Search Results (1,378)

The livestock sector underpins food security, employment, and rural livelihoods across the Southern African Development Community (SADC), contributing up to 50% of agricultural GDP and supporting more than 60% of rural households. Yet climate change poses escalating threats through heat stress, declining pasture productivity, water scarcity, and vector-borne diseases that compromise productivity and economic resilience. This review identifies and locates effective climate change mitigation strategies along the livestock value chain, spanning production, processing, transport, and consumption, to promote sustainable, low-emission, and inclusive growth in the SADC region. A broad review of 46 peer-reviewed and institutional sources (2000–2024) was undertaken, focusing on livestock-related mitigation within SADC and comparable agro-ecological systems. Strategies were thematically categorized by value-chain stage and assessed for their emission-reduction and livelihood-enhancement potential. Local strategies include genetic improvement for low-methane and heat-tolerant breeds, adaptive rangeland and feed management, renewable-energy adoption in processing, climate-resilient transport infrastructure, and consumer awareness of low-emission products. Evidence suggests potential GHG-emission reductions of 18–30%, coupled with productivity gains and improved smallholder incomes. Coordinated implementation through the SADC Regional Agricultural Investment Plan (2021–2030) and national policies can transform the livestock sector into a climate-resilient driver of inclusive growth. Further research should quantify the socioeconomic feasibility and scaling potential of these strategies across production systems. Successful integration of climate change mitigation imperatives must be tailored to local biophysical conditions (e.g., rainfall, soil type) and socioeconomic contexts (e.g., market access, cultural practices). Full article

Dengue Hemorrhagic Fever (DHF) is a tropical infectious disease transmitted by the Aedes aegypti mosquito and exhibits seasonal patterns with periodic increases in cases throughout the year. The control of vector-borne diseases such as DHF is very important for strengthening public health resilience against climate change, in line with the Sustainable Development Goals (SDGs) for Good Health, Well-being, and Climate Action. Therefore, this study was focused on Bogor city, which experiences high rainfall and continues to face an elevated risk of DHF. The objective was to develop a time series forecasting model to predict DHF outbreaks using Singular Spectrum Analysis (SSA). This is a statistical method for identifying patterns in time series data. Lunar and Solar calendars were adopted to capture seasonal patterns and determine the optimal window length for prediction. The results showed that the Lunar calendar more accurately captured local seasonal variation related to DHF risk. Moreover, the SSA model with one component and a window length of 7 achieved the best performance with a Mean Absolute Percentage Error (MAPE) of 0.0757. The forecast accuracy decreased with longer horizons, but the model provided reliable predictions for short-term periods (approximately 1 month, i.e., up to 4 weeks ahead), which were considered useful for planning DHF mitigation. The results emphasized that the combination of SSA with appropriate calendar systems could improve the accuracy of epidemiological predictions and support vector control policymaking in tropical regions. Full article

Ticks harbor complex microbial communities composed of symbionts, commensals, and tick-borne pathogens (TBPs). Together, these microorganisms form the tick holobiont. Within this system, the tick’s physiological architecture structures microbial communities by distributing microorganisms across distinct tissues. This compartmentalization creates spatially distinct ecological niches, which in turn shape how microbial communities assemble and interact. In this review, we integrate ecological theory with current knowledge of tick microbiome research to examine how pathogen–pathogen and pathogen–microbiome interactions emerge within these tissue-structured microbial communities. We first outline how baseline ecological filters, including tick species, developmental stage, tissue identity, vertical transmission, and environmental context, shape the microbiome configuration through community assembly processes. We then examined how TBPs, as high-impact colonizers, can further modify microbial networks by altering host-mediated selective pressures, influencing interaction topology, and reshaping community stability. Based on these observations, we propose a dual selective pressure framework in which (i) baseline ecological structuring processes and (ii) pathogen-associated selective pressures interact to determine the microbial network configuration and functional outcomes within the tick holobiont. These interacting forces may drive shifts in diversity, modularity, keystone taxa emergence, and network resilience, ultimately influencing vector competence. This review frames the microbial communities within the tick holobiont as spatially structured ecological systems shaped by multilevel selective pressures. This conceptual foundation provides a coherent framework for understanding microbial interactions in arthropod vectors and highlights avenues for mechanistic research and microbiome-based strategies to mitigate tick-borne diseases. Full article

Lyme disease is the most common reported vector-borne disease in North America and is also highly prevalent across Europe. Although tick-borne diseases are uncommon in Singapore, there remains a risk of imported tick-borne diseases among travelers from endemic regions. We present a case series of three pediatric patients with imported Lyme disease managed at a tertiary children’s hospital in Singapore, illustrating the varied clinical presentations of Lyme disease in children. One child developed meningitis following prior antibiotic therapy for Lyme disease, although causality cannot be definitively established. This series aims to highlight key diagnostic considerations and management principles relevant to clinicians practicing in non-endemic regions. Full article

Yellow fever (YF) remains a re-emerging vector-borne zoonotic disease in tropical regions of the Americas despite the availability of an effective vaccine. In South America, the virus is maintained through a jungle transmission cycle involving Haemagogus and Sabethes mosquitoes and non-human primates (NHPs), which act as amplifying hosts and key epidemiological sentinels. This narrative review examines the current status of YF epizootics in South America, with a focus on the role of NHPs in viral circulation, early detection, and spillover risk to human populations. We synthesize recent evidence on epizootic patterns across endemic countries, the differential susceptibility of neotropical primates, and the ecological and environmental drivers influencing transmission, including deforestation, habitat fragmentation, and human encroachment into forested areas. In addition, we analyze current surveillance strategies, including wildlife monitoring, entomological and genomic surveillance, and their integration within a One Health framework. This review highlights that YF epizootics are expanding geographically and are closely linked to environmental change and human–ecosystem interactions. Strengthening integrated, multidisciplinary surveillance systems is essential to improve early detection, guide vaccination strategies, and prevent human outbreaks. These findings underscore the critical importance of operationalizing the One Health approach to enhance preparedness and response to YF in South America. Full article

In 2024 Kenya had a population of 4.78 million camels that contributed to the livelihoods of pastoralist communities in northern Kenya. Previous studies in Kenya, Saudi Arabia and eastern Africa demonstrated high seroprevalence of Middle East respiratory syndrome coronavirus (MERS-CoV)-specific antibodies in dromedary camels, as well as sporadic transmission of MERS-CoV from camels to humans. Based on the MERS-CoV data and the very close contact between owners and their camels in northern Kenya, we speculated that camels may also transmit other zoonotic viruses, such as Rift Valley fever virus (RVFV). In this study, 493 camel and 197 human sera were collected in Marsabit, Kenya, through a cross-sectional survey in 2018 and analyzed for the presence of RVFV IgG antibodies using a laboratory-developed indirect enzyme-linked immunosorbent assay (ELISA). Overall, 15.6% of camels and 7.6% of humans were RVFV IgG-positive; IgG-positive camels were predominantly females in large population herds and IgG-positive humans were engaged in farming-related activities and were greater than 18 years old. Of the eight location groups sampled, two had high camel (site 2 and site 6) and two had high human (site 5 and site 6) RVFV seropositivity rates. These data suggest that camelids, such as dromedary camels, may serve as amplifying hosts for vector-borne zoonotic diseases, such as RVFV, and that humans with frequent farming and camel meat, milk, or camel product contact may have increased risk for RVFV exposure or infection. Full article

The increasing burden of dengue represents a growing global public health concern. Among the factors associated with rising dengue incidence, climate change, particularly increasing temperatures, has been frequently highlighted, alongside other environmental, biological, and social determinants. The emergence of dengue in previously non-endemic areas and its sustained increase in incidence have become increasingly common in recent decades. Objective: The aim of this study was to describe national dengue case trends in Mexico from 1990 to 2023 and to assess their association with temperature over the same period using a descriptive, retrospective analysis of epidemiological surveillance and temperature data. Methods: Epidemiological data on confirmed dengue cases and incidence were obtained from the Morbidity Yearbook of the General Directorate of Epidemiology (DGE) of the Mexican Ministry of Health. These data were used to construct epidemic curves and to analyze the geographic distribution of incidence using quartiles. Temperature data were derived from the national annual mean calculated from monthly reports issued by the National Water Commission (CONAGUA). Associations between temperature and dengue cases and incidence were explored over the study period. Results: Temporal analysis revealed a significant increase in both dengue cases and incidence in Mexico, with a positive association with temperature during the same period. Quartile-based geographic analysis showed that state-level classifications remained relatively stable across periods, with several states clustering within or tending toward the group considered endemic. Conclusions: The results of this study show an increase in cases and incidence of dengue over time, as well as a positive association between cases/incidence of dengue in Mexico and the increase in the national average temperature during the study period; however, due to its descriptive and retrospective design, causal inference is not possible. Dengue transmission is inherently multifactorial, and the observed trends likely reflect the combined influence of climatic conditions, historical expansion of transmission cycles, vector establishment, and unmeasured socio-epidemiological factors. The absence of entomological indicators, additional climatic variables, and spatially or seasonally disaggregated analyses limits the ability to capture localized dynamics. Overall, temperature should be interpreted as a contributing factor within a complex system rather than as the sole driver of dengue trends, underscoring the need for integrated surveillance and control strategies in both endemic and non-endemic regions. Full article

Infections caused by arboviruses, a diverse group of viral pathogens transmitted by biting arthropod vectors, mainly mosquitoes, ticks, and midges, can cause a range of illnesses in humans, from mild, influenza-like symptoms to severe neurological complications including encephalitis and viral hemorrhagic fever. According to 2024 World Health Organization statistics, vector-borne diseases collectively account for over 700,000 human deaths annually, with mosquito-borne infections such as dengue, chikungunya, Zika, and yellow fever constituting a growing and significant proportion of this burden. What was once considered a problem localized to poorly resourced settings in tropical and subtropical regions is now becoming a pervasive global challenge. This is due largely to a combination of factors including climate change, transcontinental travel, and urbanization, with the geographical spread and intensity of arboviral outbreaks reaching unprecedented levels during the current century. In much the same way that the escalating global burden of bacterial infections resistant to antibiotics has been described as a silent pandemic, the insidious rise of arboviruses begs questions regarding outbreak preparedness, prevention and control. Here, we highlight the pressing need for comprehensive strategies that incorporate various health sectors to mitigate the emergence and resurgence of arboviral diseases. Future directives that should be prioritized are outlined. As demonstrated by epidemiological trends and historical outbreak data, an orchestrated global response is critical not only for managing current threats but also for preventing future epidemics. Full article

Ticks serve as primary vectors for a wide array of RNA viruses, yet the diversity and distribution of tick-associated RNA viruses remain incompletely characterized in Hebei province. To address this gap, we conducted a systematic metatranscriptomic investigation of 986 ticks representing six species, collected from the diverse ecological landscapes of Hebei Province in northern China. Our analysis recovered 25 complete or near-complete viral genomes spanning 12 families, including Phenuiviridae, Flaviviridae, and Nairoviridae. Of critical public health significance, we identified Severe Fever with Thrombocytopenia Syndrome Virus (SFTSV) in both Haemaphysalis longicornis and Dermacentor nuttalli. Phylogenetic reconstruction revealed marked geographic stratification where strains from the coastal plains clustered with the dominant Genotype F, while those from the mountainous north formed a characteristic and divergent lineage phylogenetically linked to isolates from Inner Mongolia. Furthermore, a novel viral agent provisionally named Zhangjiakou Hepacivirus was discovered in Haemaphysalis japonica. This virus shared less than 80% nucleotide identity with the rodent-associated Hepacivirus P, consistent with a rodent origin and possible cross-species transmission. Collectively, these findings reveal descriptive variation associated with vector identity, physiological status, and ecological context in shaping viral evolution and underscore the need for continuous metagenomic surveillance to mitigate emerging tick-borne disease risks within a One Health framework. Full article

Lyme disease is the most common vector-borne disease in North America. Predicting Lyme disease incidence is a key component of public health preparedness. Previously, we demonstrated that the volume of data searches on Google Trends for terms related to Lyme disease, such as “Lyme” and “tick bite”, can be used as a tool to predict monthly human Lyme disease incidence at the state level. The objective of this project was to build upon our previous work by adding environmental and canine data to our predictive models for the prediction of state-level human and canine Lyme disease incidence. Human data were acquired from state health departments. Canine data were acquired from IDEXX Laboratories. We hypothesized that incorporating a One Health approach with human, animal, and environmental data would improve the predictive ability of the models. The One Health model performed significantly better (Mean Absolute Error [MAE] = 12.1) in predicting human disease incidence in 6 out of 16 states compared to the environmental data model (MAE = 16.5), human search terms model (MAE = 21.4), canine data (search terms + case count) model (MAE = 31.1), and the canine case data model (MAE = 32.0). For canine Lyme disease incidence, the One Health model performed worse (MAE = 330.5) compared to the canine search data model (MAE = 282.3), the human data (search terms + cases) model (MAE = 248.4), and the environmental data (MAE = 221.5) model. However, even the best-performing models had large prediction errors, which limit practical utility. Future studies should incorporate alternative data streams, such as electronic health records and insurance claims, to test predictive ability. Full article

Dengue fever (DF) is an acute mosquito-borne infectious disease caused by dengue virus (DENV), primarily transmitted by Aedes aegypti and Aedes albopictus. Nearly 4 billion people worldwide are at risk of infection, and the 2024 epidemic reached an unprecedented scale. Severe cases can lead to hemorrhage, shock, and even death, prompting the WHO to classify it as a potential pandemic pathogen. Current prevention and control measures face prominent bottlenecks, including limited applicable populations for vaccines, lack of specific antiviral drugs, and increasing insecticide resistance in mosquito vectors. Notably, susceptible animal models serve as core tools for elucidating the pathogenic mechanisms of dengue virus, screening antiviral drugs, and evaluating vaccine protective efficacy, holding irreplaceable significance. This review systematically summarizes the characteristics, application scenarios, and research progress of mainstream and potential susceptible animal models, including non-human primates, mice, pigs, tree shrews, and bats. It covers model systems with different immune statuses, genetically modified types, and species-specific traits. Among these, mouse models are the most widely used due to their high flexibility and controllable cost, while non-human primate models have become key carriers for preclinical vaccine evaluation by virtue of their high homology with human immune responses. However, current models generally suffer from core bottlenecks, such as incomplete simulation of core severe phenotypes, insufficient restoration of immune mechanisms, unclear viral receptor mechanisms, and lack of unified standards for inoculation doses and evaluation indicators. These limitations make it difficult to accurately replicate key severe disease mechanisms, including antibody-dependent enhancement (ADE) and cytokine storms. Future model development should focus on core requirements—including intact immunity, broad-spectrum susceptibility, and accurate simulation of clinical pathological features—prioritize solving the simulation challenges of ADE and cytokine storms, and establish standardized experimental systems and evaluation criteria. By comprehensively summarizing the advantages and limitations of the existing models, this review provides a systematic reference for the optimization and upgrading of dengue virus-susceptible animal models. It also holds important guiding significance for promoting the in-depth development of basic dengue research, innovation in prevention and control technologies, and clinical transformation and application. Full article

Outbreaks of emerging and re-emerging diseases in both animals and plants are increasing due to climate change, globalization, land-use change, and agricultural intensification. While most pathogen monitoring programs focus on zoonotic threats, wildlife and other organisms in natural habitats can also serve as reservoirs and sentinels for pathogens of agricultural and ecological concern. Plant communities and the pathogens circulating within them are underrepresented in integrated disease monitoring frameworks. This study demonstrates how biodiversity and zoonosis monitoring programs conducted in protected habitats (tallgrass prairies and woodlands) across Illinois, together with insect specimens preserved in biorepositories, can be leveraged to improve knowledge of the identities and ecological associations of a wide range of potential pathogens. We developed an integrative workflow combining taxonomic identification, molecular screening, and epidemiological inference to detect vector-borne plant pathogens from archived insect material. Focusing on Hemiptera (Auchenorrhyncha), we screened specimens for phytoplasmas (Mollicutes), uncultured bacterial plant pathogens transmitted by sap-feeding insects, and characterized host–pathogen associations. At least three distinct phytoplasma strains were detected: ‘Candidatus Phytoplasma asteris’ (16SrI-B), ‘Candidatus Phytoplasma pruni’-related strains (16SrIII), and ‘Candidatus Phytoplasma sacchari’-related strains (16SrXI-H). The latter represents the first documented occurrence of a 16SrXI-H phytoplasma subgroup in Illinois. Overall, we identified five insect specimens harboring phytoplasmas across four preserved sites, all of which were previously unreported for insect–phytoplasma associations. These findings demonstrate the value of existing biodiversity and zoonosis monitoring infrastructures for proactive surveillance of plant pathogens and extending the One Health paradigm to explicitly include natural ecosystems. Full article

Ticks are vectors of pathogens affecting wildlife, livestock, and humans. Knowledge of their abundance, species composition, and ecological drivers is essential for an understanding of tick-borne disease ecology. In this study, four natural sites in Sicily (southern Italy) were selected, and 39 sampling events from April 2024 to August 2025 yielded 1200 ticks: 407 larvae, 474 nymphs, and 319 adults. Five genera were identified (Dermacentor, Haemaphysalis, Rhipicephalus, Ixodes, and Hyalomma); Rhipicephalus bursa was the most abundant species, followed by Haemaphysalis punctata. Most ticks were collected in spring–summer, except at one site where abundance peaked in autumn–winter. A significant association was found between site and season (p < 0.0001), but tick abundance per sampling event did not differ among sites (p = 0.431) or seasons (p = 0.769). No significant correlations emerged between tick abundance and environmental variants. However, significant associations were detected between site and sex (p < 0.0001), site and developmental stage (p < 0.0001), and species and developmental stage (p < 0.0001). Sites exclusively hosting wild boar showed higher species richness. These findings underscore the ecological complexity of tick populations in Mediterranean protected areas and provide baseline data useful for integrated tick surveillance and wildlife health management. Full article

Mosquitoes of the genera Aedes and Culex are major vectors of mosquito-borne diseases, posing serious threats to public health. Accurate detection of these species is therefore crucial for disease prevention and vector control. Traditional identification methods are time-consuming, labor-intensive, and prone to human error. With the rapid development of deep learning, automated mosquito detection has become feasible; however, existing object detection models still struggle with small-object recognition and high computational complexity. To address these limitations, this study constructs a self-developed dataset and proposes a lightweight mosquito detection model based on YOLOv8, termed LW-YOLO. The model integrates HGNetv2, Rep-Ghost, and SCDH modules into the backbone, neck, and head, respectively, enhancing both detection accuracy and computational efficiency. Experimental results show that LW-YOLO achieves a precision of 0.978, recall of 0.972, and mAP50 of 0.987, improving by 1.6%, 1.25%, and 0.7% over the baseline YOLOv8. Meanwhile, its parameter count and computational cost are reduced from 3.0 M and 8.1 GFLOPs to 1.2 M and 4.4 GFLOPs, corresponding to decreases of 60% and 45.7%, respectively. The proposed LW-YOLO model not only achieves accurate detection of Aedes and Culex mosquitoes, providing technical support for mosquito-borne disease prevention, but also offers a promising lightweight solution for deployment on resource-constrained embedded or edge devices. Full article

Inflammatory myocardial involvement has been reported in canine leishmaniosis (CanL); however, studies evaluating the degree of myocardial dysfunction in affected dogs are limited. This prospective study aimed to investigate myocardial involvement in dogs with CanL using conventional and speckle-tracking echocardiography (STE), focusing on the assessment of left ventricular systolic function and myocardial strain. Symptomatic, initially untreated dogs with a diagnosis of leishmaniosis and free from other vector-borne diseases or underlying heart diseases were enrolled (Leish group). Healthy dogs matched for age, body weight, breed, and sex were selected for the control group (C group). At the time of inclusion (T0) and at each follow-up, laboratory tests as well as conventional echocardiographic examination and STE were performed. For strain analysis, apical longitudinal long-axis 4-chamber, 3-chamber, and 2-chamber views were used (2C, 3C, 4C, respectively) to obtain the average global longitudinal strain (GLSAV), which is recognised to have the maximum reliability as an indicator of left ventricular dysfunction in humans. The software obtains GLSAV by averaging the longitudinal strain values from all left-ventricular segments derived from the multiple apical views. After enrolment, dogs were treated with a combination of meglumine and allopurinol and were monitored for six months. Clinical-pathological and echocardiographic data were collected at follow-up at 1, 3, and 6 months after the start of treatment (T1, T2, T3) and compared between the two study groups using appropriate statistical tests. Sixteen dogs composed the C group and nine dogs the Leish group. At T0, none of these dogs had abnormalities in cardiac auscultation, plasma cardiac troponin concentration was within the reference range, and standard echocardiographic examination excluded underlying cardiac diseases. The comparison between C and Leish groups did not show a statistically significant difference in any of the strain parameters analysed (GLSAV, GLS4C, GLS3C, GLS2C). Moreover, strain values in the Leish group did not change significantly over time. In conclusion, in this preliminary study on a limited population of dogs with leishmaniosis, both conventional echocardiography and STE failed to reveal clear changes suggestive of left ventricular systolic dysfunction secondary to possible myocarditis or as a consequence of the systemic disease in dogs with active leishmaniosis. However, further STE studies in larger cohorts of dogs with leishmaniosis are needed to confirm and expand our findings. Full article