On Understanding Catastrophe — The Case of Highly Severe Influenza-Like Illness

Computational epidemiology is a form of spatiotemporal reasoning in which social link structures are employed, and spatially explicit models are specified and executed. We point to issues thus far addressed neither by engineers, nor scientists, in the light of a use case focusing on catastrophic scenarios that assume the emergence of a highly unlikely but lethal and contagious strain of influenza. Our conclusion is that important perspectives are missing when dealing with policy issues resulting from scenario execution and analyses in computational epidemiology

A Workflow for Software Development within Computational Epidemiology

A critical investigation into computational models developed for studying the spread of communicable disease is presented. The case in point is a spatially explicit micro-meso-macro model for the entire Swedish population built on registry data, thus far used for smallpox and for influenza-like illnesses. The lessons learned from a software development project of more than 100 person months are collected into a check list. The list is intended for use by computational epidemiologists and policy makers, and the workflow incorporating these two roles is described in detail.NOTICE: This is the author’s version of a work that was accepted for publication in Journal of Computationa Science. Changes resulting from the publishing process, such as peer review, editing, corrections, structural formatting, and other quality control mechanisms may not be reflected in this document. Changes may have been made to this work since it was submitted for publication. A definitive version was subsequently published in Journal of Computational Science, VOL 2, ISSUE 3, 6 June 2011 DOI 10.1016/j.jocs.2011.05.004.</p

Arrival Time Statistics in Global Disease Spread

Metapopulation models describing cities with different populations coupled by the travel of individuals are of great importance in the understanding of disease spread on a large scale. An important example is the Rvachev-Longini model [{\it Math. Biosci.} {\bf 75}, 3-22 (1985)] which is widely used in computational epidemiology. Few analytical results are however available and in particular little is known about paths followed by epidemics and disease arrival times. We study the arrival time of a disease in a city as a function of the starting seed of the epidemics. We propose an analytical Ansatz, test it in the case of a spreading on the world wide air transportation network, and show that it predicts accurately the arrival order of a disease in world-wide cities

Computational epidemiology: Bayesian disease surveillance

Disease monitoring plays a crucial role in the implementation of public health measures. The demographic profiles of the people and the disease prevalence in a geographic region are analyzed for inter-causal relationships. Bayesian analysis of the data identifies the pertinent characteristics of the disease under study. The vital components of control and prevention of the disease spread are identified by Bayesian learning for the efficient utilization of the limited public health resources. Bayesian computing, layered with epidemiological expertise, provides the public health personnel to utilize their available resources optimally to minimize the prevalence of the disease. Bayesian analysis is implemented using synthetic data for two different demographic and geographic scenarios for pneumonia and influenza, that exhibit similar symptoms. The analysis infers results on the effects of the demographic parameters, namely ethnicity, gender, age, and income levels, on the evidence of the prevalence of the diseases. Bayesian learning brings in the probabilistic reasoning capabilities to port the inferences derived from one region to another

THE ROLE OF RISK ASSESSMENT IN MANAGING RISK

Risk and Uncertainty,

Modeling the Epidemic Outbreak and Dynamics of COVID-19 in Croatia

The paper deals with a modeling of the ongoing epidemic caused by Coronavirus disease 2019 (COVID-19) on the closed territory of the Republic of Croatia. Using the official public information on the number of confirmed infected, recovered and deceased individuals, the modified SEIR compartmental model is developed to describe the underlying dynamics of the epidemic. Fitted modified SEIR model provides the prediction of the disease progression in the near future, considering strict control interventions by means of social distancing and quarantine for infected and at-risk individuals introduced at the beginning of COVID-19 spread on February, 25th by Croatian Ministry of Health. Assuming the accuracy of provided data and satisfactory representativeness of the model used, the basic reproduction number is derived. Obtained results portray potential positive developments and justify the stringent precautionary measures introduced by the Ministry of Health.Comment: 5 pages, 6 figures, to appear in the Proceedings of the SpliTech2020 conferenc

Broadwick: A Framework for Computational Epidemiology

Motivation: Modelling disease outbreaks often involves incorporating the wealth of data that is gathered during modern outbreaks into complex mathematical or computational models of disease transmission. Incorporating this data into simple compartmental models is often difficult so complex computational models are required. In this paper we introduce a new framework written in Java for building and running epidemiological models that efficiently handles epidemiological data and reduces much of the boilerplate code that often accompanies these types of models. Results: Broadwick version 1.1 consists of 5,289 lines of Java source code (72 classes and 20 top-level packages, not including examples and unit test code) providing packages for stochastic simulations, parameter inference using Approximate Bayesian Computation (ABC) and Markov Chain Monte Carlo (MCMC) methods. Each algorithm used is fully customisable with sensible defaults (e.g. the MCMC algorithm uses a Gaussian random walk for the Markov Chain and a Metropolis-Hastings algorithm as the rejection algorithm) but these can be easily overridden by custom algorithms as required. Availability: The source code is object-oriented, modular in design and freely available at https://github.com/EPICScotland/Broadwick under the Apache License, Version 2.0 license