Ecological and Socio-Economic Determinants of Livestock Animal Leptospirosis in the Russian Arctic

Identifying the Drivers Related to Animal Reservoirs, Environment, and Socio-Demography of Human Leptospirosis in Different Community Types of Southern Chile: An Application of Machine Learning Algorithm in One Health Perspective

Leptospirosis is a zoonosis with global public health impact, particularly in poor socio-economic settings in tropical regions. Transmitted through urine-contaminated water or soil from rodents, dogs, and livestock, leptospirosis causes over a million clinical cases annually. Risk factors include outdoor activities, livestock production, and substandard housing that foster high densities of animal reservoirs. This One Health study in southern Chile examined Leptospira serological evidence of exposure in people from urban slums, semi-rural settings, and farm settings, using the Extreme Gradient Boosting algorithm to identify key influencing factors. In urban slums, age, shrub terrain, distance to Leptospira-positive households, and neighborhood housing density were contributing factors. Human exposure in semi-rural communities was linked to environmental factors (trees, shrubs, and lower vegetation terrain) and animal variables (Leptospira-positive dogs and rodents and proximity to Leptospira-positive households). On farms, dog counts, animal Leptospira prevalence, and proximity to Leptospira-contaminated water samples were significant drivers. The study underscores that disease dynamics vary across landscapes, with distinct drivers in each community setting. This case study demonstrates how the integration of machine learning with comprehensive cross-sectional epidemiological and geospatial data provides valuable insights into leptospirosis eco-epidemiology. These insights are crucial for informing targeted public health strategies and generating hypotheses for future research.

Evaluation of protective efficacy, serological responses, and cytokine modulation induced by polyvalent Leptospira vaccines in hamsters

Whole-cell inactivated vaccines (bacterins) are the only licensed vaccines available for leptospirosis prevention and control, especially in domestic and farm animals. However, despite their widespread use, inconsistencies in their efficacy have been reported. Because immunity induced by bacterins is mainly mediated by antibodies against leptospiral lipopolysaccharides, the involvement of cellular responses is not well-known. The aim of this study was to investigate the efficacy and characterize the humoral and cellular immune responses induced by whole-cell inactivated leptospirosis bacterin formulations containing serovars Bratislava, Canicola, Copenhageni, Grippotyphosa, Hardjoprajitno, and Pomona. For the potency test, hamsters were immunized with one dose of polyvalent bacterins (either commercial or experimental) and then challenged with a virulent Pomona strain. Serological (MAT and IgM and IgG-ELISA) and cellular (cytokine transcription in blood evaluated by RT-qPCR) analyses were performed. The results revealed that vaccination with either bacterin formulation was able to protect 90-100% of the hamsters infected with the Pomona serovar, although most of the surviving animals remained as renal carriers. Specific agglutinating antibodies and significant levels of IgM, IgG, and IgG2 (P < 0.05) that were able to react with the six serovars present in the vaccine formulations were produced, indicating that the vaccines can potentially provide immunity against all strains. The protective immunity of these vaccines was mainly mediated by balanced a Th1/Th2 response, characterized by increased IFN-γ, IL-10 and IL-α transcription. These data support the importance of characterizing immunological responses involved in bacterin efficacy and investing in the improvement of these vaccine formulations.

The Effect of Temperature on the Distribution of Zoonotic Pathogens in Livestock and Wildlife Populations: A Systematic Review

Background

Evidence for the impact of climate change on the distribution of zoonoses has largely focussed on the burden in humans and is lacking information on the effect of temperature on nonvectorborne zoonoses that are transmitted indirectly through contaminated environments. We present a systematic literature review on the impact of temperature on the distribution of zoonotic pathogens in mammalian livestock and wildlife populations, with a focus on nonvectorborne zoonoses that can be spread through air, water, food, and soil.

Methods

We systematically searched PubMed, Scopus, and Web of Science, as well as grey literature, and screened titles, abstracts, and full text. English, peer-reviewed, and full text studies were included if they: focused on temperature; considered incursion, distributional burden or risk; and focused on a zoonotic pathogen in livestock and/or wildlife populations of mammalian vertebrates that can be transmitted through indirect pathways without a nonmammalian and nonvertebrate intermediate host.

Results

Temperature was an important determinant of zoonoses distribution across all 17 studies included in the final review, with 11 studies finding a positive association. The majority of studies focused on parasites (7) and bacteria (9) and were conducted in the northern hemisphere. Two studies provided future climate projections that identified areas of increasing prevalence and expanded risk for pathogens that were already established. However, no studies specifically investigated the risk of zoonotic incursion with increasing temperature. Few studies explored how local variations in temperature and urbanisation interact with distal changes like Arctic warming to affect the distribution and spread of nonvectorborne pathogens through food, water, and soil.

Conclusions

The review's findings point to the value of a One Health approach to biosecurity that builds on the interconnected relationship between human, animal, plant, and environmental health. Such research is urgently needed to inform the prioritisation and risk assessment of zoonoses more comprehensively in a rapidly changing climate.

A Review of Circumpolar Arctic Marine Mammal Health-A Call to Action in a Time of Rapid Environmental Change

The impacts of climate change on the health of marine mammals are increasingly being recognised. Given the rapid rate of environmental change in the Arctic, the potential ramifications on the health of marine mammals in this region are a particular concern. There are eleven endemic Arctic marine mammal species (AMMs) comprising three cetaceans, seven pinnipeds, and the polar bear (Ursus maritimus). All of these species are dependent on sea ice for survival, particularly those requiring ice for breeding. As air and water temperatures increase, additional species previously non-resident in Arctic waters are extending their ranges northward, leading to greater species overlaps and a concomitant increased risk of disease transmission. In this study, we review the literature documenting disease presence in Arctic marine mammals to understand the current causes of morbidity and mortality in these species and forecast future disease issues. Our review highlights potential pathogen occurrence in a changing Arctic environment, discussing surveillance methods for 35 specific pathogens, identifying risk factors associated with these diseases, as well as making recommendations for future monitoring for emerging pathogens. Several of the pathogens discussed have the potential to cause unusual mortality events in AMMs. Brucella, morbillivirus, influenza A virus, and Toxoplasma gondii are all of concern, particularly with the relative naivety of the immune systems of endemic Arctic species. There is a clear need for increased surveillance to understand baseline disease levels and address the gravity of the predicted impacts of climate change on marine mammal species.

Leptospirosis modelling using hydrometeorological indices and random forest machine learning

Leptospirosis is a zoonosis that has been linked to hydrometeorological variability. Hydrometeorological averages and extremes have been used before as drivers in the statistical prediction of disease. However, their importance and predictive capacity are still little known. In this study, the use of a random forest classifier was explored to analyze the relative importance of hydrometeorological indices in developing the leptospirosis model and to evaluate the performance of models based on the type of indices used, using case data from three districts in Kelantan, Malaysia, that experience annual monsoonal rainfall and flooding. First, hydrometeorological data including rainfall, streamflow, water level, relative humidity, and temperature were transformed into 164 weekly average and extreme indices in accordance with the Expert Team on Climate Change Detection and Indices (ETCCDI). Then, weekly case occurrences were classified into binary classes "high" and "low" based on an average threshold. Seventeen models based on "average," "extreme," and "mixed" indices were trained by optimizing the feature subsets based on the model computed mean decrease Gini (MDG) scores. The variable importance was assessed through cross-correlation analysis and the MDG score. The average and extreme models showed similar prediction accuracy ranges (61.5-76.1% and 72.3-77.0%) while the mixed models showed an improvement (71.7-82.6% prediction accuracy). An extreme model was the most sensitive while an average model was the most specific. The time lag associated with the driving indices agreed with the seasonality of the monsoon. The rainfall variable (extreme) was the most important in classifying the leptospirosis occurrence while streamflow was the least important despite showing higher correlations with leptospirosis.