Future of Pandemic Prevention and Response CCC Workshop Report

This report summarizes the discussions and conclusions of a 2-day multidisciplinary workshop that brought together researchers and practitioners in healthcare, computer science, and social sciences to explore what lessons were learned and what actions, primarily in research, could be taken. One consistent observation was that there is significant merit in thinking not only about pandemic situations, but also about peacetime advances, as many healthcare networks and communities are now in a perpetual state of crisis. Attendees discussed how the COVID-19 pandemic amplified gaps in our health and computing systems, and how current and future computing technologies could fill these gaps and improve the trajectory of the next pandemic. Three major computing themes emerged from the workshop: models, data, and infrastructure. Computational models are extremely important during pandemics, from anticipating supply needs of hospitals, to determining the care capacity of hospital and social service providers, to projecting the spread of the disease. Accurate, reliable models can save lives, and inform community leaders on policy decisions. Health system users require accurate, reliable data to achieve success when applying models. This requires data and measurement standardization across health care organizations, modernizing the data infrastructure, and methods for ensuring data remains private while shared for model development, validation, and application. Finally, many health care systems lack the data, compute, and communication infrastructures required to build models on their data, use those models in ordinary operations, or even to reliably access their data. Robust and timely computing research has the potential to better support healthcare works to save lives in times of crisis (e.g., pandemics) and today during relative peacetime

Use of early biological detection data by decision makers to minimize the consequences of no-notice infectious disease outbreaks

New and reemerging diseases pose significant challenges to the United States. Providing decision makers with data early to help characterize the event may allow for better-informed decisions and the initiation of appropriate responses. There is a limited amount of literature on the factors that lead decision makers to implement the appropriate response. The purpose of this study was to generate knowledge about the use of early biological detection by decision makers. A case study design of two cities was employed to determine if early biological detection capability affected the decisions to implement public health interventions. Multiple methodologies were used to collect and analyze data from primary and secondary sources. A review of previous outbreaks provided insights into disease characteristics and response activities which were used to build realistic disease scenarios for use in key informant interviews. Interviews with decision makers in each of two cities were conducted to understand how early biologic data were used, the availability of data, and to determine decision making processes. Several overarching themes emerged: data types, sources, and confidence is varied among different professional types of decision makers; strong relationships support the notification of an event and assist in effective, rapid response; public relationships and the media are beneficial partners in response with ability to rapidly communicate guidance; authority for decision making is unclear during crisis; significant events initiated preparedness activities in each city; and the 2009 H1N1 experience tested the US's capability to respond to a public health crisis. Federal and local stakeholders have a role to play in improving the level of preparedness of cities for a public health emergency. At the federal level, an assessment of federally funded biological detection capabilities and an appropriate realignment of federal support based on actual threat is required to improve the capacity for our cities to rapidly respond. In addition, the federal government has a unique opportunity to identify and fund cities to participate in National Special Security Events and National Level Exercises which improves their preparedness posture as a community. Our nation's cities have the responsibility to understand their information requirements and create an infrastructure that supports appropriate decision making. This study presents a plan to help local governments assess their information requirements and create an information network

Disasters Preparedness and Emergency Response: Prevention, Surveillance and Mitigation Planning

This Special Issue welcomes research papers on new approaches that have been applied or are under development to improve preparedness and emergency response. We especially encourage the submission of inter-disciplinary and crosscutting research. We also encourage the submission of manuscripts that focus on various types of disasters, disaster and emergency research, and on policy or management solutions at multiple scales

Methods matter: computational modelling in public health policy and planning

This work is aimed at understanding and unifying information on epidemiological modelling methods and how those methods relate to public policy addressing human health, specifically in the context of infectious disease prevention, pandemic planning, and health behaviour change. This thesis employs multiple qualitative and quantitative methods, and presents as a manuscript of several individual, data-driven projects that are combined in a narrative arc. The first chapter introduces the scope and complexity of this interdisciplinary undertaking, describing several topical intersections of importance. The second chapter begins the presentation of original data, and describes in detail two exercises in computational epidemiological modelling pertinent to pandemic influenza planning and policy, and progresses in the next chapter to present additional original data on how the confidence of the public in modelling methodology may have an effect on their planned health behaviour change as recommended in public health policy. The thesis narrative continues in the final data-driven chapter to describe how health policymakers use modelling methods and scientific evidence to inform and construct health policies for the prevention of infectious diseases, and concludes with a narrative chapter that evaluates the breadth of this data and recommends strategies for the optimal use of modelling methodologies when informing public health policy in applied public health scenarios

State and local policy considerations for implementing the National Response Plan

CHDS State/LocalThreatened with the loss of federal funding for Homeland Security and emergency management preparedness programs, state and local entities must implement the National Response Plan and the National Incident Management System, which includes the Incident Command System, Unified Command, and the Multiagency Coordination System. Although mandated by Congress and implemented by Homeland Security Presidential Directive 5, underdeveloped areas of Indian country and small towns, especially farming and ranching communities and agriculturally-based counties are likely to find that they do not have the capacity to fully implement these mandated federal response programs. A theoretical terrorist-induced multistate Foot and Mouth Disease (FMD) outbreak is used to examine the impact of implementing newly established federally mandated response management programs on rural and tribal communities in agrarian states. Recovering from such an agroterrorism bioattack would require a coordinated multi-disciplinary response that is heavily dependent on local, tribal, state, and private sector personnel. However, because the United States has not experienced an outbreak of FMD since 1929, many of the skills required to quickly diagnose and respond may no longer exist. This thesis identifies potential methods for obtaining and deploying the FMD virus in a coordinated bioattack on the U.S. economy.http://archive.org/details/statendlocalpoli109452305Director, Idaho Bureau of Disaster ServicesApproved for public release; distribution is unlimited

Exitus: An Agent-Based Evacuation Simulation Model For Heterogeneous Populations

Evacuation planning for private-sector organizations is an important consideration given the continuing occurrence of both natural and human-caused disasters that inordinately affect them. Unfortunately, the traditional management approach that is focused on fire drills presents several practical challenges at the scale required for many organizations but especially those responsible for national critical infrastructure assets such as airports and sports arenas. In this research we developed Exitus, a comprehensive decision support system that may be used to simulate large-scale evacuations of such structures. The system is unique because it considers individuals with disabilities explicitly in terms of physical and psychological attributes. It is also capable of classifying the environment in terms of accessibility characteristics encompassing various conditions that have been shown to have a disproportionate effect upon the behavior of individuals with disabilities during an emergency. The system was applied to three unique test beds: a multi-story office building, an international airport, and a major sports arena. Several simulation experiments revealed specific areas of concern for both building managers and management practice in general. In particular, we were able to show (a) how long evacuations of heterogeneous populations may be expected to last, (b) who the most vulnerable groups of people are, (c) the risk engendered from particular design features for individuals with disabilities, and (d) the potential benefits from adopting alternate evacuation strategies, among others. Considered together, the findings provide a useful foundation for the development of best practices and policies addressing the evacuation concerns surrounding heterogeneous populations in large, complex environments. Ultimately, a capabilities based approach featuring both tactical and strategic planning with an eye toward the unique problems presented by individuals with disabilities is recommended

International Society for Disease Surveillance Conference 2011: Building the Future of Public Health Surveillance: Building the Future of Public Health Surveillance

Daniel Reidpath - ORCID: 0000-0002-8796-0420 https://orcid.org/0000-0002-8796-04204pubpub1117

Data Science for Finance: Targeted Learning from (Big) Data to Economic Stability and Financial Risk Management

A thesis submitted in partial fulfillment of the requirements for the degree of Doctor in Information Management, specialization in Statistics and EconometricsThe modelling, measurement, and management of systemic financial stability remains a critical issue in most countries. Policymakers, regulators, and managers depend on complex models for financial stability and risk management. The models are compelled to be robust, realistic, and consistent with all relevant available data. This requires great data disclosure, which is deemed to have the highest quality standards. However, stressed situations, financial crises, and pandemics are the source of many new risks with new requirements such as new data sources and different models. This dissertation aims to show the data quality challenges of high-risk situations such as pandemics or economic crisis and it try to theorize the new machine learning models for predictive and longitudes time series models. In the first study (Chapter Two) we analyzed and compared the quality of official datasets available for COVID-19 as a best practice for a recent high-risk situation with dramatic effects on financial stability. We used comparative statistical analysis to evaluate the accuracy of data collection by a national (Chinese Center for Disease Control and Prevention) and two international (World Health Organization; European Centre for Disease Prevention and Control) organizations based on the value of systematic measurement errors. We combined excel files, text mining techniques, and manual data entries to extract the COVID-19 data from official reports and to generate an accurate profile for comparisons. The findings show noticeable and increasing measurement errors in the three datasets as the pandemic outbreak expanded and more countries contributed data for the official repositories, raising data comparability concerns and pointing to the need for better coordination and harmonized statistical methods. The study offers a COVID-19 combined dataset and dashboard with minimum systematic measurement errors and valuable insights into the potential problems in using databanks without carefully examining the metadata and additional documentation that describe the overall context of data. In the second study (Chapter Three) we discussed credit risk as the most significant source of risk in banking as one of the most important sectors of financial institutions. We proposed a new machine learning approach for online credit scoring which is enough conservative and robust for unstable and high-risk situations. This Chapter is aimed at the case of credit scoring in risk management and presents a novel method to be used for the default prediction of high-risk branches or customers. This study uses the Kruskal-Wallis non-parametric statistic to form a conservative credit-scoring model and to study its impact on modeling performance on the benefit of the credit provider. The findings show that the new credit scoring methodology represents a reasonable coefficient of determination and a very low false-negative rate. It is computationally less expensive with high accuracy with around 18% improvement in Recall/Sensitivity. Because of the recent perspective of continued credit/behavior scoring, our study suggests using this credit score for non-traditional data sources for online loan providers to allow them to study and reveal changes in client behavior over time and choose the reliable unbanked customers, based on their application data. This is the first study that develops an online non-parametric credit scoring system, which can reselect effective features automatically for continued credit evaluation and weigh them out by their level of contribution with a good diagnostic ability. In the third study (Chapter Four) we focus on the financial stability challenges faced by insurance companies and pension schemes when managing systematic (undiversifiable) mortality and longevity risk. For this purpose, we first developed a new ensemble learning strategy for panel time-series forecasting and studied its applications to tracking respiratory disease excess mortality during the COVID-19 pandemic. The layered learning approach is a solution related to ensemble learning to address a given predictive task by different predictive models when direct mapping from inputs to outputs is not accurate. We adopt a layered learning approach to an ensemble learning strategy to solve the predictive tasks with improved predictive performance and take advantage of multiple learning processes into an ensemble model. In this proposed strategy, the appropriate holdout for each model is specified individually. Additionally, the models in the ensemble are selected by a proposed selection approach to be combined dynamically based on their predictive performance. It provides a high-performance ensemble model to automatically cope with the different kinds of time series for each panel member. For the experimental section, we studied more than twelve thousand observations in a portfolio of 61-time series (countries) of reported respiratory disease deaths with monthly sampling frequency to show the amount of improvement in predictive performance. We then compare each country’s forecasts of respiratory disease deaths generated by our model with the corresponding COVID-19 deaths in 2020. The results of this large set of experiments show that the accuracy of the ensemble model is improved noticeably by using different holdouts for different contributed time series methods based on the proposed model selection method. These improved time series models provide us proper forecasting of respiratory disease deaths for each country, exhibiting high correlation (0.94) with Covid-19 deaths in 2020. In the fourth study (Chapter Five) we used the new ensemble learning approach for time series modeling, discussed in the previous Chapter, accompany by K-means clustering for forecasting life tables in COVID-19 times. Stochastic mortality modeling plays a critical role in public pension design, population and public health projections, and in the design, pricing, and risk management of life insurance contracts and longevity-linked securities. There is no general method to forecast the mortality rate applicable to all situations especially for unusual years such as the COVID-19 pandemic. In this Chapter, we investigate the feasibility of using an ensemble of traditional and machine learning time series methods to empower forecasts of age-specific mortality rates for groups of countries that share common longevity trends. We use Generalized Age-Period-Cohort stochastic mortality models to capture age and period effects, apply K-means clustering to time series to group countries following common longevity trends, and use ensemble learning to forecast life expectancy and annuity prices by age and sex. To calibrate models, we use data for 14 European countries from 1960 to 2018. The results show that the ensemble method presents the best robust results overall with minimum RMSE in the presence of structural changes in the shape of time series at the time of COVID-19. In this dissertation’s conclusions (Chapter Six), we provide more detailed insights about the overall contributions of this dissertation on the financial stability and risk management by data science, opportunities, limitations, and avenues for future research about the application of data science in finance and economy