Hasil untuk "Nutritional diseases. Deficiency diseases"

Zohreh Sadat Sangsefidi, Abbas Ali Sangouni, Faezeh Yarhosseini et al.

Abstract Background Available evidence regarding effect of Cornus mas L. fruit extract (CMFE) as an anthocyanins source on glycemic control, insulin resistance, and adiponectin levels among metabolic dysfunction-associated steatotic liver disease (MASLD) patients is limited and controversial. Thus, this study aimed to assess effect of CMFE on glycemic control, insulin resistance, and adiponectin levels in MASLD patients. Methods This double-blind, randomized controlled clinical trial was carried out among 50 MASLD patients for 12 weeks. The participants were randomly categorized into the CMFE (CMFE, 20 cc/day; providing 32 mg/day total anthocyanin) or the placebo groups. Fasting plasma glucose and insulin levels, adiponectin, homeostatic model assessment of insulin resistance, triglyceride and glucose index, metabolic score for insulin resistance, quantitative insulin sensitivity check index, and visceral adiposity index were evaluated at baseline and after 12 weeks. Finally, a total number of 40 individuals completed the study (CMFE group: n = 18, placebo group: n = 22). Results Adiponectin levels significantly reduced after 12 weeks versus baseline in the placebo group among total population (ITT approach: baseline: 610.0 (430.0 to 710.0), after 12 weeks: 460.0 (390.0 to 580.0); P = 0.01) and among women (ITT approach: baseline: 675.0 (605.0 to 742.50), after 12 weeks: 435.0 (417.5 to 582.50), P = 0.01) without any significant difference in compared with the CMFE group. Moreover, a significant decreasing trend for changes of adiponectin levels was found in the placebo group versus the CMFE group only in women at the end of study (ITT approach: Placebo group: -220.0 (-290 to -37.50), CMFE group: -10.0 (-210.0 to 172.5); P = 0.01). However, CMFE consumption had no significant effect on glycemic control and insulin resistant indices (P > 0.05). Conclusions CMFE consumption may have a beneficial impact on adiponectin levels especially in women. However, CMFE had no significant effect on glycemic control and insulin resistant indices. To clarify our vision, further studies with longer durations, larger sample sizes and different extract dosages are required. Moreover, mechanistic investigations should be conducted in this field. Trial registration Registered on 30/09/2018 at Iranian Registry of Clinical Trials under code IRCT20180419039359N1, with URL: https://www.irct.ir/trial/30707 .

Shishir Paudel, Tulsi Ram Bhandari, Yoko Oda Thapa et al.

Malnutrition remains a critical public health issue among women of reproductive age (WRA) worldwide, and Nepal is no exception, as the country experiences a triple burden of malnutrition among WRA, including undernutrition, overnutrition, and anemia. This scoping review aimed to map and synthesize the existing literature to explore the scope of research on the prevalence and predictors of malnutrition (underweight, obesity, and anemia) among WRA in Nepal. A comprehensive search was conducted using several databases, including MEDLINE, DOAJ, CINAHL, and NepJOL, covering studies published between January 1, 2000, and June 15, 2024. Studies reporting the prevalence and/or predictors of undernutrition, obesity, or anemia among women aged 15–49 years in Nepal were included in this review. The exclusion criteria were studies focusing on disease-specific malnutrition, review articles, and clinical trials. Studies relying solely on secondary analyses of Nepal Demographic and Health Survey (NDHS) data were excluded to prevent duplication of estimates. A total of 1448 records were retrieved, and 751 duplicates were removed, leaving 697 records for screening. After excluding 577 records at the title/abstract stage, 120 full texts (including 6 from citation searching) were assessed, and 16 studies met the inclusion criteria. The prevalence of underweight ranged from 2.0% to 30.3%; overweight/obesity ranged from 4.8% to 55.0%; and anemia ranged from 12.83% to 66.8%. Definitions and measurement methods varied considerably across studies, contributing to wide variability in reported prevalence estimates. The studies identified numerous factors associated with malnutrition. Demographic and socioeconomic characteristics such as age, ethnicity, family size, educational status, occupation, food consumption patterns, and health-seeking behavior were linked to different forms of malnutrition. Anemia was associated with women’s ethnicity, deworming medication, and reproductive health factors, such as menarcheal status and number of antenatal visits. This review maps the existing research on malnutrition among WRA in Nepal, identifying key trends and critical gaps that require further investigation. It also emphasizes the need for multisector-targeted interventions to address the diverse factors contributing to malnutrition, including anemia.

Yunxia Li, Qi Zhao, Yan Wang et al.

Lauren Thompson, Yvonne E Goh, Manu Jamwal et al.

Background: Young children in India often face multiple micronutrient deficiencies, yet interventions such as micronutrient powders have raised concerns about potential adverse effects on the gut microbiome. Large-scale food fortification is an effective strategy to improve micronutrient intake; however, its impact on the gut microbiome of children remains unclear. Objectives: To determine whether intake of quintuply-fortified salt (QFS) for 12 mo adversely affects gut microbiome composition in children aged 1–5 y. Methods: In a double-blind, randomized, controlled trial in Punjab, India, children received: 1) QFS with iron as encapsulated ferrous fumarate [eFF], zinc, vitamin B12, folic acid, and iodine (eFF-QFS); 2) QFS with the same micronutrients, but iron as encapsulated ferric pyrophosphate [eFePP] plus ethylenediaminetetraacetic acid (eFePP-QFS); or 3) standard iodized salt for 12 mo. Stool samples were collected from 125 children (eFF-QFS, n = 43; eFePP-QFS, n = 45; iodized salt, n= 37) at baseline and 12 mo and analyzed via 16S rRNA gene sequencing. Changes in alpha diversity (Shannon, abundance-based estimator index) between groups were assessed with linear mixed models, beta diversity (Bray-Curtis dissimilarity) with linear regression and permutational multivariate analysis of variance, and relative abundance of Enterobacteriaceae, Lactobacillus, Bifidobacterium, Bacteroides, Prevotella, or Escherichia-Shigella with zero-inflated negative binomial mixed models. Results: Average discretionary salt utilization was estimated to be 3.5 g/child equivalent/d across groups. Abundance-based estimator index was higher in the iodized salt arm compared with eFePP-QFS, but similar to eFF-QFS. Permutational multivariate analysis of variance revealed no overall group differences; however, pairwise Bray-Curtis distances from baseline were modestly greater in eFF-QFS compared with the other groups. No significant changes in relative abundance were identified. Conclusions: After 12 mo, QFS resulted no major changes in abundance of key taxa and minimal, inconsistent shifts in certain diversity metrics and relative to the iodized salt control, suggesting no adverse effects on microbiome composition among young children in this setting. Additional studies in settings with improved iron status are needed.This trial was registered at clinicaltrials.gov as NCT05166980 and at Clinical Trials Registry–India as CTRI/2022/02/040333.

Ayesha Baig, Li Zhouxin

As global living standards improve and medical technology advances, many infectious diseases have been effectively controlled. However, certain diseases, such as the recent COVID-19 pandemic, continue to pose significant threats to public health. This paper explores the evolution of infectious disease modeling, from early ordinary differential equation-based models like the SIR framework to more complex reaction-diffusion models that incorporate both temporal and spatial dynamics. The study highlights the importance of numerical methods, such as the Runge-Kutta method, implicit-explicit time-discretization techniques, and finite difference methods, in solving these models. By analyzing the development and application of these methods, this research underscores their critical role in predicting disease spread, informing public health strategies, and mitigating the impact of future pandemics.

Thien-Minh Le, Jukka-Pekka Onnela

Infectious disease modeling is used to forecast epidemics and assess the effectiveness of intervention strategies. Although the core assumption of mass-action models of homogeneously mixed population is often implausible, they are nevertheless routinely used in studying epidemics and provide useful insights. Network models can account for the heterogeneous mixing of populations, which is especially important for studying sexually transmitted diseases. Despite the abundance of research on mass-action and network models, the relationship between them is not well understood. Here, we attempt to bridge the gap by first identifying a spreading rule that results in an exact match between disease spreading on a fully connected network and the classic mass-action models. We then propose a method for mapping epidemic spread on arbitrary networks to a form similar to that of mass-action models. We also provide a theoretical justification for the procedure. Finally, we show the advantages of the proposed methods using synthetic data that is based on an empirical network. These findings help us understand when mass-action models and network models are expected to provide similar results and identify reasons when they do not.

Gerold Schmitt-Ulms, Xinzhu Wang, Joel Watts et al.

Decades after their initial observation in prion-infected brain tissues, the identities of virus-like dense particles, varicose tubules, and oval bodies containing parallel bands and fibrils have remained elusive. Our recent work revealed that a phenotype of dilation of the endoplasmic reticulum (ER), most notable for the perinuclear space (PNS), contributes to spongiform degeneration. To assess the significance of this phenotype for the etiology of prion diseases, we explored whether it can be functionally linked to other neuropathological hallmarks observed in these diseases, as this would indicate it to be a central event. Having surveyed the neuropathological record and other distant literature niches, we propose a model in which pathogenic forms of the prion protein poison raft domains, including essential Na+, K+-ATPases (NKAs) embedded within them, thereby triggering an ER-centered cellular rescue program coordinated by the unfolded protein response (UPR). The execution of this program stalls general protein synthesis, causing the deterioration of synaptic spines. As the disease progresses, cells selectively increase sterol biosynthesis, along with ribosome and ER biogenesis. These adaptive rescue attempts cause morphological changes to the ER which manifest as ER dilation or ER hypertrophy in a manner that is influenced by Ca2+ influx into the cell. The nuclear-to-cytoplasmic transport of mRNAs and tRNAs interrupts in late stage disease, thereby depriving ribosomes of supplies and inducing them to aggregate into a paracrystalline form. In support of this model, we share previously reported data, whose features are consistent with the interpretation that 1) the phenotype of ER dilation is observed in major prion diseases, 2) varicose tubules and oval bodies represent ER hypertrophy, and 3) virus-like dense particles are paracrystalline aggregates of inactive ribosomes.

Thalita Mendonça Antico, Larissa F. Rodrigues Moreira, Rodrigo Moreira

The diagnosis of diseases in food crops based on machine learning seemed satisfactory and suitable for use on a large scale. The Convolutional Neural Networks (CNNs) perform accurately in the disease prediction considering the image capture of the crop leaf, being extensively enhanced in the literature. These machine learning techniques fall short in data privacy, as they require sharing the data in the training process with a central server, disregarding competitive or regulatory concerns. Thus, Federated Learning (FL) aims to support distributed training to address recognized gaps in centralized training. As far as we know, this paper inaugurates the use and evaluation of FL applied in maize leaf diseases. We evaluated the performance of five CNNs trained under the distributed paradigm and measured their training time compared to the classification performance. In addition, we consider the suitability of distributed training considering the volume of network traffic and the number of parameters of each CNN. Our results indicate that FL potentially enhances data privacy in heterogeneous domains.

Rafi Andyah Arum Kedaton, Mimi Ruspita, Hanifa Andisetyana Putri

The number of maternal deaths in Semarang City in 2021 was 21 cases out of 22,030 live births, or around 95.32 deaths per 100,000 live births, with the causes of death dominated by bleeding (14.29%) and hypertension (9.52%). Postpartum hemorrhage is caused by four main factors known as the 4T: tone, trauma, tissue, and thrombin. This study was conducted to determine the relationship between preeclampsia, maternal age, and type of delivery with the incidence of postpartum hemorrhage at Central General Hospital Dr. Kariadi Semarang in 2020–2022. The study was an analytic observational quantitative research with a retrospective case control study. The study was conducted in February 2023 with a total sample of 100 samples divided into case (total sampling) and control (simple random sampling) groups with a ratio of 1: 1 for each group. Statistical tests used Chi-square and odds ratio (OR) tests. The results showed that there was no relationship between preeclampsia and the incidence of postpartum hemorrhage (p-value = 0.063; OR = 0.347), there was a relationship between maternal age and the incidence of postpartum hemorrhage (p-value = 0.011; OR = 3.455), and there was a relationship between the type of delivery and postpartum hemorrhage (p-value = 0.012; OR = 2.923). The community can be expected to play an active role in integrated service post cadre activities and ante natal care assistance. Health workers and educational institutions also need to improve their knowledge and skills to form qualified health workers through certified training.

Chester Kalinda, Million Phiri, Simona J. Simona et al.

Abstract Childhood stunting in its moderate and severe forms is a major global problem and an important indicator of child health. Rwanda has made progress in reducing the prevalence of stunting. However, the burden of stunting and its geographical disparities have precipitated the need to investigate its spatial clusters and attributable factors. Here, we assessed the determinants of under‐5 stunting and mapped its prevalence to identify areas where interventions can be directed. Using three combined rounds of the nationally representative Rwanda Demographic and Health Surveys of 2010, 2015 and 2020, we employed the Blinder‐Oaxaca decomposition analysis and the hotspot and cluster analyses to quantify the contributions of key determinants of stunting. Overall, there was a 7.9% and 10.3% points reduction in moderate stunting among urban and rural areas, respectively, and a 2.8% and 8.3% points reduction in severe stunting in urban and rural areas, respectively. Child age, wealth index, maternal education and the number of antenatal care visits were key determinants for the reduction of moderate and severe stunting. Over time, persistent statistically significant hotspots for moderate and severe stunting were observed in Northern and Western parts of the country. There is a need for an adaptive scaling approach when implementing national nutritional interventions by targeting high‐burden regions. Stunting hotspots in Western and Northern provinces underscore the need for coordinated subnational initiatives and strategies such as empowering the rural poor, enhancing antenatal health care, and improving maternal health and education levels to sustain the gains made in reducing childhood stunting.

Yeganeh Madadi, Mohammad Delsoz, Priscilla A. Lao et al.

Objective: To evaluate the efficiency of large language models (LLMs) such as ChatGPT to assist in diagnosing neuro-ophthalmic diseases based on detailed case descriptions. Methods: We selected 22 different case reports of neuro-ophthalmic diseases from a publicly available online database. These cases included a wide range of chronic and acute diseases that are commonly seen by neuro-ophthalmic sub-specialists. We inserted the text from each case as a new prompt into both ChatGPT v3.5 and ChatGPT Plus v4.0 and asked for the most probable diagnosis. We then presented the exact information to two neuro-ophthalmologists and recorded their diagnoses followed by comparison to responses from both versions of ChatGPT. Results: ChatGPT v3.5, ChatGPT Plus v4.0, and the two neuro-ophthalmologists were correct in 13 (59%), 18 (82%), 19 (86%), and 19 (86%) out of 22 cases, respectively. The agreement between the various diagnostic sources were as follows: ChatGPT v3.5 and ChatGPT Plus v4.0, 13 (59%); ChatGPT v3.5 and the first neuro-ophthalmologist, 12 (55%); ChatGPT v3.5 and the second neuro-ophthalmologist, 12 (55%); ChatGPT Plus v4.0 and the first neuro-ophthalmologist, 17 (77%); ChatGPT Plus v4.0 and the second neuro-ophthalmologist, 16 (73%); and first and second neuro-ophthalmologists 17 (17%). Conclusions: The accuracy of ChatGPT v3.5 and ChatGPT Plus v4.0 in diagnosing patients with neuro-ophthalmic diseases was 59% and 82%, respectively. With further development, ChatGPT Plus v4.0 may have potential to be used in clinical care settings to assist clinicians in providing quick, accurate diagnoses of patients in neuro-ophthalmology. The applicability of using LLMs like ChatGPT in clinical settings that lack access to subspeciality trained neuro-ophthalmologists deserves further research.

Gustavo Frechtel

Type 1 diabetes (T1D) is caused by the autoimmune destruction of β cells of the pancreas, driven by CD4+ and CD8+ T lymphocytes. The humoral branch represented by B lymphocytes is responsible for producing autoantibodies (AAs) against specific antigens such as glutamate decarboxylase (GADA), cellular membrane phosphatase (IA2A/ICA512A), zincT8 transporter (ZincT8A), and the insulin molecule (IAA), all markers of the autoimmune process. These AAs can be detected before the clinical onset of T1D, at the prediabetes type 1 stage (1). The overall risk of progression to T1D is regarded as the presence of 2 or more AAs but differs across patients, being higher with a greater number of AAs (2).

Martin Röhlig, Oliver Stachs, Heidrun Schumann

Advances in optical coherence tomography (OCT) have enabled noninvasive imaging of substructures of the human retina with high spatial resolution. OCT examinations are now a standard procedure in clinics and an integral part of ophthalmic research. The interpretation of the OCT helps ophthalmologists understand the impact of various retinal and systemic diseases on the structure of the retina in a way not previously possible. In the early stages of retinal diseases, however, the identification and analysis of small and localized substructural changes in the retina remains a challenge. We present an overview of novel visual analytics approaches for the interactive exploration of early retinal changes in single and multiple patients, the comparison of the changes with normative data, and automated quantification and measurement of diagnosis-relevant information. We developed these approaches in close collaboration with ophthalmology researchers and industry experts from a leading OCT device manufacturer. As a result, they not only significantly reduced the time and effort required for OCT data analysis, especially in the context of cross-sectional studies, but have also led to several new discoveries published in biomedical journals.

Muhammad Salman Pathan, Avishek Nag, Muhammad Mohisn Pathan et al.

Heart Disease has become one of the most serious diseases that has a significant impact on human life. It has emerged as one of the leading causes of mortality among the people across the globe during the last decade. In order to prevent patients from further damage, an accurate diagnosis of heart disease on time is an essential factor. Recently we have seen the usage of non-invasive medical procedures, such as artificial intelligence-based techniques in the field of medical. Specially machine learning employs several algorithms and techniques that are widely used and are highly useful in accurately diagnosing the heart disease with less amount of time. However, the prediction of heart disease is not an easy task. The increasing size of medical datasets has made it a complicated task for practitioners to understand the complex feature relations and make disease predictions. Accordingly, the aim of this research is to identify the most important risk-factors from a highly dimensional dataset which helps in the accurate classification of heart disease with less complications. For a broader analysis, we have used two heart disease datasets with various medical features. The classification results of the benchmarked models proved that there is a high impact of relevant features on the classification accuracy. Even with a reduced number of features, the performance of the classification models improved significantly with a reduced training time as compared with models trained on full feature set.

Shabana Urooj, Astha Sharma, Chitransh Sinha et al.

The usage of Information, Communication and Technology (ICT) in health sector has a great potential in improving the health of individuals and communities, disease detection, prevention and overall strengthening the healthcare systems, vital for development and poverty reduction. Large ICT establishments offer a variety of Artificial Intelligence (AI) based solutions; and their tenacities are inclusive of wearable therapeutic devices, healthcare management arrangements, extrapolative healthcare diagnostics, ailment prevention systems, detection and screening of diseases and automated tactics. In the field of healthcare related instrumentation, AI plays a prevalent role with the amalgamation of several technological progressions. This enables machines to sense, comprehend, act and learn to perform organisational and clinical healthcare functions as well as serves the research and training purposes. Additionally, it enables to accomplish the anticipated directorial and medicinal benefits. The major causes of life threats reported in literature are; heart and brain diseases. In this paper, an extensive review is presented exploring the evolving ICT technologies in machine learning and AI to help ICT enthusiasts to be able to catch up with the emerging trends in healthcare.

Hang Dong, Víctor Suárez-Paniagua, Huayu Zhang et al.

The identification of rare diseases from clinical notes with Natural Language Processing (NLP) is challenging due to the few cases available for machine learning and the need of data annotation from clinical experts. We propose a method using ontologies and weak supervision. The approach includes two steps: (i) Text-to-UMLS, linking text mentions to concepts in Unified Medical Language System (UMLS), with a named entity linking tool (e.g. SemEHR) and weak supervision based on customised rules and Bidirectional Encoder Representations from Transformers (BERT) based contextual representations, and (ii) UMLS-to-ORDO, matching UMLS concepts to rare diseases in Orphanet Rare Disease Ontology (ORDO). Using MIMIC-III US intensive care discharge summaries as a case study, we show that the Text-to-UMLS process can be greatly improved with weak supervision, without any annotated data from domain experts. Our analysis shows that the overall pipeline processing discharge summaries can surface rare disease cases, which are mostly uncaptured in manual ICD codes of the hospital admissions.

Rondanelli M, Faliva MA, Peroni G et al.

Mariangela Rondanelli,1,2 Milena Anna Faliva,3 Gabriella Peroni,3 Vittoria Infantino,2 Clara Gasparri,3 Giancarlo Iannello,4 Simone Perna,5 Tariq AbdulKarim Alalwan,5 Salwa Al-Thawadi,5 Angelo Guido Corsico6,7 1IRCCS Mondino Foundation, Pavia 27100, Italy; 2Department of Public Health, Experimental and Forensic Medicine, Unit of Human and Clinical Nutrition, University of Pavia, Pavia 27100, Italy; 3Endocrinology and Nutrition Unit, Azienda di Servizi alla Persona “Istituto Santa Margherita”, University of Pavia, Pavia 27100, Italy; 4General Management, Azienda di Servizi alla Persona “Istituto Santa Margherita”, Pavia 27100, Italy; 5Department of Biology, College of Science, University of Bahrain, Sakhir 32038, Bahrain; 6Center for Diagnosis of Inherited Alpha 1-Antitrypsin Deficiency, Department of Internal Medicine and Therapeutics, University of Pavia, Pavia 27100, Italy; 7Division of Respiratory Diseases, IRCCS Policlinico San Matteo Foundation, Pavia 27100, ItalyCorrespondence: Gabriella PeroniEndocrinology and Nutrition Unit, Azienda di Servizi alla Persona “Istituto Santa Margherita”, University of Pavia, Pavia 27100, Italy, Tel +39 0382381739Fax +39 0382381218Email gabriella.peroni01@universitadipavia.itAbstract: Nutritional problems are an important part of rehabilitation for chronic obstructive pulmonary disease (COPD) patients. COPD patients often present with malnutrition, sarcopenia, and osteoporosis with possible onset of cachexia, with an inadequate dietary intake and a poor quality of life. Moreover, diet plays a pivotal role in patients with COPD through three mechanisms: regulation of carbon dioxide produced/oxygen consumed, inflammation, and oxidative stress. A narrative review based on 99 eligible studies was performed to evaluate current evidence regarding optimum diet therapy for the management of COPD, and then a food pyramid was built accordingly. The food pyramid proposal will serve to guide energy and dietary intake in order to prevent and treat nutritionally related COPD complications and to manage progression and COPD-related symptoms. The nutrition pyramid described in our narrative review is hypothetical, even in light of several limitations of the present review; the main limitation is the fact that to date there are no randomized controlled trials in the literature clearly showing that improved nutrition, via the regulation of carbon dioxide produced/oxygen consumed, inflammation and oxidative stress, improves symptoms and/or progression of COPD. Even if this nutritional pyramid is hypothetical, we hope that it can serve the valuable purpose of helping researchers focus on the often-ignored possible connections between body composition, nutrition, and COPD.Keywords: COPD, nutrients, inflammation, fat free mass, antioxidants, gas exchanges

Xinwu Qian, Satish V. Ukkusuri

In this study, we develop the mathematical model to understand the coupling between the spreading dynamics of infectious diseases and the mobility dynamics through urban transportation systems. We first describe the mobility dynamics of the urban population as the process of leaving from home, traveling to and from the activity locations, and engaging in activities. We then embed the susceptible-exposed-infectious-recovered (SEIR) process over the mobility dynamics and develops the spatial SEIR model with travel contagion (Trans-SEIR), which explicitly accounts for contagions both during travel and during daily activities. We investigate the theoretical properties of the proposed model and show how activity contagion and travel contagion contribute to the average number of secondary infections. In the numerical experiments, we explore how the urban transportation system may alter the fundamental dynamics of the infectious disease, change the number of secondary infections, promote the synchronization of the disease across the city, and affect the peak of the disease outbreaks. The Trans-SEIR model is further applied to the understand the disease dynamics during the COVID-19 outbreak in New York City, where we show how the activity and travel contagion may be distributed and how effective travel control can be implemented with only limited resources. The Trans-SEIR model along with the findings in our study may have significant contributions to improving our understanding of the coupling between urban transportation and disease dynamics, the development of quarantine and control measures of disease system, and promoting the idea of disease-resilient urban transportation networks.

Kory D. Johnson, Mathias Beiglböck, Manuel Eder et al.

A primary quantity of interest in the study of infectious diseases is the average number of new infections that an infected person produces. This so-called reproduction number has significant implications for the disease progression. There has been increasing literature suggesting that superspreading, the significant variability in number of new infections caused by individuals, plays an important role in the spread of SARS-CoV-2. In this paper, we consider the effect that such superspreading has on the estimation of the reproduction number and subsequent estimates of future cases. Accordingly, we employ a simple extension to models currently used in the literature to estimate the reproduction number and present a case-study of the progression of COVID-19 in Austria. Our models demonstrate that the estimation uncertainty of the reproduction number increases with superspreading and that this improves the performance of prediction intervals. Of independent interest is the derivation of a transparent formula that connects the extent of superspreading to the width of credible intervals for the reproduction number. This serves as a valuable heuristic for understanding the uncertainty surrounding diseases with superspreading.

Yesin Sahraoui, Ahmed Korichi, Chaker Abdelaziz Kerrache et al.

The respiratory viral diseases, such as those caused by the family of coronaviruses, can be extremely contagious and spread through saliva droplets generated by coughing, sneezing, or breathing. In humans, the most common symptoms of the infection include fever and difficulty in breathing. In order to reduce the diffusion of the current "Coronavirus disease 2019 (COVID-19)" pandemic, the Internet of Things (IoT) technologies can play an important role; for instance, they can be effectively used for implementing a real-time patient tracking and warning system at a city scale. Crucial places to install the tracking IoT devices are the public/private vehicles that, augmented with multiple connectivity solutions, can implement the Internet of Vehicles (IoV) paradigm. In such a ubiquitous network environment, vehicles are equipped with a variety of sensors, including regular cameras that can be replaced with thermal cameras. Therefore, this paper proposes a new design for widely detecting respiratory viral diseases that leverages IoV to collect real-time body temperature and breathing rate measurements of pedestrians. This information can be used to recognize geographic areas affected by possible COVID-19 cases and to implement proactive preventive strategies that would further limit the spread of the disease.