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Abstract Background The effects of a diverse spectrum of malaria interventions were evaluated through a deterministic Plasmodium vivax transmission model. This approach aimed to provide theoretical evidence of the performance of these interventions once implemented for achieving malaria elimination. Methods An integrated intervention portfolio, including mass drug administration, insecticide treatment, and untreated bed nets, was analyzed through modeling. Additionally, data-driven calibration was implemented to infer coverages that effectively reproduced historical malaria patterns in China from 1971 to 1983. Results MDA utilizing primaquine emerged as the most effective single intervention, achieving a 70% reduction in malaria incidence when implemented at full coverage. Furthermore, a strategic combination of MDA with primaquine, chloroquine, untreated bed nets, and seasonal insecticide treatments effectively eradicated malaria, attaining elimination at a coverage level of 70%. It was conclusively demonstrated that an integrated approach combining MDA and vector control measures is essential for the successful elimination of malaria. Conclusion High coverage of mass drug administration with primaquine and chloroquine before transmission was the key driver of the malaria decline in China from 1971 to 1983. The best-fit intervention coverage combinations derived from calibration are provided as a reference for malaria control in other countries.

Abstract Background This study investigates the impact of the COVID-19 pandemic on the prevalence, management, and control of the neglected tropical diseases (NTDs) highlighting the current or prospective impact of COVID-19 on research and development funding for, and execution of, NTD programmes. This review was conducted to determine if, and how, NTDs were affected by COVID-19, and whether those effects will delay the elimination goals of the Sustainable Development goals. Methods Using open-source available data from policy and documentation from official websites of the relevant stakeholders including but not limited to World Health Organization (WHO) documents and policies, government foreign aid documents, and the Policy Cures G-Finder reports, this scoping review explored ongoing challenges to supporting research and development (R&D) for the NTDs and in maintaining NTD control programs; examined the constraints posed for NTD management by the pandemic from disruptions to healthcare services, reduction of finance and explored the potential long-term implications and consequences for those poorer, neglected populations in low and middle income-countries (LMICs). This was done by a scoping review literature search, publications were subject to an initial practical screening step to ensure the most relevant publications were selected for full screening, with the focus on scoping the designated topic of the impact of COVID-19 on NTDs. We further undertook an evaluation of the socio-economic factors exacerbating the impact of COVID-19 on NTD burden. Results Multiple disruptions and setbacks, likely to affect NTD programmes and progress towards their elimination targets were identified in this study. R&D funding for the NTDs and AIDs and TB has declined since the funding high point of 2019, and for malaria since the high point of 2018. Significant changes in allocation of R&D funding within the NTDs are observed post pandemic, likely because of prioritization among donors. Diseases for which the least R&D investment was reported in place, prior to the pandemic (mycetoma, taeniasis/cysticercosis, trachoma and Buruli ulcer) have been particularly impacted post pandemic. We identified specific NTDs including schistosomiasis, leprosy, and rabies that have been affected by the COVID-19 pandemic and disruptions caused to on ongoing NTD control and elimination programs. Pandemic restrictions disrupted essential medical supply manufacturing and distribution impacting immunization programs and hindered efforts to control the spread of infectious diseases. NTD programmes have experienced numerous setbacks including delays in mass drug administration programs (e.g. for schistosomiasis), cancelled or delayed vaccination programs (e.g. for rabies) and closure of testing facilities has resulted in reduced diagnosis, treatment, and disease elimination for all NTDs. Lockdowns and clinic closures causing disruption to essential healthcare services restricted NTD surveillance and treatment programs. Community fears around contracting COVID-19 exacerbated the constraints to service delivery. Disparities in global vaccine distribution have widened with LMICs facing limited access to vaccines and disruption to immunization programs. Finally, the pandemic has led to increased poverty with poor and marginalized communities, impacting nutrition, healthcare access and education all of which have long term implications for NTD management and control. Conclusions The COVID-19 pandemic profoundly impacted global health research and global health equity. Attention and funding were diverted from all sectors, significantly affecting research and development efforts set out in the World Health Organization’s NTD elimination Roadmaps. Ongoing changes to funding, economic crises, logistics and supply chain disruptions as well as deepening poverty has put a strain on already weak healthcare systems and exacerbated LMIC healthcare challenges. In particular, the delays and constraints to NTD management and elimination programs will have long-reaching consequences highlighting the need for global cooperation and renewed investment to put the NTD roadmap back on track. Targets and milestones are unlikely to be met without significant investment for recovery, in place. Graphical Abstract

Abstract Background Poverty contributes to the transmission of schistosomiasis via multiple pathways, with the insufficiency of appropriate interventions being a crucial factor. The aim of this article is to provide more economical and feasible intervention measures for endemic areas with varying levels of poverty. Methods We collected and analyzed the prevalence patterns along with the cost of control measures in 11 counties over the last 20 years in China. Seven machine learning models, including XGBoost, support vector machine, generalized linear model, regression tree, random forest, gradient boosting machine and neural network, were used for developing model and calculate marginal benefits. Results The XGBoost model had the highest prediction accuracy with an R 2 of 0.7308. Results showed that risk surveillance, snail control with molluscicides and treatment were the most effective interventions in controlling schistosomiasis prevalence. The best combination of interventions was interlacing seven interventions, including risk surveillance, treatment, toilet construction, health education, snail control with molluscicides, cattle slaughter and animal chemotherapy. The marginal benefit of risk surveillance is the most effective intervention among nine interventions, which was influenced by the prevalence of schistosomiasis and cost. Conclusions In the elimination phase of the national schistosomiasis program, emphasizing risk surveillance holds significant importance in terms of cost-saving. Graphical Abstract

Abstract Background Rabies continues to be a serious threat to global public health endangering people’s health and public health safety. In the People’s Republic of China, multi-sectoral and comprehensive prevention and control strategies have aimed to extensively curb human rabies transmission. Here, we examine the current state of rabies infection in China, explore strategic interventions put in place in response to WHO’s ambition of “Zero rabies deaths by 2030” and critically assess the constraints and feasibility of dog-mediated rabies elimination in China. Methods This study analyzed and evaluated the process towards dog-mediated rabies elimination in China from five perspectives: namely, human, dog, policy, challenge, and prospects. Evidence-based data on progress of dog-mediated rabies elimination in China was derived from a number of sources; a literature search was undertaken using PubMed, Web of Science and CNKI databases, distribution data for human rabies cases as derived from the Data-center of the China Public Health Science and policy and document data were obtained from official websites of the relevant China ministries and commissions. Results The incidence of human rabies cases in China have shown a downward trend year-on-year since 2007. Implementation of a government-led, multi-sectoral “One Health” approach to combating rabies has driven down the total number of rabies deaths nationwide to around 200 in 2020. The number of provincial-level administrative divisions (PLADs) reporting human cases of rabies has also decreased to 21 in 2020, 13 of which reported less than 10 cases. Furthermore, the number of outpatient visits seeking rabies post-exposure prophylaxis has risen dramatically over the past two decades, with demand being 15 times higher than it was initially. There remain however, significant gaps in rabies elimination outcomes across the different regions of China. To date the target of achieving a canine rabies vaccination rate of > 75% has not been met. The challenges of rabies immunization of dogs and dog management in underdeveloped cities and rural areas need to be addressed together with more effective animal surveillance and rabies risk from and too wildlife and livestock. Conclusions The Chinese government-led, multi-sectoral “One Health” approach to combating rabies and has made significant progress over the past decade. Development and adoption of more cost-effective One Health strategies can achieve more nationally beneficial rabies elimination outcomes. The ambitious target of “Zero rabies deaths by 2030” can be met through establishment of long-lasting herd immunity in dogs by means of dog mass vaccination campaigns, dog population management, epidemiological surveillance and the application of large-scale oral rabies vaccine to eliminate rabies in wild animals coupled with deployment of cost-effective human post-exposure prophylaxis, and community education. Graphical Abstract

Abstract Background Substantial research is underway to develop next-generation interventions that address current malaria control challenges. As there is limited testing in their early development, it is difficult to predefine intervention properties such as efficacy that achieve target health goals, and therefore challenging to prioritize selection of novel candidate interventions. Here, we present a quantitative approach to guide intervention development using mathematical models of malaria dynamics coupled with machine learning. Our analysis identifies requirements of efficacy, coverage, and duration of effect for five novel malaria interventions to achieve targeted reductions in malaria prevalence. Methods A mathematical model of malaria transmission dynamics is used to simulate deployment and predict potential impact of new malaria interventions by considering operational, health-system, population, and disease characteristics. Our method relies on consultation with product development stakeholders to define the putative space of novel intervention specifications. We couple the disease model with machine learning to search this multi-dimensional space and efficiently identify optimal intervention properties that achieve specified health goals. Results We apply our approach to five malaria interventions under development. Aiming for malaria prevalence reduction, we identify and quantify key determinants of intervention impact along with their minimal properties required to achieve the desired health goals. While coverage is generally identified as the largest driver of impact, higher efficacy, longer protection duration or multiple deployments per year are needed to increase prevalence reduction. We show that interventions on multiple parasite or vector targets, as well as combinations the new interventions with drug treatment, lead to significant burden reductions and lower efficacy or duration requirements. Conclusions Our approach uses disease dynamic models and machine learning to support decision-making and resource investment, facilitating development of new malaria interventions. By evaluating the intervention capabilities in relation to the targeted health goal, our analysis allows prioritization of interventions and of their specifications from an early stage in development, and subsequent investments to be channeled cost-effectively towards impact maximization. This study highlights the role of mathematical models to support intervention development. Although we focus on five malaria interventions, the analysis is generalizable to other new malaria interventions. Graphical abstract

Abstract Background With the recent certification by World Health Organization that the People’s Republic of China is malaria-free, it is timely to consider how elimination of malaria was completed in People’s Republic of China over the last 7 decades. Of the four widespread species of human malaria, Plasmodium vivax was the last to be eliminated by the national program of China. Understanding the incubation periods and relapses patterns of P. vivax through historical data from China is relevant for planning disease elimination in other malaria-endemic countries, with residual P. vivax malaria. Methods We collated data from both published and unpublished malaria parasite inoculation experiments conducted between 1979 and 1988 with parasites from different regions of the People’s Republic of China. The studies had at least two years of follow-up. We categorized P. vivax incubation patterns via cluster analysis and investigated relapse studies by adapting a published within-host relapse model for P. vivax temperate phenotypes. Each model was fitted using the expectation-maximization (EM) algorithm initialized by hierarchical model-based agglomerative clustering. Results P. vivax parasites from the seven studies of five southern and central provinces in the People’s Republic of China covering geographies ranging from the south temperate to north tropical zones. The parasites belonged to two distinct phenotypes: short- (10–19 days) or long-incubation (228–371 days). The larger the sporozoite inoculation, the more likely short incubation periods were observed, and with more subsequent relapses (Spearman’s rank correlation between the number of inoculated sporozoites and the number of relapses of 0.51, p-value = 0.0043). The median of the posterior distribution for the duration of the first relapse interval after primary infection was 168.5 days (2.5% quantile: 89.7; 97.5% quantile: 227.69 days). The predicted survival proportions from the within-host model fit well to the original relapse data. The within-host model also captures the hypnozoite activation rates and relapse frequencies, which consequently influences the transmission possibility of P. vivax. Conclusions Through a within-host model, we demonstrate the importance of clearance of hypnozoites. A strategy of two rounds of radical hypnozoite clearance via mass drug administration (MDA) deployed during transmission (summer and autumn) and non-transmission (late spring) seasons had a pronounced effect on outbreaks during the malaria epidemics in China. This understanding can inform malaria control strategies in other endemic countries with similar settings.

Abstract “One Health” has increasingly been accepted as a contemporary theoretical framework to target complex health issues. Historical documents and original records from China’s history can provide important perspectives on how Chinese traditional philosophy dealt with the harmonious relationship between humans, animals, plants, and the environment in the millennial years of civilization. In this commentary, we also presented the active engagements of Chinese scholars in promoting “One Health” and “One Health Action” in China to build a community with a shared future for mankind. One Health Impact Statement One Health Impact StatementHistorical documents and original records from China’s history can help us learn how Chinese traditional philosophy has dealt with the harmonious relationship between human, animal, plants and the environment in the thousands of years of civilization. In this commentary, we make an in-depth elaboration on the Chinese meaning of the concept of “One Health” which has been translated into “全健康” in Chinese, meaning as holistic health, based on the history and culture in China, so as to promote “One Health” to play a greater role in building a “community with a shared future for mankind”. China should take a global perspective, pivoting on the “Healthy China” strategy, based on the three interaction fields of “human, animal and environment”, and continuously improve the capabilities in the fields of public health and “One Health” to face the emerging challenge.

Abstract Background In December 2019, an outbreak of coronavirus disease (later named as COVID-19) was identified in Wuhan, China and, later on, detected in other parts of China. Our aim is to evaluate the effectiveness of the evolution of interventions and self-protection measures, estimate the risk of partial lifting control measures and predict the epidemic trend of the virus in the mainland of China excluding Hubei province based on the published data and a novel mathematical model. Methods A novel COVID-19 transmission dynamic model incorporating the intervention measures implemented in China is proposed. COVID-19 daily data of the mainland of China excluding Hubei province, including the cumulative confirmed cases, the cumulative deaths, newly confirmed cases and the cumulative recovered cases between 20 January and 3 March 2020, were archived from the National Health Commission of China (NHCC). We parameterize the model by using the Markov Chain Monte Carlo (MCMC) method and estimate the control reproduction number (R c ), as well as the effective daily reproduction ratio- R e (t), of the disease transmission in the mainland of China excluding Hubei province. Results The estimation outcomes indicate that R c is 3.36 (95% CI: 3.20–3.64) and R e (t) has dropped below 1 since 31 January 2020, which implies that the containment strategies implemented by the Chinese government in the mainland of China are indeed effective and magnificently suppressed COVID-19 transmission. Moreover, our results show that relieving personal protection too early may lead to a prolonged disease transmission period and more people would be infected, and may even cause a second wave of epidemic or outbreaks. By calculating the effective reproduction ratio, we prove that the contact rate should be kept at least less than 30% of the normal level by April, 2020. Conclusions To ensure the pandemic ending rapidly, it is necessary to maintain the current integrated restrict interventions and self-protection measures, including travel restriction, quarantine of entry, contact tracing followed by quarantine and isolation and reduction of contact, like wearing masks, keeping social distance, etc. People should be fully aware of the real-time epidemic situation and keep sufficient personal protection until April. If all the above conditions are met, the outbreak is expected to be ended by April in the mainland of China apart from Hubei province.

Abstract Background Paragonimiasis, caused by helminths of the genus Paragonimus spp., is a neglected tropical disease. Human suffering from paragonimiasis is often misunderstood and its quantification by the disability weight of the disability-adjusted life years largely varies in different global burden of disease (GBD) estimates. This paper is to systematically review clinical paragonimiasis cases and requantify the disability weight of human paragonimiasis. Methods A systematic analysis was conducted using articles from the following databases: PubMed, Institute for Scientific Information Web of Science, China National Knowledge Infrastructure, the Chinese scientific journal databases Wanfang Data and CQVIP, Africa Journal Online, and the System for Information on Grey Literature in Europe. Search terms were the combination of “paragonim*” with “clinical” or “infection”. Only articles fulfilling the following conditions were recruited for this study: the occurrence of clinical signs and symptoms of paragonimiasis in human beings were reported; diagnosis was confirmed; no comorbidities were reported; the reviewed clinical cases or epidemiological findings were not already included in any other articles. The information and frequencies of paragonimiasis outcomes from included articles using predefined data fields were extracted two times by two separate individuals. Outcome disability weights were selected mainly from the GBD 2004 and GBD 2013 datasets. Frequencies and disability weights of paragonimiasis outcomes were modelled into a decision tree using the additive approach and multiplicative approach, respectively. Monte Carlo simulations were run 5000 times for an uncertainty analysis. Results The disability weight estimates of paragonimiasis were simulated with 5302 clinical cases from 80 general articles. The overall disability weight was estimated at 0.1927 (median 0.1956) with a 95% uncertainty interval (UI) of 0.1632–0.2378 using the additive approach, and 0.1791 (median 0.1816) with a 95% UI of 0.1530–0.2182 using the multiplicative approach. The simulated disability weights of Paragonimus westermani cases were higher than that of P. skrjabini cases. Lung outcomes and headache were the top two contributors to disability weight for both species. Conclusions The use of paragonimiasis disability weight needs to be reconsidered with regard to availability of morbidity data and species variation. Calculating the disease burden of paragonimiasis requires further modification and thus has considerable implications for public health prioritization in research, monitoring, and control.

Abstract Background In China since the first human infection of avian influenza A (H7N9) virus was identified in 2013, it has caused serious public health concerns due to its wide spread and high mortality rate. Evidence shows that bird migration plays an essential role in global spread of avian influenza viruses. Accordingly, in this paper, we aim to identify key bird species and geographical hotspots that are relevant to the transmission of avian influenza A (H7N9) virus in China. Methods We first conducted phylogenetic analysis on 626 viral sequences of avian influenza A (H7N9) virus isolated in chicken, which were collected from the Global Initiative on Sharing All Influenza Data (GISAID), to reveal geographical spread and molecular evolution of the virus in China. Then, we adopted the cross correlation function (CCF) to explore the relationship between the identified influenza A (H7N9) cases and the spatiotemporal distribution of migratory birds. Here, the spatiotemporal distribution of bird species was generated based on bird observation data collected from China Bird Reports, which consists of 157 272 observation records about 1145 bird species. Finally, we employed a kernel density estimator to identify geographical hotspots of bird habitat/stopover that are relevant to the influenza A (H7N9) infections. Results Phylogenetic analysis reveals the evolutionary and geographical patterns of influenza A (H7N9) infections, where cases in the same or nearby municipality/provinces are clustered together with small evolutionary differences. Moreover, three epidemic waves in chicken along the East Asian–Australasian flyway in China are distinguished from the phylogenetic tree. The CCF analysis identifies possible migratory bird species that are relevant to the influenza A(H7N9) infections in Shanghai, Jiangsu, Zhejiang, Fujian, Jiangxi, and Guangdong in China, where the six municipality/provinces account for 91.2% of the total number of isolated H7N9 cases in chicken in GISAID. Based on the spatial distribution of identified bird species, geographical hotspots are further estimated and illustrated within these typical municipality/provinces. Conclusions In this paper, we have identified key bird species and geographical hotspots that are relevant to the spread of influenza A (H7N9) virus. The results and findings could provide sentinel signal and evidence for active surveillance, as well as strategic control of influenza A (H7N9) transmission in China.

Abstract Background In order to achieve the goal of malaria elimination, the Chinese government launched the National Malaria Elimination Programme in 2010. However, as a result of increasing cross-border population movements, the risk of imported malaria cases still exists at the border areas of China, resulting in a potential threat of local transmission. The focus of this paper is to assess the Plasmodium vivax incidences in Tengchong, Yunnan Province, at the border areas of China and Myanmar. Methods Time series of P. vivax incidences in Tengchong from 2006 to 2010 are collected from the web-based China Information System for Disease Control and Prevention, which are further separated into time series of imported and local cases. First, the seasonal and trend decomposition are performed on time series of imported cases using Loess method. Then, the impact of climatic factors on the local transmission of P. vivax is assessed using both linear regression models (LRM) and generalized additive models (GAM). Specifically, the notion of vectorial capacity (VCAP) is used to estimate the transmission potential of P. vivax at different locations, which is calculated based on temperature and rainfall collected from China Meteorological Administration. Results Comparing with Ruili County, the seasonal pattern of imported cases in Tengchong is different: Tengchong has only one peak, while Ruili has two peaks during each year. This may be due to the different cross-border behaviors of peoples in two locations. The vectorial capacity together with the imported cases and the average humidity, can well explain the local incidences of P. vivax through both LRM and GAM methods. Moreover, the maximum daily temperature is verified to be more suitable to calculate VCAP than the minimal and average temperature in Tengchong County. Conclusion To achieve malaria elimination in China, the assessment results in this paper will provide further guidance in active surveillance and control of malaria at the border areas of China and Myanmar.

Based on an ensemble of global circulation models (GCMs), four representative concentration pathways (RCPs) and several ongoing and planned Coupled Model Intercomparison Projects (CMIPs), the Intergovernmental Panel on Climate Change (IPCC) predicts that global, average temperatures will increase by at least 1.5 °C in the near future and more by the end of the century if greenhouse gases (GHGs) emissions are not genuinely tempered. While the RCPs are indicative of various amounts of GHGs in the atmosphere the CMIPs are designed to improve the workings of the GCMs. We chose RCP4.5 which represented a medium GHG emission increase and CMIP5, the most recently completed CMIP phase. Combining this meteorological model with a biological counterpart model accounted for replication and survival of the snail intermediate host as well as maturation of the parasite stage inside the snail at different ambient temperatures. The potential geographical distribution of the three main schistosome species: Schistosoma japonicum, S. mansoni and S. haematobium was investigated with reference to their different transmission capabilities at the monthly mean temperature, the maximum temperature of the warmest month(s) and the minimum temperature of the coldest month(s). The set of six maps representing the predicted situations in 2021⁻2050 and 2071⁻2100 for each species mainly showed increased transmission areas for all three species but they also left room for potential shrinkages in certain areas.

A basic framework for the rapid assessment of the risk for schistosomiasis was developed by combining spatial data from Google Earth® with a geographical information system (GIS) package, bundling the modules together with an Internet connection into a WebGIS platform. It operates through functions such as “search“, “evaluation“, “risk analysis“ and “prediction“ and is primarily aimed to be a dynamic, early-warning system (EWS) providing user-friendly, evidencebased, near real-time awareness of the status of an important endemic disease. It contributes to rapid information-sharing at all levels of decision-making, facilitating “point-of-care“ response, i.e. treatment provided at newly discovered transmission sites. The experience using the platform is encouraging and it has the potential to improve support systems and strengthen schistosomiasis control activities, in particular with regard to surveillance and EWS. It can quickly and intuitively locate early, high-risk areas, retrieve all important data needed as well as provide detailed, up-to-date information on the performance of the control programme. This WebGIS, the first of its kind in the People’s Republic of China, is not only applicable for schistosomiasis but can easily be adapted for improving control of any endemic disease in any geographical area.

The purpose of this study was to deepen our understanding of Plasmodium vivax malaria transmission patterns in the People’s Republic of China (P.R. China). An integrated modeling approach was employed, combining biological and statistical models. A Delphi approach was used to determine environmental factors that govern malaria transmission. Key factors identified (i.e. temperature, rainfall and relative humidity) were utilized for subsequent mapping and modeling purposes. Yearly growing degree days, annual rainfall and effective yearly relative humidity were extracted from a 15-year time series (1981-1995) of daily environmental data readily available for 676 locations in P.R. China. A suite of eight multinomial regression models, ranging from the null model to a fully saturated one were constructed. Two different information criteria were used for model ranking, namely the corrected Akaike’s information criterion and the Bayesian information criterion. Mapping was based on model output data, facilitated by using ArcGIS software. Temperature was found to be the most important environmental factor, followed by rainfall and relative humidity in the Delphi evaluation. However, relative humidity was found to be more important than rainfall and temperature in the ranking list according to the three single environmental factor regression models. We conclude that the distribution of the mosquito vector is mainly related to relative humidity, which thus determines the extent of malaria transmission. However, in regions with relative humidity >60%, temperature is the major driver of malaria transmission intensity. By integrating biology-driven models with statistical regression models, reliable risk maps indicating the distribution of transmission and the intensity can be produced. In a next step, we propose to integrate social and health systems factors into our modeling approach, which should provide a platform for rigorous surveillance and monitoring progress towards P. vivax malaria elimination in P.R. China.

With the aim of exploring the usefulness of spatial analysis in the formulation of a strategy for schistosomiasis japonica control in different environmental settings, a population-based database was established in Dangtu county, China. This database, containing the human prevalence of schistosomiasis at the village level from 2001 to 2004, was analyzed by directional trend analysis supported with ArcGIS 9.0 to select the optimum predictive approach. Based on the approach selected, different strata of prevalence were classified and the spatial distribution of human infection with Schistosoma japonicum was estimated. The second-order ordinary kriging approach of spatial analysis was found to be optimal for prediction of human prevalence of S. japonicum infection. The mean prediction error was close to 0 and the root-mean-square standardised error was close to 1. Starting with the different environmental settings for each stratum of transmission, four areas were classified according to human prevalence, and different strategies to control transmission of schistosomiasis were put forward. We conclude that the approach to use spatial analysis as a tool to predict the spatial distribution of human prevalence of S. japonicum infection improves the formulation of strategies for schistosomiasis control in different environmental settings at the county level.

BACKGROUND:Schistosomiasis japonica is a parasitic disease that remains endemic in seven provinces in the People's Republic of China (P.R. China). One of the most important measures in the process of schistosomiasis elimination in P.R. China is control of Oncomelania hupensis, the unique intermediate host snail of Schistosoma japonicum. Compared with plains/swamp and lake regions, the hilly/mountainous regions of schistosomiasis endemic areas are more complicated, which makes the snail survey difficult to conduct precisely and efficiently. There is a pressing call to identify the snail habitats of mountainous regions in an efficient and cost-effective manner. METHODS:Twelve out of 56 administrative villages distributed with O. hupensis in Eryuan, Yunnan Province, were randomly selected to set up the ecological model. Thirty out of the rest of 78 villages (villages selected for building model were excluded from the villages for validation) in Eryuan and 30 out of 89 villages in Midu, Yunnan Province were selected via a chessboard method for model validation, respectively. Nine-year-average Normalized Difference Vegetation Index (NDVI) and Land Surface Temperature (LST) as well as Digital Elevation Model (DEM) covering Eryuan and Midu were extracted from MODIS and ASTER satellite images, respectively. Slope, elevation and the distance from every village to its nearest stream were derived from DEM. Suitable survival environment conditions for snails were defined by comparing historical snail presence data and remote sensing derived images. According to the suitable conditions for snails, environment factors, i.e. NDVI, LST, elevation, slope and the distance from every village to its nearest stream, were integrated into an ecological niche model to predict O. hupensis potential habitats in Eryuan and Midu. The evaluation of the model was assessed by comparing the model prediction and field investigation. Then, the consistency rate of model validation was calculated in Eryuan and Midu Counties, respectively. RESULTS:The final ecological niche model for potential O. hupensis habitats prediction comprised the following environmental factors, namely: NDVI (≥ 0.446), LST (≥ 22.70°C), elevation (≤ 2,300 m), slope (≤ 11°) and the distance to nearest stream (≤ 1,000 m). The potential O. hupensis habitats in Eryuan distributed in the Lancang River basin and O. hupensis in Midu shows a trend of clustering in the north and spotty distribution in the south. The consistency rates of the ecological niche model in Eryuan and Midu were 76.67% and 83.33%, respectively. CONCLUSIONS:The ecological niche model integrated with NDVI, LST, elevation, slope and distance from every village to its nearest stream adequately predicted the snail habitats in the mountainous regions.

Political and health sector reforms, along with demographic, environmental and socio-economic transformations in the face of global warming, could cause the re-emergence of schistosomiasis in areas where transmission has been successfully interrupted and its emergence in previously non-endemic areas in China. In the present study, we used geographic information systems and remote sensing techniques to predict potential habitats of Oncomelania hupensis, the intermediate host snail of Schistosoma japonicum. Focussing on the Hongze, Baima and Gaoyou lakes in Jiangsu province in eastern China, we developed a model using the normalized difference vegetation index, a tasseled-cap transformed wetness index, and flooding areas to predict snail habitats at a small scale. Data were extracted from two Landsat images, one taken during a typical dry year and the other obtained three years later during a flooding event. An area of approximately 163.6 km2 was predicted as potential O. hupensis habitats around the three lakes, which accounts for 4.3% of the estimated snail habitats in China. In turn, these predicted snail habitats are risk areas for transmission of schistosomiasis, and hence illustrate the scale of the possible impact of climate change and other ecological transformations. The generated risk map can be used by health policy makers to guide mitigation policies targetting the possible spread of O. hupensis, and with the aim of containing the transmission of S. japonicum.

BackgroundThe transmission networks of Plasmodium vivax characterize how the parasite transmits from one location to another, which are informative and insightful for public health policy makers to accurately predict the patterns of its geographical spread. However, such networks are not apparent from surveillance data because P. vivax transmission can be affected by many factors, such as the biological characteristics of mosquitoes and the mobility of human beings. Here, we pay special attention to the problem of how to infer the underlying transmission networks of P. vivax based on available tempo-spatial patterns of reported cases.MethodologyWe first define a spatial transmission model, which involves representing both the heterogeneous transmission potential of P. vivax at individual locations and the mobility of infected populations among different locations. Based on the proposed transmission model, we further introduce a recurrent neural network model to infer the transmission networks from surveillance data. Specifically, in this model, we take into account multiple real-world factors, including the length of P. vivax incubation period, the impact of malaria control at different locations, and the total number of imported cases.Principal findingsWe implement our proposed models by focusing on the P. vivax transmission among 62 towns in Yunnan province, People's Republic China, which have been experiencing high malaria transmission in the past years. By conducting scenario analysis with respect to different numbers of imported cases, we can (i) infer the underlying P. vivax transmission networks, (ii) estimate the number of imported cases for each individual town, and (iii) quantify the roles of individual towns in the geographical spread of P. vivax.ConclusionThe demonstrated models have presented a general means for inferring the underlying transmission networks from surveillance data. The inferred networks will offer new insights into how to improve the predictability of P. vivax transmission.

BACKGROUND: The transmission of schistosomiasis japonica in a local setting is still poorly understood in the lake regions of the People's Republic of China (P. R. China), and its transmission patterns are closely related to human, social and economic factors. METHODOLOGY/PRINCIPAL FINDINGS: We aimed to apply the integrated approach of artificial neural network (ANN) and logistic regression model in assessment of transmission risks of Schistosoma japonicum with epidemiological data collected from 2339 villagers from 1247 households in six villages of Jiangling County, P.R. China. By using the back-propagation (BP) of the ANN model, 16 factors out of 27 factors were screened, and the top five factors ranked by the absolute value of mean impact value (MIV) were mainly related to human behavior, i.e. integration of water contact history and infection history, family with past infection, history of water contact, infection history, and infection times. The top five factors screened by the logistic regression model were mainly related to the social economics, i.e. village level, economic conditions of family, age group, education level, and infection times. The risk of human infection with S. japonicum is higher in the population who are at age 15 or younger, or with lower education, or with the higher infection rate of the village, or with poor family, and in the population with more than one time to be infected. CONCLUSION/SIGNIFICANCE: Both BP artificial neural network and logistic regression model established in a small scale suggested that individual behavior and socioeconomic status are the most important risk factors in the transmission of schistosomiasis japonica. It was reviewed that the young population (≤15) in higher-risk areas was the main target to be intervened for the disease transmission control.

In this paper, the Disease Reference Group on Helminth Infections (DRG4), established in 2009 by the Special Programme for Research and Training in Tropical Diseases (TDR), with the mandate to review helminthiases research and identify research priorities and gaps, focuses on the environmental, social, behavioural, and political determinants of human helminth infections and outlines a research and development agenda for the socioeconomic and health systems research required for the development of sustainable control programmes. Using Stockols' social-ecological approach, we describe the role of various social (poverty, policy, stigma, culture, and migration) and environmental determinants (the home environment, water resources development, and climate change) in the perpetuation of helminthic diseases, as well as their impact as contextual factors on health promotion interventions through both the regular and community-based health systems. We examine these interactions in regard to community participation, intersectoral collaboration, gender, and possibilities for upscaling helminthic disease control and elimination programmes within the context of integrated and interdisciplinary approaches. The research agenda summarises major gaps that need to be addressed.

Diagnostic tools appropriate for undertaking interventions to control helminth infections are key to their success. Many diagnostic tests for helminth infection have unsatisfactory performance characteristics and are not well suited for use in the parasite control programmes that are being increasingly implemented. Although the application of modern laboratory research techniques to improve diagnostics for helminth infection has resulted in some technical advances, uptake has not been uniform. Frequently, pilot or proof of concept studies of promising diagnostic technologies have not been followed by much needed product development, and in many settings diagnosis continues to rely on insensitive and unsatisfactory parasitological or serodiagnostic techniques. In contrast, PCR-based xenomonitoring of arthropod vectors, and use of parasite recombinant proteins as reagents for serodiagnostic tests, have resulted in critical advances in the control of specific helminth parasites. The Disease Reference Group on Helminths Infections (DRG4), established in 2009 by the Special Programme for Research and Training in Tropical Diseases (TDR) was given the mandate to review helminthiases research and identify research priorities and gaps. In this review, the diagnostic technologies relevant to control of helminth infections, either available or in development, are reviewed. Critical gaps are identified and opportunities to improve needed technologies are discussed.

Mathematical modelling of helminth infections has the potential to inform policy and guide research for the control and elimination of human helminthiases. However, this potential, unlike in other parasitic and infectious diseases, has yet to be realised. To place contemporary efforts in a historical context, a summary of the development of mathematical models for helminthiases is presented. These efforts are discussed according to the role that models can play in furthering our understanding of parasite population biology and transmission dynamics, and the effect on such dynamics of control interventions, as well as in enabling estimation of directly unobservable parameters, exploration of transmission breakpoints, and investigation of evolutionary outcomes of control. The Disease Reference Group on Helminth Infections (DRG4), established in 2009 by the Special Programme for Research and Training in Tropical Diseases (TDR), was given the mandate to review helminthiases research and identify research priorities and gaps. A research and development agenda for helminthiasis modelling is proposed based on identified gaps that need to be addressed for models to become useful decision tools that can support research and control operations effectively. This agenda includes the use of models to estimate the impact of large-scale interventions on infection incidence; the design of sampling protocols for the monitoring and evaluation of integrated control programmes; the modelling of co-infections; the investigation of the dynamical relationship between infection and morbidity indicators; the improvement of analytical methods for the quantification of anthelmintic efficacy and resistance; the determination of programme endpoints; the linking of dynamical helminth models with helminth geostatistical mapping; and the investigation of the impact of climate change on human helminthiases. It is concluded that modelling should be embedded in helminth research, and in the planning, evaluation, and surveillance of interventions from the outset. Modellers should be essential members of interdisciplinary teams, propitiating a continuous dialogue with end users and stakeholders to reflect public health needs in the terrain, discuss the scope and limitations of models, and update biological assumptions and model outputs regularly. It is highlighted that to reach these goals, a collaborative framework must be developed for the collation, annotation, and sharing of databases from large-scale anthelmintic control programmes, and that helminth modellers should join efforts to tackle key questions in helminth epidemiology and control through the sharing of such databases, and by using diverse, yet complementary, modelling approaches.

Human helminthiases are of considerable public health importance in sub-Saharan Africa, Asia, and Latin America. The acknowledgement of the disease burden due to helminth infections, the availability of donated or affordable drugs that are mostly safe and moderately efficacious, and the implementation of viable mass drug administration (MDA) interventions have prompted the establishment of various large-scale control and elimination programmes. These programmes have benefited from improved epidemiological mapping of the infections, better understanding of the scope and limitations of currently available diagnostics and of the relationship between infection and morbidity, feasibility of community-directed or school-based interventions, and advances in the design of monitoring and evaluation (M&E) protocols. Considerable success has been achieved in reducing morbidity or suppressing transmission in a number of settings, whilst challenges remain in many others. Some of the obstacles include the lack of diagnostic tools appropriate to the changing requirements of ongoing interventions and elimination settings; the reliance on a handful of drugs about which not enough is known regarding modes of action, modes of resistance, and optimal dosage singly or in combination; the difficulties in sustaining adequate coverage and compliance in prolonged and/or integrated programmes; an incomplete understanding of the social, behavioural, and environmental determinants of infection; and last, but not least, very little investment in research and development (R&D). The Disease Reference Group on Helminth Infections (DRG4), established in 2009 by the Special Programme for Research and Training in Tropical Diseases (TDR), was given the mandate to undertake a comprehensive review of recent advances in helminthiases research, identify research gaps, and rank priorities for an R&D agenda for the control and elimination of these infections. This review presents the processes undertaken to identify and rank ten top research priorities; discusses the implications of realising these priorities in terms of their potential for improving global health and achieving the Millennium Development Goals (MDGs); outlines salient research funding needs; and introduces the series of reviews that follow in this PLoS Neglected Tropical Diseases collection, "A Research Agenda for Helminth Diseases of Humans."

Recognising the burden helminth infections impose on human populations, and particularly the poor, major intervention programmes have been launched to control onchocerciasis, lymphatic filariasis, soil-transmitted helminthiases, schistosomiasis, and cysticercosis. The Disease Reference Group on Helminth Infections (DRG4), established in 2009 by the Special Programme for Research and Training in Tropical Diseases (TDR), was given the mandate to review helminthiases research and identify research priorities and gaps. A summary of current helminth control initiatives is presented and available tools are described. Most of these programmes are highly dependent on mass drug administration (MDA) of anthelmintic drugs (donated or available at low cost) and require annual or biannual treatment of large numbers of at-risk populations, over prolonged periods of time. The continuation of prolonged MDA with a limited number of anthelmintics greatly increases the probability that drug resistance will develop, which would raise serious problems for continuation of control and the achievement of elimination. Most initiatives have focussed on a single type of helminth infection, but recognition of co-endemicity and polyparasitism is leading to more integration of control. An understanding of the implications of control integration for implementation, treatment coverage, combination of pharmaceuticals, and monitoring is needed. To achieve the goals of morbidity reduction or elimination of infection, novel tools need to be developed, including more efficacious drugs, vaccines, and/or antivectorial agents, new diagnostics for infection and assessment of drug efficacy, and markers for possible anthelmintic resistance. In addition, there is a need for the development of new formulations of some existing anthelmintics (e.g., paediatric formulations). To achieve ultimate elimination of helminth parasites, treatments for the above mentioned helminthiases, and for taeniasis and food-borne trematodiases, will need to be integrated with monitoring, education, sanitation, access to health services, and where appropriate, vector control or reduction of the parasite reservoir in alternative hosts. Based on an analysis of current knowledge gaps and identification of priorities, a research and development agenda for intervention tools considered necessary for control and elimination of human helminthiases is presented, and the challenges to be confronted are discussed.

BackgroundClonorchiasis is among the most neglected tropical diseases. It is caused by ingesting raw or undercooked fish or shrimp containing the larval of Clonorchis sinensis and mainly endemic in Southeast Asia including China, Korea and Vietnam. The global estimations for population at risk and infected are 601 million and 35 million, respectively. However, it is still not listed among the Global Burden of Disease (GBD) and no disability weight is available for it. Disability weight reflects the average degree of loss of life value due to certain chronic disease condition and ranges between 0 (complete health) and 1 (death). It is crucial parameter for calculating the morbidity part of any disease burden in terms of disability-adjusted life years (DALYs).Methodology/principal findingsAccording to the probability and disability weight of single sequelae caused by C. sinensis infection, the overall disability weight could be captured through Monte Carlo simulation. The probability of single sequelae was gained from one community investigation, while the corresponding disability weight was searched from the literatures in evidence-based approach. The overall disability weights of the male and female were 0.101 and 0.050, respectively. The overall disability weights of the age group of 5-14, 15-29, 30-44, 45-59 and 60+ were 0.022, 0.052, 0.072, 0.094 and 0.118, respectively. There was some evidence showing that the disability weight and geometric mean of eggs per gram of feces (GMEPG) fitted a logarithmic equation.Conclusion/significanceThe overall disability weights of C. sinensis infection are differential in different sex and age groups. The disability weight captured here may be referred for estimating the disease burden of C. sinensis infection.

The transmission of mosquito-borne diseases is strongly linked to the abundance of the host vector. Identifying the environmental and biological precursors which herald the onset of peaks in mosquito abundance would give health and land-use managers the capacity to predict the timing and distribution of the most efficient and cost-effective mosquito control. We analysed a 15-year time series of monthly abundance of Aedes vigilax, a tropical mosquito species from northern Australia, to determine periodicity and drivers of population peaks (high-density outbreaks). Two sets of density-dependent models were used to examine the correlation between mosquito abundance peaks and the environmental drivers of peaks or troughs (low-density periods). The seasonal peaks of reproduction (r) and abundance (N(peak)) occur at the beginning of September and early November, respectively. The combination of low mosquito abundance and a low frequency of a high tide exceeding 7 m in the previous low-abundance (trough) period were the most parsimonious predictors of a peak's magnitude, with this model explaining over 50% of the deviance in N(peak). Model weights, estimated using AIC(c), were also relatively high for those including monthly maximum tide height, monthly accumulated tide height or total rainfall per month in the trough, with high values in the trough correlating negatively with the onset of a high-abundance peak. These findings illustrate that basic environmental monitoring data can be coupled with relatively simple density feedback models to predict the timing and magnitude of mosquito abundance peaks. Decision-makers can use these methods to determine optimal levels of control (i.e., least-cost measures yielding the largest decline in mosquito abundance) and so reduce the risk of disease outbreaks in human populations.