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Predicting the human burden of vector-borne diseases from limited surveillance data remains a major challenge, particularly in the presence of nonlinear transmission dynamics and delayed effects arising from vector ecology and human behavior. We develop a data-driven framework based on an extension of Sparse Identification of Nonlinear Dynamics (SINDy) to systems with distributed memory, enabling discovery of transmission mechanisms directly from time series data. Using severe fever with thrombocytopenia syndrome (SFTS) as a case study, we show that this approach can uncover key features of tick-borne disease dynamics using only human incidence and local temperature data, without imposing predefined assumptions on human case reporting. We further demonstrate that predictive performance is substantially enhanced when the data-driven model is coupled with mechanistic representations of tick-host transmission pathways informed by empirical studies. The framework supports systematic sensitivity analysis of memory kernels and behavioral parameters, identifying those most influential for prediction accuracy. Although the approach prioritizes predictive accuracy over mechanistic transpare

Computational point-of-care (POC) sensors enable rapid, low-cost, and accessible diagnostics in emergency, remote and resource-limited areas that lack access to centralized medical facilities. These systems can utilize neural network-based algorithms to accurately infer a diagnosis from the signals generated by rapid diagnostic tests or sensors. However, neural network-based diagnostic models are subject to hallucinations and can produce erroneous predictions, posing a risk of misdiagnosis and inaccurate clinical decisions. To address this challenge, here we present an autonomous uncertainty quantification technique developed for POC diagnostics. As our testbed, we used a paper-based, computational vertical flow assay (xVFA) platform developed for rapid POC diagnosis of Lyme disease, the most prevalent tick-borne disease globally. The xVFA platform integrates a disposable paper-based assay, a handheld optical reader and a neural network-based inference algorithm, providing rapid and cost-effective Lyme disease diagnostics in under 20 min using only 20 uL of patient serum. By incorporating a Monte Carlo dropout (MCDO)-based uncertainty quantification approach into the diagnostics pi

Globally, vector-borne diseases are increasing in distribution and frequency, affecting humans, domestic animals and livestock, and wildlife. Science-based management and prevention of these diseases requires a sound understanding of the distribution and environmental requirements of the vectors and hosts involved in disease transmission. Integrated Species Distribution Models (ISDM) account for diverse data types through hierarchical modeling and represent a significant advancement in species distribution modeling that have not yet been leveraged in disease ecology. We used this approach, as implemented in the recently developed R package RISDM, to assess the distribution of the soft tick subspecies Ornithodoros turicata americanus. We created an ISDM for O. t. americanus, using systematically collected field data and historical records of this tick species in the southeastern US, to predict its distribution and assess potential correlations with environmental variables. Given the novelty of this method, we compared the results to a conventional Maxent SDM and validated the results through data partitioning using true skills statistics (TSS), sensitivity, and area under the ROC cu

This chapter presents a practical guide for conducting Sentiment Analysis using Natural Language Processing (NLP) techniques in the domain of tick-borne disease text. The aim is to demonstrate the process of how the presence of bias in the discourse surrounding chronic manifestations of the disease can be evaluated. The goal is to use a dataset of 5643 abstracts collected from scientific journals on the topic of chronic Lyme disease to demonstrate using Python, the steps for conducting sentiment analysis using pre-trained language models and the process of validating the preliminary results using both interpretable machine learning tools, as well as a novel methodology of using emerging state-of-the-art large language models like ChatGPT. This serves as a useful resource for researchers and practitioners interested in using NLP techniques for sentiment analysis in the medical domain.

With a single circulating vector-borne virus, the basic reproduction number incorporates contributions from tick-to-tick (co-feeding), tick-to-host and host-to-tick transmission routes. With two different circulating vector-borne viral strains, resident and invasive, and under the assumption that co-feeding is the only transmission route in a tick population, the invasion reproduction number depends on whether the model system of ordinary differential equations possesses the property of neutrality. We show that a simple model, with two populations of ticks infected with one strain, resident or invasive, and one population of co-infected ticks, does not have Alizon's neutrality property. We present model alternatives that are capable of representing the invasion potential of a novel strain by including populations of ticks dually infected with the same strain. The invasion reproduction number is analysed with the next-generation method and via numerical simulations.

Arbovirus is a vital, life-threatening disease worldwide and continues to be a significant problem while the world is dealing with the major coronavirus (COVID-19) pandemic. Vectors, mostly mosquitoes and ticks, transmit this disease. Dengue fever, chikungunya, and Zika viruses are the major threats because of their high incidence, public health burden, and clinically significant disease spectrum. These vector-borne disease causes one-fourth of annual deaths, leading to various infectious diseases. The arbovirus represents eight different families and 14 genera; most viruses belong to the family Bunyaviridae, and some also belong to Togaviridae, Reoviridae, and Flaviviridae. The arbovirus disease was isolated first in tropical and subtropical regions of South America and Africa and has high significance because of suitable environmental conditions for virus transmission and vector expansion. Its transmission cycle ranges from simple to highly complex. DENV is the most prevalent, results in febrile illness, and has transmission in 128 different countries. CHIKV causes infection in asymptomatic people, and the problems include nephritis, arthritis, myelitis, and acute encephalopathy.

Vector-borne diseases (VBDs) are a kind of infection caused through the transmission of vectors generated by the bites of infected parasites, bacteria, and viruses, such as ticks, mosquitoes, triatomine bugs, blackflies, and sandflies. If these diseases are not properly treated within a reasonable time frame, the mortality rate may rise. In this work, we propose a set of ontologies that will help in the diagnosis and treatment of vector-borne diseases. For developing VBD's ontology, electronic health records taken from the Indian Health Records website, text data generated from Indian government medical mobile applications, and doctors' prescribed handwritten notes of patients are used as input. This data is then converted into correct text using Optical Character Recognition (OCR) and a spelling checker after pre-processing. Natural Language Processing (NLP) is applied for entity extraction from text data for making Resource Description Framework (RDF) medical data with the help of the Patient Clinical Data (PCD) ontology. Afterwards, Basic Formal Ontology (BFO), National Vector Borne Disease Control Program (NVBDCP) guidelines, and RDF medical data are used to develop ontologies

Ticks and tick-borne diseases present a well known threat to the health of people in many parts of the globe. The scientific literature devoted both to field observations and to modeling the propagation of ticks continues to grow. So far the majority of the mathematical studies were devoted to models based on ordinary differential equations, where spatial variability was taken into account by a discrete parameter. Only few papers use spatially nontrivial diffusion models, and they are devoted mostly to spatially homogeneous equilibria. Here we develop diffusion models for the propagation of ticks stressing spatial heterogeneity. This allows us to assess the sizes of control zones that can be created (using various available techniques) to produce a patchy territory, on which ticks will be eventually eradicated. Using averaged parameters taken from various field observations we apply our theoretical results to the concrete cases of the lone star ticks of North America and of the taiga ticks of Russia.

Crimean-Congo haemorrhagic fever (CCHF) is a tick-borne zoonotic disease caused by the Crimean-Congo hemorrhagic fever virus (CCHFV). Ticks belonging to the genus \textit{Hyalomma} are the main vectors and reservoir for the virus. It is maintained in nature in an endemic vertebrate-tick-vertebrate cycle. CCHFV is prevalent in wide geographical areas including Asia, Africa, South-Eastern Europe and the Middle East. Over the last decade, several outbreaks of CCHFV have been observed in Europe, mainly in Mediterranean countries. Due to the high case/fatality ratio of CCHFV in human sometimes, it is of great importance for public health. Climate change and the invasion of CCHFV vectors in Central Europe suggest that the establishment of the transmission in Central Europe may be possible in future. We developed a compartment-based nonlinear Ordinary Differential Equation (ODE) system to model the disease transmission cycle including blood sucking ticks, livestock and human. Sensitivity analysis of the basic reproduction number $R_0$ shows that decreasing in the tick survival time is an efficient method to eradicate the disease. The model supports us in understanding the influence of dif

Avian Spirochaetosis is an acute endemic tick-borne disease of birds, caused by Borrelia anserins, a species of Borrelia bacteria. In this paper, we present a compartmental Mathematical model of the disease for the bird population and Tick population. The model so constructed was analyzed using methods from dynamical systems theory. \tcr{The disease steady (equilibrium) state was determined and the conditions for the disease-free steady state to be stable were determined}. The analysis showed that the disease-free steady state is locally stable if $d\geq τ_B$ and $δ\geq τ_T$, that is, the natural death rate of birds (d) will be greater than the per capita birth rate of birds $τ_B$ and the death rate of tick $δ)$ is greater than the per capita birth rate of tick $τ_T$. This means that for the disease to be under control and eradicated within a while from its outbreak, the natural death rate of birds $d$ will be greater than the per capita birth rate of bird $τ_B$ and the death rate of tick $δ$ is greater than the per capita birth rate of tick $τ_T$. It was also proved the disease-free equilibrium (DFE) and the endemic equilibrium (EE) are globally stable using Lyaponov method. Three

In this paper we describe the dynamics of a vector-borne relapsing disease, such as tick-borne relapsing fever, using the methods of compartmental models. After some motivation, model description, and a brief overview of the theory of compartmental models, we compute a general form of the reproductive ratio $R_0$, which is the average number of new infections produced by a single infected individual. A disease free equilibrium undergoes a bifurcation at $R_0 =1$ and we show that for an arbitrary number of relapses it is a transcritical bifurcation with a single branch of endemic equilibria that is locally asymptotically stable for $R_0$ sufficiently close to 1. We close with some discussion and directions for future research.

With the resurgence of tick-borne diseases such as Lyme disease and the emergence of new pathogens such as Powassan virus, understanding what distinguishes vector from non-vector species, and predicting undiscovered tick vectors is an important step towards mitigating human disease risk. We apply generalized boosted regression to interrogate over 90 features for over 240 species of Ixodes ticks. Our model predicted vector status with ~97% accuracy and implicated 14 tick species whose intrinsic trait profiles confer high probabilities (~80%) that they are capable of transmitting infections from animal hosts to humans. Distinguishing characteristics of zoonotic tick vectors include several anatomical structures that facilitate efficient host seeking and blood-feeding from a wide variety of host species. Boosted regression analysis produced both actionable predictions to guide ongoing surveillance as well as testable hypotheses about the biological underpinnings of vectorial capacity across tick species.

Researchers have finally resolved a key problem in a 100-year-old theory of color, showing that the qualities we perceive in colors are intrinsic to the mathematics of color space itself。 The discovery sharpens our understanding of human vision and could lead to more precise color technologies and visualizations

A breakthrough hydrogen-production method could make clean fuel far cheaper and easier to generate。 Researchers at the University of Birmingham developed a perovskite-based catalyst that splits water into hydrogen at much lower temperatures than existing technologies, potentially allowing factories, steel plants, cement works, and renewable energy