Dynamics-informed deconvolutional neural networks for super-resolution identification of regime changes in epidemiological time series

Inferring the timing and amplitude of perturbations in epidemiological systems from their stochastically spread low-resolution outcomes is as relevant as challenging. It is a requirement for current approaches to overcome the need to know the details of the perturbations to proceed with the analyses. However, the general problem of connecting epidemiological curves with the underlying incidence lacks the highly effective methodology present in other inverse problems, such as super-resolution and dehazing from computer vision. Here, we develop an unsupervised physics-informed convolutional neural network approach in reverse to connect death records with incidence that allows the identification of regime changes at single-day resolution. Applied to COVID-19 data with proper regularization and model-selection criteria, the approach can identify the implementation and removal of lockdowns and other nonpharmaceutical interventions with 0.93-day accuracy over the time span of a year.Comment: 18 pages, 5 figure

Meta-analysis of the severe acute respiratory syndrome coronavirus 2 serial intervals and the impact of parameter uncertainty on the coronavirus disease 2019 reproduction number

This is the final version. Available on open access from SAGE Publications via the DOI in this recordThe serial interval of an infectious disease, commonly interpreted as the time between the onset of symptoms in sequentially infected individuals within a chain of transmission, is a key epidemiological quantity involved in estimating the reproduction number. The serial interval is closely related to other key quantities, including the incubation period, the generation interval (the time between sequential infections), and time delays between infection and the observations associated with monitoring an outbreak such as confirmed cases, hospital admissions, and deaths. Estimates of these quantities are often based on small data sets from early contact tracing and are subject to considerable uncertainty, which is especially true for early coronavirus disease 2019 data. In this paper, we estimate these key quantities in the context of coronavirus disease 2019 for the UK, including a meta-analysis of early estimates of the serial interval. We estimate distributions for the serial interval with a mean of 5.9 (95% CI 5.2; 6.7) and SD 4.1 (95% CI 3.8; 4.7) days (empirical distribution), the generation interval with a mean of 4.9 (95% CI 4.2; 5.5) and SD 2.0 (95% CI 0.5; 3.2) days (fitted gamma distribution), and the incubation period with a mean 5.2 (95% CI 4.9; 5.5) and SD 5.5 (95% CI 5.1; 5.9) days (fitted log-normal distribution). We quantify the impact of the uncertainty surrounding the serial interval, generation interval, incubation period, and time delays, on the subsequent estimation of the reproduction number, when pragmatic and more formal approaches are taken. These estimates place empirical bounds on the estimates of most relevant model parameters and are expected to contribute to modeling coronavirus disease 2019 transmission.Engineering and Physical Sciences Research Council (EPSRC)NHS EnglandAlan Turing InstituteMedical Research Council (MRC)National Institute for Health Research (NIHR

Spatial Dynamics of the Severe Acute Respiratory Syndrome (SARS) Epidemic in Hong Kong in 2003

The Severe Acute Respiratory Syndrome (SARS) epidemic in 2003 was the first infectious disease outbreak caused by a novel pathogen in the twenty-first century. The outbreak in Hong Kong was the second largest worldwide and was characterised by a large proportion of hospital infections and a super-spreading event caused by environmental factors in residential buildings. Hospitals treating SARS cases were at high risk for transmission. I found that hospital outbreaks triggered community transmission as well as the formation of spatial clusters of community cases. The size of the community outbreak in an area increased with the size of the outbreak in the nearest hospital treating SARS, and an area was more likely to have no community-infected cases if it was far from hospitals treating SARS, or had less hospital-infected cases within the area. To quantify the transmission between hospital and community, I developed a spatial epidemic-tree-reconstruction method that uses gravity models to spatially define the probability of contact between individuals in the community. From the reconstructed probabilistic infection tree, I estimated that 24% of community transmission was likely to be infected by cases infected in hospitals, with infected patients discharged during their incubation period and hospital visitors the most important drivers of transmission from healthcare settings to the community. Healthcare workers were key drivers of hospital transmission, with the hospital-to-hospital reproduction number, excluding a single hospital super-spreading event, estimated to be 0.8. A typical community-acquired case was estimated to generate 0.6 cases in the community and 0.2 cases in the hospital in which they were subsequently hospitalised. My findings suggest that hospital infection control could be improved. Restricted hospital visitor policies could have been imposed for longer time during the outbreak and quarantine could be considered for those who recently visited or have been discharged from hospitals treating SARS cases

Data Science in Healthcare

Data science is an interdisciplinary field that applies numerous techniques, such as machine learning, neural networks, and deep learning, to create value based on extracting knowledge and insights from available data. Advances in data science have a significant impact on healthcare. While advances in the sharing of medical information result in better and earlier diagnoses as well as more patient-tailored treatments, information management is also affected by trends such as increased patient centricity (with shared decision making), self-care (e.g., using wearables), and integrated care delivery. The delivery of health services is being revolutionized through the sharing and integration of health data across organizational boundaries. Via data science, researchers can deliver new approaches to merge, analyze, and process complex data and gain more actionable insights, understanding, and knowledge at the individual and population levels. This Special Issue focuses on how data science is used in healthcare (e.g., through predictive modeling) and on related topics, such as data sharing and data management

The politics of crisis management in China

This thesis investigates how the Chinese Communist Party (CCP) has tactically managed and defused major crises between 2002 and 2008 which put its credibility and legitimacy to the test. Contrary to conventional wisdom that major crises are likely to challenge and threaten regime stability in authoritarian systems or even undermine their viability, this thesis argues that the CCP has managed to sustain its political hegemony to date through the manipulation of these major crises and through the maximum tinkering with the current political system it reigns over. In order to explain why manipulation is the key in the CCP’s successful crisis management, this thesis first develops a critical reassessment of the conception of crisis and elaborates on crisis’s tripartite political utilities. These are (a) shift the dominating paradigm, (b) centralise political power and (c) (re) gain popularity and legitimacy. These altogether form an analytical framework for crisis, which is followed by a chapter that sets the backdrop against which our case studies unfold and explains why the Chinese context is particularly favourable for crisis manipulation. The thesis then proceeds with three case studies: the 2003 SARS epidemic, the 2008 Sichuan earthquake and the Sanlu milk scandal occurred in the same year. The thesis suggests that although the CCP’s responses were not flawless, and not always timely, it managed to manipulate all three crises in its favour via the aforementioned political utilities and subsequently defused these crises. At the same time, its Leninist structure was able to unleash formidable mobilisation capacity to help the regime rapidly bring situations under control. Overall, the CCP’s crisis management efficacy was satisfactory in the short term. Nevertheless, the thesis concludes that despite the short term usefulness of crisis manipulation, in the long term the efficacy of the same strategy as well as the political utility of crisis are decaying, as illustrated in reference to more recent crises that stretched the CCP’s credibility. Therefore, the CCP is in need of embarking on substantive political reform in order to develop an alternative crisis displacement mechanism. This thesis makes an original contribution to the existing literature in the field. It complements the public administration and public management literature by bringing politics back in. It also updates the empirical knowledge base of past studies as well as offering a comparison of crisis responses. This is a timely contribution to the study of Chinese crisis management and to the study of the nature of Chinese politics

Coronavirus Disease (COVID-19): Socio-Economic Systems in the Post-Pandemic World; Design Thinking, Strategic Planning, Management, and Public Policy

On 11 March 2020, the World Health Organization declared a pandemic of the COVID-19 coronavirus disease that was first recognized in China in late 2019. Among the primary effects caused by the pandemic, there was the dissemination of health preventive measures such as physical distancing, travel restrictions, self-isolation, quarantines, and facility closures. This includes the global disruption of socio-economic systems including the postponement or cancellation of various public events (e.g., sporting, cultural, or religious), supply shortages and fears of the same, schools and universities closure, evacuation of foreign citizens, a rise of unemployment, changes in the international aid schemes, misinformation, and incidents of discrimination toward people affected by or suspected of having the COVID-19 disease. The pandemic has brought to the fore unpreparedness in critical areas that require attention, amid prospects and challenges. Moreover, considerable reorganization efforts are required with implications for assets, resources, norms, and value systems. COVID-19 is challenging the concept of globalization and stimulating responses at the levels of local and regional socio-economic systems that lead to the mobilization of assets that have been unrecognized earlier on, such as various forms of economic capital, social capital, cultural capital, human capital, and creative capital. For example, through digital channels, local groups are forming to create schemes of support for physical and mental wellbeing. These emerging exchanges lead to various social and technological innovations by building on skills and assets that are less important in the free-market economy, such as empathy, skills for crafts, making and fixing; locally grown microgreens; and micromanufacturing. Isolation and local living are also making it much harder to ignore the civic responsibilities towards communities, meant as individuals, vulnerable groups, and local businesses. Whilst the pandemic is limiting physical participation, this challenging time is uncovering alternative ways of mutual support, which may create long-term benefits for socio-economic systems, including environmental and biodiversity protection, reduction of the air pollution, and climate action. The pandemic's threat to public health will hopefully be overcome with implications for disruption for an extended period that we are unable to forecast at this stage. It is key to focus on studies recognizing the activities and interventions leading to the recovery of socio-economic systems after the pandemic. Reflecting and planning on how societies and economies will go back to "business as usual" requires new forms of communication and cooperation, imaginative design thinking, new styles of management, as well as new tools and forms of participation in various public policies. Many questions related to the care of the vulnerable, economic restart, and the risk of future pandemics, to mention but a few, are already occupying the academic, scientific, experts, and activist communities, who have started to imagine the "new normal.

Covipendium : information available to support the development of medical countermeasures and interventions against COVID-19

The living paper on the new coronavirus disease (COVID-19) provides a structured compilation of scientific data about the virus, the disease and its control. Its objective is to help scientists identify the most relevant publications on COVID-19 in the mass of information that appears every day. It is also expected to foster a global understanding of disease control and stimulate transdisciplinary initiatives

MERS-CoV

Middle East respiratory syndrome coronavirus (MERS-CoV) is an emerging zoonotic coronavirus. First identified in 2012, MERS-CoV has caused over 2460 infections and a fatality rate of about 35% in humans. Similar to severe acute respiratory syndrome coronavirus (SARS-CoV), MERS-CoV likely originated from bats; however, different from SARS-CoV, which potentially utilized palm civets as its intermediate hosts, MERS-CoV likely transmits to humans through dromedary camels. Animal models, such as humanized mice and nonhuman primates, have been developed for studying MERS-CoV infection. Currently, there are no vaccines and therapeutics approved for the prevention and treatment of MERS-CoV infection, although a number of them have been developed preclinically or tested clinically. This book covers one editorial and 16 articles (including seven review articles and nine original research papers) written by researchers working in the field of MERS-CoV. It describes the following three main aspects: (1) MERS-CoV epidemiology, transmission, and pathogenesis; (2) current progress on MERS-CoV animal models, vaccines, and therapeutics; and (3) challenges and future prospects for MERS-CoV research. Overall, this book will help researchers in the MERS-CoV field to further advance their work on the virus. It also has important implications for other coronaviruses as well as viruses outside the coronavirus family with pandemic potentials

Social World Sensing via Social Image Analysis from Social Media

Social imagery, the visuals shared by users via various platforms and applications, may be analyzed to elicit something of massmind (and individual) thinking. This work involves the exploration of seven topics from various subject areas (global public health, environmentalism, human rights, political expression, and human predation) through social imagery and data from social media. The coding techniques involve manual coding, the integration of multiple social data streams, computational text analysis, data visualizations, and other combinations of approaches.https://newprairiepress.org/ebooks/1037/thumbnail.jp