2022年 · 第11卷第03期

The One Health (OH) concept has been promoted widely around the globe. OH framework is expected to be applied as an integrated approach to support addressing zoonotic diseases as a significant global health issue and to improve the efficiency and effectiveness of zoonosis prevention and control. This review is intended to overview the social impact of the implementation of OH on zoonosis prevention and control.

A scoping review of studies in the past 10 years was performed to overview the integration feature of OH in zoonosis prevention and control and the social impacts of OH. PubMed and Web of Science were searched for studies published in English between January 2011 and June 2021. The included studies were selected based on predefined criteria.

Thirty-two studies were included in this review, and most of them adopted qualitative and semi-qualitative methods. More than 50% of the studies focused on zoonosis prevention and control. Most studies were conducted in low- and middle-income countries in Africa and Asia. Applying OH approach in diseases control integrates policymakers, stakeholders, and academics from various backgrounds. The impact of OH on economic is estimated that it may alleviate the burden of diseases and poverty in the long term, even though more financial support might be needed at the initial stage of OH implementation. OH implementation considers social and ecological factors related to zoonosis transmission and provides comprehensive strategies to assess and address related risks in different communities according to regions and customs.

Based on reviewed literature, although there seems to be a lack of guidelines for assessing and visualizing the outcomes of OH implementation, which may limit the large-scale adoption of it, evidence on the contributions of implementing OH concepts on zoonosis prevention and control indicates long-term benefits to society, including a better integration of politics, stakeholders and academics to improve their cooperation, a potential to address economic issues caused by zoonosis, and a comprehensive consideration on social determinants of health during zoonosis prevention and control.

A One Health approach has been increasingly mainstreamed by the international community, as it provides for holistic thinking in recognizing the close links and inter-dependence of the health of humans, animals and the environment. However, the dearth of real-world evidence has hampered application of a One Health approach in shaping policies and practice. This study proposes the development of a potential evaluation tool for One Health performance, in order to contribute to the scientific measurement of One Health approach and the identification of gaps where One Health capacity building is most urgently needed.

We describe five steps towards a global One Health index (GOHI), including (i) framework formulation; (ii) indicator selection; (iii) database building; (iv) weight determination; and (v) GOHI scores calculation. A cell-like framework for GOHI is proposed, which comprises an external drivers index (EDI), an intrinsic drivers index (IDI) and a core drivers index (CDI). We construct the indicator scheme for GOHI based on this framework after multiple rounds of panel discussions with our expert advisory committee. A fuzzy analytical hierarchy process is adopted to determine the weights for each of the indicators.

The weighted indicator scheme of GOHI comprises three first-level indicators, 13 second-level indicators, and 57 third-level indicators. According to the pilot analysis based on the data from more than 200 countries/territories the GOHI scores overall are far from ideal (the highest score of 65.0 out of a maximum score of 100), and we found considerable variations among different countries/territories (31.8-65.0). The results from the pilot analysis are consistent with the results from a literature review, which suggests that a GOHI as a potential tool for the assessment of One Health performance might be feasible.

GOHI—subject to rigorous validation—would represent the world’s first evaluation tool that constructs the conceptual framework from a holistic perspective of One Health. Future application of GOHI might promote a common understanding of a strong One Health approach and provide reference for promoting effective measures to strengthen One Health capacity building. With further adaptations under various scenarios, GOHI, along with its technical protocols and databases, will be updated regularly to address current technical limitations, and capture new knowledge.

Giardia intestinalis is one of the most common causes of diarrhoea worldwide. Molecular techniques have greatly improved our understanding of the taxonomy and epidemiology of this parasite. Co-infection with mixed (sub-) assemblages has been reported, however, Sanger sequencing is sometimes unable to identify shared subtypes between samples involved in the same epidemiologically linked event, due to samples showing multiple dominant subtypes within the same outbreak. Here, we aimed to use a metabarcoding approach to uncover the genetic diversity within samples from sporadic and outbreak cases of giardiasis to characterise the subtype diversity, and determine if there are common sequences shared by epidemiologically linked cases that are missed by Sanger sequencing.

We built a database with 1109 unique glutamate dehydrogenase (gdh) locus sequences covering most of the assemblages of G. intestinalis and used gdh metabarcoding to analyse 16 samples from sporadic and outbreak cases of giardiasis that occurred in New Zealand between 2010 and 2018.

There is considerable diversity of subtypes of G. intestinalis present in each sample. The utilisation of meta-barcoding enabled the identification of shared subtypes between samples from the same outbreak. Multiple variants were identified in 13 of 16 samples, with Assemblage B variants most common, and Assemblages E and A present in mixed infections.

This study showed that G. intestinalis infections in humans are frequently mixed, with multiple subtypes present in each host. Shared sequences among epidemiologically linked cases not identified through Sanger sequencing were detected. Considering the variation in symptoms observed in cases of giardiasis, and the potential link between symptoms and (sub-) assemblages, the frequency of mixed infections could have implications for our understanding of host-pathogen interactions.

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.

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.

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.

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.

Due to sustained control activities, the prevalence of Schistosoma japonicum infection in humans, livestock and snails has decreased significantly in P. R. China, and the target has shifted from control to elimination according to the Outline of Healthy China 2030 Plan. Applying highly sensitive methods to explore the presence of S. japonicum infection in its intermediate host will benefit to assess the endemicity or verify the transmission interruption of schistosomiasis accurately. The aim of this study was to access the presence of S. japonicum infection by a loop-mediated isothermal amplification (LAMP) method through a 5-year longitudinal study in five lake provinces along the Yangtze River.

Based on previous epidemiological data, about 260 villages with potential transmission risk of schistosomiasis were selected from endemic counties in five lake provinces along the Yangtze River annually from 2015 to 2019. Snail surveys were conducted in selected villages by systematic sampling method and/or environmental sampling method each year. All live snails collected from field were detected by microscopic dissection method, and then about one third of them were detected by LAMP method to assess the presence of S. japonicum infection with a single blind manner. The infection rate and nucleic acid positive rate of schistosomes in snails, as well as the indicators reflecting the snails’ distribution were calculated and analyzed. Fisher’s exact test was used to examine any change of positive rate of schistosomes in snails over time.

The 5-year survey covered 94,241 ha of environment with 33,897 ha of snail habitats detected accumulatively. Totally 145.3 ha new snail habitats and 524.4 ha re-emergent snail habitats were found during 2015-2019. The percentage of frames with snails decreased from 5.93% [45,152/761,492, 95% confidence intervals (CI): 5.88-5.98%] in 2015 to 5.25% (30,947/589,583, 95% CI: 5.19-5.31%) in 2019, while the mean density of living snails fluctuated but presented a downward trend generally from 0.20 snails/frame (155,622/761,492, 95% CI: 0.17-0.37) in 2015 to 0.13 snails/frame (76,144/589,583, 95% CI: 0.11-0.39) in 2019. A total of 555,393 live snails were collected, none of them was positive by dissection method. Totally 17 pooling snail samples were determined as positives by LAMP method among 8716 pooling samples with 174,822 of living snails, distributed in 12 villages of Hubei, Hunan, Jiangxi and Anhui provinces. The annual average positive rate was 0.41% (95% CI: 0.13-0.69%) in 2015, 0% in 2016, 0.36% (95% CI: 0.09-0.63%) in 2017, 0.05% (95% CI: 0-0.16%) in 2018, 0.05% (95% CI: 0-0.15%) in 2019, respectively, presenting a downward trend from 2015 to 2019 with statistical significance (χ² = 11.64, P < 0.05).

The results suggest that S. japonicum infection still persisted in nature along the Yangtze River and traditional techniques might underestimate the prevalence of schistosomiasis in its intermediate hosts. Exploring and integrating molecular techniques into national surveillance programme could improve the sensitivity of surveillance system and provide guidance on taking actions against schistosomiasis.

Border malaria is one of the most intractable problems hindering malaria elimination worldwide. Movement of both the human population and anopheline mosquitoes infected with Plasmodium spp. can cause cross-border malaria transmission. The Yunnan border area was still hyperendemic for malaria in the early part of this century. The objective of this case study was to analyze the strategies, interventions and impacts of malaria control and elimination in the Yunnan border area.

A total of 10,349 malaria cases and 17.1 per 10,000 person-years of annual parasite incidence (API) were reported in the border area in 2003. Based on natural village-based stratification, integrated interventions, including mass drug administration for radical cures and preventive treatment, clinically presumptive treatment of all febrile patients for malaria and indoor residual spraying or dipping bed nets with insecticides were successfully carried out from 2003 to 2013. The overall API was reduced to 0.6 per 10,000 person-years by 2013, while effective cross-border collaboration interventions dramatically reduced the malaria burden in the neighbouring border areas of Myanmar. From 2014 forward, the comprehensive strategy, including universal coverage of surveillance to detect malaria cases, a rapid response to possible malaria cases and effective border collaboration with neighbouring areas, successfully eliminated malaria and prevented reintroduction of malaria transmission in the Yunnan border area.

In Yunnan malaria burden has successfully reduced by dynamically accurate stratification and comprehensive interventions; and then the region achieved elimination and prevented reintroduction of malaria transmission through intensive surveillance, rapid response and border collaboration. Other border areas should perform their own intervention trials to develop their own effective strategy.

Infectious diseases pandemic can lead to explosive effect with unpredictability on the world, as exemplified the bubonic-pneumonic plague pandemic in the fourteenth century [1], the 1918 influenza pandemic and the coronavirus disease 2019 (COVID-19) pandemic caused by severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2). From January 2020 to May 2022, a total of 527.5 million individuals were suffered from an COVID-19, and more than 6.2 million individuals were died [2]. The global all-age rate of excess mortality due to the COVID-19 pandemic was 120.3 deaths per 100,000 [3]. Its threat to human beings, especially those with underlying health issues, no one overlook this outcome.