Policy measures, information technology, and people's collective behavior in Taiwan's COVID-19 response

COVID-19 has rampaged globally with devastating consequences on human lives and livelihoods. Taiwan, a democratic island-country with 23.5 million population, was predicted to have the second highest risk of imported cases at the outset and instead registered among the lowest COVID-19 prevalence and mortality rates during year one. This chapter describes Taiwan's emergency response policies, including the Communicable Disease Control Act (CDCA), pandemic measures in different segments of the population, the role of national health insurance and accessible healthcare, innovative integrations of information technology, and amendments to laws and regulations adapted to the evolving situation. Taiwan's CDCA was introduced in 1944. In addition to posting travel advisories, the Taiwan Centers for Disease Control (CDC) enhanced border control, as required by the CDCA. Taiwan CDC officers stationed at airports implemented health screening of passengers and provided healthcare advice. © 2023 John Wiley & Sons, Inc.

Rapid Expert Consultation on Critical Federal Capabilities Needed to Evaluate Real-World Safety, Effectiveness, and Equitable Distribution and Use of Medical Countermeasures During a Public Health Emergency

This rapid expert consultation was produced by individual members of the Standing Committee for CDC Center for Preparedness and Response (SCPR). Its aim is to review and propose modifications to an initial draft list of critical federal capabilities presented by the Centers for Disease Control and Prevention (CDC) and the U.S. Food and Drug Administration (FDA) that are needed to evaluate real-world safety, effectiveness, equitable distribution, access, and use of medical countermeasures (MCMs) during a public health emergency (PHE). This effort draws from expert input, published literature, and lessons learned from previous public health emergencies, as well as the ongoing COVID-19 pandemic. National Academies of Sciences, Engineering, and Medicine. 2022. Rapid Expert Consultation on Critical Federal Capabilities Needed to Evaluate Real-World Safety, Effectiveness, and Equitable Distribution and Use of Medical Countermeasures During a Public Health Emergency. Washington, DC: The National Academies Press. https://doi.org/10.17226/26574. © 2022 National Academy of Sciences. All rights reserved.

Safety and Tolerability of SARS-Cov-2 Vaccination in Patients with Neuromuscular Disorders: A Single Center Experience

Objective: To describe side effects from SARS-Cov-2 vaccination and its effect on underlying neuromuscular disease amongst patients followed at the University of California, Irvine Neuromuscular Center. Background: Extensive data on safety and tolerability of SARS-Cov-2 vaccines exists for healthy individuals. However, patients with neuromuscular conditions and especially those on immune modulatory therapy were not included in the pivotal vaccine trials. It is primarily through expert consensus that vaccination is recommended for this patient population. Design/Methods: Patients were advised to inform the study team about their vaccination status. We collected data during in-person clinic visits or via telehealth encounters using a standardized questionnaire between December 2020 and August 2021. When information was provided about upcoming vaccination dates, patients were contacted within 2 weeks for follow up. Results: Information on 363 administered vaccine doses in 214 patients was recorded, including 199 Pfizer-BioNT, 155 Moderna and 9 Johnson & Johnson doses. Our cohort included 84 patients with myasthenia gravis (MG) and 34 with motor neuron disease (Amyotrophic Lateral Sclerosis, Primary Lateral Sclerosis). The remainder (96 patients) included other immunemediated disorders (idiopathic inflammatory myopathies, Chronic Inflammatory Demyelinating Neuropathy, Guillain-Barre Syndrome and other immune neuropathies) as well as acquired and inherited neuromuscular disorders (Inclusion Body Myositis, muscular dystrophy, inherited and acquired neuropathies). One patient with generalized MG had MG exacerbation, and another experienced impending crisis within one week of vaccine administration;both recovered with appropriate therapy. Detailed analyses of the dataset are being performed and will be presented at the meeting. Conclusions: SARS-Cov-2 vaccinations were well tolerated for the majority of our neuromuscular cohort, similar to what has been reported in healthy individuals. Vaccination did not result in disease exacerbation in the majority of patients with immune-mediated neuromuscular disorders.

Spatial Temporal Analysis of 40,000,000,000,000 Internet Darkspace Packets

The Internet has never been more important to our society, and understanding the behavior of the Internet is essential. The Center for Applied Internet Data Analysis (CAIDA) Telescope observes a continuous stream of packets from an unsolicited darkspace representing 1/256 of the Internet. During 2019 and 2020 over 40,000,000,000,000 unique packets were collected representing the largest ever assembled public corpus of Internet traffic. Using the combined resources of the Supercomputing Centers at UC San Diego, Lawrence Berkeley National Laboratory, and MIT, the spatial temporal structure of anonymized source-destination pairs from the CAIDA Telescope data has been analyzed with GraphBLAS hierarchical hypersparse matrices. These analyses provide unique insight on this unsolicited Internet darkspace traffic with the discovery of many previously unseen scaling relations. The data show a significant sustained increase in unsolicited traffic corresponding to the start of the COVID19 pandemic, but relatively little change in the underlying scaling relations associated with unique sources, source fan-outs, unique links, destination fan-ins, and unique destinations. This work provides a demonstration of the practical feasibility and benefit of the safe collection and analysis of significant quantities of anonymized Internet traffic.

Supercomputing Enabled Deployable Analytics for Disaster Response

First responders and other forward deployed essential workers can benefit from advanced analytics. Limited network access and software security requirements prevent the usage of standard cloud based microservice analytic platforms that are typically used in industry. One solution is to precompute a wide range of analytics as files that can be used with standard preinstalled software that does not require network access or additional software and can run on a wide range of legacy hardware. In response to the COVID-19 pandemic, this approach was tested for providing geo-spatial census data to allow quick analysis of demographic data for better responding to emergencies. These data were processed using the MIT SuperCloud to create several thousand Google Earth and Microsoft Excel files representative of many advanced analytics. The fast mapping of census data using Google Earth and Microsoft Excel has the potential to give emergency responders a powerful tool to improve emergency preparedness. Our approach displays relevant census data (total population, population under 15, population over 65, median age) per census block, sorted by county, through a Microsoft Excel spreadsheet (xlsx file) and Google Earth map (kml file). The spreadsheet interface includes features that allow users to convert between different longitude and latitude coordinate units. For the Google Earth files, a variety of absolute and relative colors maps of population density have been explored to provide an intuitive and meaningful interface. Using several hundred cores on the MIT SuperCloud, new analytics can be generated in a few minutes. © 2021 IEEE.

Fast Mapping onto Census Blocks

Pandemic measures such as social distancing and contact tracing can be enhanced by rapidly integrating dynamic location data and demographic data. Projecting billions of longitude and latitude locations onto hundreds of thousands of highly irregular demographic census block polygons is computationally challenging in both research and deployment contexts. This paper describes two approaches labeled "simple" and "fast". The simple approach can be implemented in any scripting language (Matlab/Octave, Python, Julia, R) and is easily integrated and customized to a variety of research goals. This simple approach uses a novel combination of hierarchy, sparse bounding boxes, polygon crossing-number, vectorization, and parallel processing to achieve 100,000,000+ projections per second on 100 servers. The simple approach is compact, does not increase data storage requirements, and is applicable to any country or region. The fast approach exploits the thread, vector, and memory optimizations that are possible using a low-level language (C++) and achieves similar performance on a single server. This paper details these approaches with the goal of enabling the broader community to quickly integrate location and demographic data.