Disease Dynamics Research Articles

Unleashing the relationship between climate change and infectious diseases

Climate change is rapidly emerging as a powerful driver of human health, understanding the relation between infectious disease dynamics and climate change is crucial for public health, particularly in developing nations. This article review delves into the intricate relationship between climate change and infectious diseases, with a particular focus on how climate factors are influencing the spread of vector borne diseases and water borne diseases such as prevalent diseases like malaria, cholera, and dengue in India. The objective is to assess the relationship between climate change and the burden of infectious diseases, with a focus on understanding how climate factors influence the prevalence and spread of infectious diseases. A literature search using PubMed, Science Direct, and government and international health reports focused on the terms 'climate change', 'infectious diseases' and 'health impact’. The review highlights that climate change accelerates vector lifecycles and creates favourable conditions for pathogens, leading to more frequent and severe outbreaks. Extreme weather events are worsening these risks, leading to more frequent and severe outbreaks. This poses a growing threat to global health, particularly in vulnerable regions like India. Despite government efforts, significant challenges remain. Strengthening healthcare infrastructure, improving disease surveillance, and adopting climate-resilient practices are essential to safeguard vulnerable populations and mitigate the impact of climate change on public health.

Research examining microcirculatory dynamics in diseases that cause oedema using skin blood flow and skin reperfusion pressure

Oedema is a build-up of fluid in the tissues in the body. It can develop due to diseases including heart failure and kidney disease. It usually occurs in the limbs, where it can cause discomfort, joint stiffness or pain. However, fluid can also accumulate in the brain, which is called cerebral oedema or in the blood vessels serving the lungs, called pulmonary oedema. A type of oedema called pitting oedema is more prevalent than the other type, non-pitting oedema. In order to treat the condition, the underlying cause must be resolved. Treatment can involve diuretics to help the release of fluids, positioning to encourage draining or compression to force fluids away. Researchers at the Faculty of Rehabilitation at Reiwa Health Sciences University led by Dr Haruki Kogo are examining the microcirculatory dynamics of diseases that cause oedema using skin blood flow and skin reperfusion pressure. They have developed a device that can diagnose oedema and confirmed the reproducibility and validity of the device. They have registered the trademark and plan to commercialise the device for use in clinical and research settings. The device is able to measure the depth of the surface imprint in pitting oedema patients, simplifying the evaluation of pitting oedema, which will be extremely useful in clinical settings. The researchers tested the device across two hospitals, comparing the results with results obtained from the popular circumference measurement method and ultrasound imaging measurement. The team found that there was a strong correlation between measurements from the device and the tissue thickness measurement derived from the ultrasound. This confirmed that the device is a reliable gauge for the measurement of oedema.

From agent-based simulations of disease dynamics to expert support on public health interventions

Abstract Agent-based epidemiological simulators have long been used to predict the dynamics of infectious diseases. They allow to combine compartment approaches with representing contact networks derived from real or synthetic populations. Often, scenario techniques are applied to model the impact of public health interventions. During the COVID-19 pandemics the agent-based simulator COVASIM has been established for modelling the spreading of SARS-CoV-2. It includes four different contact networks related to homes, schools, workplaces and environment. During the pandemic it became obvious that the current simulation techniques were not sufficient to cover important needs in public health administrations and society. While most simulations were based on scenarios predicting expected numbers for the infected, hospitalized or critically ill, these studies could not provide easily strategic options on a variety of equivalent public health interventions, which are all suitable to keep disease dynamics within acceptable bounds, set e.g. by hospital capacity. Knowing about a variety of equivalent options, however, is of crucial importance to enable a fair burden sharing throughout all groups of society. Therefore, we applied machine learning techniques to create an inverse model for epidemiological simulations based on COVASIM. For given transmission rates it outputs a range of quantitative interventions which are equivalently suitable to keep epidemiological dynamics within predefined bounds. Currently, it covers interventions like school and workplace closings and other mobility restrictions. Future work will be directed to include other interventions, like vaccination campaigns, and we will work to make the approach more generalizable for other infectious diseases. The tool may turn out helpful fur public health administrations and stimulate the public debate on the best way a society may take in a pandemic event or other situations where public health action has to be imposed. Key messages • Machine Learning allows to invert agent based simulators. • Instead of disease dynamics simulations a set of equivalent public health interventions consistent with pre-defined constraints can be calculated.

Spatiotemporally weighted regression (STWR) for assessing Lyme disease and landscape fragmentation dynamics in Connecticut towns

Understanding the landscape determinants that escalate Lyme disease (LD) risk through various times and regions is vital for appraising disease susceptibility and shaping precise intervention and prevention strategies. This research introduces a novel data-driven framework to identify potential indicators from an extensive array of potential variables. We then deployed an advanced spatiotemporal weighted regression (STWR) model to investigate how landscape fragmentation metrics correlate with the spatiotemporal variability of LD incidence rate in Connecticut towns. We proposed a data-driven filtering framework to select five variables from a large data pool. The analysis unveils that LD incidence rates exhibit heightened sensitivity to proportional or exponential shifts in landscape fragmentation; logarithmic and squared transformations of landscape metrics shed light on lesser effects and venue for potential parabolic relationships. Observations also disclose significant spatial trends, showing elevated LD incidence rates in locales with vast, uninterrupted deciduous forests, alongside contributions from wetland ecosystem-related variables to the rise in disease occurrence. Compared with Geographically Weighted Regression (GWR), the STWR model proved more potent and reliable with higher R2 and lower estimated standard errors (SE). The STWR model is highly flexible in terms of spatiotemporal variations in data. The STWR results further reversely indicate the changes made by the Center for Disease and Prevention (CDC) in the case classification of LD in 2008. The integration of data-driven and model-driven approaches in this study delivers a robust framework that combines empirical pattern detection with theoretical insight, enhancing the robustness and predictive power of ecological studies.

A canine onchocercosis report from the portuguese west region in 2024: implications for Public Health

Abstract Issue/problem Onchocerca lupi parasitosis, known as onchocercosis, affects dogs, sporadically cats and humans, posing a public health concern due to possible vectors in the environment. This neglected tropical disease(NTD), has no known human cases outside endemic areas. However, recent reports in non-endemic areas raise concerns about future implications. Climate change has increased the potential risk of disease vectors establishing themselves in previously unaffected areas. Description of the problem A case of canine onchocercosis in the West Region of Portugal, made the pet owner look for medical counselling. Primary health care providers contacted the Public Health Unit(PHU) for guidance and actions. There had been no previous reports in this region. In this case report, we explore the potential public health implications, through a one-health approach. Results History of the disease was collected, and travel history of the dog revealed that he had lived in the Algarve during 11 years with only a one-and-a-half-month travel to Italy in this period. Literature review revealed reports of previous cases of oncochercosis in the Iberian Peninsula. Collaboration between the PHU, research institutes, and veterinary authorities led to the assumption of probable transmission in the Algarve. Lessons Increased vector surveillance is needed, especially in areas associated with animal onchocercosis. Proactive measures reduce transmission risks, protecting both humans and animals. Tracking animal cases is vital for understanding regional environmental factors. PHU’s must conduct thorough risk evaluations and maintain assertive risk communication strategies to address public and pet owner concerns effectively. Adopting a One Health approach improves the understanding of the disease dynamics and enables targeted interventions. Key messages • NTD’s transmitted by vectors pose a potential future challenge. This report underscores the need for a One Health approach, with collaborative multidisciplinary efforts to address these challenges. • Furthermore, systematic interaction and reporting between human, animal, and environmental health authorities are crucial to ensure comprehensive surveillance and mitigation strategies.

Analysis of control measures inducing the evolution of infectious viral diseases

With the continuation of the infectious disease, pathogens mutate and interact with the resident pathogen. In this paper, we develop an SIR model that considers the evolution of viral virulence under the regulation of different defense strengths. By using infectious disease dynamics and evolutionary adaptive dynamics, we obtain the evolutionary criteria allowing for evolutionary branching and continuously stable strategy across varying intensities of prevention and control measures. Our study finds that robust prevention and control measures can contribute to a rapid evolutionary trend toward higher virulence. We perform a critical function analysis to reveal the evolutionary consequences of trade-offs of arbitrary shape and analyze a diverse range of evolutionary dynamics. Furthermore, we investigate the coexistence of two different strains and construct an evolutionary model to obtain their invasion fitness functions and the evolutionarily singular coalition of the dimorphism. We contemplate the presence of secondary branching on a significantly extended evolutionary time scale. Last, we conduct the numerical simulations to prove our theoretical findings. This reveals the law that the evolutionary state of pathogens is affected by the intensity of prevention and control strategies. The results and methods can be used as references for studying the effects of other factors on virus evolution.

Clinical and immunological characteristics of COVID-19 in patients with disease dynamics

The aim is to study the clinical and immunological indicators of COVID-19 in the dynamics of the disease with the determination of predictors of the development of changes in the lungs. Materials and methods. The object of the study was 35 patients aged 22 to 75 years in the acute period and dynamics of COVID-19 disease, divided into two groups according to the identified clinical and immunological disorders. Markers of cellular (CD-3, CD-4, CD-8, CD-19) and humoral immunity, cytokines (IL-6,8, TGF-β, TNF-α), CEC were determined in all examined patients. Statistical processing of the obtained results was carried out using the computer program Statistica 10.0. Results. It was found that all the subjects retained clinical and immunological changes during the entire follow-up period, which indicates an ongoing disease. At the same time, significant differences in the severity of changes in individuals were revealed, taking into account age and the presence of chronic somatic pathology, expressed primarily in violation of the parameters of the T-system of immunity. In patients with the development of postcovoid changes in the lungs, characteristic immunological features were revealed, taking into account age. Conclusions. A violation of the detected indicators of the immune system may indicate the persistence of the virus, which means a prolongation of a specific inflammatory response with the risk of extensive tissue damage. Within three months after recovery, the tendency towards the formation of humoral immunity persists in both groups, which occurs by the end of 1 month after the disease in both groups and continues throughout the entire follow-up period. In the risk group for the development of pneumofibrosis in the outcome of a new coronavirus infection, the combination of IL-8 and TGF-β is the most optimal, despite their significant decrease in dynamics compared with the acute period.

Mathematical Models for Infectious Disease Dynamics and Control Strategies

The study explores mathematical models related to the dynamics of infectious diseases and approaches to manage them. These models are essential resources for comprehending how illnesses spread throughout people and assessing how well control strategies work. The study of infectious disease epidemiology heavily relies on mathematical modeling and analysis. Here, we give a clear overview of the spread of disease, explain how to mathematically model this stochastic process, and show how to utilize this mathematical representation to analyze the emergent dynamics of real-world epidemics. The advancement of mathematical analysis and modeling is crucial to our expanding comprehension of the evolution and ecology of pathogens. This study emphasizes how important mathematical modeling is to improving our knowledge of the dynamics of infectious diseases and how crucial it is to creating efficient management plans to lessen the burden of infectious diseases on public health.

Estimating the impact of sarcoptic mange epidemic on the population size of wild raccoon dogs (Nyctereutes procyonoides) from wildlife rescue data

The impact of infectious diseases on host populations is often not quantified because it is difficult to observe the host population and infectious disease dynamics. To address this problem, we developed a state-space model to simultaneously estimate host population and disease dynamics using wildlife rescue data. Using this model, we aimed to quantify the impact of sarcoptic mange on a Japanese raccoon dog population by estimating the change in their relative population size. We classified the status of rescued raccoon dogs into four categories: i) rescued due to infection with mange, ii) rescued due to traffic accidents without mange, iii) rescued due to traffic accidents with mange, and iv) rescued due to causes other than traffic accidents or mange. We modelled the observation process for each category and fitted the model to the reported number of raccoon dogs rescued between 1990 and 2010 at three wildlife rescue facilities in Kanagawa Prefecture, Japan. The mortality rate induced by mange was estimated to be 1.09 (95% credible interval (CI): 0.47–1.72) per year. The estimated prevalence of sarcoptic mange ranged between 4 and 80% in the study period. When a substantial prevalence of mange was observed (1995–2002), the host population size decreased by 91.2% (95% credible intervals: 86.3–94.7). We show that the impact of infectious disease outbreak on the wildlife population can be estimated from the time-series data of wildlife rescue events due to multiple causes. Our estimates suggest that sarcoptic mange triggered a substantial decrease in the Japanese wild raccoon dog populations.

Decoding Infectious Disease Dynamics: A Stochastic Approach to Population Heterogeneity and Epidemic Outcomes

Decoding Infectious Disease Dynamics: A Stochastic Approach to Population Heterogeneity and Epidemic Outcomes

Predictive modelling of the effectiveness of vaccines against COVID-19 in Bogotá: Methodological innovation involving different variants and computational optimisation efficiency

The uncertainty associated with the future of viruses such as SARS-CoV-2 poses a challenge to public health officials because of its implications for welfare, economics and population health. In this document, we develop an age-stratified epidemiological-mathematical model to predict various health outcomes, considering the effectiveness of COVID-19 vaccines. The analytical model proposed and developed for this research is based on the approach constructed by the COVID-19 International Modelling Consortium. Following this approach, this paper innovates at the frontier of knowledge by including the various variants of SARS-CoV-2 in the Consortium model. Furthermore, for the first time in international literature, a complete compilation of the formal mathematical development of this entire quantitative model is presented. Our model accurately fits the observed historical data of new infections, cumulative mortality, symptomatic infections, hospitalisations, and Intensive Care Units admissions, capturing the waves of contagion that have occurred in Bogotá, Colombia. In turn, the prognosis obtained indicates a considerable decrease in the incidence and lethality caused by SARS-CoV-2 under current conditions, thus evidencing the effectiveness of vaccines against infection, hospitalisation, and death. This model enables the evaluation of different scenarios in response to changes in the dynamics of this infectious disease, providing information to policymakers for real-world evidence-based decision-making.

Enhancing GeoHealth: A step-by-step procedure for spatiotemporal disease mapping.

Cartography, or geographical visualization of disease is an essential aspect of the field of GeoHealth, yet there is limited guidance on the visualization of spatiotemporal disease maps. In order to adequately contribute to understanding disease outbreaks, disease maps should be crafted carefully and according to relevant cartographic guidelines. This article aims to increase the understanding of space-time visualization techniques that are relevant to the field of GeoHealth, by providing a step-by-step framework for the creation of space-time disease visualizations. This study introduces a systematic approach to spatiotemporal disease mapping by integrating operations from the Generalized Space Time Cube (GSTC) Framework with established cartographic symbology guidelines. This resulted in an overview table that contains both the relevant GSTC operations and cartographic guidelines, as well as a step-by-step procedure that guides users through the process of creating informative spatiotemporal disease maps. The practical application of this step-by-step procedure is demonstrated with an example using Dutch COVID-19 data. By providing a clear, practical step by step procedure, this study enhances the capacity of public health professionals, policymakers, and researchers to monitor, understand, and respond to the spatial and temporal dynamics of diseases.

Estimating dynamics of dengue disease in Colombo district of Sri Lanka with environmental impact by quantifying the per-capita vector density

Dengue is a vector-borne disease transmitted to humans by vectors of genus Aedes causing a global threat to health, social, and economic sectors in many of the tropical countries including Sri Lanka. In Sri Lanka, the tropical climate, marked by seasonal weather primarily influenced by monsoons, fosters optimal conditions for the virus to spread efficiently. This heightened transmission results in increased per-capita vector density. In this work, we investigate the dynamic influence of environmental conditions on dengue emergence in Colombo district − the geographical region with the highest recorded dengue threat in Sri Lanka. An iterative approach is employed to dynamically estimate dengue cases leveraging the Markov chain Monte Carlo simulations, utilizing the dynamics of four seasons per year influenced by monsoon weather patterns governing in the region. The developed algorithm allows to estimate the risk of dengue outbreaks in 2017 and 2019 with high precision, facilitating accurate forecasts of upcoming disease emergence patterns for better preparedness. The uncertainty quantification not only validated the accuracy of outbreak estimates but also showcased the model’s capacity to capture extreme cases and revealed undisclosed external factors such as human mobility and environmental pollution that might affect dengue transmission in the Colombo district of Sri Lanka.

Qualitative and optimal control analysis of a two-serotype dengue model with saturated incidence in co-infection

ABSTRACT The arboviral disease dengue is a life-threatening global public health problem due to its high prevalence rate, morbidity and mortality. Recent studies suggest that morbidity during secondary dengue infection is higher than during primary infection. To deepen our understanding, we have explored a two-serotype co-infected dengue model with bilinear incidence for primary infection and saturated incidence for secondary infection. In the model, we have also included four control measures: mosquito bite prevention control, treatment control, vaccine control and mosquito killing control. The existence and local stability of the disease free, serotype II free, serotype I free and endemic equilibrium points have been studied. The effect of inhibitory or awareness factors on disease dynamics has also been exhaustively examined. By using sensitivity analysis we have identified and quantified the most influential parameters to prevent as well as to control the dengue outbreak. To optimize our strategies, we have characterized the time-dependent optimal control to reduce dengue prevalence and the total implementation cost of the aforementioned controls. Efficiency analysis has been done to identify the best effective combination of the applied controls to reduce dengue infection. Mosquito bites with treatment control is the most effective combined strategy to control dengue infection.

Modeling climate-related global risk maps of rice bacterial blight caused by Xanthomonas oryzae (Ishiyama 1922) using geographical information system (GIS).

Rice is a critical staple crop that feeds more than half of the world's population. Still, its production confronts various biotic risks, notably the severe bacterial blight disease produced by Xanthomonas oryzae. Understanding the possible effects of climate change on the geographic distribution of this virus is critical to ensuring food security. This work used ecological niche modeling and the Maxent algorithm to create future risk maps for the range of X. oryzae under several climate change scenarios between 2050 and 2070. The model was trained using 93 occurrence records of X. oryzae and five critical bioclimatic variables. It has an excellent predictive performance, with an AUC of 0.889. The results show that X. oryzae's potential geographic range and habitat suitability are expected to increase significantly under low (RCP2.6) and high (RCP8.5) emission scenarios. Key climatic drivers allowing this development include increased yearly precipitation, precipitation during the wettest quarter, and the wettest quarter's mean temperature. These findings are consistent with broader research revealing that climate change is allowing many plant diseases and other dangerous microbes to spread across the globe. Integrating these spatial predictions with data on host susceptibility, agricultural practices, and socioeconomic vulnerabilities can help to improve targeted surveillance, preventative, and management methods for reducing the growing threat of bacterial blight to rice production. Proactive, multidisciplinary efforts to manage the changing disease dynamics caused by climate change will be critical to assuring global food security in the future decades.

Investigating the impact of stochasticity on HIV infection dynamics in CD4+\\documentclass[12pt]{minimal} \\usepackage{amsmath} \\usepackage{wasysym} \\usepackage{amsfonts} \\usepackage{amssymb} \\usepackage{amsbsy} \\usepackage{mathrsfs} \\usepackage{upgreek} \\setlength{\\oddsidemargin}{-69pt} \\begin{document}$$^{+}$$\\end{document} T cells using a reaction-diffusion model

The disease dynamics affect the human life. When one person is affected with a disease and if it is not treated well, it can weaken the immune system of the body. Human Immunodeficiency Virus (HIV) is a virus that attacks the immune system, of the body which is the defense line against diseases. If it is not treated well then HIV progresses to its advanced stages and it is known as Acquired Immunodeficiency Syndrome (AIDS). HIV is typically a disease that can transferred from one person to another in several ways such as through blood, breastfeeding, sharing needles or syringes, and many others. So, the need of the hour is to consider such important disease dynamics and that will help mankind to save them from such severe disease. For the said purpose the reaction-diffusion HIV CD4+\\documentclass[12pt]{minimal} \\usepackage{amsmath} \\usepackage{wasysym} \\usepackage{amsfonts} \\usepackage{amssymb} \\usepackage{amsbsy} \\usepackage{mathrsfs} \\usepackage{upgreek} \\setlength{\\oddsidemargin}{-69pt} \\begin{document}$$^{+}$$\\end{document} T cell model with drug therapy under the stochastic environment is considered. The underlying model is numerically investigated with two time-efficient schemes and the effects of various parameters used in the model are analyzed and explained in a real-life scenario. Additionally, the obtained results will help the decision-makers to avoid such diseases. The random version of the HIV model is numerically investigated under the influence of time noise in Ito^\\documentclass[12pt]{minimal} \\usepackage{amsmath} \\usepackage{wasysym} \\usepackage{amsfonts} \\usepackage{amssymb} \\usepackage{amsbsy} \\usepackage{mathrsfs} \\usepackage{upgreek} \\setlength{\\oddsidemargin}{-69pt} \\begin{document}$${\\hat{o}}$$\\end{document} sense. The proposed stochastic backward Euler (SBE) scheme and proposed stochastic Implicit finite difference (SIFD) scheme are developed for the computational study of the underlying model. The consistency of the schemes is proven in the mean square sense and the given system of equations is compatible with both schemes. The stability analysis proves that both schemes and schemes are unconditionally stable. The given system of equations has two equilibria, one is disease-free equilibrium (DFE) and the other is endemic equilibrium. The simulations are drawn for the different values of the parameters. The proposed SBE scheme showed the convergent behavior towards the equilibria for the given values of the parameters but also showed negative behavior that is not biological. The proposed SIFD scheme showed better results as compared with the stochastic SBE scheme. This scheme has convergent and positive behavior towards the equilibria points for the given values of the parameters. The effect of various parameters is also analyzed. Simulations are drawn to evaluate the efficacy of the schemes.

Molecular Detection of Anaplasma marginale in Capybara (Hydrochoerus hydrochaeris) from Corrientes, Argentina.

Monitoring wildlife health is essential for understanding global disease patterns, particularly as vector-borne infections extend the geographic ranges and thereby hosts due to environmental shifts. Anaplasma marginale, primarily impacting cattle, has economic implications and has been found in diverse hosts, yet its presence in capybaras (Hydrochoerus hydrochaeris), influential in tick-borne pathogen spread, lacks comprehensive understanding. From 2015 to 2022, 14 capybaras were surveyed across two different areas of northeastern Argentina. In 1 of 14 (7%) capybaras, the presence of A. marginale was confirmed through the amplification of specific genes, msp5 and msp1β. In addition, A. marginale DNA was detected in the capybara's blood sample through quantitative PCR, with a cycle threshold value of 30.81 (800 copies per reaction). Amplification of a fragment of the msp1α gene revealed PCR products of three different sizes, suggesting the presence of at least three coinfecting A. marginale variants in the capybara host. This study suggests that capybaras are wild hosts for A. marginale in the Ibera Wetlands in Argentina, potentially influencing the infection dynamics of both domestic and wild species. This finding highlights the necessity for thorough studies on the role of capybaras in disease dynamics, crucial for understanding wildlife health and the spread of disease.

Communication dynamics of congestion warning information considering the attitudes of travelers

Traffic congestion is a serious problem faced by many cities worldwide today. Congestion warning information is one of the important influencing factors of urban road congestion; To this end, based on the dynamics of infectious diseases, a congestion warning information dissemination model considering the attitudes of travelers and the network structure was constructed. The existence and stability of the equilibrium points of non congestion warning information and congestion warning information in the model were analyzed, and the optimal control strategy of the model was proposed. Numerical simulation was conducted to verify the results of theoretical analysis, simulate and analyze the impact of changes in various parameters in the model on the dissemination of congestion warning information, and perform sensitivity analysis on several parameters. The results indicate that travelers are more inclined towards “fast” modes of transportation and have a stronger willingness to share congestion warning information. The dissemination range of warning information is wider, which can play a positive role in reducing traffic congestion pressure.

Associations between Ornithodoros spp. Ticks and Mojave Desert Tortoises (Gopherus agassizii) Obtained from Health Assessment Documents.

Soft ticks in the genus Ornithodoros occur throughout the Mojave Desert in southern Nevada, southeastern California, and parts of southwestern Utah and northwestern Arizona, USA, and are frequently observed parasitizing Mojave desert tortoises (Gopherus agassizii). However, limited research exists examining the relationship between ticks and desert tortoises. Mojave desert tortoises are listed as threatened by the US Fish and Wildlife Service, and as such, their populations are monitored and individual tortoise health is routinely assessed. These health assessments document the presence and abundance of ticks present on tortoises, but detailed examination of the relationship between ticks and tortoise health has been lacking. This study analyzed the relationship between tick presence and desert tortoise health assessments as a function of season, location, age (adult vs. juvenile), foraging behavior, evidence of clinical signs of disease, body condition score, and sex. Our results indicate that more ticks were found on tortoises in the summer than in any other season. Ticks were observed more frequently on captive tortoises versus wild tortoises, and more ticks were likely to be present on adult tortoises than on juveniles. Ticks were also more likely to be observed on tortoises that lacked evidence of foraging and on tortoises with observed clinical signs of disease. These findings provide valuable insights into the biology of ticks in relation to tortoises that may be useful for management of both captive and free-living threatened tortoise populations where ticks are detected. Our study also may improve understanding of potential tick-borne disease dynamics in the Mojave desert tortoise habitat, including Borrelia sp. carried by Ornithodoros ticks, which cause tick-borne relapsing fever in people.

Toscana Virus in Wild-Caught Sand Flies in Portugal, Findings from the National Vector Surveillance Network, 2023.

Phlebotomine sand flies play a crucial role in both human and veterinary medicine, acting as vectors for Leishmania parasites and most known phleboviruses. In Portugal, the REVIVE program, a comprehensive national surveillance network under the Ministry of Health, has included sand fly surveys since 2016. REVIVE aims to identify existing sand fly species in the country, determine which pathogens are circulating among them, and provide actionable insights for prevention and control measures when necessary. In this way, annually, from May to October, health technicians collect sand flies across mainland Portugal with CDC light traps. The collected sand flies are sent to the Centre for Vectors and Infectious Diseases Research for species identification and molecular screening of pathogens. On 21 September 2023, Toscana virus (TOSV), a well-known phlebovirus in the Mediterranean region due to its capacity to cause neurological disease, was detected in a pool of 30 sand flies collected in Algarve, the southernmost region of Portugal. A 668 bp partial sequence of the nucleoprotein gene shows similarity with TOSV strains from Spain. To our knowledge, this is the first detection of TOSV in its vector in this country, having previously only been reported in vertebrate hosts. These findings highlight the important role of ongoing surveillance efforts in monitoring and understanding the dynamics of sand fly-borne diseases in Portugal.