Hasil untuk "Infectious and parasitic diseases"

Alex Viguerie, Malú Grave, Alvaro L. G. A. Coutinho et al.

Partial differential equation (PDE) models for infectious diseases, while less common than their ordinary differential equation (ODE) counterparts, have found successful applications for many years. Such models are typically of reaction-diffusion type, and model spatial propagation as a diffusive process. However, given the complex nature of human mobility, such models are limited in their ability to describe airborne infectious diseases in human populations. Recent work has advocated for the inclusion of an additional chemotaxis-type term as an alternative; spatial propagation of infection fronts is assumed additionally to flow from low-to-high concentrations of susceptible populations. The present work extends the study of such models by providing an epidemiologically interpretable analysis, directly connecting model behavior to information readily available to policymakers. In particular, we derive a spatially-aware basic reproduction number, which accounts for spatial heterogeneity in population density. Furthermore, we discuss several important aspects concerning the numerical solution of the model, including the introduction of a stabilization scheme. Finally, we perform a series of simulation studies in the Italian region of Lombardy (severely affected by the COVID-19 outbreak in 2020) and in the US state of Georgia, in which we demonstrate the model's potential to better capture important spatiotemporal dynamics observed in real-world data compared to pure reaction-diffusion models.

Dirk Douwes-Schultz, Rob Deardon, Alexandra M. Schmidt

Individual-level epidemic models are increasingly being used to help understand the transmission dynamics of various infectious diseases. However, fitting such models to individual-level epidemic data is challenging, as we often only know when an individual's disease status was detected (e.g., when they showed symptoms) and not when they were infected or removed. We propose an autoregressive coupled hidden Markov model to infer unknown infection and removal times, as well as other model parameters, from a single observed detection time for each detected individual. Unlike more traditional data augmentation methods used in epidemic modelling, we do not assume that this detection time corresponds to infection or removal or that infected individuals must at some point be detected. Bayesian coupled hidden Markov models have been used previously for individual-level epidemic data. However, these approaches assumed each individual was continuously tested and that the tests were independent. In practice, individuals are often only tested until their first positive test, and even if they are continuously tested, only the initial detection times may be reported. In addition, multiple tests on the same individual may not be independent. We accommodate these scenarios by assuming that the probability of detecting the disease can depend on past observations, which allows us to fit a much wider range of practical applications. We illustrate the flexibility of our approach by fitting two examples: an experiment on the spread of tomato spot wilt virus in pepper plants and an outbreak of norovirus among nurses in a hospital.

Muhammad Yousaf, Bassem Alhariri, Dore C. Ananthegowda et al.

Background: Hydatid disease, which is caused by Echinococcus granulosus, mainly impacts the liver and lungs. It is extremely uncommon for the pleura to be primarily affected, and this presents difficulties for diagnosis because it resembles a malignant condition. This report underscores a distinctive instance of an isolated pleural hydatid cyst, focusing on diagnostic challenges, multidisciplinary management, and treatment results. Case presentation: A Lebanese man aged 60, with no notable exposure history, showed up with an incidental pleural mass. Initial imaging indicated a potential malignancy based on rib erosion and rapid growth. A hydatid cyst was confirmed by a CT-guided biopsy, despite the presence of normal eosinophil counts and negative serology. The MRI ruled out hepatic/pulmonary involvement. Complete resolution, confirmed through serial imaging, was achieved with albendazole monotherapy (400 mg twice daily for five months). Discussion: This instance highlights how uncommon primary pleural hydatidosis is, as well as the shortcomings of serologic testing. The review of comparative literature shows just 12 documented instances, with our case being the first to exhibit rib erosion without hepatic or pulmonary involvement (Sudan et al., 2025 [2], Soner Gürsoy et al., 2009 [3], Brunetti et al., 2010 [7]). It encompasses radiological, pathological, and therapeutic nuances, promoting biopsy in equivocal cases (Santivanez et al., 2010 [1], Mardani et al., 2017 [6]). Conclusion: Even though primary pleural hydatid cysts are uncommon, they should be considered in the differential diagnoses of pleural masses, especially in areas where they are endemic. For an accurate diagnosis, histopathology and collaboration across disciplines are essential. For isolated cysts, albendazole monotherapy may be adequate; however, long-term monitoring is crucial (Mardani et al., 2017 [6], Qian, 1988 [9]).

Ralph Huits, Dora Buonfrate, Kevin O'Laughlin et al.

Background: The global resurgence of measles is a threat to measles elimination campaigns. Measles importations by international travelers have been identified as a risk factor for outbreaks. Methods: We reviewed measles cases among international travelers and migrants reported to the GeoSentinel network. Results: From May 2019 through June 2025, GeoSentinel recorded 53 measles cases among travelers imported into 15 different countries. Travelers of all age groups were affected, and 74 % were 21 years or older. Thirty-three travelers (61 %) were hospitalized. Seventy-nine percent of cases reported no or unknown history of vaccination against measles. Conclusions: Against a background of increasing numbers of measles cases and outbreaks globally, GeoSentinel observed a stable trend of measles importations by international travelers. Measles caused considerable morbidity among travelers. Immunization effectively prevents measles in more than 97 % of individuals. Pretravel consultations provide an important opportunity to promote vaccination coverage for all vaccine-preventable diseases, including measles.

Alexander E. Zarebski, Nefel Tellioglu, Jessica E. Stockdale et al.

Decisions on public health interventions to control infectious disease are often informed by computational models. Interpreting the predicted outcomes of a public health decision requires not only high-quality modelling, but also an ethical framework for assessing the benefits and harms associated with different options. The design and specification of ethical frameworks matured independently of computational modelling, so many values recognised as important for ethical decision-making are missing from computational models. We demonstrate a proof-of-concept approach to incorporate multiple public health values into the evaluation of a simple computational model for vaccination against a pathogen such as SARS-CoV-2. By examining a bounded space of alternative prioritisations of values (outcome equity and aggregate benefit) we identify value trade-offs, where the outcomes of optimal strategies differ depending on the ethical framework. This work demonstrates an approach to incorporating diverse values into decision criteria used to evaluate outcomes of models of infectious disease interventions.

Chudan Liang, Zequn Wang, Linjin Fan et al.

The increase in emerging and reemerging infectious diseases has underscored the need for the prompt monitoring of intact infectious viruses and the quick assessment of their infectivity. However, molecular techniques cannot distinguish between intact infectious and noninfectious viruses. Here, two distinct methodologies have been developed for the expeditious and dependable quantification of intact infectious H1N1 virus, and several experiments have been conducted to substantiate their efficacy. One is an integrated cell absorption quantitative polymerase chain reaction (qPCR) method (ICA-qPCR), and the other is a combined propidium monoazide qPCR method (PMA-qPCR). The quantification limit is 100 cell culture infective dose 50 % (CCID50)/mL in ICA-qPCR following a 1.5-hour cell absorption or 126 CCID50/mL after a 15-minute incubation. For PMA-qPCR, the limit was 2,512 CCID50/mL. The number of genome copies quantified by the ICA-qPCR and PMA-qPCR methods was strongly correlated with the infectious titer determined by the CCID50 assay, thereby enabling the estimation of virus infectivity. The ICA-qPCR and PMA-qPCR methods are both suitable for the identification and quantification of intact infectious H1N1 virus in inactivated samples, wastewater, and biological materials. In conclusion, the ICA-qPCR and PMA-qPCR methods have distinct advantages and disadvantages, and can be used to quantify intact infectious viruses rapidly. These methodologies can facilitate the identification of the presence of intact infectious viruses in wastewater or on pathogen-related physical surfaces in high-level biosafety laboratories and medical facilities. Furthermore, these methodologies can also be utilized to detect other highly pathogenic pathogens.

Sebastián Muñoz-Leal, Valentina Nova-Cancino, Adam Sobieski et al.

Accumulation of DNA sequence data and its use in systematics of the family Argasidae reveals new incongruencies between genera and subgenera, since several groups defined by classical taxonomy appear to be paraphyletic, which is the case of the subgenus Pavlovskyella. In order to identify morphological characters unique to one of the monophyletic groupings within Pavlovskyella and improve its system, we describe all active stages of Ornithodoros (Pavlovskyella) tartakovskyi, a species with an incomplete original description. Larvae, nymphs, males and females from Iran were examined with scanning electron microscopy (SEM). The larva of O. (P.) tartakovskyi lacks dorsal plate, posteromedian seta, postcoxal setae and a spinose area in palpal article I; the dorsal surface has 13 pairs of setae and the hypostome has two short rows of denticles in the apex only. The first nymphal instar (N1) has a micromammillated body with faint dorsal and ventral disks. The second nymphal instar (N2) shows mammillated body with an incipient hood, and dorsal disks outlined as in subsequent instars. Both N1 and N2 have a small patch of glabrous surface in the anterior margin of preanal groove, absent in posterior stages. Third nymphal instar (N3) has a small hood and lack cheeks, which are incipient in the fourth nymphal instar (N4). The four nymphal instars (N1–N4) have blunt hypostomes, striated capitula, non-mammillated legs, and 3 humps on tarsi I. Females and males of O. (P.) tartakovskyi have mammillated bodies, small cheeks not covering the capitulum, the anteromedian disk is placed anteriorly to anterior central disks, and a posterior median file of disks is merged with the median disk. We describe all postembryonic stages of O. (P.) tartakovskyi providing morphological characters that define Pavlovskyella sensu stricto and Theriodoros, a sister subgenus that includes similarly shaped species.

Styliani-Christina Fragkouli, Dhwani Solanki, Leyla J Castro et al.

Over the last three to five years, it has become possible to generate machine learning synthetic data for healthcare-related uses. However, concerns have been raised about potential negative factors associated with the possibilities of artificial dataset generation. These include the potential misuse of generative artificial intelligence (AI) in fields such as cybercrime, the use of deepfakes and fake news to deceive or manipulate, and displacement of human jobs across various market sectors. Here, we consider both current and future positive advances and possibilities with synthetic datasets. Synthetic data offers significant benefits, particularly in data privacy, research, in balancing datasets and reducing bias in machine learning models. Generative AI is an artificial intelligence genre capable of creating text, images, video or other data using generative models. The recent explosion of interest in GenAI was heralded by the invention and speedy move to use of large language models (LLM). These computational models are able to achieve general-purpose language generation and other natural language processing tasks and are based on transformer architectures, which made an evolutionary leap from previous neural network architectures. Fuelled by the advent of improved GenAI techniques and wide scale usage, this is surely the time to consider how synthetic data can be used to advance infectious disease research. In this commentary we aim to create an overview of the current and future position of synthetic data in infectious disease research.

Michael Morris

Infectious diseases pose significant human and economic burdens. Accurately forecasting disease incidence can enable public health agencies to respond effectively to existing or emerging diseases. Despite progress in the field, developing accurate forecasting models remains a significant challenge. This thesis proposes two methodological frameworks using neural networks (NNs) with associated uncertainty estimates - a critical component limiting the application of NNs to epidemic forecasting thus far. We develop our frameworks by forecasting influenza-like illness (ILI) in the United States. Our first proposed method uses Web search activity data in conjunction with historical ILI rates as observations for training NN architectures. Our models incorporate Bayesian layers to produce uncertainty intervals, positioning themselves as legitimate alternatives to more conventional approaches. The best performing architecture: iterative recurrent neural network (IRNN), reduces mean absolute error by 10.3% and improves Skill by 17.1% on average in forecasting tasks across four flu seasons compared to the state-of-the-art. We build on this method by introducing IRNNs, an architecture which changes the sampling procedure in the IRNN to improve the uncertainty estimation. Our second framework uses neural ordinary differential equations to bridge the gap between mechanistic compartmental models and NNs; benefiting from the physical constraints that compartmental models provide. We evaluate eight neural ODE models utilising a mixture of ILI rates and Web search activity data to provide forecasts. These are compared with the IRNN and IRNN0 - the IRNN using only ILI rates. Models trained without Web search activity data outperform the IRNN0 by 16% in terms of Skill. Future work should focus on more effectively using neural ODEs with Web search data to compete with the best performing IRNN.

Zhen Li, Changcheng Wu, Li-An Tang et al.

Metagenomic next-generation sequencing (mNGS) has been widely applied to identify pathogens associated with infectious diseases. However, limited studies have explored the use of mNGS-based dynamic pathogen monitoring in intensive care unit patients with severe pneumonia. Here, we present a clinical case of an 86-year-old male patient with severe pneumonia caused by a fungal infection. During the clinical treatment, four mNGS analyses were performed within two consecutive weeks. Various respiratory fungal pathogens, including Candida orthopsilosis, Candida albicans, and Aspergillus fumigatus were detected by mNGS of bronchoalveolar lavage fluid (BALF). Based on conventional pathogen identification and clinical symptoms, the patient was diagnosed with severe pneumonia caused by a fungal infection. The abundance of fungal species decreased gradually in response to antifungal and empirical therapies, and the fungal infections were effectively controlled. In summary, our results demonstrated that mNGS could effectively identify pathogens in patients with severe pneumonia. Additionally, dynamic pathogen monitoring based on mNGS could assist in the precise diagnosis of complex infections and may facilitate rapid induction of the most appropriate therapy.

Xin Wang, Xiaoping Qiu

Moustafa A. Mansour, Mahmoud Bayoumi, Abdou Hamdi et al.

Mamadou Ciss, Alessandra Giacomini, Mame Nahé Diouf et al.

Livestock mobility, particularly that of small and large ruminants, is one of the main pillars of production and trade in West Africa: livestock is moved around in search of better grazing or sold in markets for domestic consumption and for festival-related activities. These movements cover several thousand kilometers and have the capability of connecting the whole West African region thus facilitating the diffusion of many animal and zoonotic diseases. Several factors shape mobility patterns even in normal years and surveillance systems need to account for such changes. In this paper, we present a procedure based on temporal network theory to identify possible sentinel locations using two indicators: vulnerability (i.e. the probability of being reached by the disease) and time of infection (i.e. the time of first arrival of the disease). Using these indicators in our structural analysis of the changing network enabled us to identify a set of nodes that could be used in an early warning system. As a case study we simulated the introduction of F.A.S.T. (Foot and Mouth Similar Transboundary) diseases in Senegal and used data taken from 2020 Sanitary certificates (LPS, laissez-passer sanitaire) issued by the Senegalese Veterinary Services to reconstruct the national mobility network. Our analysis showed that a static approach can significantly overestimate the speed and the extent of disease propagation, whereas temporal analysis revealed that the reachability and vulnerability of the different administrative departments (used as nodes of the mobility network) change over the course of the year. For this reason, several sets of sentinel nodes were identified in different periods of the year, underlining the role of temporality in shaping patterns of disease diffusion.

Christina S. Thornton, Kevin Huntley, Byron M. Berenger et al.

Abstract Background Detection of severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) RNA is completed through reverse transcriptase-PCR (RT-PCR) from either oropharyngeal or nasopharyngeal swabs, critically important for diagnostics but also from an infection control lens. Recent studies have suggested that COVID-19 patients can demonstrate prolonged viral shedding with immunosuppression as a key risk factor. Case presentation We present a case of an immunocompromised patient with SARS-CoV-2 infection demonstrating prolonged infectious viral shedding for 189 days with virus cultivability and clinical relapse with an identical strain based on whole genome sequencing, requiring a multi-modal therapeutic approach. We correlated clinical parameters, PCR cycle thresholds and viral culture until eventual resolution. Conclusions We successfully demonstrate resolution of viral shedding, administration of COVID-19 vaccination and maintenance of viral clearance. This case highlights implications in the immunosuppressed patient towards infection prevention and control that should consider those with prolonged viral shedding and may require ancillary testing to fully elucidate viral activity. Furthermore, this case raises several stimulating questions around complex COVID-19 patients around the role of steroids, effect of antiviral therapies in absence of B-cells, role for vaccination and the requirement of a multi-modal approach to eventually have successful clearance of the virus.

Josef Finsterer, Fulvio A Scorza

Said Ali, Jeanne N. Samake, Joseph Spear et al.

Abstract Malaria control in Somaliland depends on the effective identification of potential malaria vectors, particularly those that may be invasive. The malaria vector Anopheles stephensi has been detected in multiple countries in the Horn of Africa (HOA), but data on its geographic distribution and population genetic diversity are incomplete. We implemented a vector surveillance program and performed molecular analysis of Anopheles in three urban areas in Somaliland. Our study confirmed the presence of both the invasive An. stephensi and the long-established HOA malaria vector Anopheles arabiensis. Further analysis of An. stephensi genetic diversity revealed three cytochrome oxidase I (COI) haplotypes, all of which have been observed in other countries in East Africa and one also observed in South Asia. We also detected the knockdown resistance (kdr) L1014F mutation, which is associated with pyrethroid resistance; this finding supports the need for further assessment of the potential for insecticide resistance. The detection of multiple haplotypes previously observed in other regions of East Africa indicates that An. stephensi is an established population in Somaliland and likely shares its origin with other newly identified An. stephensi populations in East Africa. The detection of genetic diversity in An. stephensi in Somaliland provides a basis for future studies on the history of the species in the region and its dispersal throughout East Africa. Graphical Abstract

Teng Li, Siwei Yin, Runze Yu et al.

To fight against infectious diseases (e.g., SARS, COVID-19, Ebola, etc.), government agencies, technology companies and health institutes have launched various contact tracing approaches to identify and notify the people exposed to infection sources. However, existing tracing approaches can lead to severe privacy and security concerns, thereby preventing their secure and widespread use among communities. To tackle these problems, this paper proposes CoAvoid, a decentralized, privacy-preserved contact tracing system that features good dependability and usability. CoAvoid leverages the Google/Apple Exposure Notification (GAEN) API to achieve decent device compatibility and operating efficiency. It utilizes GPS along with Bluetooth Low Energy (BLE) to dependably verify user information. In addition, to enhance privacy protection, CoAvoid applies fuzzification and obfuscation measures to shelter sensitive data, making both servers and users agnostic to information of both low and high-risk populations. The evaluation demonstrates good efficacy and security of CoAvoid. Compared with four state-of-art contact tracing applications, CoAvoid can reduce upload data by at least 90% and simultaneously resist wormhole and replay attacks in various scenarios.

Mohammad Meraj, Surendra Pal Singh, Prashant Johri et al.

An infectious kind of disease affects a huge number of human beings. A lot of investigation being conducted throughout the world. There are many interactive hardware platform packages like IoT in healthcare including smart tracking, smart sensors, and clinical device integration available in the market. Emerging technology like IoT has a notable ability to hold patients secure and healthful and also enhance how physicians supply care. Healthcare IoT also can bolster affected person pride by permitting patients to spend more time interacting with their medical doctors due to the fact docs aren't as taken with the mundane and rote aspects of their career. The most considerable advantage to IoT in healthcare is that it supports doctors in undertaking extra significant clinical work in a profession that already is experiencing a worldwide professional hard work shortage. This paper investigates the basis exploration of the applicability of IoT in the healthcare System.

A. Kotian, A. Vankadari, K. Jazeela et al.

Masrizal Masrizal, Tria Syananda Putri, Imraatul Hasni

Background: West Sumatra is a target area for malaria elimination in 2020; the Annual Parasite Incidence (API) in Padang City increased from 0.12 per 1000 inhabitants to 0.13 per 1000 inhabitants between 2015 and 2016. Purpose: This study aimed to analyze the effect of factors contributing to malaria events based on the environmental and behavioral conditions of people in Padang City. Method: This is a quantitative study using a case-control approach. The research was conducted in Padang from August 2017 until January 2018. The case-control study was conducted on a sample of 62 people, consisting of 31 cases and 31 controls. Cases were identified through random sampling and controls were selected by purposive sampling. Data collection was via observation and questionnaires and both univariate and bivariate analyses were conducted. Results: Descriptively, malaria patients were more likely to live in at-risk physical conditions at home (74.12%), had a history of visiting endemic areas (41.90%), did not use mosquito repellent equipment (58%), and had the habit of being outdoors at night (32.28%). Statistical tests showed the risk factors for the incidence of malaria were the physical condition of the house (OR = 3.43; 95% CI 1.20–9.20) and a history of visiting endemic areas (OR = 9; 95% CI 1.20–394). Conclusion: Environmental and behavioral factors affect the incidence of malaria. It is recommended that the Padang City Health Office provide counseling through health promotion officers about healthy homes and advise people not to go to endemic areas.