ORAN: A meta-modeling platform to drive real-life and online outbreak simulations

Presented virtually during the New Faculty Talks session at the 25th Annual University of Massachusetts Medical School Research Retreat 2020 on October 27, 2020

Shader programming for computational arts and design: A comparison between creative coding frameworks

We describe an Application Program Interface (API) that facilitates the use of GLSL shaders in computational design, interactive arts, and data visualization. This API was first introduced in the version 2.0 of Processing, a programming language and environment widely used for teaching and production in the context of media arts and design, and has been recently completed in the 3.0 release. It aims to incorporate low-level shading programming into code-based design, by integrating traditional models of graphics programming with more expressive approaches afforded by the penGL pipeline on modern GPUs. We contrast Processing's shader API with similar interfaces available in other frameworks used in computational arts and design, in order to better understand its advantages and shortcomings.Organismic and Evolutionary Biolog

Containing the spread of mumps on college campuses

College campuses are vulnerable to infectious disease outbreaks, and there is an urgent need to develop better strategies to mitigate their size and duration, particularly as educational institutions around the world adapt to in-person instruction during the COVID-19 pandemic. Towards addressing this need, we applied a stochastic compartmental model to quantify the impact of university-level responses to contain a mumps outbreak at Harvard University in 2016. We used our model to determine which containment interventions were most effective and study alternative scenarios without and with earlier interventions. This model allows for stochastic variation in small populations, missing or unobserved case data and changes in disease transmission rates post-intervention. The results suggest that control measures implemented by the University\u27s Health Services, including rapid isolation of suspected cases, were very effective at containing the outbreak. Without those measures, the outbreak could have been four times larger. More generally, we conclude that universities should apply (i) diagnostic protocols that address false negatives from molecular tests and (ii) strict quarantine policies to contain the spread of easily transmissible infectious diseases such as mumps among their students. This modelling approach could be applied to data from other outbreaks in college campuses and similar small population settings

Containing the Spread of Infectious Disease on College Campuses [preprint]

College campuses are highly vulnerable to infectious disease outbreaks, and there is a pressing need to develop better strategies to mitigate their size and duration, particularly as educational institutions around the world reopen to in-person instruction in the midst of the COVID-19 pandemic. Towards addressing this need, we applied a stochastic compartmental model to quantify the impact of university-level responses to past mumps outbreaks in college campuses and used it to determine which control interventions are most effective. Mumps is a very relevant disease in such settings, given its airborne mode of transmission, high infectivity, and recurrence of outbreaks despite availability of a vaccine. Our model aims to simultaneously overcome three crucial issues: stochastic variation in small populations, missing or unobserved case data, and changes in disease transmission rates post-intervention. We tested the model and assessed various interventions using data from the 2014 and 2016 mumps outbreaks at Ohio State University and Harvard University, respectively. Our results suggest that in order to decrease infectious disease incidence on their campuses, universities should apply diagnostic protocols that address false negatives from molecular tests, stricter quarantine policies, and effective awareness campaigns among their students and staff. Our model can be applied to data from other outbreaks in college campuses and similar small-population settings

Transforming Clinical Data into Actionable Prognosis Models: Machine-Learning Framework and Field-Deployable App to Predict Outcome of Ebola Patients

We introduce a machine-learning framework and field-deployable app to predict outcome of Ebola patients from their initial clinical symptoms. Recent work from other authors also points out to the clinical factors that can be used to better understand patient prognosis, but there is currently no predictive model that can be deployed in the field to assist health care workers. Mobile apps for clinical diagnosis and prognosis allow using more complex models than the scoring protocols that have been traditionally favored by clinicians, such as Apgar and MTS. Furthermore, the WHO Ebola Interim Assessment Panel has recently concluded that innovative tools for data collection, reporting, and monitoring are needed for better response in future outbreaks. However, incomplete clinical data will continue to be a serious problem until more robust and standardized data collection systems are in place. Our app demonstrates how systematic data collection could lead to actionable knowledge, which in turn would trigger more and better collection, further improving the prognosis models and the app, essentially creating a virtuous cycle.Organismic and Evolutionary Biolog

The case for altruism in institutional diagnostic testing

Amid COVID-19, many institutions deployed vast resources to test their members regularly for safe reopening. This self-focused approach, however, not only overlooks surrounding communities but also remains blind to community transmission that could breach the institution. To test the relative merits of a more altruistic strategy, we built an epidemiological model that assesses the differential impact on case counts when institutions instead allocate a proportion of their tests to members\u27 close contacts in the larger community. We found that testing outside the institution benefits the institution in all plausible circumstances, with the optimal proportion of tests to use externally landing at 45% under baseline model parameters. Our results were robust to local prevalence, secondary attack rate, testing capacity, and contact reporting level, yielding a range of optimal community testing proportions from 18 to 58%. The model performed best under the assumption that community contacts are known to the institution; however, it still demonstrated a significant benefit even without complete knowledge of the contact network

Clinical Illness and Outcomes in Patients with Ebola in Sierra Leone

Background Limited clinical and laboratory data are available on patients with Ebola virus disease (EVD). The Kenema Government Hospital in Sierra Leone, which had an existing infrastructure for research regarding viral hemorrhagic fever, has received and cared for patients with EVD since the beginning of the outbreak in Sierra Leone in May 2014. Methods We reviewed available epidemiologic, clinical, and laboratory records of patients in whom EVD was diagnosed between May 25 and June 18, 2014. We used quantitative reverse-transcriptase–polymerase-chain-reaction assays to assess the load of Ebola virus (EBOV, Zaire species) in a subgroup of patients. Results Of 106 patients in whom EVD was diagnosed, 87 had a known outcome, and 44 had detailed clinical information available. The incubation period was estimated to be 6 to 12 days, and the case fatality rate was 74%. Common findings at presentation included fever (in 89% of the patients), headache (in 80%), weakness (in 66%), dizziness (in 60%), diarrhea (in 51%), abdominal pain (in 40%), and vomiting (in 34%). Clinical and laboratory factors at presentation that were associated with a fatal outcome included fever, weakness, dizziness, diarrhea, and elevated levels of blood urea nitrogen, aspartate aminotransferase, and creatinine. Exploratory analyses indicated that patients under the age of 21 years had a lower case fatality rate than those over the age of 45 years (57% vs. 94%, P=0.03), and patients presenting with fewer than 100,000 EBOV copies per milliliter had a lower case fatality rate than those with 10 million EBOV copies per milliliter or more (33% vs. 94%, P=0.003). Bleeding occurred in only 1 patient. Conclusions The incubation period and case fatality rate among patients with EVD in Sierra Leone are similar to those observed elsewhere in the 2014 outbreak and in previous outbreaks. Although bleeding was an infrequent finding, diarrhea and other gastrointestinal manifestations were common. (Funded by the National Institutes of Health and others.

Genomic surveillance elucidates Ebola virus origin and transmission during the 2014 outbreak

In its largest outbreak, Ebola virus disease is spreading through Guinea, Liberia, Sierra Leone, and Nigeria. We sequenced 99 Ebola virus genomes from 78 patients in Sierra Leone to ~2000× coverage. We observed a rapid accumulation of interhost and intrahost genetic variation, allowing us to characterize patterns of viral transmission over the initial weeks of the epidemic. This West African variant likely diverged from central African lineages around 2004, crossed from Guinea to Sierra Leone in May 2014, and has exhibited sustained human-to-human transmission subsequently, with no evidence of additional zoonotic sources. Because many of the mutations alter protein sequences and other biologically meaningful targets, they should be monitored for impact on diagnostics, vaccines, and therapies critical to outbreak response.Organismic and Evolutionary Biolog

Sciviewer enables interactive visual interrogation of single-cell RNA-Seq data from the Python programming environment [preprint]

Visualizing two-dimensional (2D) embeddings (e.g. UMAP or tSNE) is a key step in interrogating single-cell RNA sequencing (scRNA-Seq) data. Subsequently, users typically iterate between programmatic analyses (e.g. clustering and differential expression) and visual exploration (e.g. coloring cells by interesting features) to uncover biological signals in the data. Interactive tools exist to facilitate visual exploration of embeddings such as performing differential expression on user-selected cells. However, the practical utility of these tools is limited because they don’t support rapid movement of data and results to and from the programming environments where the bulk of data analysis takes place, interrupting the iterative process. Here, we present the Single-cell Interactive Viewer (Sciviewer), a tool that overcomes this limitation by allowing interactive visual interrogation of embeddings from within Python. Beyond differential expression analysis of user-selected cells, Sciviewer implements a novel method to identify genes varying locally along any user-specified direction on the embedding. Sciviewer enables rapid and flexible iteration between interactive and programmatic modes of scRNA-Seq exploration, illustrating a useful approach for analyzing high-dimensional data