed models to real-world schistosomiasis-endemic regions, there were 63 bovine objectives in the original image, 53 records were extracted using the ENVINet5 model, and 57 records were extracted using the Mask R-CNN model. The successful recognition ratios were 84.1% and 90.5% for the respectively developed models. Conclusion The ENVINet5 model is very feasible when the bovine distribution is low in structure with few samples. The Mask R-CNN model has a good framework design and runs highly efficiently. The livestock recognition models developed using deep learning methods with high-resolution remote sensing images accurately recognize the spatial distribution of livestock, which could enable precise control of schistosomiasis. Graphical Abstract

Abstract Background Zoonoses are public health threats that cause severe damage worldwide. Zoonoses constitute a key indicator of One Health (OH) and the OH approach is being applied for zoonosis control programmes of zoonotic diseases. In a very recent study, we developed an evaluation system for OH performance through the global OH index (GOHI). This study applied the GOHI to evaluate OH performance for zoonoses in sub-Saharan Africa. Methods The framework for the OH index on zoonoses (OHIZ) was constructed including five indicators, 15 subindicators and 28 datasets. Publicly available data were referenced to generate the OHIZ database which included both qualitative and quantitative indicators for all sub-Sahara African countries (n = 48). The GOHI algorithm was used to estimate scores for OHIZ. Indicator weights were calculated by adopting the fuzzy analytical hierarchy process. Results Overall, five indicators associated with weights were generated as follows: source of infection (23.70%), route of transmission (25.31%), targeted population (19.09%), capacity building (16.77%), and outcomes/case studies (15.13%). Following the indicators, a total of 37 sub-Sahara African countries aligned with OHIZ validation, while 11 territories were excluded for unfit or missing data. The OHIZ average score of sub-Saharan Africa was estimated at 53.67/100. The highest score was 71.99 from South Africa, while the lowest score was 40.51 from Benin. It is also worth mentioning that Sub-Sahara African countries had high performance in many subindicators associated with zoonoses, e.g., surveillance and response, vector and reservoir interventions, and natural protected areas, which suggests that this region had a certain capacity in control and prevention or responses to zoonotic events. Conclusions This study reveals that it is possible to perform OH evaluation for zoonoses in sub-Saharan Africa by OHIZ. Findings from this study provide preliminary research information in advancing knowledge of the evidenced risks to strengthen strategies for effective control of zoonoses and to support the prevention of zoonotic events.

Abstract Background A One Health approach has been increasingly mainstreamed by the international community, as it provides for holistic thinking in recognizing the close links and inter-dependence of the health of humans, animals and the environment. However, the dearth of real-world evidence has hampered application of a One Health approach in shaping policies and practice. This study proposes the development of a potential evaluation tool for One Health performance, in order to contribute to the scientific measurement of One Health approach and the identification of gaps where One Health capacity building is most urgently needed. Methods We describe five steps towards a global One Health index (GOHI), including (i) framework formulation; (ii) indicator selection; (iii) database building; (iv) weight determination; and (v) GOHI scores calculation. A cell-like framework for GOHI is proposed, which comprises an external drivers index (EDI), an intrinsic drivers index (IDI) and a core drivers index (CDI). We construct the indicator scheme for GOHI based on this framework after multiple rounds of panel discussions with our expert advisory committee. A fuzzy analytical hierarchy process is adopted to determine the weights for each of the indicators. Results The weighted indicator scheme of GOHI comprises three first-level indicators, 13 second-level indicators, and 57 third-level indicators. According to the pilot analysis based on the data from more than 200 countries/territories the GOHI scores overall are far from ideal (the highest score of 65.0 out of a maximum score of 100), and we found considerable variations among different countries/territories (31.8–65.0). The results from the pilot analysis are consistent with the results from a literature review, which suggests that a GOHI as a potential tool for the assessment of One Health performance might be feasible. Conclusions GOHI—subject to rigorous validation—would represent the world’s first evaluation tool that constructs the conceptual framework from a holistic perspective of One Health. Future application of GOHI might promote a common understanding of a strong One Health approach and provide reference for promoting effective measures to strengthen One Health capacity building. With further adaptations under various scenarios, GOHI, along with its technical protocols and databases, will be updated regularly to address current technical limitations, and capture new knowledge. Graphical Abstract

Abstract Background Schistosomiasis control is striving forward to transmission interruption and even elimination, evidence-lead control is of vital importance to eliminate the hidden dangers of schistosomiasis. This study attempts to identify high risk areas of schistosomiasis in China by using information value and machine learning. Methods The local case distribution from schistosomiasis surveillance data in China between 2005 and 2019 was assessed based on 19 variables including climate, geography, and social economy. Seven models were built in three categories including information value (IV), three machine learning models [logistic regression (LR), random forest (RF), generalized boosted model (GBM)], and three coupled models (IV + LR, IV + RF, IV + GBM). Accuracy, area under the curve (AUC), and F1-score were used to evaluate the prediction performance of the models. The optimal model was selected to predict the risk distribution for schistosomiasis. Results There is a more prone to schistosomiasis epidemic provided that paddy fields, grasslands, less than 2.5 km from the waterway, annual average temperature of 11.5–19.0 °C, annual average rainfall of 1000–1550 mm. IV + GBM had the highest prediction effect (accuracy = 0.878, AUC = 0.902, F1 = 0.920) compared with the other six models. The results of IV + GBM showed that the risk areas are mainly distributed in the coastal regions of the middle and lower reaches of the Yangtze River, the Poyang Lake region, and the Dongting Lake region. High-risk areas are primarily distributed in eastern Changde, western Yueyang, northeastern Yiyang, middle Changsha of Hunan province; southern Jiujiang, northern Nanchang, northeastern Shangrao, eastern Yichun in Jiangxi province; southern Jingzhou, southern Xiantao, middle Wuhan in Hubei province; southern Anqing, northwestern Guichi, eastern Wuhu in Anhui province; middle Meishan, northern Leshan, and the middle of Liangshan in Sichuan province. Conclusions The risk of schistosomiasis transmission in China still exists, with high-risk areas relatively concentrated in the coastal regions of the middle and lower reaches of the Yangtze River. Coupled models of IV and machine learning provide for effective analysis and prediction, forming a scientific basis for evidence-lead surveillance and control. Graphic Abstract

Abstract Background Mosquito-based arbovirus surveillance can serve as an early warning in evaluating the status of mosquito-borne virus prevalence and thus prevent local outbreaks. Although Tengchong County in Yunnan Province—which borders Myanmar—is abundant and diverse in mosquitoes, very few mosquito-based arbovirus investigations have been conducted in the recent decade. Herein, this study aims to evaluate the presence and the diffusion of mosquito-borne pathogens, currently prevalent in this region. Methods We collected 9486 mosquitoes, representing eight species, with Culex tritaeniorhynchus and Anopheles sinensis as the dominant species, during high mosquito activity seasons (July–October) in Tengchong, in 2018. Samples collected from 342 pools were tested using reverse-transcription PCR to determine the species, distribution, and infection rates of virus and parasite, and further analyze their genotypes, phylogenetic relationships, infection rate, and potential pathogenicity. Results Fifteen Japanese encephalitis virus (JEV) strains from Cx. tritaeniorhynchus pools were detected. Seven strains of insect-specific flaviviruses (ISFVs), including two Aedes flavivirus (AeFV) and Yunnan Culex flavivirus strains each, one Culex theileri flavivirus, Yamadai flavivirus (YDFV) and Anopheles-associated flavivirus (AAFV) strains each were detected in Aedes albopictus, Cx. tritaeniorhynchus, Cx. vagans, Cx. pseudovihnui, and An. sinensis pools, respectively. The whole-genome was successfully amplified in one strain of JEV and AeFV each. Phylogenetic analysis using the E gene placed all the newly detected JEV strains into the GI-b genotype. They showed highly nucleotide identities, and were most closely related to the strain detected in Tengchong in 2010. The comparison of the E protein of JEV strains and vaccine-derived strain, showed six amino residue differences. The bias-corrected maximum likelihood estimation values (and 95% confidence interval) for JEV in Cx. tritaeniorhynchus collected in Tengchong in 2018 were 2.4 (1.4–3.9). Conclusions A potential Japanese encephalitis epidemic focus with the abundance of host mosquitoes and high JEV infection rate was observed in Tengchong. In addition, at least five species of ISFVs co-circulate in this area. This study highlights the importance of widespread and sustained mosquito-based arbovirus surveillance in local areas to prevent the transmission of JEV, and other emerging/re-emerging mosquito-borne pathogens. Graphic Abstract

Abstract Background The emergence and spread of multidrug resistance poses a significant risk to malaria control and eradication goals in the world. There has been no indigenous malaria cases reported in China since 2017, and China is approaching national malaria elimination. Therefore, anti-malarial drug resistance surveillance and tracking the emergence and spread of imported drug-resistant malaria cases will be necessary in a post-elimination phase in China. Methods Dried blood spots were obtained from Plasmodium falciparum-infected cases returned from Africa to China between 2012 and 2015, prior to anti-malarial drug treatment. Whole DNA were extracted and known polymorphisms relating to drug resistance of pfcrt, pfmdr1 gene, and the propeller domain of pfk13 were evaluated by nested PCR and sequencing. The haplotypes and prevalence of these three genes were evaluated separately. Chi-squared test and Fisher's exact test were used to evaluate differences among the different sub-regions of Africa. A P value

Abstract Background Flooding is considered to be one of the most important factors contributing to the rebound of Oncomelania hupensis, a small tropical freshwater snail and the only intermediate host of Schistosoma japonicum, in endemic foci. The aim of this study was to assess the risk of intestinal schistosomiasis transmission impacted by flooding in the region around Poyang Lake using multi-source remote sensing images. Methods Normalized Difference Vegetation Index (NDVI) data collected by the Landsat 8 satellite were used as an ecological and geographical suitability indicator of O. hupensis habitats in the Poyang Lake region. The expansion of the water body due to flooding was estimated using dual-polarized threshold calculations based on dual-polarized synthetic aperture radar (SAR). The image data were captured from the Sentinel-1B satellite in May 2020 before the flood and in July 2020 during the flood. A spatial database of the distribution of snail habitats was created using the 2016 snail survey in Jiangxi Province. The potential spread of O. hupensis snails after the flood was predicted by an overlay analysis of the NDVI maps in the flood-affected areas around Poyang Lake. The risk of schistosomiasis transmission was classified based on O. hupensis snail density data and the related NDVI. Results The surface area of Poyang Lake was approximately 2207 km2 in May 2020 before the flood and 4403 km2 in July 2020 during the period of peak flooding; this was estimated to be a 99.5% expansion of the water body due to flooding. After the flood, potential snail habitats were predicted to be concentrated in areas neighboring existing habitats in the marshlands of Poyang Lake. The areas with high risk of schistosomiasis transmission were predicted to be mainly distributed in Yongxiu, Xinjian, Yugan and Poyang (District) along the shores of Poyang Lake. By comparing the predictive results and actual snail distribution, we estimated the predictive accuracy of the model to be 87%, which meant the 87% of actual snail distribution was correctly identified as snail habitats in the model predictions. Conclusions Data on water body expansion due to flooding and environmental factors pertaining to snail breeding may be rapidly extracted from Landsat 8 and Sentinel-1B remote sensing images. Applying multi-source remote sensing data for the timely and effective assessment of potential schistosomiasis transmission risk caused by snail spread during flooding is feasible and will be of great significance for more precision control of schistosomiasis.

Abstract Background Malaria cases have declined significantly along the China-Myanmar border in the past 10 years and this region is going through a process from control to elimination. The aim of this study is to investigate the epidemiology of malaria along the border, will identify challenges in the progress from control to elimination. Methods National reported malaria cases from China and Myanmar, along with the data of 18 Chinese border counties and 23 townships in Myanmar were obtained from a web-based diseases information reporting system in China and the national malaria control program of Myanmar, respectively. Epidemiological data was analyzed, including the number of reported cases, annual parasite index and proportion of vivax infection. Spatial mapping of the annual parasite index (API) at county or township level in 2014 and 2018 was performed by ArcGIS. The relationship of malaria endemicity on both sides of the border was evaluated by regression analysis. Results The number of reported malaria cases and API declined in the border counties or townships. In 2014, 392 malaria cases were reported from 18 Chinese border counties, including 8.4% indigenous cases and 91.6% imported cases, while the highest API (0.11) was occurred in Yingjiang County. There have been no indigenous cases reported since 2017, but 164 imported cases were reported in 2018 and 97.6% were imported from Myanmar. The average API in 2014 in 23 Myanmar townships was significantly greater than that of 18 Chinese counties (P 10. Plasmodium vivax was the predominant species along the border. The number of reported malaria cases and the proportion of vivax infection in the 18 Chinese counties were strongly correlated with those of the 23 Myanmar townships (P

Abstract Background In the final phase of China’s national programme to eliminate malaria by 2020, it is vitally important to monitor the resistance of malaria vectors for developing effective vector control strategies. In 2017 Shanghai declared that it had eliminated malaria; however, the insecticide resistance status of the primary malaria vector Anopheles sinensis remains unknown. Methods We examined the pyrethroid and organophosphate resistance of An. sinensis via a bioassay of two populations from the Chongming District of Shanghai. The voltage-gated sodium channel (VGSC) and acetylcholinesterase 1 (ace-1) genes were partially sequenced to examine the association between resistance phenotype and target site genotype. In addition, the geographical distribution, polymorphism and genotype frequencies of insecticide resistance genes were examined using samples collected during routine mosquito surveillance in 2016 and 2017 from Chongming, Songjiang, Jiading and Qingpu Districts. Results In Chongming District, the An. sinensis population near Dongtan National Nature Reserve showed resistance to pyrethroids, sensitivity to organophosphate, no knockdown resistance (kdr) mutations in the VGSC gene, and a low frequency (1.71%) of the ace-1 119S allele. An An. sinensis population near the Chongming central area (CM-Xinhe population) showed high resistance to pyrethroids and organophosphates and high frequencies of kdr 1014F and 1014C (80.73%) and ace-1 119S (85.98%) alleles. A significant association was detected between the homozygous kdr mutation 1014F/1014F and pyrethroid resistance in the CM-Xinhe population, indicating that the kdr mutation is probably recessive. Eight kdr genotypes with 1014F and 1014C substitutions were detected in the four surveyed districts of Shanghai. TTT and GGC/AGC were the dominant kdr allele and ace-1 genotype, respectively, and were prevalent in most Shanghai An. sinensis populations. Conclusions On the basis of our assessment of insecticide resistance gene mutations in Shanghai, we identified a kdr mutation-free population in Chongming Dongtan. However, high frequencies of target-site mutations of insecticide resistance genes were observed in most areas of Shanghai. Bioassays of An. sinensis populations in the central Chongming District indicated the high insecticide resistance status of An. sinensis populations in Shanghai. We accordingly recommend a restriction on insecticide usage and development of effective integrated pest/vector management interventions to support disease control efforts.

Abstract Background Continuous vector pathogen surveillance is essential for preventing outbreaks of mosquito-borne diseases. Several mosquito species acting as vectors of Japanese encephalitis virus (JEV), dengue virus, Zika virus, malaria parasites and other pathogens are primary mosquito species in Shanghai, China. However, few surveys of human pathogenic arboviruses in mosquitoes in Shanghai have been reported in the last ten years. Therefore, in this study, we evaluated mosquito activity in Shanghai, China during 2016 and tested for the presence of alphaviruses, flaviviruses, orthobunyaviruses and several parasitic pathogens. Results Five pooled samples were JEV-positive [4/255 pools of Culex tritaeniorhynchus and 1/256 pools of Cx. pipiens (s.l.)] based on analysis of the NS5 gene. Alphaviruses, orthobunyaviruses, Plasmodium and filariasis were not found in this study. Phylogenetic and molecular analyses revealed that the JEV strains belonged to genotype I. Moreover, newly detected Shanghai JEV strains were genetically close to previously isolated Shandong strains responsible for transmission during the 2013 Japanese encephalitis (JE) outbreak in Shandong Province, China but were more distantly related to other Shanghai strains detected in the early 2000s. The E proteins of the newly detected Shanghai JEV strains differed from that in the live attenuated vaccine SA14-14-2-derived strain at six amino residues: E130 (Ile→Val), E222 (Ala→Ser), E327 (Ser→Thr), E366 (Arg→Ser/Pro), E393 (Asn→Ser) and E433 (Val→Ile). However, no differences were observed in key amino acid sites related to antigenicity. Minimum JEV infection rates were 1.01 and 0.65 per 1000 Cx. tritaeniorhynchus and Cx. pipiens (s.l.), respectively. Conclusions Five new Shanghai JEV genotype I strains, detected after a ten-year hiatus in local mosquito surveillance, were genetically close to strains involved in the 2013 Shandong JE outbreak. Because JEV is still circulating, vaccination in children should be extensively and continuously promoted. Moreover, JEV mosquito surveillance programmes should document the genotype variation, intensity and distribution of circulating viruses for use in the development and implementation of disease prevention and control strategies.

Abstract Background Pulmonary tuberculosis (PTB,both smear positive and smear negative) is an airborne infectious disease of major public health concern in China and other parts of the world where PTB endemicity is reported. This study aims at identifying PTB spatio-temporal clusters and associated risk factors in Zhaotong prefecture-level city, located in southwest China, where the PTB notification rate was higher than the average rate in the entire country. Methods Space-time scan statistics were carried out using PTB registered data in the nationwide TB online registration system from 2011 to 2015, to identify spatial clusters. PTB patients diagnosed between October 2015 and February 2016 were selected and a structured questionnaire was administered to collect a set of variables that includes socio-economic status, behavioural characteristics, local environmental and biological characteristics. Based on the discovery of detailed town-level spatio-temporal PTB clusters, we divided selected subjects into two groups including the cases that resides within and outside identified clusters. Then, logistic regression analysis was applied comparing the results of variables between the two groups. Results A total of 1508 subjects consented and participated in the survey. Clusters for PTB cases were identified in 38 towns distributed over south-western Zhaotong. Logistic regression analysis showed that history of chronic bronchitis (OR = 3.683, 95% CI: 2.180–6.223), living in an urban area (OR = 5.876, 95% CI: 2.381–14.502) and using coal as the main fuel (OR = 9.356, 95% CI: 5.620–15.576) were independently associated with clustering. While, not smoking (OR = 0.340, 95% CI: 0.137–0.843) is the protection factor of spatial clustering. Conclusions We found PTB specially clustered in south-western Zhaotong. The strong associated factors influencing the PTB spatial cluster including: the history of chronic bronchitis, living in the urban area, smoking and the use of coal as the main fuel for cooking and heating. Therefore, efforts should be made to curtail these associated factors.

Abstract Background Many tick species have great morphological similarity and are thus grouped into species complexes. Molecular methods are therefore useful in the classification and identification of ticks. However, little is known about the genetic diversity of hard ticks in China, especially at the subspecies level. Tengchong is one of the epidemic foci of tick-borne diseases in China, but the tick species inhabiting the local area are still unknown. Methods Eighteen villages in Tengchong County, China, were selected for sampling carried out from September to October 2014. Infesting hard ticks were removed from the body surface of domestic animals and questing ticks were collected from grazing fields. After morphological identification, molecular characteristics of each tick species were analyzed based on both 16S rRNA and cytochrome c oxidase subunit 1 (cox1) gene fragments. Results Six tick species were identified based on morphology: Rhipicephalus microplus, R. haemaphysaloides, Ixodes ovatus, Haemaphysalis longicornis, H. shimoga and H. kitaokai. Phylogenetic analysis using the cox1 gene revealed that R. microplus ticks from the present study belong to clade C. For tick samples of both R. haemaphysaloides and I. ovatus, three phylogenetic groups were recognized, and the intergroup genetic distances exceeded the usual tick species boundaries. Haemaphysalis longicornis ticks were clustered into two separate clades based on the cox1 gene. For ticks from both H. shimoga and H. kitaokai, two phylogenetic groups were recognized based on the phylogenetic analysis of the 16S rRNA gene, and the intergroup genetic distances also exceeded the known boundaries for closely related tick species. Conclusions According to molecular analyses, new species or subspecies closely related to R. haemaphysaloides, I. ovatus, H. shimoga and H. kitaokai probably exist in the China-Myanmar border Tengchong County, or these ticks form species complexes with highly divergent mitochondrial lineages. Morphological comparisons are warranted to further confirm the taxonomic status of these tick species.

Abstract Background The prevalence of schistosomiasis remains a key public health issue in China. Jiangling County in Hubei Province is a typical lake and marshland endemic area. The pattern analysis of schistosomiasis prevalence in Jiangling County is of significant importance for promoting schistosomiasis surveillance and control in the similar endemic areas. Methods The dataset was constructed based on the annual schistosomiasis surveillance as well the socio-economic data in Jiangling County covering the years from 2009 to 2013. A village clustering method modified from the K-mean algorithm was used to identify different types of endemic villages. For these identified village clusters, a matrix-based predictive model was developed by means of exploring the one-step backward temporal correlation inference algorithm aiming to estimate the predicative correlations of schistosomiasis prevalence among different years. Field sampling of faeces from domestic animals, as an indicator of potential schistosomiasis prevalence, was carried out and the results were used to validate the results of proposed models and methods. Results The prevalence of schistosomiasis in Jiangling County declined year by year. The total of 198 endemic villages in Jiangling County can be divided into four clusters with reference to the 5 years’ occurrences of schistosomiasis in human, cattle and snail populations. For each identified village cluster, a predictive matrix was generated to characterize the relationships of schistosomiasis prevalence with the historic infection level as well as their associated impact factors. Furthermore, the results of sampling faeces from the front field agreed with the results of the identified clusters of endemic villages. Conclusion The results of village clusters and the predictive matrix can be regard as the basis to conduct targeted measures for schistosomiasis surveillance and control. Furthermore, the proposed models and methods can be modified to investigate the schistosomiasis prevalence in other regions as well as be used for investigating other parasitic diseases.