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

Heterogeneity and Network Structure in the Dynamics of Diffusion: Comparing Agent-Based and Differential Equation Models

When is it better to use agent based (AB) models, and when should differential equation (DE) models be used? Where DE models assume homogeneity and perfect mixing within compartments, AB models can capture heterogeneity in agent attributes and in the network of interactions among them. Using contagious disease as an example, we contrast the dynamics of AB models with those of the corresponding mean-field DE model, specifically, comparing the standard SEIR model-a nonlinear DE-to an explicit AB model of the same system. We examine both agent heterogeneity and the impact of different network structures, including fully connected, random, Watts-Strogatz small world, scale-free, and lattice networks. Surprisingly, in many conditions the AB and DE dynamics are quite similar. Differences between the DE and AB models are not statistically significant on key metrics relevant to public health, including diffusion speed, peak load on health services infrastructure and total disease burden. We explore the conditions under which the AB and DE dynamics differ, and consider implications for managing infectious disease. The results extend beyond epidemiology: from innovation adoption to the spread of rumor and riot to financial panics, many important social phenomena involve analogous processes of diffusion and social contagion

An agent-based approach for modeling dynamics of contagious disease spread

Background: The propagation of communicable diseases through a population is an inherentspatial and temporal process of great importance for modern society. For this reason a spatiallyexplicit epidemiologic model of infectious disease is proposed for a greater understanding of thedisease\u27s spatial diffusion through a network of human contacts.Objective: The objective of this study is to develop an agent-based modelling approach theintegrates geographic information systems (GIS) to simulate the spread of a communicable diseasein an urban environment, as a result of individuals\u27 interactions in a geospatial context.Methods: The methodology for simulating spatiotemporal dynamics of communicable diseasepropagation is presented and the model is implemented using measles outbreak in an urbanenvironment as a case study. Individuals in a closed population are explicitly represented by agentsassociated to places where they interact with other agents. They are endowed with mobility,through a transportation network allowing them to move between places within the urbanenvironment, in order to represent the spatial heterogeneity and the complexity involved ininfectious diseases diffusion. The model is implemented on georeferenced land use dataset fromMetro Vancouver and makes use of census data sets from Statistics Canada for the municipality ofBurnaby, BC, Canada study site.Results: The results provide insights into the application of the model to calculate ratios ofsusceptible/infected in specific time frames and urban environments, due to its ability to depict thedisease progression based on individuals\u27 interactions. It is demonstrated that the dynamic spatialinteractions within the population lead to high numbers of exposed individuals who performstationary activities in areas after they have finished commuting. As a result, the sick individuals areconcentrated in geographical locations like schools and universities.Conclusion: The GIS-agent based model designed for this study can be easily customized to studythe disease spread dynamics of any other communicable disease by simply adjusting the modeleddisease timeline and/or the infection model and modifying the transmission process. This type ofsimulations can help to improve comprehension of disease spread dynamics and to take bettersteps towards the prevention and control of an epidemic outbreak

Estimating the impact and economic trade-offs of infectious disease control strategies using metapopulation models

Infectious diseases remain the main cause of death in low-income countries. Because of this, efforts to control the circulation of infectious agents are a priority for public policy makers. This control is challenged by a combination of complex disease dynamics, funding constraints or lack of political and societal commitment. These challenges are generally heterogeneous between geographical settings making the impact of control strategies hard to assess. In view of this, the purpose of this research is to integrate economic and epidemiological tools in order to improve support for disease control planning and implementation. To do this, I develop a metapopulation model framework to analyse the impact of control strategies when there are neighbouring populations with different epidemiological conditions. The results from this framework can be incorporated into further economic analysis and optimisations. The first section of this project aims to understand interventions’ effects when transmission intensity varies between populations. As a first approach, I implement the framework to analyse indirect effects of interventions for a transmission-stratified population, using generic models. Then, to contextualise the findings from the generic model, I analyse optimal intervention allocation for malaria control. Results from this section evidenced the importance of aligning local and global control strategies. The second section of this project focuses on understanding the consequences of disease control when intervention uptake varies between populations. For this, the metapopulation framework is applied to estimate the burden populations undergo due to the presence of an anti-vaccination movement. First, I analyse the burden of an outbreak of a vaccine preventable disease in a population where there are opposing vaccine acceptance views, implementing a measles transmission. Finally, I use the same approach to estimate the likely impact of vaccine hesitancy on the control of the COVID-19 pandemic. Results of this section highlight the importance of addressing vaccine hesitancy as a public health priorityOpen Acces

Literature Review - the vaccine supply chain

Vaccination is one of the most effective ways to prevent the outbreak of an infectious disease. This medical intervention also brings about many logistical quest

Disease surveillance systems

Recent advances in information and communication technologies have made the development and operation of complex disease surveillance systems technically feasible, and many systems have been proposed to interpret diverse data sources for health-related signals. Implementing these systems for daily use and efficiently interpreting their output, however, remains a technical challenge. This thesis presents a method for understanding disease surveillance systems structurally, examines four existing systems, and discusses the implications of developing such systems. The discussion is followed by two papers. The first paper describes the design of a national outbreak detection system for daily disease surveillance. It is currently in use at the Swedish Institute for Communicable Disease Control. The source code has been licenced under GNU v3 and is freely available. The second paper discusses methodological issues in computational epidemiology, and presents the lessons learned from a software development project in which a spatially explicit micro-meso-macro model for the entire Swedish population was built based on registry data

Literature review: The vaccine supply chain

Vaccination is one of the most effective ways to prevent and/or control the outbreak of infectious diseases. This medical intervention also brings about many logistical questions. In the past years, the Operations Research/Operations Management community has shown a growing interest in the logistical aspects of vaccination. However, publications on vaccine logistics often focus on one specific logistical aspect. A broader framework is needed so that open research questions can be identified more easily and contributions are not overlooked.In this literature review, we combine the priorities of the World Health Organization for creating a flexible and robust vaccine supply chain with an Operations Research/Operations Management supply chain perspective. We propose a classification for the literature on vaccine logistics to structure this relatively new field, and identify promising research directions. We classify the literature into the following four components: (1) product, (2) production, (3) allocation, and (4) distribution. Within the supply chain classification, we analyze the decision problems for existing outbreaks versus sudden outbreaks and developing countries versus developed countries. We identify unique characteristics of the vaccine supply chain: high uncertainty in both supply and demand; misalignment of objectives and decentralized decision making between supplier, public health organization and end customer; complex political decisions concerning allocation and the crucial