Prediction and prevention of the next pandemic zoonosis.

Most pandemics--eg, HIV/AIDS, severe acute respiratory syndrome, pandemic influenza--originate in animals, are caused by viruses, and are driven to emerge by ecological, behavioural, or socioeconomic changes. Despite their substantial effects on global public health and growing understanding of the process by which they emerge, no pandemic has been predicted before infecting human beings. We review what is known about the pathogens that emerge, the hosts that they originate in, and the factors that drive their emergence. We discuss challenges to their control and new efforts to predict pandemics, target surveillance to the most crucial interfaces, and identify prevention strategies. New mathematical modelling, diagnostic, communications, and informatics technologies can identify and report hitherto unknown microbes in other species, and thus new risk assessment approaches are needed to identify microbes most likely to cause human disease. We lay out a series of research and surveillance opportunities and goals that could help to overcome these challenges and move the global pandemic strategy from response to pre-emption

A DATA ANALYTICAL FRAMEWORK FOR IMPROVING REAL-TIME, DECISION SUPPORT SYSTEMS IN HEALTHCARE

In this dissertation we develop a framework that combines data mining, statistics and operations research methods for improving real-time decision support systems in healthcare. Our approach consists of three main concepts: data gathering and preprocessing, modeling, and deployment. We introduce the notion of offline and semi-offline modeling to differentiate between models that are based on known baseline behavior and those based on a baseline with missing information. We apply and illustrate the framework in the context of two important healthcare contexts: biosurveillance and kidney allocation. In the biosurveillance context, we address the problem of early detection of disease outbreaks. We discuss integer programming-based univariate monitoring and statistical and operations research-based multivariate monitoring approaches. We assess method performance on authentic biosurveillance data. In the kidney allocation context, we present a two-phase model that combines an integer programming-based learning phase and a data-analytical based real-time phase. We examine and evaluate our method on the current Organ Procurement and Transplantation Network (OPTN) waiting list. In both contexts, we show that our framework produces significant improvements over existing methods

Method selection and adaptation for distributed monitoring of infectious diseases for syndromic surveillance

AbstractBackgroundAutomated surveillance systems require statistical methods to recognize increases in visit counts that might indicate an outbreak. In prior work we presented methods to enhance the sensitivity of C2, a commonly used time series method. In this study, we compared the enhanced C2 method with five regression models.MethodsWe used emergency department chief complaint data from US CDC BioSense surveillance system, aggregated by city (total of 206 hospitals, 16 cities) during 5/2008–4/2009. Data for six syndromes (asthma, gastrointestinal, nausea and vomiting, rash, respiratory, and influenza-like illness) was used and was stratified by mean count (1–19, 20–49, ⩾50 per day) into 14 syndrome-count categories. We compared the sensitivity for detecting single-day artificially-added increases in syndrome counts. Four modifications of the C2 time series method, and five regression models (two linear and three Poisson), were tested. A constant alert rate of 1% was used for all methods.ResultsAmong the regression models tested, we found that a Poisson model controlling for the logarithm of total visits (i.e., visits both meeting and not meeting a syndrome definition), day of week, and 14-day time period was best. Among 14 syndrome-count categories, time series and regression methods produced approximately the same sensitivity (<5% difference) in 6; in six categories, the regression method had higher sensitivity (range 6–14% improvement), and in two categories the time series method had higher sensitivity.DiscussionWhen automated data are aggregated to the city level, a Poisson regression model that controls for total visits produces the best overall sensitivity for detecting artificially added visit counts. This improvement was achieved without increasing the alert rate, which was held constant at 1% for all methods. These findings will improve our ability to detect outbreaks in automated surveillance system data

Method selection and adaptation for distributed monitoring of infectious diseases for syndromic surveillance

AbstractBackgroundAutomated surveillance systems require statistical methods to recognize increases in visit counts that might indicate an outbreak. In prior work we presented methods to enhance the sensitivity of C2, a commonly used time series method. In this study, we compared the enhanced C2 method with five regression models.MethodsWe used emergency department chief complaint data from US CDC BioSense surveillance system, aggregated by city (total of 206 hospitals, 16 cities) during 5/2008–4/2009. Data for six syndromes (asthma, gastrointestinal, nausea and vomiting, rash, respiratory, and influenza-like illness) was used and was stratified by mean count (1–19, 20–49, ⩾50 per day) into 14 syndrome-count categories. We compared the sensitivity for detecting single-day artificially-added increases in syndrome counts. Four modifications of the C2 time series method, and five regression models (two linear and three Poisson), were tested. A constant alert rate of 1% was used for all methods.ResultsAmong the regression models tested, we found that a Poisson model controlling for the logarithm of total visits (i.e., visits both meeting and not meeting a syndrome definition), day of week, and 14-day time period was best. Among 14 syndrome-count categories, time series and regression methods produced approximately the same sensitivity (<5% difference) in 6; in six categories, the regression method had higher sensitivity (range 6–14% improvement), and in two categories the time series method had higher sensitivity.DiscussionWhen automated data are aggregated to the city level, a Poisson regression model that controls for total visits produces the best overall sensitivity for detecting artificially added visit counts. This improvement was achieved without increasing the alert rate, which was held constant at 1% for all methods. These findings will improve our ability to detect outbreaks in automated surveillance system data

Construction of Fuzzy Signature from Data: An Example of SARS Pre-clinical Diagnosis System

There are many areas where objects with very complex and sometimes interdependent features are to be classified; similarities and dissimilarities are to be evaluated. This makes a complex decision model difficult to construct effectively. Fuzzy signatures are introduced to handle complex structured data and interdependent feature problems. Fuzzy signatures can also used in cases where data is missing. This paper presents the concept of a fuzzy signature and how its flexibility can be used to quickly construct a medical pre-clinical diagnosis system. A Severe Acute Respiratory Syndrome (SARS) pre-clinical diagnosis system using fuzzy signatures is constructed as an example to show many advantages of the fuzzy signature. With the use of this fuzzy signature structure, complex decision models in the medical field should be able to be constructed more effectively

Surveillance of febrile patients in a district and evaluation of their spatiotemporal associations: a pilot study

<p>Abstract</p> <p>Background</p> <p>Fever is an undifferentiated clinical feature that may enhance the sensitivity of syndromic surveillance systems. By studying the spatiotemporal associations of febrile patients, it may allow early detection of case clustering that indicates imminent threat of infectious disease outbreaks in the community.</p> <p>Methods</p> <p>We captured consecutive emergency department visits that led to hospitalization in a district hospital in Hong Kong during the period of 12 Sep 2005 to 14 Oct 2005. We recorded demographic data, provisional diagnoses, temperature on presentation and residential location for each patient-episode, and geocoded the residential addresses. We applied Geographical Information System technology to study the geographical distribution these cases, and their associations within a 50-m buffer zone spatially. A case cluster was defined by three or more spatially associated febrile patients within each three consecutive days.</p> <p>Results</p> <p>One thousand and sixty six patient-episodes were eligible for analysis; 42% of them had fever (>37°C; oral temperature) on presentation. Two hundred and four patient-episodes (19.1%) came from residential care homes for elderly (RCHE). We detected a total of 40 case clusters during the study period. Clustered cases were of older age; 57 (33.3%) were residents of RCHE. We found a median of 3 patients (range: 3 - 8) and time span of 3 days (range: 2 - 8 days) in each cluster. Twenty five clusters had 2 or more patients living in the same building block; 18 of them were from RCHE.</p> <p>Conclusions</p> <p>It is technically feasible to perform surveillance on febrile patients and studying their spatiotemporal associations. The information is potentially useful for early detection of impending infectious disease threats.</p

Towards using Cough for Respiratory Disease Diagnosis by leveraging Artificial Intelligence: A Survey

Cough acoustics contain multitudes of vital information about pathomorphological alterations in the respiratory system. Reliable and accurate detection of cough events by investigating the underlying cough latent features and disease diagnosis can play an indispensable role in revitalizing the healthcare practices. The recent application of Artificial Intelligence (AI) and advances of ubiquitous computing for respiratory disease prediction has created an auspicious trend and myriad of future possibilities in the medical domain. In particular, there is an expeditiously emerging trend of Machine learning (ML) and Deep Learning (DL)-based diagnostic algorithms exploiting cough signatures. The enormous body of literature on cough-based AI algorithms demonstrate that these models can play a significant role for detecting the onset of a specific respiratory disease. However, it is pertinent to collect the information from all relevant studies in an exhaustive manner for the medical experts and AI scientists to analyze the decisive role of AI/ML. This survey offers a comprehensive overview of the cough data-driven ML/DL detection and preliminary diagnosis frameworks, along with a detailed list of significant features. We investigate the mechanism that causes cough and the latent cough features of the respiratory modalities. We also analyze the customized cough monitoring application, and their AI-powered recognition algorithms. Challenges and prospective future research directions to develop practical, robust, and ubiquitous solutions are also discussed in detail.Comment: 30 pages, 12 figures, 9 table

Automated machine learning optimizes and accelerates predictive modeling from COVID-19 high throughput datasets

COVID-19 outbreak brings intense pressure on healthcare systems, with an urgent demand for effective diagnostic, prognostic and therapeutic procedures. Here, we employed Automated Machine Learning (AutoML) to analyze three publicly available high throughput COVID-19 datasets, including proteomic, metabolomic and transcriptomic measurements. Pathway analysis of the selected features was also performed. Analysis of a combined proteomic and metabolomic dataset led to 10 equivalent signatures of two features each, with AUC 0.840 (CI 0.723–0.941) in discriminating severe from non-severe COVID-19 patients. A transcriptomic dataset led to two equivalent signatures of eight features each, with AUC 0.914 (CI 0.865–0.955) in identifying COVID-19 patients from those with a different acute respiratory illness. Another transcriptomic dataset led to two equivalent signatures of nine features each, with AUC 0.967 (CI 0.899–0.996) in identifying COVID-19 patients from virus-free individuals. Signature predictive performance remained high upon validation. Multiple new features emerged and pathway analysis revealed biological relevance by implication in Viral mRNA Translation, Interferon gamma signaling and Innate Immune System pathways. In conclusion, AutoML analysis led to multiple biosignatures of high predictive performance, with reduced features and large choice of alternative predictors. These favorable characteristics are eminent for development of cost-effective assays to contribute to better disease management