Prevalence and correlations of schistosomiasis mansoni and schistosomiasis haematobium among humans and intermediate snail hosts: a systematic review and meta-analysis.

BACKGROUND: The control of schistosomiasis is particularly difficult in sub-Saharan Africa, which currently harbours 95% of this disease. The target population for preventive chemotherapy (PC) is expanded to all age group at risk of infection, thus increasing the demands of praziquantel (PZQ) tablets according to the new released guideline by World Health Organization. Due to the gap between available PZQ for PC and requirements, alternative approaches to assess endemicity of schistosomiasis in a community, are urgently needed for more quick and precise methods. We aimed to find out to which degree the infection status of snails can be used to guide chemotherapy against schistosomiasis. METHODS: We searched literature published from January 1991 to December 2022, that reported on the prevalence rates of Schistosoma mansoni, S. haematobium in the intermediate snails Biomphalaria spp. and Bulinus spp., respectively, and in humans. A random effect model for meta-analyses was used to calculate the pooled prevalence estimate (PPE), with heterogeneity assessed using I-squared statistic (I2), with correlation and regression analysis for the exploration of the relationship between human S. mansoni and S. haematobium infections and that in their specific intermediate hosts. RESULTS: Forty-seven publications comprising 59 field investigations were included. The pooled PPE of schistosomiasis, schistosomiasis mansoni and schistosomiasis haematobium in humans were 27.5% [95% confidence interval (CI): 24.0-31.1%], 25.6% (95% CI: 19.9-31.3%), and 28.8% (95% CI: 23.4-34.3%), respectively. The snails showed an overall infection rate of 8.6% (95% CI: 7.7-9.4%), with 12.1% (95% CI: 9.9-14.2%) in the Biomphalaria spp. snails and 6.9% (95% CI: 5.7-8.1%) in the Bulinus spp. snails. The correlation coefficient was 0.3 (95% CI: 0.01-0.5%, P < 0.05) indicating that the two variables, i.e. all intermediate host snails on the one hand and the human host on the other, were positively correlated. CONCLUSIONS: The prevalence rate of S. mansoni and S. haematobium is still high in endemic areas. Given the significant, positive correlation between the prevalence of schistosomes in humans and the intermediate snail hosts, more attention should be paid to programme integration of snail surveillance in future.

Profile and Determinants for Complications of Imported Malaria in 5 Chinese Provinces From 2014 to 2021: Retrospective Analysis.

Background: In 2021, the World Health Organization officially declared the People's Republic of China as malaria-free. However, despite this milestone achievement, the continued occurrence of severe and fatal cases of imported malaria in China, due to globalization and increased international communication, remains a significant public health concern. Objective: The aim of this study was to elucidate the epidemiological characteristics of imported malaria in 5 Chinese provinces from 2014 to 2021 and to identify the factors that influence complications in imported malaria cases. The findings will provide a basis for enhancing prevention and control measures, thereby consolidating China's achievements in malaria elimination. Methods: A case-based retrospective study was performed, using surveillance data collected from the representative provinces of China from 2014 to 2021. Epidemiological characteristics were analyzed using descriptive statistics. Logistic regression was used to identify the factors influencing the occurrence of complications. Results: A total of 5559 malaria cases were included during the study period. The predominant species was Plasmodium falciparum (3940/5559, 70.9%), followed by Plasmodium ovale (1054/5559, 19%), Plasmodium vivax (407/5559, 7.3%), Plasmodium malariae (157/5559, 2.8%), and 1 case of Plasmodium knowlesi. Most of the cases were male (5343/5559, 96.1%). The complication rates for P falciparum and P ovale were 11.4% and 3.3%, respectively. Multivariate logistic regression analysis of the relevant factors of malaria complications revealed potential protective factors, including a previous infection by Plasmodium (P<.001; odds ratio [OR] 0.512, 95% CI 0.422-0.621), and risk factors, including increased age (P=.004; OR 1.014, 95% CI 1.004-1.024), misdiagnosis at the first clinical visit (P<.001; OR 3.553, 95% CI 2.886-4.375), and the time interval from onset to treatment (P=.001; OR 1.026, 95% CI 1.011-1.042). Subgroup analyses identified risk factors associated with P falciparum, which include advanced age (P=.004; OR 1.015, 95% CI 1.005-1.026), initial misdiagnosis during the first clinical visit (P<.001; OR 3.549, 95% CI 2.827-4.455), the time interval from onset to treatment (P<.001; OR 1.043, 95% CI 1.022-1.063), and a delay of more than 3 days from the first treatment to diagnosis (P<.001; OR 2.403, 95% CI 1.823-3.164). Additionally, the risk factors pertaining to P ovale involve misdiagnosis at the initial clinical visit (P=.01; OR 2.901, 95% CI 1.336-6.298), the time interval from onset to treatment (P=.002; OR 1.095, 95% CI 1.033-1.160), and the duration from the initial treatment to diagnosis (P=.43; OR 1.032, 95% CI 0.953-1.118). Previous infections can prevent the progression of both P falciparum and P ovale. Conclusions: This study showed that the increasing proportion of P ovale in recent years should not be ignored. Furthermore, there is a need to improve diagnostic awareness, enhance the capacity of medical institutions, and provide health education for high-risk groups.

Intervention portfolios analysis of Plasmodium vivax control in central China.

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.

Integrated Eco-Health approach significantly reduces helminth infections in endemic Khong islands with emphasis on Schistosoma mekongi.

BACKGROUND: Helminth infections, including Opisthorchis viverrini, hookworm, and Trichuris trichiura, are prevalent in Khong district, Champasack province, southern Lao People's Democratic Republic (PDR). Schistosomiasis caused by Schistosoma mekongi is of public health concern on the islands of the Khong district. This study aimed to assess the impact of an Eco-Health/One-Health approach in combination with mass drug administration (MDA) to reduce these helminth infections. METHODS: We conducted a community intervention using a stepped-wedge trial approach on two endemic islands (Donsom and Donkhone) of the Khong district, Champasack province, Lao PDR, between April 2012 and March 2013. In each study village, 30-40 households were randomly selected. All members of selected households, who were at home during the study period were invited to participate in the study. A baseline study was conducted to assess helminth infections, knowledge attitudes and practices toward Schistosoma mekongi infection, behavior of open defecation and availability of latrine at home. After the baseline (T0), the Eco-Health/One-Health approach was implemented on Donsom (intervention) and Donkhone island (control). An assessment was conducted in 2014 (T1), one year after the completion of intervention implementation, to assess the short-term impact of the Eco-Health/One-Health approach on helminth infections and compare intervention and control islands. Later in 2015, the Eco-Health/One-Health approach was implemented on control island (Donkhone). After the implementation of intervention, the parasitological assessments were conducted annually in humans in 2015 (T2), in 2016 (T3) and in 2017 (T4), and in dogs in 2017 (T4) to evaluate the long-term impact of the intervention on helminth infections. Frequency was used to describe the prevalence of helminth infections. Logistic regression was applied to associate the KAP (knowledge, attitudes, and practices and open defecation behavior) and the reduction of helminth infections between intervention and control islands. The reduction in prevalence pre- and post-intervention was associated using a McNemar test. A two-independent sample t-test was applied to compare the mean eggs per gram (EPG) of helminth infections between control and intervention islands. A paired t-test test was used to compare the mean EPG of stool samples before (baseline) and after (follow-up) interventions for the two islands separately. A P-value lower than 0.05 was considered statistically significant. RESULTS: Eco-Health/One-Health approach appears to be associated with reduction in prevalence of S. mekongi by 9.0% [odds ratio (OR) = 0.49, P = 0.003] compared to the use of mass drug administration alone (control island). Additionally, this intervention package significantly reduced O. viverrini infection by 20.3% (OR = 1.92, P < 0.001) and hookworm by 17.9% (OR = 0.71, P = 0.045), respectively. Annual parasitological assessments between 2012 and 2017 showed that the Eco-Health/One-Health approach, coupled with MDA, steadily reduced the prevalence of S. mekongi on the intervention island from 29.1% to 1.8% and on the control island from 28.4% to 3.1%, respectively. CONCLUSIONS: The study findings suggest that the Eco-Health/One-Health approach appears to be associated with a significant reduction in prevalence of S. mekongi and helminth co-infections, particularly hookworm and T. trichiura. Therefore, implementing the Eco-Health/One-Health approach in schistosomiasis-endemic areas could accelerate the achievement of national goals for transmission interruption by 2025 and elimination by 2030.

Assessment of the influence of levees along Yangtze River on Oncomelania hupensis, the intermediate host of Schistosoma japonicum.

BACKGROUND: Oncomelania hupensis is the exclusive intermediate host of Schistosoma japonicum in China. Snail control is an essential component of schistosomiasis elimination programme. With 70 years of continuous efforts, the range of O. hupensis had reduced significantly, but slowed down in last decades. A large number of levees against flooding were constructed along Yangtze River and its affiliated lakes in the middle and lower reaches, which influenced the hydrology and ecology in the alluvial plains. The purpose of this study was to assess the impact of levees on the distribution of O. hupensis in the middle and lower reaches of the Yangtze River. METHODS: The snail habitats were digitalised by hand-held GPS system. The years for discovery and elimination of snail habitats were extracted from historical records. The accumulated snail-infested range for each habitat was calculated on the basis of annual reports. The current distribution of O. hupensis was determined by systematic and environmental sampling. The geographical distribution of levees was obtained from satellite imagery. To assess the impact of levees, the data pertaining to O. hupensis were divided into two parts: inside and outside the Yangtze River. Joinpoint regression was utilised to divide the study time span and further characterise the regression in each period. The 5-year-period moving averages of eliminated area infested by snails were calculated for the habitats inside and outside Yangtze River. The moving routes of corresponding geographical median centres were simulated in ArcGIS. Hotspot analysis was used to determine the areas with statistical significance clustering of O. hupensis density. RESULTS: Three periods were identified according to Joinpoint regression both inside and outside Yangtze River. The area infested by O. hupensis increased in the first two periods. It decreased rapidly outside Yangtze River year over year after 1970, while that inside the Yangtze River did not change significantly. Furthermore, the latter was significantly higher than the former. It was observed that the present density of O. hupensis inside Yangtze River was lower than outside the Yangtze River. The median centre for eliminated ranges inside Yangtze River wavered between the east (lower reach) and the west (middle reach). In contrast, the median centre for eliminated ranges continuously moved from the east to the west. CONCLUSIONS: Our findings indicated that the levees had a considerable negative impact on the distribution of O. hupensis outside Yangtze River. Some hotspots observed in the irrigation areas need a sluice system at the inlet of branch for snail control. The major distribution of O. hupensis located in Hubei might be caused by severe waterlogging. The intensive surveillance should be implemented there. The biggest two freshwater lakes, the major endemic regions historically, were identified as cold spots. The long-term impact of Three Gorges Dam on the distribution of O. hupensis in the lakes should be monitored and evaluated.

Schistosomiasis transmission in Zimbabwe: Modelling based on machine learning.

Zimbabwe, located in Southern Africa, faces a significant public health challenge due to schistosomiasis. We investigated this issue with emphasis on risk prediction of schistosomiasis for the entire population. To this end, we reviewed available data on schistosomiasis in Zimbabwe from a literature search covering the 1980-2022 period considering the potential impact of 26 environmental and socioeconomic variables obtained from public sources. We studied the population requiring praziquantel with regard to whether or not mass drug administration (MDA) had been regularly applied. Three machine-learning algorithms were tested for their ability to predict the prevalence of schistosomiasis in Zimbabwe based on the mean absolute error (MAE), the root mean squared error (RMSE) and the coefficient of determination (R2). The findings revealed different roles of the 26 factors with respect to transmission and there were particular variations between Schistosoma haematobium and S. mansoni infections. We found that the top-five correlation factors, such as the past (rather than current) time, unsettled MDA implementation, constrained economy, high rainfall during the warmest season, and high annual precipitation were closely associated with higher S. haematobium prevalence, while lower elevation, high rainfall during the warmest season, steeper slope, past (rather than current) time, and higher minimum temperature in the coldest month were rather related to higher S. mansoni prevalence. The random forest (RF) algorithm was considered as the formal best model construction method, with MAE = 0.108; RMSE = 0.143; and R2 = 0.517 for S. haematobium, and with the corresponding figures for S. mansoni being 0.053; 0.082; and 0.458. Based on this optimal model, the current total schistosomiasis prevalence in Zimbabwe under MDA implementation was 19.8%, with that of S. haematobium at 13.8% and that of S. mansoni at 7.1%, requiring annual MDA based on a population of 3,003,928. Without MDA, the current total schistosomiasis prevalence would be 23.2%, that of S. haematobium 17.1% and that of S. mansoni prevalence at 7.4%, requiring annual MDA based on a population of 3,521,466. The study reveals that MDA alone is insufficient for schistosomiasis elimination, especially that due to S. mansoni. This study predicts a moderate prevalence of schistosomiasis in Zimbabwe, with its elimination requiring comprehensive control measures beyond the currently used strategies, including health education, snail control, population surveillance and environmental management.

Identification of habitat suitability for the dominant zoonotic tick species Haemaphysalis flava on Chongming Island, China.

Haemaphysalis ticks are pathogenic vectors that threaten human and animal health and were identified in Chongming, the third largest island in China. To understand the distribution of these ticks and determine their potential invasion risk, this study aimed to identify the habitat suitability of the dominant tick H. flava based on natural environmental factors. Geographic information system (GIS) images were combined with sample points from tick investigations to map the spatial distribution of H. flava. Data on 19 bioclimatic variables, environmental variables, and satellite-based landscapes of Chongming Island were retrieved to create a landcover map related to natural environmental determinants of H. flava. These data included 38 sites associated with the vectors to construct species distribution models with MaxEnt, a model based on the maximum entropy principle, and to predict habitat suitability for H. flava on Chongming Island in 2050 and 2070 under different climate scenarios. The model performed well in predicting the H. flava distribution, with a training area under the curve of 0.84 and a test area under the curve of 0.73. A habitat suitability map of the whole study area was created for H. flava. The resulting map and natural environment analysis highlighted the importance of the normalized difference vegetation index and precipitation in the driest month for the bioecology of H. flava, with 141.61 km2 (11.77%), 282.94 km2 (23.35%), and 405.30 km2 (33.69%) of highly, moderately, and poorly suitable habitats, respectively. The distribution decreased by 135.55 km2 and 138.82 km2 in 2050 and 2070, respectively, under the shared socioeconomic pathway (SSP) 1.2.6 climate change scenario. However, under SSP 5.8.5, the total area will decrease by 128.5 km2 in 2050 and increase by 151.64 km2 in 2070. From a One Health perspective, this study provides good knowledge that will guide tick control efforts to prevent the spread of Haemaphysalis ticks or transmission risk of Haemaphysalis-borne infections at the human-animal-environment interface on the island.

How far has the globe gone in achieving One Health? Current evidence and policy implications based on global One Health index.

Background: In the 21st century, as globalization accelerates and global public health crises occur, the One Health approach, guided by the holistic thinking of human-animal-environment and emphasizing interdisciplinary collaboration to address global health issues, has been strongly advocated by the international community. An immediate requirement exists for the creation of an assessment tool to foster One Health initiatives on both global and national scales. Methods: Built upon extensive expert consultations and dialogues, this follow-up study enhances the 2022 global One Health index (GOHI) indicator system. The GOHI framework is enriched by covering three indices, e.g. external drivers index (EDI), intrinsic drivers index (IDI), and core drivers index (CDI). The comprehensive indicator system incorporates 13 key indicators, 50 indicators, and 170 sub I-indicators, utilizing a fuzzy analytic hierarchy process to ascertain the weight for each indicator. Weighted and summed, the EDI, IDI, and CDI scores contribute to the computation of the overall GOHI 2022 score. By comparing the ranking and the overall scores among the seven regions and across 160 countries/territories, we have not only derived an overall profile of the GOHI 2022 scores, but also assessed the GOHI framework. We also compared rankings of indicators and sub I-indicators to provide greater clarity on the strengths and weaknesses of each region within the One Health domains. Results: The GOHI 2022 performance reveals significant disparities between countries/territories ranged from 39.03 to 70.61. The global average score of the GOHI 2022 is 54.82. The average score for EDI, IDI, and CDI are 46.57, 58.01, and 57.25, respectively. In terms of global rankings, countries from North America, Europe and Central Asia, East Asia and Pacific present higher scores. In terms of One Health domains of CDI, the lowest scores are observed in antimicrobial resistance (median: 43.09), followed by food security (median: 53.78), governance (median: 54.77), climate change (median: 64.12) and zoonotic diseases (median: 69.23). Globally, the scores of GOHI vary spatially, with the highest score in North America while lowest in sub-Saharan Africa. In addition, evidence shows associations between the socio-demographic profile of countries/territories and their GOHI performance in certain One Health scenarios. Conclusion: The objective of GOHI is to guide impactful strategies for enhancing capacity building in One Health. With advanced technology and an annually updated database, intensifying efforts to refine GOHI's data-mining methodologies become imperative. The goal is to offer profound insights into disparities and progressions in practical One Health implementation, particularly in anticipation of future pandemics.

Synergistic integration of climate change and zoonotic diseases by artificial intelligence: a holistic approach for sustainable solutions.

Artificial intelligence (AI) is a rapidly evolving field that can impel research in communicable diseases with respect to climate projections, ecological indicators and environmental impact, at the same time revealing new, previously overlooked events. A number of zoonotic and vector-borne diseases already show signs of expanding their northern geographical ranges and appropriate risk assessment and decision support are urgently needed. The deployment of AI-enabled monitoring systems tracking animal populations and environmental changes is of immense potential in the study of transmission under different climate scenarios. In addition, AI's capability to identify new treatments should not only accelerate drug and vaccine discovery but also help predicting their effectiveness, while its contribution to genetic pathogen speciation would assist the evaluation of spillover risks with regard to viral infections from animals to human. Close collaboration between AI experts, epidemiologists and other stakeholders is not only crucial for responding to challenges interconnected with a variety of variables effectively, but also necessary to warrant responsible AI use. Despite its wider successful implementation in many fields, AI should be seen as a complement to, rather than a replacement of, traditional public health measures.

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).

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.

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.

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.

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.

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.

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.