Modelling tick bite risk by combining random forests and count data regression models

Spatiotemporal trends and covariates of Lyme borreliosis incidence in Poland, 2010-2019

Lyme borreliosis (LB) is the most commonly diagnosed tick-borne disease in the northern hemisphere. Since an efficient vaccine is not yet available, prevention of transmission is essential. This, in turn, requires a thorough comprehension of the spatiotemporal dynamics of LB transmission as well as underlying drivers. This study aims to identify spatiotemporal trends and unravel environmental and socio-economic covariates of LB incidence in Poland, using consistent monitoring data from 2010 through 2019 obtained for 320 (aggregated) districts. Using yearly LB incidence values, we identified an overall increase in LB incidence from 2010 to 2019. Additionally, we observed a large variation of LB incidences between the Polish districts, with the highest risks of LB in the eastern districts. We applied spatiotemporal Bayesian models in an all-subsets modeling framework to evaluate potential associations between LB incidence and various potentially relevant environmental and socio-economic variables, including climatic conditions as well as characteristics of the vegetation and the density of tick host species. The best-supported spatiotemporal model identified positive relationships between LB incidence and forest cover, the share of parks and green areas, minimum monthly temperature, mean monthly precipitation, and gross primary productivity. A negative relationship was found with human population density. The findings of our study indicate that LB incidence in Poland might increase as a result of ongoing climate change, notably increases in minimum monthly temperature. Our results may aid in the development of targeted prevention strategies.

Predicting the current and future risk of ticks on livestock farms in Britain using random forest models

The most abundant tick species in northern Europe, Ixodes ricinus, transmits a range of pathogens that cause disease in livestock. As I. ricinus distribution is influenced by climate, tick-borne disease risk is expected to change in the future. The aims of this work were to build a spatial model to predict current and future risk of ticks on livestock farms across Britain. Variables relating both to tick hazard and livestock exposure were included, to capture a niche which may be missed by broader scale models. A random forest machine learning model was used due to its ability to cope with correlated variables and interactions. Data on tick presence and absence on sheep and cattle farms was obtained from a retrospective questionnaire survey of 926 farmers. The ROC of the final model was 0.80. The model outputs matched observed patterns of tick distribution, with areas of highest tick risk in southwest and northwest England, Wales, and west Scotland. Overall, the probability of tick presence on livestock farms was predicted to increase by 5-7 % across Britain under future climate scenarios. The predicted increase is greater at higher altitudes and latitudes, further increasing the risk of tick-borne disease on farms in these areas.

Models of spatial analysis for vector-borne diseases studies: A systematic review

Background and Aim: Vector-borne diseases (VBDs) constitute a global problem for humans and animals. Knowledge related to the spatial distribution of various species of vectors and their relationship with the environment where they develop is essential to understand the current risk of VBDs and for planning surveillance and control strategies in the face of future threats. This study aimed to identify models, variables, and factors that may influence the emergence and resurgence of VBDs and how these factors can affect spatial local and global distribution patterns. Materials and Methods: A systematic review was designed based on identification, screening, selection, and inclusion described in the research protocols according to the preferred reporting items for systematic reviews and meta-analyses guide. A literature search was performed in PubMed, ScienceDirect, Scopus, and SciELO using the following search strategy: Article type: Original research, Language: English, Publishing period: 2010–2020, Search terms: Spatial analysis, spatial models, VBDs, climate, ecologic, life cycle, climate variability, vector-borne, vector, zoonoses, species distribution model, and niche model used in different combinations with "AND" and "OR." Results: The complexity of the interactions between climate, biotic/abiotic variables, and non-climate factors vary considerably depending on the type of disease and the particular location. VBDs are among the most studied types of illnesses related to climate and environmental aspects due to their high disease burden, extended presence in tropical and subtropical areas, and high susceptibility to climate and environment variations. Conclusion: It is difficult to generalize our knowledge of VBDs from a geospatial point of view, mainly because every case is inherently independent in variable selection, geographic coverage, and temporal extension. It can be inferred from predictions that as global temperatures increase, so will the potential trend toward extreme events. Consequently, it will become a public health priority to determine the role of climate and environmental variations in the incidence of infectious diseases. Our analysis of the information, as conducted in this work, extends the review beyond individual cases to generate a series of relevant observations applicable to different models.

Predicting downed woody material carbon stocks in forests of the conterminous United States

Downed woody material (DWM) is a unique part of the forest carbon cycle serving as a pool between living biomass and subsequent atmospheric emission or transference to other forest pools. Thus, DWM is an individually defined pool in national greenhouse gas inventories. The diversity of DWM carbon drivers (e.g., decay, tree mortality, or wildfire) and associated high spatial variability make this a difficult-to-predict component of forest ecosystems. Using the now fully established nationwide inventory of DWM across the United States (US), we developed models, which substantially improved predictions of stand-level DWM carbon density relative to the current national-reporting model ('previous' model, here). The previous model was developed from published DWM carbon densities prior to the NFI DWM inventory. Those predictions were tested using NFI DWM carbon densities resulting in a poor fit to the data (coefficient of determination, or R² = 0.03). We present new random forest (RF) and stochastic gradient boosted (SGB) regression models to prediction DWM carbon density on all NFI plots and spatially on all forest land pixels. We evaluated various biotic and abiotic regression predictors, and the most important were standing dead trees, long-term annual precipitation, and long-term maximum summer temperature. A RF model scored best for expanding predictions to NFI plots (R² = 0.31), while an SGB model was identified for DWM carbon predictions based on purely spatial data (i.e., NFI-plot-independent, with R² = 0.23). The new RF model predicts conterminous US DWM carbon stocks to be 15% lower than the previous model and 2% higher than NFI data expanded according to inventory design-based inference. The new NFI data-driven models not only improve the predictions of DWM carbon density on all plots, they also provide flexibility in extending these predictions beyond the NFI to make spatially explicit and spatially continuous estimates of DWM carbon on all forest land in the US.

Assessment of vector-host-pathogen relationships using data mining and machine learning

Infectious diseases, including vector-borne diseases transmitted by arthropods, are a leading cause of morbidity and mortality worldwide. In the era of big data, addressing broad-scale, fundamental questions regarding the complex dynamics of these diseases will increasingly require the integration of diverse datasets to produce new biological knowledge. This review provides a current snapshot of the systematic assessment of the relationships between microbial pathogens, arthropod vectors and mammalian hosts using data mining and machine learning. We employ PRISMA to identify 32 key papers relevant to this topic. Our analysis shows an increasing use of data mining and machine learning tasks and techniques, including prediction, classification, clustering, association rules mining, and deep learning, over the last decade. However, it also reveals a number of critical challenges in applying these to the study of vector-host-pathogen interactions at various systems biology levels. Here, relevant studies, current limitations and future directions are discussed. Furthermore, the quality of data in relevant papers was assessed using the FAIR (Findable, Accessible, Interoperable, Reusable) compliance criteria to evaluate and encourage reproducibility and shareability of research outcomes. Although shortcomings in their application remain, data mining and machine learning have significant potential to break new ground in understanding fundamental aspects of vector-host-pathogen relationships and their application in this field should be encouraged. In particular, while predictive modeling, feature engineering and supervised machine learning are already being used in the field, other data mining and machine learning methods such as deep learning and association rules analysis lag behind and should be implemented in combination with established methods to accelerate hypothesis and knowledge generation in the domain.