Disinformation and misinformation, spread over online social media, can be harmful to society. While there are a number of models that predict or explain the dynamic behavior of fake news dissemination over social media, the limited availability of datasets hinders the ability to fully test these models under a wide range of possible conditions. Simulation, however, offers a low-cost approach to evaluate models over many different scenarios. This paper proposes a novel approach for simulating fake news dissemination process on Twitter. Our approach combines a Multivariate Hawkes Point Processes with concepts from Agent-Based models, to generate realistic data that incorporates the core elements of Twitter including user networks, tweet type, user stance toward the news event, and time. The flexible and efficient simulation approach can capture a wide range of realistic behavior through a rich set of tuning parameters. We show how closely the simulated data can replicate real fake news events.

Temporal abnormality localization in long Wireless Capsule Endoscopy (WCE) videos is an important problem. The cost of obtaining frame level label for long WCE videos is prohibitive. In this paper, we propose an end-to-end temporal abnormality localization for long WCE videos using only weak video level labels. Physicians use Capsule Endoscopy (CE) as a non-surgical and non-invasive method to examine the entire digestive tract in order to diagnose diseases or abnormalities. While CE has revolutionized traditional endoscopy procedures, a single CE examination could last up to 8 hours generating as much as 100,000 frames. Physicians must review the entire video, frame-by-frame, in order to identify the frames capturing relevant lesion or abnormality. This, sometimes could be as few as just a single frame. Given this very high level of redundancy, analysing long CE videos can be very tedious, time consuming and also error prone. This paper presents a novel multi-step method for an end-to-end localization of target frames capturing abnormalities of interest in the long video using only weak video labels. First we developed an automatic temporal segmentation using change point detection technique to temporally segment the video into uniform, homogeneous and identifiable segments. Then we employed Graph Convolutional Neural Network (GCNN) to learn a representation of each video segment. Using weak video segment labels, we trained our GCNN model to recognize each video segment as abnormal if it contains at least a single abnormal frame. Finally, leveraging the parameters of the trained GCNN model, we replaced the final layer of the network with a temporal pool layer to localize the relevant abnormal frames within each abnormal video segment. We experimented with multiple real patients’ endoscopy videos and achieved an accuracy of 89.9% on the graph classification task and a specificity of 97.5% on the abnormal frames localization task.

Using wastewater surveillance as a continuous pooled sampling technique has been in place in many countries since the early stages of the outbreak of COVID-19. Since the beginning of the outbreak, many research works have emerged, studying different aspects of viral SARS-CoV-2 DNA concentrations (viral load) in wastewater and its potential as an early warning method. However, one of the questions that has remained unanswered is the quantitative relation between viral load and clinical indicators such as daily cases, deaths, and hospitalizations. Few studies have tried to couple viral load data with an epidemiological model to relate the number of infections in the community to the viral burden. This paper proposes a stochastic wastewater-based SEIR model to showcase the importance of viral load in the early detection and prediction of an outbreak in a community. We built three models based on whether or not they use the case count and viral load data and compared their simulations and forecasting quality. Our results demonstrate that a simple SEIR model based on viral load data can reliably predict the number of infections in the future. Therefore, wastewater-based surveillance is a promising way of monitoring the spread of COVID19 and can provide city officials with timely information about the circulation of COVID-19 in the community.