Background: Strategic investment in new interventions is now crucial for Neglected Tropical Diseases (NTDs) control and elimination programs as progress has stalled and existing tools are challenged. The objective of this study is to propose a novel, One Health-driven framework to guide and accelerate the elimination of NTDs.Methods: The proposed simulation framework consists of three parts: the disease dynamics model (the white box), the emulation process (the black box with its series of supervised machine learning methods) and their joint contribution to the optimisation of the intervention strategy (the grey box). We describe how this optimised strategy supports the development of new disease intervention tools using leishmaniasis as example.Findings: A leishmaniasis transmission dynamics model was established and three intervention measures proposed. A total of six epidemiological settings were defined and five health goals were chosen. The comparison of a number of simulators consistently indicated Gaussian process (GP) to be the best ML core function. The most promising leishmaniasis control tool was found to be the innovative tool, i.e. sandfly repellent dog collar. Our result demonstrates the multi-functional utility of our approach in guiding the development of intervention products and focusing resources where they would be most beneficial.Interpretation: This innovative framework provides a theoretical basis and technological pipeline for R&D investment and development of new intervention measures and/or products in countries with limited resources.Funding: This work research was supported by the National Natural Science Foundation of China, (42177264), Hainan Natural Science Foundation (821CXTD440). National Key Research and Development Program of People’s Republic of China (grant no. 2021YFC2300800 and 2021YFC2300804).Declaration of Interest: The authors declare that they have no known competing financial interests or personal relationships that could have appeared to influence the work reported in this paper.
Background: Due to emerging issues such as global climate change and zoonotic disease pandemic, the One Health approach has gained more attention since the turn of the 21st century. Although One Health thinking has its deep roots and early application in Chinese history, significant gaps exist in its implementation in China’s real-world practice at the complex interface of the human-animal-environment.Methods: We abstracted the data from the global One Health index (GOHI) study and analysed China's performance in selected fields based on Structure-Process-Outcome (SPO) model. By comparing China to the Belt & Road and G20 countries, the advances and gaps in China's One Health performance were determined and analysed.Findings: For the selected scientific fields, China performs generally better in ensuring food security and controlling AMR and worse on addressing climate change. Based on the SPO model, the 'structure' indicators have the highest proportion (80·00%) of high ranking and the 'outcome' indicators have the highest proportion (20·00%) of low ranking. When compared with Belt and Road countries, China scores higher than the median of Belt & Road countries in almost all indicators (16 out of 18) under the selected scientific fields. When compared with G20 countries, China ranks highest in food security (scores 72·56, ranks 6th), and lowest in climate change (48·74, 11th).Interpretation: The results indicated that China has made significant efforts to enhance the application of One Health approach in national policies, while still faces challenges of translating policies into practical measures. It is recommended that a holistic One Health action framework should be established for China in accordance with diverse social and cultural contexts, with a particular emphasis on braking data barrier and mobilizing stakeholders both domestically and globally. Implementation mechanisms, with clarified stakeholder responsibilities and incentives, should be improved along with top-level design.Funding: National Natural Science Foundation of China.Declaration of Interests: The authors declare no conflicts of interest.Ethics Approval: Not applicable.
Abstract Background: In 2015, a China-UK-Tanzania tripartite pilot project was implemented in south-eastern Tanzania to explore a new model for reducing malaria burden and possibly scaling-out the approach into other malaria endemic countries. The 1,7-malaria Reactive Community-based Testing and Response (1,7-RCTR) which is a locally-tailored approach for reporting febrile malaria cases in endemic villages was developed to stop transmission and plasmodium life-cycle. The (1,7-RCTR) utilizes existing health facility data and locally trained community health workers to conduct community-level testing and treatment. Methods: The pilot project was implemented from September 2015 to June 2018. Matched malaria incidence pairs of control and intervention wards were chosen. The latter arm was selected for the 1,7-mRCTR approach leaving control wards relying on existed programs. The 1,7-mRCTR activities included community testing and treatment of malaria infection. Malaria case-to-suspect ratios at health facilities (HF) were aggregated by villages, weekly to identify the village with the highest ratio. Community-based mobile test stations (cMTS) were used for conducting mass testing and treatment. Random household surveys were done in the control and intervention wards before (baseline) and after (endline) the program. The primary outcome was the baseline and endline difference of malaria prevalence in the control and intervention wards measured by the interaction term of ‘time’ (post vs. pre) and group in a logistic model. We also studied the malaria incidence reported at the health facilities during the intervention. Results: Overall 85 rounds of 1,7-mRCT conducted in the intervention wards significantly reduced the odds of malaria infection by 66% (adjusted OR 0.34, 95%CI 0.26,0.44, p<0001) beyond the effect of the standard programs. Malaria prevalence in the intervention wards declined by 81% (from 26% (95% CI, 23.7, 7.8), at baseline to 4.9% (95% CI, 4.0,5.9) at endline). Villages receiving the 1,7-mRCT had a case ratio decreased by over 15.7% (95%CI, -33, 6) compared to baseline. Conclusion: The 1,7-mRCTR approach reduced significantly the malaria burden in the areas of moderate and high transmission in southern Tanzania. This locally-tailored approach could accelerate malaria control and elimination efforts. The results provide the impetus for further evaluation of the effectiveness and scaling up of this type of approach in other high malaria burden countries in Africa, including Tanzania.
With the development and application of distributed cloud computing, the problem of assigning workflow tasks to computational resources has become more and more prominent. It involves multiple constraints and optimization objectives, and is a typical NP-hard problem. Existing evolutionary algorithms face local optimum and premature convergence problems. Considering these facts, we proposed a multi-objective evolutionary algorithm with elitism strategy (MOEAES) in this paper. To avoid local optimum, MOEAES uses a new crossover operator called Random Sub-Sequence Exchange Crossover (RSSEX), and it introduces a multi-population-based elitism strategy to accelerate the algorithm. Finally, experimental validation is carried out, which shows that MOEAES achieves performance improvement in terms of solution quality and convergence speed comparing to other methods.
In this paper, the history and current situation of environmental health standardization in China are reviewed, and the experience and shortcomings in the process of environmental health standardization in China are analyzed, suggestions for the next step of environmental health standards are also put forward.
Abstract Background Visceral leishmaniasis (VL) is one of the most important neglected tropical diseases. Although VL was controlled in several regions of China during the last century, the mountain-type zoonotic visceral leishmaniasis (MT-ZVL) has reemerged in the hilly areas of China in recent decades. The purpose of this study was to construct an indicator framework for assessing the risk of the MT-ZVL in China, and to provide guidance for preventing disease. Methods Based on a literature review and expert interview, a 3-level indicator framework was initially established in November 2021, and 28 experts were selected to perform two rounds of consultation using the Delphi method. The comprehensive weight of the tertiary indicators was determined by the Delphi and the entropy weight methods. Results Two rounds of Delphi consultation were conducted. Four primary indicators, 11 secondary indicators, and 35 tertiary indicators were identified. The Delphi-entropy weight method was performed to calculate the comprehensive weight of the tertiary indicators. The normalized weights of the primary indicators were 0.268, 0.261, 0.242, and 0.229, respectively, for biological factors, interventions, environmental factors, and social factors. The normalized weights of the top four secondary indicators were 0.122, 0.120, 0.098, and 0.096, respectively, for climatic features, geographical features, sandflies, and dogs. Among the tertiary indicators, the top four normalized comprehensive weights were the population density of sandflies (0.076), topography (0.057), the population density of dogs, including tethering (0.056), and use of bed nets or other protective measures (0.056). Conclusions An indicator framework of transmission risk assessment for MT-ZVL was established using the Delphi-entropy weight method. The framework provides a practical tool to evaluate transmission risk in endemic areas.
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