Mr. Keil Werner - Stem demo camp

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STEM:The Spatiotemporal Epidemiological ModelerOpen Source Disease Modeling Application Available Through http://www.eclipse.org/stem/ IBM Almaden Research IBM Haifa Research

2 Introduction General Information About STEM Analysis of Seasonal Influenza Data from ICDC and Maccabi Health Simulations based on Seasonal Flu Data Swine Flu Public Health Information Affinity Domain and Collaboration with MECIDS

3 STEM is Open Source http://www.eclipse.org/stem/downloads.php

4 STEM is a Framework Epidemiological Compartment Models Standard SEIR model Standard SIR model Births Births S E I R S I R Deaths Deaths S: susceptible E: exposed but not yet infectious I: infectious R: recovered

5 Human Flu Swine Flu “Mixing Cell The Many Vectors of Infectious Disease

6 US Counties (3109) Edges Common Border Edges Interstate Highway Edges Air Travel Edges

7 STEM Core Integration Engine Choice Between Finite Difference Solver (fast, good for demos) Runge-Kutta-Fehlberg (RKF45) adaptive integration Adaptive step size Very efficient Synchronized across threads (multi-core engine) Accurate Computational error is estimated and controlled and an appropriate step size set automatically User Contributed Solvers

8 Goal: Evaluation of Public Health Policies Masks No Air travel HCN/HL7 Vaccinate Weather Air Traffic Masks/No Air Multiple parallel scenarios identically initialized from current real world conditions and simulate each simultaneously forward in time. ?

9 MECIDS iCDC France (Government, Universities) Northrop Grumman Why Open Source?Need for an International Community Eclipse Foundation UPMC Pitt Stanford MIT University of Vermont (UVM) U. Helsinki In discussion/meetings planned University of Edinburgh UC Davis

10 Demo

11 Simulations Based on ICDC Influenza Dataas initial condition

12 Recent H1N1 Outbreak

13 What are the parameters for the recent H1N1 outbreak What is Ro? Case report data not complete What could we learn from public data When first cases (~10) showed up in NY there were thousands in Mexico city Flu in Mexico peaked shortly after spread to air transport system Can we use the spread by air transport as an indicator for Ro ?

14 How Many Cases had developed in Mexico when Flu first appears in NY ? County/State National

15 Time to epidemic peak [days after I(NY)>~10]

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Highlighted notes while preparing for project on Computational Epidemics: Computational Epidemiology (Review) By Madhav Marathe, Anil Kumar S. Vullikanti Communications of the ACM, July 2013, Vol. 56 No. 7, Pages 88-96 10.1145/2483852.2483871 An epidemic is said to arise in a community or region when cases of an illness or other health-related events occur in excess of normal expectancy. Epidemics are considered to have influenced significant historical events, including the plagues in Roman times and Middle Ages, the fall of the Han empire in the 3rd century in China, and the defeat of the Aztecs in the 1500s, due to a smallpox outbreak. The 1918 flu pandemic in the U.S. was responsible for more deaths than those due to World War I. The last 50 years have seen epidemics caused by HIV/AIDS, SARS, and influenza-like illnesses. Despite significant medical advances, according to the World Health organization (WHO), infectious diseases account for more than 13 million deaths a year.

Computational Epidemiology (Review) : Notes

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This paper describes the design, prototype implementation and performance characteristics of a Disease Outbreak Notification System (DONS). The prototype was implemented in a hybrid cloud environment as an online/real-time system. It detects potential outbreaks of both listed and unknown diseases. It uses data mining techniques to choose the correct algorithm to detect outbreaks of unknown diseases. Our experiments showed that the proposed system has very high accuracy rate in choosing the correct detection algorithm. To our best knowledge, DONS is the first of its kind to detect outbreaks of unknown diseases using data mining techniques.

A Cloud-Based Prototype Implementation of a Disease Outbreak Notification System

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This paper describes the design, prototype implementation and performance characteristics of a Disease Outbreak Notification System (DONS). The prototype was implemented in a hybrid cloud environment as an online/real-time system. It detects potential outbreaks of both listed and unknown diseases. It uses data mining techniques to choose the correct algorithm to detect outbreaks of unknown diseases. Our experiments showed that the proposed system has very high accuracy rate in choosing the correct detection algorithm. To our best knowledge, DONS is the first of its kind to detect outbreaks of unknown diseases using data mining techniques.

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Highlighted notes while preparing for project on Computational Epidemics: Parallel Programming Approaches for an Agent-based Simulation of Concurrent Pandemic and Seasonal Influenza Outbreaks Milton Soto-Ferraria Peter Holvenstot Diana Prietoa Elise de Doncker John Kapenga 2013 International Conference on Computational Science Procedia Computer Science 18 ( 2013 ) 2187 – 2192 In this paper we propose parallelized versions of an agent-based simulation for concurrent pandemic and seasonal influenza outbreaks. The objective of the implementations is to significantly reduce the replication time and allow faster evaluation of mitigation strategies during an ongoing emergency. The simulation was initially parallelized using the g++ OpenMP library. We simulated the outbreak in a population of 1,000,000 individuals to evaluate algorithm performance and results. In addition to the OpenMP parallelization, a proposed CUDA implementation is also presented.

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The SIR model is used extensively in the field of epidemiology, in particular, for the analysis of communal diseases. One problem with SIR and other existing models is that they are tailored to random or Erdos type networks since they do not consider the varying probabilities of infection or immunity per node. In this paper, we present the application and the simulation results of the pSEIRS model that takes into account the probabilities, and is thus suitable for more realistic scale free networks. In the pSEIRS model, the death rate and the excess death rate are constant for infective nodes. Latent and immune periods are assumed to be constant and the infection rate is assumed to be proportional to I (t) N(t) , where N (t) is the size of the total population and I(t) is the size of the infected population. A node recovers from an infection temporarily with a probability p and dies from the infection with probability (1-p).

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Coronavirus disease 2019 (COVID-19) is caused a large number of death since has declared as an international pandemic in December 2019, and it is spreading all over the world (more than 200 countries). This situation puts the health organizations in an aberrant demand for urgent needs to develop significant early detection and monitoring smart solutions. Therefore, that new system or solution might be capable to identify COVID-19 quickly and accurately. Nowadays, the science of artificial intelligence (AI), and internet of things (IoT) techniques have an extensive range of applications, it can be initiated a possible solution for early detection and accurate decisions. We believe, combine both of the IoT revolution and machine learning (ML) methods are expected to reshape healthcare treatment strategies to provide smart (diagnosis, treatments, monitoring, and hospitals). This work aims to overview the recent solutions that have been used for early detection, and to provide the researchers a comprehensive summary that contribute to the pandemic control such AI, IoT, cloud, fog, algorithms, and all the dataset and their sources that recently published. In addition, all models, frameworks, monitoring systems, devices, and ideas (in four sections) have been sufficiently presented with all clarifications and justifications. Also, we propose a new vision for early detection based on IoT sensors data entry using 1 million patients-data to verify three proposed methods.

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Disease outbreak detection, monitoring and notification systems are increasingly gaining popularity since these systems are designed to assess threats to public health and disease outbreaks are becoming increasingly common world-wide. A variety of systems are in use around the world, with coverage of national, international and global disease outbreaks. These systems use different taxonomies and classifications for the detection and prioritization of potential disease outbreaks. In this paper, we study and analyze the current disease outbreak systems. Subsequently, we extract features and functions of typical and generic disease outbreak systems. We then propose a generic model for disease outbreak notification systems. Our effort is directed towards standardizing the design process for typical disease outbreak systems.

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Most web-based disease surveillance systems that give epidemic alerts are based on very large and unstructured data from various news sources, social media and online queries that are parsed by complex algorithms. This has the tendency to generate results that are so diverse and non-specific. When considered along with the fact that there are no existing standards for mining and analyzing data from the internet, the results or decisions reached based on internet sources have been classified as low-quality. This paper proposes a web-based grassroots epidemic alert system that is based on data collected specifically from primary health centers, hospitals and registered laboratories. It takes a more traditional approach to indicator-based disease surveillance as a step towards standardizing web-based disease surveillance. It makes use of a threshold value that is based on the third quartile (75 th percentile) to determine the need to trigger the alarm for the onset of an epidemic. It also includes, for deeper analysis, demographic information.

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Madhav Marathe and Anil Vullikanti will present a tutorial on computational epidemiology, along with Thomas L. Phillips Professor of Engineering Naren Ramakrishnan, at the 20th ACM SIGKDD Conference on Knowledge Discovery and Data Mining on August 24th, 2014 in New York City. In this tutorial, the researchers will approach epidemics based on diffusion processes on complex networks, which are able to capture more realistic problems. They will provide a state of the art overview of computational epidemiology, a multi-disciplinary research area that overlaps different areas in computer science, including data mining, machine learning, high performance computing and theoretical computer science, as well as mathematics, economics and statistics.

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The design of intelligent systems for analyzing information and predicting the epidemiological trends of the disease is rapidly expanding because of the coronavirus disease (COVID-19) pandemic. The COVID-19 datasets provided by Johns Hopkins University were included in the analysis. This dataset contains some missing data that is imputed using the multi-objective particle swarm optimization method. A time series model based on nonlinear autoregressive exogenou (NARX) neural network is proposed to predict the recovered and death COVID-19 cases. This model is trained and evaluated for two modes: predicting the situation of the affected areas for the next day and the next month. After training the model based on the data from January 22 to February 27, 2020, the performance of the proposed model was evaluated in predicting the situation of the areas in the coming two weeks. The error rate was less than 5%. The prediction of the proposed model for April 9, 2020, was compared with the actual data for that day. The absolute percentage error (AE) worldwide was 12%. The lowest mean absolute error (MAE) of the model was for South America and Australia with 3 and 3.3, respectively. In this paper, we have shown that geographical areas with mortality and recovery of COVID-19 cases can be predicted using a neural network-based model.

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A generic model for disease outbreak

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1. STEM:The Spatiotemporal Epidemiological ModelerOpen Source Disease Modeling Application Available Through http://www.eclipse.org/stem/ IBM Almaden Research IBM Haifa Research

2. 2 Introduction General Information About STEM Analysis of Seasonal Influenza Data from ICDC and Maccabi Health Simulations based on Seasonal Flu Data Swine Flu Public Health Information Affinity Domain and Collaboration with MECIDS

3. 3 STEM is Open Source http://www.eclipse.org/stem/downloads.php

4. 4 STEM is a Framework Epidemiological Compartment Models Standard SEIR model Standard SIR model Births Births S E I R S I R Deaths Deaths S: susceptible E: exposed but not yet infectious I: infectious R: recovered

5. 5 Human Flu Swine Flu “Mixing Cell The Many Vectors of Infectious Disease

6. 6 US Counties (3109) Edges Common Border Edges Interstate Highway Edges Air Travel Edges

7. 7 STEM Core Integration Engine Choice Between Finite Difference Solver (fast, good for demos) Runge-Kutta-Fehlberg (RKF45) adaptive integration Adaptive step size Very efficient Synchronized across threads (multi-core engine) Accurate Computational error is estimated and controlled and an appropriate step size set automatically User Contributed Solvers

8. 8 Goal: Evaluation of Public Health Policies Masks No Air travel HCN/HL7 Vaccinate Weather Air Traffic Masks/No Air Multiple parallel scenarios identically initialized from current real world conditions and simulate each simultaneously forward in time. ?

9. 9 MECIDS iCDC France (Government, Universities) Northrop Grumman Why Open Source?Need for an International Community Eclipse Foundation UPMC Pitt Stanford MIT University of Vermont (UVM) U. Helsinki In discussion/meetings planned University of Edinburgh UC Davis

11. 11 Simulations Based on ICDC Influenza Dataas initial condition

12. 12 Recent H1N1 Outbreak

13. 13 What are the parameters for the recent H1N1 outbreak What is Ro? Case report data not complete What could we learn from public data When first cases (~10) showed up in NY there were thousands in Mexico city Flu in Mexico peaked shortly after spread to air transport system Can we use the spread by air transport as an indicator for Ro ?

14. 14 How Many Cases had developed in Mexico when Flu first appears in NY ? County/State National

15. 15 Time to epidemic peak [days after I(NY)>~10]

16.

17. Model does not include seasonal forcing