Identification of bio-climatic determinants and potential risk areas for Kyasanur forest disease in Southern India using MaxEnt modelling approach

Hard Ticks as Vectors: The Emerging Threat of Tick-Borne Diseases in India

Hard ticks (Ixodidae) play a critical role in transmitting various tick-borne diseases (TBDs), posing significant global threats to human and animal health. Climatic factors influence the abundance, diversity, and vectorial capacity of tick vectors. It is imperative to have a comprehensive understanding of hard ticks, pathogens, eco-epidemiology, and the impact of climatic changes on the transmission dynamics of TBDs. The distribution and life cycle patterns of hard ticks are influenced by diverse ecological factors that, in turn, can be impacted by changes in climate, leading to the expansion of the tick vector's range and geographical distribution. Vector competence, a pivotal aspect of vectorial capacity, involves the tick's ability to acquire, maintain, and transmit pathogens. Hard ticks, by efficiently feeding on diverse hosts and manipulating their immunity through their saliva, emerge as competent vectors for various pathogens, such as viruses, parasites and bacteria. This ability significantly influences the success of pathogen transmission. Further exploration of genetic diversity, population structure, and hybrid tick vectors is crucial, as they play a substantial role in influencing vector competence and complicating the dynamics of TBDs. This comprehensive review deals with important TBDs in India and delves into a profound understanding of hard ticks as vectors, their biology, and the factors influencing their vector competence. Given that TBDs continue to pose a substantial threat to global health, the review emphasizes the urgency of investigating tick control strategies and advancing vaccine development. Special attention is given to the pivotal role of population genetics in comprehending the genetic diversity of tick populations and providing essential insights into their adaptability to environmental changes.

Kyasanur Forest Disease: A Comprehensive Review

Vector-borne microbial diseases are ubiquitous, and their management remains elusive. Such diseases with zoonotic potential result in public health challenges requiring additional control and preventive measures. Despite their cosmopolitan presence, vector-borne infections are neglected due to their endemicity in specified geographical regions. The Kyasanur forest disease (KFD) caused by the Kyasanur forest disease virus (KFDV) is among such diseases transmitted through ticks and localized to India. Despite its prevalence, high transmissibility, and potential to cause fatalities, KFDV has not been given the deserved attention by the governments. Further, KFDV circulates in the rural and wild geographical areas threatening infections to people living in these areas with limited access to medical and healthcare. Therefore, physicians, healthcare workers, and the general population need to understand the KFDV and its ecology, epidemiology, transmission, pathogenesis, laboratory diagnosis, and control and prevention as described comprehensively in this review.

Impact of climate change on potential distribution of Dickeya zeae causal agent of stalk rot of maize in Sialkot district Pakistan

Maize (Zea mays) is an influential crop in its production across the world. However, the invasion of many phytopathogens greatly affects the maize crop yield at various hotspot areas. Of many diseases, bacterial stalk rot of maize caused by Dickeya zeae results in severe yield reduction, thus the need for efficient management is important. Further, to produce epidemiological information for control of disease outbreaks in the hot spot regions of Sialkot District, Punjab Pakistan, extensive field surveys during 2021 showed that out of 266 visited areas, the highest disease incidence ranging from 66.5 to 78.5% while the lowest incidence was ranging from 9 to 20%. The Maxent modeling revealed that among 19 environmental variables, four variables including temperature seasonality (bio-4), mean temperature of the wettest quarter (bio-8), annual precipitation (bio-12), and precipitation of driest month (bio-14) were significantly contributing to disease distribution in current and coming years. The study outcomes revealed that disease spread will likely increase across four tehsils of Sialkot over the years 2050 and 2070. Our findings will be helpful to policymakers and researchers in devising effective disease management strategies against bacterial stalk rot of maize outbreaks in Sialkot, Pakistan.

Distribution of sika deer (Cervus nippon) and the bioclimatic impact on their habitats in South Korea

Invasive species and climate change are primary factors influencing biodiversity, and examining the behavior of invasive species is essential for effective conservation management. Here, we report the global distribution of the sika deer (Cervus nippon) based on locations reported in published literature (Google Scholar), the Global Biodiversity Information Facility (GBIF) database, and the International Union for Conservation of Nature report. We used the maximum entropy (Maxent) model to examine the impact of climate change on sika deer habitats in South Korea based on GBIF occurrence data and WorldClim bioclimatic variables. Habitat suitability analysis was performed using the Maxent model under Representative Concentration Pathways (RCPs) 4.5 and 8.5 (for predicted climatic conditions in both 2050 and 2070) to project the effects of different climate change scenarios on South Korean sika deer habitats. We identified that the sika deer is distributed in 39 countries worldwide. Due to climate change effects, South Korean sika deer habitats will decline by approximately 24.98% and 20.63% (under RCP 4.5) and by 50.51% and 57.35% (under RCP 8.5) by 2050 and 2070, respectively. Our findings shed light on sika deer ecology and provide reference data for future conservation management strategies and policy design.

Predicting Kyasanur forest disease in resource-limited settings using event-based surveillance and transfer learning

In recent years, the reports of Kyasanur forest disease (KFD) breaking endemic barriers by spreading to new regions and crossing state boundaries is alarming. Effective disease surveillance and reporting systems are lacking for this emerging zoonosis, hence hindering control and prevention efforts. We compared time-series models using weather data with and without Event-Based Surveillance (EBS) information, i.e., news media reports and internet search trends, to predict monthly KFD cases in humans. We fitted Extreme Gradient Boosting (XGB) and Long Short Term Memory models at the national and regional levels. We utilized the rich epidemiological data from endemic regions by applying Transfer Learning (TL) techniques to predict KFD cases in new outbreak regions where disease surveillance information was scarce. Overall, the inclusion of EBS data, in addition to the weather data, substantially increased the prediction performance across all models. The XGB method produced the best predictions at the national and regional levels. The TL techniques outperformed baseline models in predicting KFD in new outbreak regions. Novel sources of data and advanced machine-learning approaches, e.g., EBS and TL, show great potential towards increasing disease prediction capabilities in data-scarce scenarios and/or resource-limited settings, for better-informed decisions in the face of emerging zoonotic threats.

Using ecological niche modeling to predict the potential distribution of scrub typhus in Fujian Province, China

BACKGROUND

Despite the increasing number of cases of scrub typhus and its expanding geographical distribution in China, its potential distribution in Fujian Province, which is endemic for the disease, has yet to be investigated.

METHODS

A negative binomial regression model for panel data mainly comprising meteorological, socioeconomic and land cover variables was used to determine the risk factors for the occurrence of scrub typhus. Maximum entropy modeling was used to identify the key predictive variables of scrub typhus and their ranges, map the suitability of different environments for the disease, and estimate the proportion of the population at different levels of infection risk.

RESULTS

The final multivariate negative binomial regression model for panel data showed that the annual mean normalized difference vegetation index had the strongest correlation with the number of scrub typhus cases. With each 0.1% rise in shrubland and 1% rise in barren land there was a 75.0% and 37.0% increase in monthly scrub typhus cases, respectively. In contrast, each unit rise in mean wind speed in the previous 2 months and each 1% increase in water bodies corresponded to a decrease of 40.0% and 4.0% in monthly scrub typhus cases, respectively. The predictions of the maximum entropy model were robust, and the average area under the curve value was as high as 0.864. The best predictive variables for scrub typhus occurrence were population density, annual mean normalized difference vegetation index, and land cover types. The projected potentially most suitable areas for scrub typhus were widely distributed across the eastern coastal area of Fujian Province, with highly suitable and moderately suitable areas accounting for 16.14% and 9.42%, respectively. Of the total human population of the province, 81.63% reside in highly suitable areas for scrub typhus.

CONCLUSIONS

These findings could help deepen our understanding of the risk factors of scrub typhus, and provide information for public health authorities in Fujian Province to develop more effective surveillance and control strategies in identified high risk areas in Fujian Province.

Climate Change Influences the Spread of African Swine Fever Virus

Climate change is an inevitable and urgent issue in the current world. African swine fever virus (ASFV) is a re-emerging viral animal disease. This study investigates the quantitative association between climate change and the potential spread of ASFV to a global extent. ASFV in wild boar outbreak locations recorded from 1 January 2019 to 29 July 2022 were sampled and investigated using the ecological distribution tool, the Maxent model, with WorldClim bioclimatic data as the predictor variables. The future impacts of climate change on ASFV distribution based on the model were scoped with Representative Concentration Pathways (RCP 2.6, 4.5, 6.0, and 8.5) scenarios of Coupled Model Intercomparison Project 5 (CMIP5) bioclimatic data for 2050 and 2070. The results show that precipitation of the driest month (Bio14) was the highest contributor, and annual mean temperature (Bio1) was obtained as the highest permutation importance variable on the spread of ASFV. Based on the analyzed scenarios, we found that the future climate is favourable for ASFV disease; only quantitative ratios are different and directly associated with climate change. The current study could be a reference material for wildlife health management, climate change issues, and World Health Organization sustainability goal 13: climate action.