Forest Disease Research Articles

First evidence of transovarial transmission of Kyasanur Forest disease virus in Haemaphysalis and Rhipicephalus ticks in the wild

BackgroundKyasanur forest disease virus (KFDV) is a tick-borne flavivirus causing debilitating and potentially fatal disease in people in the Western Ghats region of India. The transmission cycle is complex, involving multiple vector and host species, but there are significant gaps in ecological knowledge. Empirical data on pathogen-vector-host interactions and incrimination have not been updated since the last century, despite significant local changes in land use and the expansion of KFD to new areas. Mathematical models predict that transovarial transmission, whereby adult female ticks pass KFDV infections to their offspring, plays an important role in the persistence of KFD, but this has not been shown in the wild. Here we set out to establish whether transovarial transmission of KFDV was occurring under natural field conditions by assessing whether host-seeking larvae were positive for KFDV.MethodsTicks were sampled by dragging and flagging across a broad range of habitats within the agro-forest matrix at 49 sites in two districts: Shivamogga, Karnataka and Wayanad, Kerala (September 2018-March 2019), and larvae were tested for KFDV by PCR.ResultsIn total, larval ticks from 7 of the 49 sites sampled tested positive for KFDV, indicating that transovarial transmission is occurring. Of the 13 KFDV-positive larval samples, 3 came from around houses and gardens, 5 from crops (3 from harvested rice paddy and 2 from areca plantation), 1 from teak plantation and 4 (2 from 1 transect) from forests. Five different tick species were found to have KFDV-positive larvae: Haemaphysalis spinigera, H. bispinosa, Rhipicephalus annulatus, R. microplus and an unidentifiable species of Haemaphysalis (no close match in GenBank).ConclusionsOur empirical confirmation of transovarial transmission has important implications for understanding and predicting KFD dynamics, suggesting that ticks may act as a reservoir for KFDV. Moreover, small mammals and cattle may play crucial roles in transmission if small mammals are the main hosts for larvae infected via transovarial transmission, and cattle support large numbers of infected female adult ticks. This first report of transovarial transmission of KFDV, and within a hitherto undescribed range of vectors and habitats, will help disease managers improve KFD surveillance and mitigation strategies, ultimately leading to communities becoming more resilient to the risk of this tick-transmitted disease.Graphical

A Pine Wilt Disease Detection Model Integrated with Mamba Model and Attention Mechanisms Using UAV Imagery

Pine wilt disease (PWD) is a highly destructive worldwide forest quarantine disease that has the potential to destroy entire pine forests in a relatively brief period, resulting in significant economic losses and environmental damage. Manual monitoring, biochemical detection and satellite remote sensing are frequently inadequate for the timely detection and control of pine wilt disease. This paper presents a fusion model, which integrates the Mamba model and the attention mechanism, for deployment on unmanned aerial vehicles (UAVs) to detect infected pine trees. The experimental dataset presented in this paper comprises images of pine trees captured by UAVs in mixed forests. The images were gathered primarily during the spring of 2023, spanning the months of February to May. The images were subjected to a preprocessing phase, during which they were transformed into the research dataset. The fusion model comprised three principal components. The initial component is the Mamba backbone network with State Space Model (SSM) at its core, which is capable of extracting pine wilt features with a high degree of efficacy. The second component is the attention network, which enables our fusion model to center on PWD features with greater efficacy. The optimal configuration was determined through an evaluation of various attention mechanism modules, including four attention modules. The third component, Path Aggregation Feature Pyramid Network (PAFPN), facilitates the fusion and refinement of data at varying scales, thereby enhancing the model’s capacity to detect multi-scale objects. Furthermore, the convolutional layers within the model have been replaced with depth separable convolutional layers (DSconv), which has the additional benefit of reducing the number of model parameters and improving the model’s detection speed. The final fusion model was validated on a test set, achieving an accuracy of 90.0%, a recall of 81.8%, a map of 86.5%, a parameter counts of 5.9 Mega, and a detection speed of 40.16 FPS. In comparison to Yolov8, the accuracy is enhanced by 7.1%, the recall by 5.4%, and the map by 3.1%. These outcomes demonstrate that our fusion model is appropriate for implementation on edge devices, such as UAVs, and is capable of effective detection of PWD.

A Digital Management System for Monitoring Epidemics and the Management of Pine Wilt Disease in East China

The precise monitoring of forest pest and disease outbreaks is a crucial prerequisite for efficient prevention and control. With the extensive application of remote sensing monitoring technology in the forest, a large amount of data on pest and disease outbreaks has been collected. It is highly necessary to practically apply these data and improve the efficiency of forest pest and disease monitoring and management. In this study, a Digital Forest Protection (DFP) system based on the geographic information system (GIS) was designed and developed for pine wilt disease (PWD) monitoring and management, a devastating forest disease caused by the pine wood nematode, Bursaphelenchus xylophilus. The DFP system consists of a mobile app for data collection and a web-based data analysis platform. Meanwhile, artificial intelligence and deep-learning methods had been conducted to integrate a real-time unmanned aerial vehicle (UAV) remote sensing monitoring with PWD detection. This system was implemented in PWD monitoring and management in Zhejiang Province, China, and has been applied in data collection under certain circumstances, including the manual epidemic survey, the UAV epidemic survey, and eradication monitoring, as well as trunk injection. Based on DFP system, the effective monitoring of PWD outbreaks could be achieved, and corresponding efficient management strategies could be formulated in a timely manner. This allows for the possibility to optimize the integrated management strategy of PWD on a large geographic scale.

МОНИТОРИНГ БИОРАЗНООБРАЗИЯ ЕЛЬНИКОВ ЕВРОПЕЙСКОЙ ЧАСТИ РОССИИ ПОСЛЕ ПРИРОДНЫХ И АНТРОПОГЕННЫХ «КАТАСТРОФ»

We analyzed the main trends of the change in the species richness of plant communities after catastrophic natural (catastrophicwindthrow, fires, large bark beetle outbreaks) and anthropogenic (clear felling) disturbances. The main factor determining the increase in species richness was the intensity of ecosystem disturbance after ca- tastrophes. The greatest disturbance was caused by clear-cut logging, when all timber was removed from the felling area by tractors. Fires also could totally destroy stands and all components of the forest ecosystem. Large- scale wind damage affected only the trees of the upper layer, while all other components of the plant communities were slightly damaged. The bark beetle outbreaks cause the death only of upper layer spruce trees, while the whole forest ecosystem remains unchanged. The degree of disturbance of stands, undergrowth, herb-dwarf scrubs layer, moss cover, litter and soil during the dieback of spruce forests as a result of ‘catastrophes’ determines the degree of vegetation recovery in disturbed areas. We studied the spruce forest in the zone of coniferous-broadleaved forests of the Moscow region, affected by an outbreak of typograph bark beetle.Dynamics of understory plant species richness was fundamentally different after stand salvage logging following beetle outbreaks and in unlogged standswith preserved deadwood. Monitor- ing observations of plant communities during 10 years on permanent sample plots revealed the main feature of the plant species succession after clear felling. For the first six years succession passed through the herb and the shrub stages with a sharp increase in species and structural diversity of plant communities. Only in the forest stage the species composition characteristic of the forest was restored. In the deadwood stand the forest communities was preserved and no differences in the dynamics of species richness of the original undisturbed forest were detected. Only dominance of species in the stand and herb-shrub layer changed. If man don't clear cut dead and wind- damaged areas of spruce forests, it will result in development of mixed forests with increased resistance to pests and forest diseases. Monocultures of spruce forests may be replaced by mixed forests, increasing the diversity of forests.

Estimation of Damaged Regions by the Bark Beetle in a Mexican Forest Using UAV Images and Deep Learning

Sustainable forestry for the management of forest resources is more important today than ever before because keeping forests healthy has an impact on human health. Recent advances in Unmanned Aerial Vehicles (UAVs), computer vision, and Deep Learning (DL) models make remote sensing for Forest Insect Pest and Disease (FIPD) possible. In this work, a UAV-based remote sensing process, computer vision, and a Deep Learning framework are used to automatically and efficiently detect and map areas damaged by bark beetles in a Mexican forest located in the Hidalgo State. First, the image dataset of the region of interest (ROI) is acquired by a UAV open hardware platform. To determine healthy trees, we use the tree crown detection prebuilt Deepforest model, and the trees diseased by pests are recognized using YOLOv5. To map the area of the damaged region, we propose a method based on morphological image operations. The system generates a comprehensive report detailing the location of affected zones, the total area of the damaged regions, GPS co-ordinates, and both healthy and damaged tree locations. The overall accuracy rates were 88% and 90%, respectively. The results obtained from a total area of 8.2743 ha revealed that 16.8% of the surface was affected and, of the 455 trees evaluated, 34.95% were damaged. These findings provide evidence of a fast and reliable tool for the early evaluation of bark beetle impact, which could be expanded to other tree and insect species.

Purification and Characterization of Kyasanur Forest Disease Virus EDIII domain of major Envelope Glycoprotein

Current research efforts are underway to create novel approaches for the efficient diagnosis, monitoring, and mitigation of Kyasanur Forest Disease Virus (KFDV) infections. Flavivirus subunit-based vaccines based on envelope glycoprotein EDIII are now in preclinical and clinical research stages. Efficient purification and isolation methods for surface immunogenic viral antigens, including the recombinant envelope immunoglobulin-like domain III (rEDIII) protein, are crucial for the production and manufacturing of promising vaccine candidates that have been extensively assessed in previous literature. Here, we describe a method for high-yield expression, purification, and refolding of a KFDV rEDIII protein from a bacterial expression system. The KFDV rEDIII protein is extracted from the inclusion bodies in urea denaturing buffer followed by nickel-nitrilotriacetic acid (Ni-NTA) affinity chromatography. The purified, denatured KFDV rEDIII protein was subsequently refolded using a step-wise gradient urea dilution via the dialysis method. The circular dichroism and Fourier transform infrared spectroscopy analysis confirms that the refolded KFDV rEDIII maintains the native secondary conformation majorly containing β-strands. Our study provides valuable insights into the design and expression strategies of rEDIII as a novel subunit vaccine candidate against KFDV.

Prediction of protein interactions between pine and pine wood nematode using deep learning and multi-dimensional feature fusion.

Pine Wilt Disease (PWD) is a devastating forest disease that has a serious impact on ecological balance ecological. Since the identification of plant-pathogen protein interactions (PPIs) is a critical step in understanding the pathogenic system of the pine wilt disease, this study proposes a Multi-feature Fusion Graph Attention Convolution (MFGAC-PPI) for predicting plant-pathogen PPIs based on deep learning. Compared with methods based on single-feature information, MFGAC-PPI obtains more 3D characterization information by utilizing AlphaFold and combining protein sequence features to extract multi-dimensional features via Transform with improved GCN. The performance of MFGAC-PPI was compared with the current representative methods of sequence-based, structure-based and hybrid characterization, demonstrating its superiority across all metrics. The experiments showed that learning multi-dimensional feature information effectively improved the ability of MFGAC-PPI in plant and pathogen PPI prediction tasks. Meanwhile, a pine wilt disease PPI network consisting of 2,688 interacting protein pairs was constructed based on MFGAC-PPI, which made it possible to systematically discover new disease resistance genes in pine trees and promoted the understanding of plant-pathogen interactions.

The potential of remote sensing for improved infectious disease ecology research and practice.

Outbreaks of COVID-19 in humans, Dutch elm disease in forests, and highly pathogenic avian influenza in wild birds and poultry highlight the disruptive impacts of infectious diseases on public health, ecosystems and economies. Infectious disease dynamics often depend on environmental conditions that drive occurrence, transmission and outbreaks. Remote sensing can contribute to infectious disease research and management by providing standardized environmental data across broad spatial and temporal extents, often at no cost to the user. Here, we (i) conduct a review of primary literature to quantify current uses of remote sensing in disease ecology; and (ii) synthesize qualitative information to identify opportunities for further integration of remote sensing into disease ecology. We identify that modern advances in airborne remote sensing are enabling early detection of forest pathogens and that satellite data are most commonly used to study geographically widespread human diseases. Opportunities remain for increased use of data products that characterize vegetation, surface water and soil; provide data at high spatio-temporal and spectral resolutions; and quantify uncertainty in measurements. Additionally, combining remote sensing with animal telemetry can support decision-making for disease management by providing insights into wildlife disease dynamics. Integrating these opportunities will advance both research and management of infectious diseases.

Forest local community perception on sengon pest and disease and forest soil properties

Abstract Banten Forest Management Unit (FMU) is undertaking an effort to develop sengon (Falcataria falcata) as a wood-producing commodity alongside teak, acacia, and mahagony. Local farmers carrying out sengon propagation assist in this development effort. Improper practices of propagating and cultivating sengon and the need for more public knowledge regarding pests and diseases that attack sengon raise questions regarding the public’s perception of cultivation techniques and control of plant pests and diseases. Training on sengon cultivation and introducing pests and diseases on sengon plants was held on 25 November 2023 at the Cigembor Wood Collecion Place (WCP), inten Jaya Village, Lebak, Banten. In total, 30 participants who were members of a forest local community (LMDH) at Perum Perhutani FMU Banten attended this activity. The training method involves presentations and direct practice in the field. The results of the pre- and post-test evaluation showed an increase of 25% in knowledge of pests and diseases in sengon plants and 10% in knowledge of sengon cultivation. The test results also showed that 8 out of 10 participants could differentiate between forest pests and diseases. Furthermore, 10 out of 10 participants knew the harvest age for sengon. In general, this training increased the participants’ knowledge and experience in cultivating sengon and identifying pests and diseases in sengon plants.

Early effects of forest diseases on the Odonata assemblage of a mountain stream in the southern Iberian Peninsula

Early effects of forest diseases on the Odonata assemblage of a mountain stream in the southern Iberian Peninsula

Insights into the virome of Hyalomma marginatum in the Danube Delta: a major vector of Crimean-Congo hemorrhagic fever virus in Eastern Europe

BackgroundTicks are significant vectors of pathogens, including viruses, bacteria, and protozoa. With approximately 900 tick species worldwide, many are expanding their geographical range due to changing socioeconomic and climate factors. The Danube Delta, one of Europe’s largest wetlands, is an ecosystem that, despite its ecological importance, remains understudied concerning the risk of introducing new tick-borne viruses. This region serves as a critical habitat for migratory birds, which can carry ticks over long distances, potentially introducing exotic tick species and their pathogens into the local ecosystem. Hyalomma marginatum ticks, the primary vector of Crimean-Congo hemorrhagic fever virus (CCHFV), are of particular concern due to their expanding presence in Europe and potential to spread other arboviruses. In addition to being the primary vector for CCHFV, Hyalomma sp. ticks are capable of transmitting other pathogens of medical and veterinary importance, including Dugbe virus, West Nile virus, African horse sickness virus, and Kyasanur forest disease virus. Therefore, it is essential to monitor the presence of Hyalomma sp. ticks while simultaneously surveilling arbovirus circulation in tick populations to mitigate the risk of arboviral outbreaks.MethodsIn this work, we used an RNA sequencing technique to analyze the virome of H. marginatum ticks collected from the Danube Delta Biosphere Reserve, Romania, one of the major bird migration hubs from Africa to Europe.ResultsAmong the viral taxa detected in H. marginatum ticks, sequences belonging to Volzhskoe tick virus (VTV), Balambala tick virus (BMTV) and Bole tick virus 4 (BTV4) were identified. In addition, we report the first identification of a novel Rhabdoviridae-related virus, Hyalomma marginatum rhabdovirus (HMRV). No CCHFV or any CCHFV-related nairovirus were detected in this study.ConclusionsTo summarize, detecting new viruses is essential for monitoring potential viral outbreaks. Our research expands the understanding of virus diversity in Eastern Europe, including the identification of novel viruses. This insight is crucial for monitoring viruses that may pose risks to both animal and human health, such as CCHFV.Graphical

Prevalence of Crimean-Congo Haemorrhagic Fever virus and Ganjam virus among livestock & ticks in Wayanad, Kerala.

The Western Ghat region of Kerala has abundant flora and domestic and wild animal species that provide an ideal ecological niche for the ticks. Kyasanur Forest Disease virus (KFDV), a zoonotic tick-borne virus causing Haemorrhagic fever, commonly known as monkey fever is prevalent in the area. The tick infestations have been documented in the majority of household animals in Wayanad and risks are further amplified as the majority of the animals reared, graze in the adjoining forest areas and these animals are often housed in and around farmer's dwellings. Therefore, the farmers are at high risk of contracting deadly tick-borne diseases, and surveillance in animals can act as an indicator of disease hotspots. Even though the tick-borne KFDV is endemic to the area, the surveillance data on other significant zoonotic tick-borne viruses such as CCHFV and GANV are scanty. Overall CCHFV seroprevalence of 2.33 was reported amongst livestock sampled. Species-wise percent seropositivity was 1.01 and 5.10 in cattle and goats, respectively. The study revealed the presence of ticks belonging to 3 genera viz. Rhipicephalus, Amblyomma, and Haemaphysalis. Despite the CCHFV seroprevalence, CCHFV and GANV could not be detected in ticks. Even though ticks were negative for the CCHFV genome, seroprevalence in adjoining taluks indicates the local virus circulation. Therefore serological as well as molecular investigation with a large number of samples collected at the peak of tick activity across the western ghat would help identify the potential hotspot for CCHFV.

Fusarium species associated with naturally regenerated Fagus sylvatica seedlings affected by Phytophthora

AbstractFusarium diseases in forests, especially in naturally regenerated seedlings, are poorly recognized. Recently the rapid and severe decline of natural regeneration of European beech caused by Phytophthora spp. has been observed in Poland. The diseased beech seedlings were also co-infected by Fusarium species. The aim of this study was to isolate and identify the role of Fusarium species that may play a role in the decline of beech seedlings in Poland. A total of 487 Fusarium isolates were obtained from 450 symptomatic and asymptomatic beech seedlings: 466 from symptomatic plants and 21 from asymptomatic plants. Based on comparisons for two genes and phylogenetic analysis, all Fusarium strains were separated into 14 phylogenetically distinct species belonging to four Fusarium species complexes (SC), namely the F. incarnatum-equiseti species complex (FIESC), the F. nisikadoi species complex (FNSC), the F. sambucinum species complex (FSSC), and the F. tricinctum species complex (FTSC). Fusarium avenaceum and F. paeoniae were the most frequently isolated species from both above- and below-ground organs of seedlings affected by Phytophthora. In the pathogenicity trials, the most virulent species for beech seedlings were F. avenaceum and F. sporotrichioides. However, Fusarium isolates were significantly less aggressive compared to the P. × cambivora (positive control). Our results demonstrate that beech seedlings were frequently colonized by various Fusarium species. Fusarium species do not play an important role in the reduction of naturally regenerated European beech seedlings due to their low aggressiveness.

Genotype-by-environment interaction in Dutch elm disease resistance

Dutch elm disease (DED) is a devastating forest disease. Recently, the deployment of native resistant cultivars has prompted initiatives of elm reintroduction in Europe and North America. It is known that DED resistance varies with the tree genotype and is influenced by climatic factors. However, genotype-by-environment interactions in DED resistance remain largely unexplored. In this work, we examined whether there is genetic variation in DED resistance plasticity and the roles of tree growth, phenology and anatomical traits in plastic responses. We established two experimental plots with 12 Ulmus minor genotypes in two environmentally contrasting locations in Spain: Madrid, under an inland continental climate, and Valencia, under a coastal Mediterranean climate. We monitored growth and phenology detecting high plasticity in both traits. In the inland plot, genotypes were taller and showed a more synchronized phenology than in the coast. A first DED-pathogen inoculation was carried out 45 days after the average flushing date in each location, after which trees exhibited more symptoms inland. A second inoculation was carried out by dividing the coastal plot trees into early and late flushing trees and inoculating each group at 45 days after its average flushing date. Therein, susceptibility rose to a level close to the inland plot. In both inoculations, we detected a significant genotype-by-location interaction in DED resistance. The xylem anatomy revealed high plasticity and a significant genotype-by-location interaction in most traits. In the coastal trial, trees formed narrower vessels and stored more starch before inoculation. The synchrony of leaf phenology, higher growth rate, lower starch reserves and higher structural vulnerability of earlywood to DED possibly favored susceptibility in the inland plot. The varying responses of genotypes in phenology, growth and anatomy at both locations were likely related to the differences in DED resistance, which can have important consequences for elm reintroduction.

Species Diversity, Host Association, and Evolutionary History of Cronartium: An Important Global Fungal Pathogen to Trees.

Pine stem rust, the most damaging and widespread forest disease occurring in pine trees in the Northern Hemisphere, is primarily caused by Cronartium species (Pucciniales, Melampsorineae). While the phylogenetic relationships of major Cronartium species have been largely elucidated, there is limited understanding of their species diversity and the evolutionary processes shaping their distribution patterns. In this work, we performed broad sampling and sequencing of Cronartium taxa in China together with additional sequence data and other accessions in NCBI to investigate the diversification and to estimate the divergence time of major evolutionary events in this genus. Molecular dating analysis suggested that the divergence of the genus Cronartium probably was around 91.78 Ma during the Upper Cretaceous. It is believed that Cronartium species may have originated in Asia and North America, with intercontinental dispersals occurring primarily during the Middle Eocene, Middle Miocene, and Pliocene. These dispersal events likely took place through the North Atlantic Land Bridge, the De Geer Route, and the Bering Land Bridge, and subsequently diverged through sporadic dispersal and vicariance events. Furthermore, our analysis of host associations revealed that the diversification of Cronartium species was correlated with their telial-hosts, and some species may have experienced host jump events, indicating a complex interplay between host specificity and pathogen-host interaction during Cronartium evolution.

Battling Kyasanur forest disease: the race for robust and long-lasting Kyasanur forest disease vaccine

Battling Kyasanur forest disease: the race for robust and long-lasting Kyasanur forest disease vaccine

Detection Methods for Pine Wilt Disease: A Comprehensive Review.

Pine wilt disease (PWD), caused by the nematode Bursaphelenchus xylophilus, is a highly destructive forest disease that necessitates rapid and precise identification for effective management and control. This study evaluates various detection methods for PWD, including morphological diagnosis, molecular techniques, and remote sensing. While traditional methods are economical, they are limited by their inability to detect subtle or early changes and require considerable time and expertise. To overcome these challenges, this study emphasizes advanced molecular approaches such as real-time polymerase chain reaction (RT-PCR), droplet digital PCR (ddPCR), and loop-mediated isothermal amplification (LAMP) coupled with CRISPR/Cas12a, which offer fast and accurate pathogen detection. Additionally, DNA barcoding and microarrays facilitate species identification, and proteomics can provide insights into infection-specific protein signatures. The study also highlights remote sensing technologies, including satellite imagery and unmanned aerial vehicle (UAV)-based hyperspectral analysis, for their capability to monitor PWD by detecting asymptomatic diseases through changes in the spectral signatures of trees. Future research should focus on combining traditional and innovative techniques, refining visual inspection processes, developing rapid and portable diagnostic tools for field application, and exploring the potential of volatile organic compound analysis and machine learning algorithms for early disease detection. Integrating diverse methods and adopting innovative technologies are crucial to effectively control this lethal forest disease.

Unraveling Sepsis Epidemiology in a Low- and Middle-Income Intensive Care Setting Reveals the Alarming Burden of Tropical Infections and Antimicrobial Resistance: A Prospective Observational Study (MARS-India).

Our study addresses the sepsis research gap in lower middle-income countries, notably India. Here, we investigate community-acquired sepsis comprehensively and explore the impact of tropical microbiology on aetiology and outcomes. MARS-India was a prospective observational study from Dec-2018 to Sep-2022 in a tertiary-care hospital in South India. Adult patients within 24hrs of ICU admission meeting the Sepsis 3.0 definition were enrolled, with 6-months follow-up (http://clinicaltrials.gov number NCT03727243). Over 4000 patients were screened on ICU admission, with 1000 unique patients meeting the inclusion criteria. Median age was 55 years (IQR: 44-65) with a male preponderance (66%). Almost half the cohort resided in villages (46.5%) and 74.6% worked in the primary sector. Mortality in-hospital was 24.1%. Overall, ∼54% had confirmed microbiological diagnosis. Over 18% had a viral cause of sepsis. Surprisingly, we identified leptospirosis (10.6%), scrub typhus (4.1%), dengue (3.7%) and Kyasanur forest disease (1.6%) as notables causes of sepsis. All these infections showed seasonal variation around the monsoon. In community-acquired infections we observed substantial resistance to 3rd generation cephalosporins and carbapenems. In India, sepsis disproportionally affects a younger and lower socio-economic demographic, yielding high mortality. Tropical and viral sepsis carry a significant burden. Analyzing local data, we pinpoint priorities for public health and resources, offering valuable insights for global sepsis research.

Study of the clinical manifestations and risk factors in people affected during the first Kyasanur Forest Disease outbreak in Goa, India: A mixed method study.

Kyasanur Forest Disease (KFD) is a tick-borne, zoonotic viral hemorrhagic fever, previously known to be endemic to the state of Karnataka, India. The first outbreak of KFD in Goa state was reported in the Sattari taluka, in North Goa in 2015. This study aimed to investigate the outbreak and report the clinical manifestations and risk factors in people diagnosed with KFD. A mixed methods approach was used, which included a case series report and 19 in-depth interviews (IDIs) conducted with people diagnosed with KFD. The recorded IDIs were transcribed and translated and themes were coded for the analysis. There were 73 suspected cases of which 30 were confirmed to have KFD using RT-PCR. There were four suspected deaths of which two were confirmed by RT-PCR. Most of the affected individuals were found to be dependent on the forest for their livelihood. Most of the people in the region were engaged in cashew plantations and had to travel to the forest to fetch firewood and cashew, hence were at a higher risk. They lived near the forest. The lack of hemorrhagic manifestation was noteworthy in the current outbreak. The 'One Health' approach should be implemented to control KFD. Tick bite prevention measures coupled with vaccination of high-risk groups and intensive health education should be carried out, especially before the transmission season. There is a need to have high clinical suspicion for KFD in the region bearing in mind the non-hemorrhagic manifestation in this outbreak.

Methodological and interpretative considerations in the study of the first Kyasanur Forest disease outbreak in Goa, India.

Methodological and interpretative considerations in the study of the first Kyasanur Forest disease outbreak in Goa, India.