Parents in north London have been told their children could be excluded from school if they are not fully vaccinated against measles amid an outbreak of the highly-contagious disease. Unvaccinated pupils identified as close contacts of people with measles could be excluded for 21 days in accordance with national guidelines, Enfield Council said in a letter to all parents in the borough in late January. At least 34 children have contracted measles in Enfield so far this year, the UK Health Security Agency (UKHSA) has said, and a number sent to hospital. A local health chief meanwhile told the BBC: We are worried because actually, this is a significantly increased number than what we're used to. Asking unvaccinated, close contacts of measles cases to stay off school is fairly standard practice when there are local outbreaks.

Plus: how wastewater tracking could help curb measles' rise in the US. Can ChatGPT Health do better? For the past two decades, there's been a clear first step for anyone who starts experiencing new medical symptoms: Look them up online. The practice was so common that it gained the pejorative moniker "Dr. But times are changing, and many medical-information seekers are now using LLMs. According to OpenAI, 230 million people ask ChatGPT health-related queries each week.

While media bias is widely studied, the epistemic strategies behind factual reporting remain computationally underexplored. This paper analyzes these strategies through a large-scale comparison of CNN and Fox News. To isolate reporting style from topic selection, we employ an article matching strategy to compare reports on the same events and apply the FactAppeal framework to a corpus of over 470K articles covering two highly politicized periods: the COVID-19 pandemic and the Israel-Hamas war. We find that CNN's reporting contains more factual statements and is more likely to ground them in external sources. The outlets also exhibit sharply divergent sourcing patterns: CNN builds credibility by citing Experts} and Expert Documents, constructing an appeal to formal authority, whereas Fox News favors News Reports and direct quotations. This work quantifies how partisan outlets use systematically different epistemic strategies to construct reality, adding a new dimension to the study of media bias.

The vaccine advisory group ACIP, not all members of which seemed to know what the group does, recommended to the CDC that combined MMRV shots not be given before age 4. A federal vaccine advisory committee made of members hand-picked by Health and Human Services Secretary Robert F. Kennedy Jr. recommended in an 8-3 vote on Thursday that the combined measles, mumps, rubella and varicella (MMRV) vaccine should not be given before age four, citing long-known evidence that shows a slightly increased risk for febrile seizures in that age group. Experts say that while frightening, febrile seizures--which are uncommon after vaccination--are usually short-lived and harmless, and removing the option for parents could cause a decline in immunization rates against measles, mumps, and rubella, some of the most dangerous childhood diseases. Known as the Advisory Committee on Immunization Practices, or ACIP, the group provides recommendations to the US Centers for Disease Control and Prevention on vaccine usage. These recommendations are typically adopted by CDC and have an impact on state vaccine requirements for school, insurance coverage of vaccines, and pharmacy access--something at least one member of the panel seemed to be unaware of. Thursday's vote is part of a new shift in vaccine policy being spearheaded by Kennedy, a longtime anti-vaccine activist.

There has been an increasing interest in detecting hallucinations in model-generated texts, both manually and automatically, at varying levels of granularity. However, most existing methods fail to precisely pinpoint the errors. In this work, we introduce QASemConsistency, a new formalism for localizing factual inconsistencies in attributable text generation, at a fine-grained level. Drawing inspiration from Neo-Davidsonian formal semantics, we propose decomposing the generated text into minimal predicate-argument level propositions, expressed as simple question-answer (QA) pairs, and assess whether each individual QA pair is supported by a trusted reference text. As each QA pair corresponds to a single semantic relation between a predicate and an argument, QASemConsistency effectively localizes the unsupported information. We first demonstrate the effectiveness of the QASemConsistency methodology for human annotation, by collecting crowdsourced annotations of granular consistency errors, while achieving a substantial inter-annotator agreement ($\kappa > 0.7)$. Then, we implement several methods for automatically detecting localized factual inconsistencies, with both supervised entailment models and open-source LLMs.

We introduce a framework for the use of large language models (LLMs) in Building Understandable Messaging for Policy and Evidence Review (BUMPER). LLMs are proving capable of providing interfaces for understanding and synthesizing large databases of diverse media. This presents an exciting opportunity to supercharge the translation of scientific evidence into policy and action, thereby improving livelihoods around the world. However, these models also pose challenges related to access, trust-worthiness, and accountability. The BUMPER framework is built atop a scientific knowledge base (e.g., documentation, code, survey data) by the same scientists (e.g., individual contributor, lab, consortium). We focus on a solution that builds trustworthiness through transparency, scope-limiting, explicit-checks, and uncertainty measures. LLMs are rapidly being adopted and consequences are poorly understood. The framework addresses open questions regarding the reliability of LLMs and their use in high-stakes applications. We provide a worked example in health policy for a model designed to inform measles control programs. We argue that this framework can facilitate accessibility of and confidence in scientific evidence for policymakers, drive a focus on policy-relevance and translatability for researchers, and ultimately increase and accelerate the impact of scientific knowledge used for policy decisions.

The work of this paper presents a dataset that contains the data of 4011 videos about the ongoing outbreak of measles published on 264 websites on the internet between January 1, 2024, and May 31, 2024. The dataset is available at https://dx.doi.org/10.21227/40s8-xf63. These websites primarily include YouTube and TikTok, which account for 48.6% and 15.2% of the videos, respectively. The remainder of the websites include Instagram and Facebook as well as the websites of various global and local news organizations. For each of these videos, the URL of the video, title of the post, description of the post, and the date of publication of the video are presented as separate attributes in the dataset. After developing this dataset, sentiment analysis (using VADER), subjectivity analysis (using TextBlob), and fine-grain sentiment analysis (using DistilRoBERTa-base) of the video titles and video descriptions were performed. This included classifying each video title and video description into (i) one of the sentiment classes i.e. positive, negative, or neutral, (ii) one of the subjectivity classes i.e. highly opinionated, neutral opinionated, or least opinionated, and (iii) one of the fine-grain sentiment classes i.e. fear, surprise, joy, sadness, anger, disgust, or neutral. These results are presented as separate attributes in the dataset for the training and testing of machine learning algorithms for performing sentiment analysis or subjectivity analysis in this field as well as for other applications. Finally, this paper also presents a list of open research questions that may be investigated using this dataset.

The recent 'Mpox' outbreak, formerly known as 'Monkeypox', has become a significant public health concern and has spread to over 110 countries globally. The challenge of clinically diagnosing mpox early on is due, in part, to its similarity to other types of rashes. Computer-aided screening tools have been proven valuable in cases where Polymerase Chain Reaction (PCR) based diagnosis is not immediately available. Deep learning methods are powerful in learning complex data representations, but their efficacy largely depends on adequate training data. To address this challenge, we present the "Mpox Skin Lesion Dataset Version 2.0 (MSLD v2.0)" as a follow-up to the previously released openly accessible dataset, one of the first datasets containing mpox lesion images. This dataset contains images of patients with mpox and five other non-mpox classes (chickenpox, measles, hand-foot-mouth disease, cowpox, and healthy). We benchmark the performance of several state-of-the-art deep learning models, including VGG16, ResNet50, DenseNet121, MobileNetV2, EfficientNetB3, InceptionV3, and Xception, to classify mpox and other infectious skin diseases. In order to reduce the impact of racial bias, we utilize a color space data augmentation method to increase skin color variability during training. Additionally, by leveraging transfer learning implemented with pre-trained weights generated from the HAM10000 dataset, an extensive collection of pigmented skin lesion images, we achieved the best overall accuracy of $83.59\pm2.11\%$. Finally, the developed models are incorporated within a prototype web application to analyze uploaded skin images by a user and determine whether a subject is a suspected mpox patient.

The recent monkeypox outbreak has become a public health concern due to its rapid spread in more than 40 countries outside Africa. Clinical diagnosis of monkeypox in an early stage is challenging due to its similarity with chickenpox and measles. In cases where the confirmatory Polymerase Chain Reaction (PCR) tests are not readily available, computer-assisted detection of monkeypox lesions could be beneficial for surveillance and rapid identification of suspected cases. Deep learning methods have been found effective in the automated detection of skin lesions, provided that sufficient training examples are available. However, as of now, such datasets are not available for the monkeypox disease. In the current study, we first develop the ``Monkeypox Skin Lesion Dataset (MSLD)" consisting skin lesion images of monkeypox, chickenpox, and measles. The images are mainly collected from websites, news portals, and publicly accessible case reports. Data augmentation is used to increase the sample size, and a 3-fold cross-validation experiment is set up. In the next step, several pre-trained deep learning models, namely, VGG-16, ResNet50, and InceptionV3 are employed to classify monkeypox and other diseases. An ensemble of the three models is also developed. ResNet50 achieves the best overall accuracy of $82.96(\pm4.57\%)$, while VGG16 and the ensemble system achieved accuracies of $81.48(\pm6.87\%)$ and $79.26(\pm1.05\%)$, respectively. A prototype web-application is also developed as an online monkeypox screening tool. While the initial results on this limited dataset are promising, a larger demographically diverse dataset is required to further enhance the generalizability of these models.

Peter Hotez deceives New York Times readers about the odds of dying from measles in the US to persuade parents to comply with the CDC's vaccine schedule. On January 9, the New York Times published an article written by Dr. Peter J. Hotez titled "You Are Unvaccinated and Got Sick. His purpose in writing is to persuade parents to vaccinate their children according to the routine schedule recommended by the Centers for Disease Control and Prevention (CDC). To that end, he purports to compare "the dangerous effects of three diseases with the minimal side effects of their corresponding vaccines." "To state it bluntly," Hotez writes, "being unvaccinated can result in illness or death. Vaccines, in contrast, are extremely unlikely to lead to side effects, even minor ones like fainting." He laments that "vaccination rates have fallen", resulting in a resurgence of measles globally. He cites the example of Samoa, where "almost 5,700 measles cases have been recorded since September, resulting in at least 83 deaths. Almost all of those who died were young children." In the US, he writes, "vaccine hesitancy is contributing to" measles outbreaks. Hotez presents data ostensibly to enable parents "to compare the risks of becoming ill with measles . . . to the minute chances of experiencing side effects from their corresponding vaccines." Hotez goes on to assert, "Moreover, new research reveals that, even when patients recover, the measles virus can suppress the immune system, rendering children susceptible to serious infections like pneumonia and the flu." "misinformation spread after an article implying a link between measles vaccinations and autism was published in The Lancet in 1998; it was retracted in 2010 over concerns about the validity of the results and the conduct of the study.