Feasibility of ultrasound radiomics based models for classification of liver fibrosis due to Schistosoma japonicum infection.

BACKGROUND: Schistosomiasis japonica represents a significant public health concern in South Asia. There is an urgent need to optimize existing schistosomiasis diagnostic techniques. This study aims to develop models for the different stages of liver fibrosis caused by Schistosoma infection utilizing ultrasound radiomics and machine learning techniques. METHODS: From 2018 to 2022, we retrospectively collected data on 1,531 patients and 5,671 B-mode ultrasound images from the Second People's Hospital of Duchang City, Jiangxi Province, China. The datasets were screened based on inclusion and exclusion criteria suitable for radiomics models. Liver fibrosis due to Schistosoma infection (LFSI) was categorized into four stages: grade 0, grade 1, grade 2, and grade 3. The data were divided into six binary classification problems, such as group 1 (grade 0 vs. grade 1) and group 2 (grade 0 vs. grade 2). Key radiomic features were extracted using Pyradiomics, the Mann-Whitney U test, and the Least Absolute Shrinkage and Selection Operator (LASSO). Machine learning models were constructed using Support Vector Machine (SVM), and the contribution of different features in the model was described by applying Shapley Additive Explanations (SHAP). RESULTS: This study ultimately included 1,388 patients and their corresponding images. A total of 851 radiomics features were extracted for each binary classification problems. Following feature selection, 18 to 76 features were retained from each groups. The area under the receiver operating characteristic curve (AUC) for the validation cohorts was 0.834 (95% CI: 0.779-0.885) for the LFSI grade 0 vs. LFSI grade 1, 0.771 (95% CI: 0.713-0.835) for LFSI grade 1 vs. LFSI grade 2, and 0.830 (95% CI: 0.762-0.885) for LFSI grade 2 vs. LFSI grade 3. CONCLUSION: Machine learning models based on ultrasound radiomics are feasible for classifying different stages of liver fibrosis caused by Schistosoma infection.

Global outreach and networking promotion to accelerate tropical diseases elimination.

Cooperation and networking are powerful tools in the combating against tropical diseases. Cooperation on a global scale is essential due to the transboundary nature of tropical diseases. Networking plays a pivotal role in facilitating such cooperation. Both cooperation and networking can foster innovation in disease control programmes. Collaborative research can lead to the development of new drugs and vaccines, while shared surveillance data can enable the early detection and control of disease epidemics. Therefore, consensus of cooperation and networking has been reached during the 7th Symposium on Surveillance-Response Systems Leading to Tropical Diseases Elimination, which reflected in the two documents, i.e., Consensus for Transboundary Tropical Diseases Control, and Action Consensus of the Network of WHO Collaborating Centres Related to NTDs. These documents will improve the efforts in the fighting against tropical diseases through collective actions to achieve the United Nations' Sustainable Development Goals (SDGs).

Geography and health: role of human translocation and access to care.

Natural, geographical barriers have historically limited the spread of communicable diseases. This is no longer the case in today's interconnected world, paired with its unprecedented environmental and climate change, emphasising the intersection of evolutionary biology, epidemiology and geography (i.e. biogeography). A total of 14 articles of the special issue entitled "Geography and health: role of human translocation and access to care" document enhanced disease transmission of diseases, such as malaria, leishmaniasis, schistosomiasis, COVID-19 (Severe acute respiratory syndrome corona 2) and Oropouche fever in spite of spatiotemporal surveillance. High-resolution satellite images can be used to understand spatial distributions of transmission risks and disease spread and to highlight the major avenue increasing the incidence and geographic range of zoonoses represented by spill-over transmission of coronaviruses from bats to pigs or civets. Climate change and globalization have increased the spread and establishment of invasive mosquitoes in non-tropical areas leading to emerging outbreaks of infections warranting improved physical, chemical and biological vector control strategies. The translocation of pathogens and their vectors is closely connected with human mobility, migration and the global transport of goods. Other contributing factors are deforestation with urbanization encroaching into wildlife zones. The destruction of natural ecosystems, coupled with low income and socioeconomic status, increase transmission probability of neglected tropical and zoonotic diseases. The articles in this special issue document emerging or re-emerging diseases and surveillance of fever symptoms. Health equity is intricately connected to accessibility to health care and the targeting of healthcare resources, necessitating a spatial approach. Public health comprises successful disease management integrating spatial surveillance systems, including access to sanitation facilities. Antimicrobial resistance caused, e.g. by increased use of antibiotics in health, agriculture and aquaculture, or acquisition of resistance genes, can be spread by horizontal gene transfer. This editorial reviews the key findings of this 14-article special issue, identifies important gaps relevant to our interconnected world and makes a number of specific recommendations to mitigate the transmission risks of infectious diseases in the post-COVID-19 pandemic era.

Global One Health index for zoonoses: A performance assessment in 160 countries and territories.

The One Health (OH) approach is used to control/prevent zoonotic events. However, there is a lack of tools for systematically assessing OH practices. Here, we applied the Global OH Index (GOHI) to evaluate the global OH performance for zoonoses (GOHI-Zoonoses). The fuzzy analytic hierarchy process algorithm and fuzzy comparison matrix were used to calculate the weights and scores of five key indicators, 16 subindicators, and 31 datasets for 160 countries and territories worldwide. The distribution of GOHI-Zoonoses scores varies significantly across countries and regions, reflecting the strengths and weaknesses in controlling or responding to zoonotic threats. Correlation analyses revealed that the GOHI-Zoonoses score was associated with economic, sociodemographic, environmental, climatic, and zoological factors. Additionally, the Human Development Index had a positive effect on the score. This study provides an evidence-based reference and guidance for global, regional, and country-level efforts to optimize the health of people, animals, and the environment.

Towards an actionable One Health approach.

BACKGROUND: Despite the increasing focus on strengthening One Health capacity building on global level, challenges remain in devising and implementing real-world interventions particularly in the Asia-Pacific region. Recognizing these gaps, the One Health Action Commission (OHAC) was established as an academic community for One Health action with an emphasis on research agenda setting to identify actions for highest impact. MAIN TEXT: This viewpoint describes the agenda of, and motivation for, the recently formed OHAC. Recognizing the urgent need for evidence to support the formulation of necessary action plans, OHAC advocates the adoption of both bottom-up and top-down approaches to identify the current gaps in combating zoonoses, antimicrobial resistance, addressing food safety, and to enhance capacity building for context-sensitive One Health implementation. CONCLUSIONS: By promoting broader engagement and connection of multidisciplinary stakeholders, OHAC envisions a collaborative global platform for the generation of innovative One Health knowledge, distilled practical experience and actionable policy advice, guided by strong ethical principles of One Health.

Developing a geographical-meteorological indicator system and evaluating prediction models for alveolar echinococcosis in China.

BACKGROUND: Geographical and meteorological factors have been reported to influence the prevalence of echinococcosis, but there's a lack of indicator system and model. OBJECTIVE: To provide further insight into the impact of geographical and meteorological factors on AE prevalence and establish a theoretical basis for prevention and control. METHODS: Principal component and regression analysis were used to screen and establish a three-level indicator system. Relative weights were examined to determine the impact of each indicator, and five mathematical models were compared to identify the best predictive model for AE epidemic levels. RESULTS: By analyzing the data downloaded from the China Meteorological Data Service Center and Geospatial Data Cloud, we established the KCBIS, including 50 basic indicators which could be directly obtained online, 15 characteristic indicators which were linear combination of the basic indicators and showed a linear relationship with AE epidemic, and 8 key indicators which were characteristic indicators with a clearer relationships and fewer mixed effects. The relative weight analysis revealed that monthly precipitation, monthly cold days, the difference between negative and positive temperature anomalies, basic air temperature conditions, altitude, the difference between positive and negative atmospheric pressure anomalies, monthy extremely hot days, and monthly fresh breeze days were correlated with the natural logarithm of AE prevalence, with sequential decreases in their relative weights. The multinomial logistic regression model was the best predictor at epidemic levels 1, 3, 5, and 6, whereas the CART model was the best predictor at epidemic levels 2, 4, and 5.

Precision Prediction for Dengue Fever in Singapore: A Machine Learning Approach Incorporating Meteorological Data.

OBJECTIVE: This study aimed to improve dengue fever predictions in Singapore using a machine learning model that incorporates meteorological data, addressing the current methodological limitations by examining the intricate relationships between weather changes and dengue transmission. METHOD: Using weekly dengue case and meteorological data from 2012 to 2022, the data was preprocessed and analyzed using various machine learning algorithms, including General Linear Model (GLM), Support Vector Machine (SVM), Gradient Boosting Machine (GBM), Decision Tree (DT), Random Forest (RF), and eXtreme Gradient Boosting (XGBoost) algorithms. Performance metrics such as Mean Absolute Error (MAE), Root Mean Square Error (RMSE), and R-squared (R2) were employed. RESULTS: From 2012 to 2022, there was a total of 164,333 cases of dengue fever. Singapore witnessed a fluctuating number of dengue cases, peaking notably in 2020 and revealing a strong seasonality between March and July. An analysis of meteorological data points highlighted connections between certain climate variables and dengue fever outbreaks. The correlation analyses suggested significant associations between dengue cases and specific weather factors such as solar radiation, solar energy, and UV index. For disease predictions, the XGBoost model showed the best performance with an MAE = 89.12, RMSE = 156.07, and R2 = 0.83, identifying time as the primary factor, while 19 key predictors showed non-linear associations with dengue transmission. This underscores the significant role of environmental conditions, including cloud cover and rainfall, in dengue propagation. CONCLUSION: In the last decade, meteorological factors have significantly influenced dengue transmission in Singapore. This research, using the XGBoost model, highlights the key predictors like time and cloud cover in understanding dengue's complex dynamics. By employing advanced algorithms, our study offers insights into dengue predictive models and the importance of careful model selection. These results can inform public health strategies, aiming to improve dengue control in Singapore and comparable regions.

Global, Regional and National Burden of Human Cystic Echinococcosis from 1990 to 2019: A Systematic Analysis for the Global Burden of Disease Study 2019.

BACKGROUND: Cystic echinococcosis (CE) is a neglected tropical parasitic disease that poses huge disease, social and economic burdens worldwide; however, there has been little knowledge on the global morbidity, mortality and disability-adjusted life years (DALYs) of CE until now. This study aimed to collect the most up-to-date data about the global, regional and national disease burden due to CE from 1990 to 2019 and to project trends in the next 10 years. METHODS: We measured the global, regional and national morbidity, mortality and DALYs of CE from 1990 to 2019 based on the Global Burden of Disease Study 2019 (GBD 2019) data, and we examined the correlation between socioeconomic development levels and the disease burden of CE. In addition, the disease burden due to CE was projected from 2020 to 2030. RESULTS: The age-standardized incidence rate (ASIR) of CE reduced from 2.65/105 [95% UI: (1.87/105 to 3.7/105)] in 1990 to 2.6/105 [95% UI: (1.72/105 to 3.79/105)] in 2019 (EAPC = -0.18%). The number of deaths, DALYs, age-standardized mortality rate (ASMR) and age-standardized DALY rate due to CE all showed a tendency to decline from 1990 to 2019. A higher disease burden of CE was measured in women than in men in 2019. There was a significant difference in the ASMR of CE by region according to the socio-demographic index (SDI), and lower burdens of CE were estimated in high-SDI regions. The global ASIR of CE is projected to decline from 2020 to 2030; however, the ASMR and age-standardized DALY rate are projected to rise. CONCLUSIONS: The global burden of CE remains high, and it is recommended that more health resources are allocated to low-SDI regions, women and the elderly aged 55 to 65 years to reduce the disease burden of CE.

Optimal decision-making in relieving global high temperature-related disease burden by data-driven simulation.

The rapid acceleration of global warming has led to an increased burden of high temperature-related diseases (HTDs), highlighting the need for advanced evidence-based management strategies. We have developed a conceptual framework aimed at alleviating the global burden of HTDs, grounded in the One Health concept. This framework refines the impact pathway and establishes systematic data-driven models to inform the adoption of evidence-based decision-making, tailored to distinct contexts. We collected extensive national-level data from authoritative public databases for the years 2010-2019. The burdens of five categories of disease causes - cardiovascular diseases, infectious respiratory diseases, injuries, metabolic diseases, and non-infectious respiratory diseases - were designated as intermediate outcome variables. The cumulative burden of these five categories, referred to as the total HTD burden, was the final outcome variable. We evaluated the predictive performance of eight models and subsequently introduced twelve intervention measures, allowing us to explore optimal decision-making strategies and assess their corresponding contributions. Our model selection results demonstrated the superior performance of the Graph Neural Network (GNN) model across various metrics. Utilizing simulations driven by the GNN model, we identified a set of optimal intervention strategies for reducing disease burden, specifically tailored to the seven major regions: East Asia and Pacific, Europe and Central Asia, Latin America and the Caribbean, Middle East and North Africa, North America, South Asia, and Sub-Saharan Africa. Sectoral mitigation and adaptation measures, acting upon our categories of Infrastructure & Community, Ecosystem Resilience, and Health System Capacity, exhibited particularly strong performance for various regions and diseases. Seven out of twelve interventions were included in the optimal intervention package for each region, including raising low-carbon energy use, increasing energy intensity, improving livestock feed, expanding basic health care delivery coverage, enhancing health financing, addressing air pollution, and improving road infrastructure. The outcome of this study is a global decision-making tool, offering a systematic methodology for policymakers to develop targeted intervention strategies to address the increasingly severe challenge of HTDs in the context of global warming.

Natural variables separate the endemic areas of Clonorchis sinensis and Opisthorchis viverrini along a continuous, straight zone in Southeast Asia.

BACKGROUND: Clonorchiasis and opisthorchiasis, caused by the liver flukes Clonorchis sinensis and Opisthorchis viverrini respectively, represent significant neglected tropical diseases (NTDs) in Asia. The co-existence of these pathogens in overlapping regions complicates effective disease control strategies. This study aimed to clarify the distribution and interaction of these diseases within Southeast Asia. METHODS: We systematically collated occurrence records of human clonorchiasis (n = 1809) and opisthorchiasis (n = 731) across the Southeast Asia countries. Utilizing species distribution models incorporating environmental and climatic data, coupled machine learning algorithms with boosted regression trees, we predicted and distinguished endemic areas for each fluke species. Machine learning techniques, including geospatial analysis, were employed to delineate the boundaries between these flukes. RESULTS: Our analysis revealed that the endemic range of C. sinensis and O. viverrini in Southeast Asia primarily spans across part of China, Vietnam, Thailand, Laos, and Cambodia. During the period from 2000 to 2018, we identified C. sinensis infections in 84 distinct locations, predominantly in southern China (Guangxi Zhuang Autonomous Region) and northern Vietnam. In a stark contrast, O. viverrini was more widely distributed, with infections documented in 721 locations across Thailand, Laos, Cambodia, and Vietnam. Critical environmental determinants were quantitatively analyzed, revealing annual mean temperatures ranging between 14 and 20 °C in clonorchiasis-endemic areas and 24-30 °C in opisthorchiasis regions (P < 0.05). The machine learning model effectively mapped a distinct demarcation zone, demonstrating a clear separation between the endemic areas of these two liver flukes with AUC from 0.9 to1. The study in Vietnam delineates the coexistence and geographical boundaries of C. sinensis and O. viverrini, revealing distinct endemic zones and a transitional area where both liver fluke species overlap. CONCLUSIONS: Our findings highlight the critical role of specific climatic and environmental factors in influencing the geographical distribution of C. sinensis and O. viverrini. This spatial delineation offers valuable insights for integrated surveillance and control strategies, particularly in regions with sympatric transmission. The results underscore the need for tailored interventions, considering regional epidemiological variations. Future collaborations integrating eco-epidemiology, molecular epidemiology, and parasitology are essential to further elucidate the complex interplay of liver fluke distributions in Asia.

Chromosome-level genome assembly of Oncomelania hupensis: the intermediate snail host of Schistosoma japonicum.

BACKGROUND: Schistosoma japonicum is a parasitic flatworm that causes human schistosomiasis, which is a significant cause of morbidity in China, the Philippines and Indonesia. Oncomelania hupensis (Gastropoda: Pomatiopsidae) is the unique intermediate host of S. japonicum. A complete genome sequence of O. hupensis will enable the fundamental understanding of snail biology as well as its co-evolution with the S. japonicum parasite. Assembling a high-quality reference genome of O. hupehensis will provide data for further research on the snail biology and controlling the spread of S. japonicum. METHODS: The draft genome was de novo assembly using the long-read sequencing technology (PacBio Sequel II) and corrected with Illumina sequencing data. Then, using Hi-C sequencing data, the genome was assembled at the chromosomal level. CAFE was used to do analysis of contraction and expansion of the gene family and CodeML module in PAML was used for positive selection analysis in protein coding sequences. RESULTS: A total length of 1.46 Gb high-quality O. hupensis genome with 17 unique full-length chromosomes (2n = 34) of the individual including a contig N50 of 1.35 Mb and a scaffold N50 of 75.08 Mb. Additionally, 95.03% of these contig sequences were anchored in 17 chromosomes. After scanning the assembled genome, a total of 30,604 protein-coding genes were predicted. Among them, 86.67% were functionally annotated. Further phylogenetic analysis revealed that O. hupensis was separated from a common ancestor of Pomacea canaliculata and Bellamya purificata approximately 170 million years ago. Comparing the genome of O. hupensis with its most recent common ancestor, it showed 266 significantly expanded and 58 significantly contracted gene families (P < 0.05). Functional enrichment of the expanded gene families indicated that they were mainly involved with intracellular, DNA-mediated transposition, DNA integration and transposase activity. CONCLUSIONS: Integrated use of multiple sequencing technologies, we have successfully constructed the genome at the chromosomal-level of O. hupensis. These data will not only provide the compressive genomic information, but also benefit future work on population genetics of this snail as well as evolutional studies between S. japonicum and the snail host.

Clonorchiasis and opisthorchiasis: epidemiology, transmission, clinical features, morbidity, diagnosis, treatment, and control.

Clonorchis sinensis, Opisthorchis viverrini, and Opisthorchis felineus are important liver flukes that cause a considerable public health burden in eastern Asia, southeastern Asia, and eastern Europe, respectively. The life cycles are complex, involving humans, animal reservoirs, and two kinds of intermediate hosts. An interplay of biological, cultural, ecological, economic, and social factors drives transmission. Chronic infections are associated with liver and biliary complications, most importantly cholangiocarcinoma. With regard to diagnosis, stool microscopy is widely used in epidemiologic surveys and for individual diagnosis. Immunologic techniques are employed for screening purposes, and molecular techniques facilitate species differentiation in reference laboratories. The mainstay of control is preventive chemotherapy with praziquantel, usually combined with behavioral change through information, education and communication, and environmental control. Tribendimidine, a drug registered in the People's Republic of China for soil-transmitted helminth infections, shows potential against both C. sinensis and O. viverrini and, hence, warrants further clinical development. Novel control approaches include fish vaccine and biological control. Considerable advances have been made using multi-omics which may trigger the development of new interventions. Pressing research needs include mapping the current distribution, disentangling the transmission, accurately estimating the disease burden, and developing new diagnostic and treatment tools, which would aid to optimize control and elimination measures.

Impact of 1,7-malaria reactive community-based testing and response (1,7-mRCTR) approach on malaria prevalence in Tanzania.

BACKGROUND: Progress in malaria control has stalled in recent years and innovative surveillance and response approaches are needed to accelerate malaria control and elimination efforts in endemic areas of Africa. Building on a previous China-UK-Tanzania pilot study on malaria control, this study aimed to assess the impact of the 1,7-malaria Reactive Community-Based Testing and Response (1,7-mRCTR) approach implemented over two years in three districts of Tanzania. METHODS: The 1,7-mRCTR approach provides community-based malaria testing via rapid diagnostic tests and treatment in villages with the highest burden of malaria incidence based on surveillance data from health facilities. We used a difference-in-differences quasi-experimental design with linear probability models and two waves of cross-sectional household surveys to assess the impact of 1,7-mRCTR on malaria prevalence. We conducted sensitivity analyses to assess the robustness of our results, examined how intervention effects varied in subgroups, and explored alternative explanations for the observed results. RESULTS: Between October 2019 and September 2021, 244,771 community-based malaria rapid tests were completed in intervention areas, and each intervention village received an average of 3.85 rounds of 1-7mRCTR. Malaria prevalence declined from 27.4% at baseline to 11.7% at endline in the intervention areas and from 26.0% to 16.0% in the control areas. 1,7-mRCTR was associated with a 4.5-percentage-point decrease in malaria prevalence (95% confidence interval: - 0.067, - 0.023), equivalent to a 17% reduction from the baseline. In Rufiji, a district characterized by lower prevalence and where larviciding was additionally provided, 1,7-mRCTR was associated with a 63.9% decline in malaria prevalence. CONCLUSIONS: The 1,7-mRCTR approach reduced malaria prevalence. Despite implementation interruptions due to the COVID-19 pandemic and supply chain challenges, the study provided novel evidence on the effectiveness of community-based reactive approaches in moderate- to high-endemicity areas and demonstrated the potential of South-South cooperation in tackling global health challenges.

Optimized strategy for schistosomiasis elimination: results from marginal benefit modeling.

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 R2 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.

Adaptation of the CUGH global health competency framework in the Chinese context: a mixed-methods study.

BACKGROUND: In 2014, the Consortium of Universities for Global Health (CUGH) developed a global health competency framework and called for its validation. Given China's increasing engagement in global health over the past decade, there is a need for a tailored competency framework to enhance the capacity of its workforce. This study aimed to localize the CUGH global health framework within the Chinese context, offering guidance to public health professionals in China to bolster their capabilities for international endeavors. METHODS: Employing a modified Delphi consultation approach, this study adapted the CUGH global health competency framework through three consultation rounds and a panel discussion. A questionnaire employing a five-point Likert scale was developed to gather opinions from 37 experts on the significance and feasibility of each competency within the Chinese setting. Profiling information, judgment criteria, and familiarity with each competency were collected to assess experts' authority levels. Furthermore, a priority survey was administered to 51 experts to identify key competencies and provide recommendations for bolstering the capabilities of China's public health professionals. Data analysis was performed using Microsoft Excel. RESULTS: The adapted framework comprises 10 domains and 37 competencies including: 1. Global Burden of Disease; 2. Social-economic, Environmental and Behavioral Determinants of Health; 3. The Impact of Globalization on Population Health, Health Systems, and Healthcare; 4. Major Global health initiatives and efforts; 5. Ethics, Health Equity and Social Justice; 6. Sociocultural, Political Awareness and Policy Promotion; 7. Personal Competencies and Professional Practice; 8. Capacity strengthening; 9. Collaboration, Partnering and Communication; 10. Programme Management. The priority survey underscored Domain 9, 10, and 4 as the foremost concern for Chinese public health professionals, urging active learning, critical thinking, open communication, experiential learning, and case-based studies. Institutions were advised to enhance their capacity, foster partnerships, and discern China's distinct role in the global health arena. CONCLUSIONS: This study adapted the CUGH framework within the Chinese context, evaluating the significance and feasibility of each competency. The adapted framework can serve as a tool for developing global health curricula and delineating roles for Chinese public health professionals. To ensure contextual compatibility, testing of the framework with diverse public health professionals is recommended, enabling precise refinement of competencies based on empirical results.

A versatile microfluidic platform for malaria infection screening and Plasmodium species genotyping.

BACKGROUND: Malaria, a widespread parasitic disease caused by Plasmodium species, remains a significant global health concern. Rapid and accurate detection, as well as species genotyping, are critical for effective malaria control. METHODS: We have developed a Flexible, Robust, Equipment-free Microfluidic (FREM) platform, which integrates recombinase polymerase amplification (RPA) and clustered regularly interspaced short palindromic repeats (CRISPR)-based detection, enabling simultaneous malaria infection screening and Plasmodium species genotyping. The microfluidic chip enabled the parallel detection of multiple Plasmodium species, each amplified by universal RPA primers and genotyped by specific crRNAs. The inclusion of a sucrose solution effectively created spatial separation between the RPA and CRISPR assays within a one-pot system, effectively resolving compatibility issues. FINDINGS: Clinical assessment of DNA extracts from patients with suspected malaria demonstrates the FREM platform's superior sensitivity (98.41%) and specificity (92.86%), yielding consistent results with PCR-sequencing for malaria detection, which achieved a positive predictive agreement of 98.41% and a negative predictive agreement of 92.86%. Additionally, the accuracy of species genotyping was validated through concordance rates of 90.91% between the FREM platform and PCR-sequencing. INTERPRETATION: The FREM platform offers a promising solution for point-of-care malaria screening and Plasmodium species genotyping. It highlights the possibility of improving malaria control efforts and expanding its applicability to address other infectious diseases. FUNDING: This work was financially supported by International Joint Laboratory on Tropical Diseases Control in Greater Mekong Subregion, National Natural Science Foundation of China, the Natural Science Foundation of Shanghai, Bill & Melinda Gates Foundation and National Research and Development Plan of China.

Molecular Identification and Genetic Characterization of Public Health Threatening Ticks - Chongming Island, China, 2021-2022.

What is already known about this topic?: Although ticks and tick-borne diseases are prevalent throughout China, there remains a knowledge gap regarding their biology and potential risk of distribution to human and animal populations on Chongming Island. The island, being China's third largest and a crucial component in the ecological preservation of the Yangtze Delta region, has yet to be comprehensively studied in this context. What is added by this report?: In this study, employing molecular methodologies, a significant prevalence of Haemaphysalis (H.) longicornis and H. flava ticks - widely recognized for their high pathogenicity - is reported from Chongming Island. Additionally, the identification of two previously unreported species on the island, namely, H. doenitzi and H. japonica, expands our understanding of both the range and evolution of tick species. What are the implications for public health practice?: The populations of humans and animals in nearly all 18 towns on Chongming Island are potentially at risk for transmission of tick-borne infectious agents. As a result, there is a pressing necessity for public health alerts, proactive tick surveillance, and effective screening of suspected clinical cases of tick-borne diseases within the Chongming population.

Understanding the China-Tanzania Malaria Control Project: lessons learned from a multi-stakeholder qualitative study.

Background: Tanzania is among the countries with the highest malaria cases and deaths worldwide, where vulnerable populations have been severely affected due to poverty and weakness in health system and infrastructure. The China-Tanzania Malaria Control Project (the Project) was a two-phase global health intervention project implemented between 2015 and 2021 that aimed to transfer project-designated intervention experience in malaria elimination to the Tanzanian health system. This study aims to identify the barriers and facilitators encountered during the Project and to improve our understanding of the emerging phenomenon of South-South global health collaboration. Methods: We conducted thematic analysis of qualitative data collected from a purposive sample of 14 participants from multiple stakeholders including project management office, project implementation agency, funding partners and external evaluators of the Project. A conceptual framework was developed to construct the interviews guides. The interviews were transcribed verbatim, crossover checked, translated into English, and analyzed with NVivo 12.0. We conducted the open coding followed by the axial coding based on the Grounded Theory to generate themes and subthemes, and identified key influencing factors that aided or hindered the malaria control in Tanzania. Results: The findings suggested that malaria control strategies should largely be tailored due to varied socioeconomic contexts. The perceived enablers in practice include project-designated intervention experiences and technologies, professional and self-learning capabilities of the implementation team, sustainable financial assistance, and support from the international partners. The barriers include the shortage of global health talents, existing gaps to meet international standards, defects in internal communication mechanisms, inadequacy of intergovernmental dialogue, and limitations in logistical arrangements. A checklist and policy implications for China's future engagement in malaria control in resource-limited settings have been proposed. Conclusions: The initiative of Health Silk Road has generated strong global interest in promoting development assistance in health. In the hope of generalizing the evidence-based interventions to high malaria-endemic countries in Africa, the need for China to carefully face the challenges of funding gaps and the lack of support from recipient governments remains ongoing. It is recommended that China should form an institutionalized scheme and sustainable funding pool to ensure the steady progress of development assistance in health.

Infectious disease control: from health security strengthening to health systems improvement at global level.

Since the twenty first century, the outbreaks of global infectious diseases have caused several public health emergencies of international concern, imposing an enormous impact on population health, the economy, and social development. The COVID-19 pandemic has once again exposed deficiencies in existing global health systems, emergency management, and disease surveillance, and highlighted the importance of developing effective evaluation tools. This article outlines current challenges emerging from infectious disease control from the perspective of global health, elucidated through influenza, malaria, tuberculosis, and neglected tropical diseases. The discordance among government actors and absent data sharing platforms or tools has led to unfulfilled targets in health system resilience and a capacity gap in infectious disease response. The current situation calls for urgent action to tackle these threats of global infectious diseases with joined forces through more in-depth international cooperation and breaking governance barriers from the purview of global health. Overall, a systematic redesign should be considered to enhance the resilience of health systems, which warrants a great need to sustain capacity-building efforts in emergency preparedness and response and raises an emerging concern of data integration in the concept of One Health that aims to address shared health threats at the human-animal-environment interface.