Transboundary and Emerging Diseases最新文献

A growing number of studies has highlighted the importance of coinfections in eco-evolutionary processes underlying host–parasite interactions and the resulting epidemiology of zoonotic agents. Small mammals, and particularly rodents, are known to be important reservoirs of many zoonotic pathogens, such as Toxoplasma gondii and Trypanosoma lewisi, that are responsible for toxoplasmosis and atypical trypanosomiasis in humans, respectively. Laboratory experiments on rodent models have shown that primary infection with T. lewisi increases the host sensitivity to other parasites, including T. gondii, following an alteration in the immune response. However, data on potential interactions between these parasites in wild small mammals remain scarce. In this study, we determined the T. lewisi prevalence in 553 small mammals from four localities of Cotonou city, Benin. The results were then combined with T. gondii data previously collected for the same individuals in order to investigate the influence of T. lewisi on T. gondii infection, and vice versa, using co-occurrence tests and generalized linear mixed models (GLMMs). Despite quite high overall prevalence (32.5% and 15.2% for T. lewisi and T. gondii, respectively), we observed a clear and significant segregation between the two parasites. This may be explained by (i) differences in the species-specific receptivity and/or sensitivity of small mammal host species to infection by these two parasites, with Rattus rattus (Rra), Rattus norvegicus (Rno), and Mastomys natalensis (Mna) being the main hosts of T. lewisi, while Crocidura olivieri (Cro) and Mus musculus domesticus (Mus) were the main hosts for T. gondii; and/or (ii) a possibly high mortality in coinfected animals in the wild. Although dedicated experimental studies are required to confirm this pattern, as they stand, our data fail to support that in nature, the infection of small mammals by one of these two parasites favors widespread infection by the second one.

In 2022, a very high number of wild bird deaths associated with the detection of highly pathogenic (HP) H5 avian influenza virus (AIV) lineage Gs/GD/96, clade 2.3.4.4b viruses were unusually observed in Europe between May and September, whereas prior to 2022 most of these HP H5 AIVs detected in wild birds in Europe were almost all detected between October and March and few between April and September. In France, wild birds affected by this virus during this unusual period were mainly seabirds, including larids and sulids. Although the abnormal mortalities in larids and sulids were reported simultaneously, sequencing of the complete genomes of the viruses identified in these seabirds showed that sulids are mainly infected with genotype EA-2020-C, whereas larids are mainly infected with genotype EA-2022-BB. The identification of these two genotypes, therefore, confirmed that there was no direct link between the abnormal mortality observed in sulids and the abnormal mortality observed in larids. These two seabird mortality events can also be distinguished by the evolutionary pattern of the number of detections. Indeed, sulid mortality associated with the EA-2020-C genotype was observed in France only between July and September, corresponding to a single epidemic wave, whereas larid mortality associated with the EA-2022-BB genotype began in France and Europe in May 2022 and then this genotype continued to spread among larids in France in the form of several successive epidemic waves until at least September 2023.

Emerging viral diseases, such as tilapia parvovirus (TiPV), are having a significant economic impact on global tilapia aquaculture. TiPV is responsible for the mass mortality of Nile tilapia (Oreochromis niloticus) and red hybrid tilapia (Oreochromis spp.) in China, India and Thailand. We, therefore, aimed to determine the current status of TiPV infection and distribution and the risk factors associated with TiPV infection in red hybrid tilapia farms in Thailand. In this cross-sectional study, a total of 101 samples, each comprising five moribund fish, were collected from 40 red hybrid tilapia farms across various provinces in Thailand between September 2022 and March 2024. The data on the farm characteristics and management practices were obtained via questionnaires and direct observation. A total of 23 factors were assessed, including six related to farm characteristics, 13 associated with farm management practices and four concerning the presence of other pathogens. The data from 101 samples were analysed using unconditional and mixed-effects logistic regression, revealing a percentage of TiPV infection was 11.88%. Two significant risk factors associated with TiPV infection were identified: the source of the fish (p = 0.020) and the initial fish weight at the stocking date (p = 0.026). Conversely, the feeding method (p = 0.039) was found to be a protective factor against TiPV infection. This study is the first to investigate the epidemiology of TiPV infection in farmed red hybrid tilapia. Our findings are important for improving farm management practices, mitigating the risk of TiPV infection and developing effective disease control strategies.

Coxiellosis, a zoonotic bacterial infection caused by Coxiella burnetii, affects diverse mammalian hosts and is prevalent worldwide, including in South Asia. This study aimed to investigate the epidemiology of Coxiellosis in South Asia, focusing on distribution, host diversity, prevalence, and associated risk factors at the human–animal–environment interface. Following the preferred reporting items for systematic reviews and meta-analyses (PRISMA) guidelines and a registered protocol, online searches were conducted in Embase, PubMed, Scopus, and Web of Science on August 6, 2023, to retrieve articles from the South Asian countries without restrictions on hosts or timeframe. Two authors independently reviewed, extracted data, and assessed quality based on predefined criteria, which were then evaluated and compiled into a single document and analyzed. The review identified 112 articles published between 1954 and 2023. Among humans, the estimated pooled seroprevalence (EPSP) was 9.2%, and the estimated pooled carrier prevalence (EPCP) was 6.2%. Ruminant herd-level EPSP and EPCP were 77.3% and 74.6%, and at the individual level, were 11.9% and 5.3%, respectively. Seroprevalence was significantly influenced by country, tick infestation, reproductive disorders, age, and body condition of ruminants. Nonruminant mammals, such as dogs (16.8%), horses (6.0%), pigs (3.9%), and rodents (14.8%), were also seropositive. Several avian and reptile species showed EPSP rates of 14.5% and 29.2%, respectively. Bacterial DNA was detected in ticks and soil samples, with EPCP of 1.0% and 3.3%, respectively. We recommend prioritizing One Health surveillance and intervention to prevent infections among humans, livestock, poultry, pets, and wildlife. Special emphasis should be placed on aged and emaciated animals, tick infestations, and animals with reproductive disorders.

Wars have devastating effects on all the components of the One Health approach: humans, animals, and ecosystems. Wars and the resulting migratory waves massively disrupt normal animal health services and surveillance. Among other consequences, they adversely impact the early detection, prevention, and control of animal diseases. Uncontrolled movement of animals or their undisposed carcasses, the destruction of wildlife habitats, and the increased interface between humans, wildlife, and domestic animals contribute to uncontrolled transmission and spread of zoonotic pathogens from animals to humans. In the last millennium, zoonotic diseases such as the “Black Death” were triggered by devastating wars and led to the deaths of a large fraction of the human population. However, also recent and ongoing wars carry the risk of an uncontrollable increase in zoonotic diseases. The most significant zoonotic diseases reported during the recent wars are African swine fever, highly pathogenic avian influenza, rabies, leptospirosis, and brucellosis, as well as foodborne and waterborne zoonotic diseases. Indeed, alarming rates of infections by antimicrobial-resistant pathogens such as Mycobacterium tuberculosis go along with wars, as seen in the current Ukraine–Russia conflict. Considering human migration, foodborne and waterborne zoonotic diseases are key health threats for refugees due to the consumption of unsafe food, lack of safe water, and disruption of the water supply and sanitation system. This review summarizes the potential factors and some data associated with the increased risk of zoonotic disease emergence and transmission during recent and ongoing conflicts.

Avian influenza (AI) is a highly contagious zoonotic disease primarily affecting birds with clinical manifestation depending on bird species and virus subtype. Globally, outbreaks have had a large socioeconomic impact. Moreover, highly pathogenic AI virus (HPAIv) outbreaks can pose a public health risk. Detection of AIv, particularly HPAIv, mainly relies on passive surveillance, risking underreporting and delayed detection. This study describes and compares the contribution of passive and active surveillance components on HPAI detection in poultry flocks in years with different HPAIv introduction risk (free, seasonal outbreaks, and year round) in the Netherlands. We drafted a flowchart representing the flow of information and samples between farmers and veterinarians, the competent authority (CA), the national reference lab (NRL), and the private organization Royal GD and identified four different surveillance components and derived the use of each of these components during 2016 (reference), 2019 (low risk), and 2022 (high risk). The first component, “notification of suspicion,” where farmers and veterinarians directly report suspicions to the CA, accounted for 88.4% of farm visits and detected 98.1% of all HPAIv outbreaks. The second component, “testing to exclude” (TTE), consisting postmortem/sample submission and contact with the veterinary helpdesk of GD detected 2% of the cases in 2022. The third and active surveillance component, “protection zone screening,” screens farms closely to a positively detected farm. No outbreaks were detected, suggesting limited between-farm transmission. The last and active surveillance component, mandatory national serological surveillance detected two low pathogenic AI outbreaks. Analysis between years for the passive surveillance components “notification of suspicion” and “TTE,” using chi-square test of independency and odd ratios, showed increased use and farm visits in the high-risk year. However, postmortem-related submissions for TTE were increased in the disease-free year. All components combined detected HPAI or provided valuable information across different risk periods.

Japanese encephalitis virus (JEV) causes ~100,000 clinical cases and 25,000 deaths annually worldwide, mainly in Southeast Asia and the Western Pacific and mostly in children. JEV is transmitted to humans through the bite of mosquitoes that have fed on competent hosts. Abiotic factors, such as seasonal rainfall, influence transmission. Transmission models have an important role in understanding disease dynamics and developing prevention and control strategies to limit the impact of infectious diseases. Our goal was to investigate how transmission models capture JEV infection dynamics and their role in predicting and controlling infection. This was achieved by identifying published JEV transmission models, describing their features and identifying their limitations, to guide future modelling. A Preferred Reporting Items for Systematic Reviews and Meta-Analyses Extension for Scoping Reviews (PRISMA-ScR)-guided scoping review of peer-reviewed JEV transmission models was conducted. Databases searched included PubMed, ProQuest, Scopus, Web of Science and Google Scholar. Of the 881 full-text papers available in English, 29 were eligible for data extraction. Publication year ranged from 1975 to 2023. The median number of host populations represented in each model was 3 (range: 1–8; usually humans, mosquitoes and pigs). Most (72% [n = 21]) models were deterministic, using ordinary differential equations to describe transmission. Ten models were applied (representing a real JEV transmission setting) and validated with field data, while the remaining 19 models were theoretical. In the applied models, data from only a small proportion of countries in Southeast Asia and the Western Pacific were used. Limitations included gaps in knowledge of local JEV epidemiology, vector attributes and the impact of prevention and control strategies, along with a lack of model validation with field data. The lack and limitations of models highlight that further research to understand JEV epidemiology is needed and that there is opportunity to develop and implement applied models to improve control strategies for at-risk populations of animals and humans.

Porcine circovirus type 3 (PCV3) has been identified worldwide and is associated with reproductive and systemic diseases, yet the dynamics of PCV3 within pig farms remain unclear. Building upon our previous study, which initialised comparisons of different sample types for the detection of PCV3 in a sow farm, this study expanded both the range of sample types and the timeline of sampling in piglets and sows to better understand the PCV3 dynamics. This study collected two additional sample types—oropharyngeal swab (OS) and oral fluid (OF) along with placental umbilical cord (PUC) blood and processing fluid (PF) that were used in the previous study. Data were collected from July to August and October 2022; the aforementioned four sample types from 51 litters were collected, and additional OS samples were collected from two to three identified piglets per litter on days 1, 7, 14, and 21 post-farrowing. Besides, blood swabs were taken from 135 sows subject to both PCR test and oestrogen measurement. PF showed the highest detection rates (50/51), while OS and OF revealed 33/51 (95% confidence interval [CI]: 51.2%–76.8%) and 37/51 (95% CI: 59.5%–83.5%) detection rates; both were higher than that of PUC blood (22/51, 95% CI: 30.2%–56.8%). Despite the similarity between OS and OF samples, they did not identify the same population as infected, as the agreement between the samples was only fair at 90% level. The Bayesian generalised linear mixed model suggested PCV3 was more likely to be detected in both OS and OF compared to PUC blood, and PCV3 was present in the farrowing room throughout the pre-weaning period using an OS. Finally, we observed higher PCV3 detection rates in sows after farrowing; however, no evidence was found that such a pattern was associated with the decreased concentration of oestrogen.

Fowl adenovirus (FAdV) is an infectious pathogen causing economic loss to the poultry industry worldwide. Nationwide surveillance was performed to determine the prevalence and distribution of FAdV with 725 samples collected from145 broiler farms in South Korea. A total of 64 strains were identified using PCR and phylogenetic analysis based on a sequence of hexon gene; 23 of the 64 were FAdV-11/D, 19 were FAdV-5/B, and 12 were FAdV-8b/E. FAdV-1/A and FAdV-8a/E were three strains, respectively; only one strain of FAdV-2/D was detected, and there was no FAdV-4/C. FAdV was detected very frequently at 44.1% of 145 farms, but inclusion body hepatitis (IBH) diagnosed by microscopy was confirmed in 13.8%. Poultry productivity was compared between farms with a single disease or noninfection and farms with multiple infectious diseases such as colibacillosis, antigenic variant infectious bursal disease virus (IBDV) infection, infectious bronchitis, and/or IBH. Coinfection of three or more diseases, including IBH, variant IBDV infection, and infectious bronchitis, had a more deleterious effect on poultry productivity. This study provides that prevalence of various species of FAdV and distribution with other diseases and highlights the need for comprehensive measures against multiple diseases concurrently affecting the broiler in South Korea.

Cutaneous leishmaniasis (CL) remains a significant global public health disease, with the critical distinction and exact detection between responsive and unresponsive cases dictating treatment strategies and patient outcomes. However, image-based methods for differentiating these groups are unexplored. This study addresses this gap by developing a deep learning (DL) model utilizing transfer learning to automatically identify responses in CL lesions. A dataset of 102 lesion images (51 per class; equally distributed across train, test, and validation sets) is employed. The DenseNet161, VGG16, and ResNet18 networks, pretrained on a massive image dataset, are fine-tuned for our specific task. The models achieved an accuracy of 76.47%, 73.53%, and 55.88% on the test data, respectively, with a sensitivity of 80%, 75%, and 100% and specificity of 73.68%, 72.22%, and 53.12%, individually. Transfer learning successfully addressed the limited sample size challenge, demonstrating the models’ potential for real-world application. This work underscores the significance of automated response detection in CL, paving the way for treatment and improved patient outcomes. While acknowledging limitations like the sample size, the need for collaborative efforts is emphasized to expand datasets and further refine the model. This approach stands as a beacon of hope in the contest against CL, illuminating the path toward a future where data-driven diagnostics guide effective treatment and alleviate the suffering of countless patients. Moreover, the study could be a turning point in eliminating this important global public health and widespread disease.