The effectiveness of policies to control a human influenza pandemic : a literature review

The studies reviewed in this paper indicate that with adequate preparedness planning and execution it is possible to contain pandemic influenza outbreaks where they occur, for viral strains of moderate infectiousness. For viral strains of higher infectiousness, containment may be difficult, but it may be possible to mitigate the effects of the spread of pandemic influenza within a country and/or internationally with a combination of policies suited to the origins and nature of the initial outbreak. These results indicate the likelihood of containment success in'frontline risk'countries, given specific resource availability and level of infectiousness; as well as mitigation success in'secondary'risk countries, given the assumption of inevitable international transmission through air travel networks. However, from the analysis of the modeling results on interventions in the U.S. and U.K. after a global pandemic starts, there is a basis for arguing that the emphasis in the secondary risk countries could shift from mitigation towards containment. This follows since a mitigation-focused strategy in such developed countries presupposes that initial outbreak containment in these countries will necessarily fail. This is paradoxical if containment success at similar infectiousness of the virus is likely in developing countries with lower public health resources, based on results using similar modeling methodologies. Such a shift in emphasis could have major implications for global risk management for diseases of international concern such as pandemic influenza or a SARS-like disease.Avian Flu,Disease Control&Prevention,Health Monitoring&Evaluation,Population Policies,HIV AIDS

Agent-based modeling for environmental management. Case study: virus dynamics affecting Norwegian fish farming in fjords

Background: Norwegian fish-farming industry is an important industry, rapidly growing, and facing significant challenges such as the spread of pathogens1, trade-off between locations, fish production and health. There is a need for research, i.e. the development of theories (models), methods, techniques and tools for analysis, prediction and management, i.e. strategy development, policy design and decision making, to facilitate a sustainable industry. Loss due to the disease outbreaks in the aquaculture systems pose a large risk to a sustainable fish industry system, and pose a risk to the coastal and fjord ecosystem systems as a whole. Norwegian marine aquaculture systems are located in open areas (i.e. fjords) where they overlap and interact with other systems (e.g. transport, wild life, tourist, etc.). For instance, shedding viruses from aquaculture sites affect the wild fish in the whole fjord system. Fish disease spread and pathogen transmission in such complex systems, is process that it is difficult to predict, analyze, and control. There are several time-variant factors such as fish density, environmental conditions and other biological factors that affect the spread process. In this thesis, we developed methods to examine these factors on fish disease spread in fish populations and on pathogen spread in the time-space domain. Then we develop methods to control and manage the aquaculture system by finding optimal system settings in order to have a minimum infection risk and a high production capacity. Aim: The overall objective of the thesis is to develop agent-based models, methods and tools to facilitate the management of aquaculture production in Norwegian fjords by predicting the pathogen dynamics, distribution, and transmission in marine aquaculture systems. Specifically, the objectives are to assess agent-based modeling as an approach to understanding fish disease spread processes, to develop agent-based models that help us predict, analyze and understand disease dynamics in the context of various scenarios, and to develop a framework to optimize the location and the load of the aquaculture systems so as to minimize the infection risk in a growing fish industry. Methods: We use agent-based method to build models to simulate disease dynamics in fish populations and to simulate pathogen transmission between several aquaculture sites in a Norwegian fjord. Also, we use particle swarm optimization algorithm to identify agent-based models’ parameters so as to optimize the dynamics of the system model. In this context, we present a framework for using a particle swarm optimization algorithm to identify the parameter values of the agent-based model of aquaculture system that are expected to yield the optimal fish densities and farm locations that avoid the risk of spreading disease. The use of particle swarm optimization algorithm helps in identifying optimal agent-based models’ input parameters depending on the feedback from the agentbased models’ outputs. Results: As the thesis is built on three main studies, the results of the thesis work can be divided into three components. In the first study, we developed many agent-based models to simulate fish disease spread in stand-alone fish populations. We test the models in different scenarios by varying the agents (i.e. fish and pathogens) parameters, environment parameters (i.e. seawater temperature and currents), and interactions (interaction between agents-agents, and agents-environment) parameters. We use sensitivity analysis method to test different key input parameters such as fish density, fish swimming behavior, seawater temperature, and sea currents to show their effects on the disease spread process. Exploring the sensitivity of fish disease dynamics to these key parameters helps in combatting fish disease spread. In the second study, we build infection risk maps in a space-time domain, by developing agent-based models to identify the pathogen transmission patterns. The agent-based method helps us advance our understanding of pathogen transmission and builds risk maps to help us reduce the spread of infectious fish diseases. By using this method, we may study the spatial and dynamic aspects of the spread of infections and address the stochastic nature of the infection process. In the third study, we developed a framework for the optimization of the aquaculture systems. The framework uses particle swarm optimization algorithm to optimize agent-based models’ parameters so as to optimize the objective function. The framework was tested by developing a model to find optimal fish densities and farm locations in marine aquaculture system in a Norwegian fjord. Results show so that the rapid convergence of the presented particle swarm optimization algorithm to the optimal solution, - the algorithm requires a maximum of 18 iterations to find the best solution which can increase the fish density to three times while keeping the risk of infection at an accepted level. Conclusion: There are many contributions of this research work. First, we assessed the agent-based modeling as a method to simulate and analyze fish disease spread dynamics as a foundation for managing aquaculture systems. Results from this study demonstrate how effective the use of agentbased method is in the simulation of infectious diseases. By using this method, we are able to study spatial aspects of the spread of fish diseases and address the stochastic nature of infections process. Agent-based models are flexible, and they can include many external factors that affect fish disease dynamics such as interactions with wild fish and ship traffic. Agent-based models successfully help us to overcome the problem associated with lack of data in fish disease transmission and contribute to our understanding of different cause-effects relationships in the dynamics of fish diseases. Secondly, we developed methods to build infection risk maps in a space-time domain conditioned upon the identification of the pathogen transmission patterns in such a space-time domain, so as to help prevent and, if needed, combat infectious fish diseases by informing the management of the fish industry in Norway. Finally, we developed a method by which we may optimize the fish densities and farm locations of aquaculture systems so as to ensure a sustainable fish industry with a minimum risk of infection and a high production capacity. This PhD study offers new research-based approaches, models and tools for analysis, predictions and management that can be used to facilitate a sustainable development of the marine aquaculture industry with a maximal economic outcome and a minimal environmental impact

Syndromic surveillance: reports from a national conference, 2003

Overview of Syndromic Surveillance -- What is Syndromic Surveillance? -- Linking Better Surveillance to Better Outcomes -- Review of the 2003 National Syndromic Surveillance Conference - Lessons Learned and Questions To Be Answered -- -- System Descriptions -- New York City Syndromic Surveillance Systems -- Syndrome and Outbreak Detection Using Chief-Complaint Data - Experience of the Real-Time Outbreak and Disease Surveillance Project -- Removing a Barrier to Computer-Based Outbreak and Disease Surveillance - The RODS Open Source Project -- National Retail Data Monitor for Public Health Surveillance -- National Bioterrorism Syndromic Surveillance Demonstration Program -- Daily Emergency Department Surveillance System - Bergen County, New Jersey -- Hospital Admissions Syndromic Surveillance - Connecticut, September 2001-November 2003 -- BioSense - A National Initiative for Early Detection and Quantification of Public Health Emergencies -- Syndromic Surveillance at Hospital Emergency Departments - Southeastern Virginia -- -- Research Methods -- Bivariate Method for Spatio-Temporal Syndromic Surveillance -- Role of Data Aggregation in Biosurveillance Detection Strategies with Applications from ESSENCE -- Scan Statistics for Temporal Surveillance for Biologic Terrorism -- Approaches to Syndromic Surveillance When Data Consist of Small Regional Counts -- Algorithm for Statistical Detection of Peaks - Syndromic Surveillance System for the Athens 2004 Olympic Games -- Taming Variability in Free Text: Application to Health Surveillance -- Comparison of Two Major Emergency Department-Based Free-Text Chief-Complaint Coding Systems -- How Many Illnesses Does One Emergency Department Visit Represent? Using a Population-Based Telephone Survey To Estimate the Syndromic Multiplier -- Comparison of Office Visit and Nurse Advice Hotline Data for Syndromic Surveillance - Baltimore-Washington, D.C., Metropolitan Area, 2002 -- Progress in Understanding and Using Over-the-Counter Pharmaceuticals for Syndromic Surveillance -- -- Evaluation -- Evaluation Challenges for Syndromic Surveillance - Making Incremental Progress -- Measuring Outbreak-Detection Performance By Using Controlled Feature Set Simulations -- Evaluation of Syndromic Surveillance Systems - Design of an Epidemic Simulation Model -- Benchmark Data and Power Calculations for Evaluating Disease Outbreak Detection Methods -- Bio-ALIRT Biosurveillance Detection Algorithm Evaluation -- ESSENCE II and the Framework for Evaluating Syndromic Surveillance Systems -- Conducting Population Behavioral Health Surveillance by Using Automated Diagnostic and Pharmacy Data Systems -- Evaluation of an Electronic General-Practitioner-Based Syndromic Surveillance System -- National Symptom Surveillance Using Calls to a Telephone Health Advice Service - United Kingdom, December 2001-February 2003 -- Field Investigations of Emergency Department Syndromic Surveillance Signals - New York City -- Should We Be Worried? Investigation of Signals Generated by an Electronic Syndromic Surveillance System - Westchester County, New York -- -- Public Health Practice -- Public Health Information Network - Improving Early Detection by Using a Standards-Based Approach to Connecting Public Health and Clinical Medicine -- Information System Architectures for Syndromic Surveillance -- Perspective of an Emergency Physician Group as a Data Provider for Syndromic Surveillance -- SARS Surveillance Project - Internet-Enabled Multiregion Surveillance for Rapidly Emerging Disease -- Health Information Privacy and Syndromic Surveillance SystemsPapers from the second annual National Syndromic Surveillance Conference convened by the New York City Department of Health and Mental Hygiene, the New York Academy of Medicine, and the CDC in New York City during Oct. 23-24, 2003. Published as the September 24, 2004 supplement to vol. 53 of MMWR. Morbidity and mortality weekly report.1571461

Optimizing Non-pharmaceutical Interventions Using Multi-coaffiliation Networks

Computational modeling is of fundamental significance in mapping possible disease spread, and designing strategies for its mitigation. Conventional contact networks implement the simulation of interactions as random occurrences, presenting public health bodies with a difficult trade off between a realistic model granularity and robust design of intervention strategies. Recently, researchers have been investigating the use of agent-based models (ABMs) to embrace the complexity of real world interactions. At the same time, theoretical approaches provide epidemiologists with general optimization models in which demographics are intrinsically simplified. The emerging study of affiliation networks and co-affiliation networks provide an alternative to such trade off. Co-affiliation networks maintain the realism innate to ABMs while reducing the complexity of contact networks into distinctively smaller k-partite graphs, were each partition represent a dimension of the social model. This dissertation studies the optimization of intervention strategies for infectious diseases, mainly distributed in school systems. First, concepts of synthetic populations and affiliation networks are extended to propose a modified algorithm for the synthetic reconstruction of populations. Second, the definition of multi-coaffiliation networks is presented as the main social model in which risk is quantified and evaluated, thereby obtaining vulnerability indications for each school in the system. Finally, maximization of the mitigation coverage and minimization of the overall cost of intervention strategies are proposed and compared, based on centrality measures

Epidemiological investigations into the 2007 outbreak of equine influenza in Australia

Equine influenza is a highly contagious and widespread viral respiratory disease of horses and other equid species, characterised by fever and a harsh dry cough. Prior to August 2007, Australia was one of only three countries to have remained free of equine influenza. An incursion of equine influenza virus H3N8 in that month resulted in a four-month outbreak during which approximately 69,000 horses were infected on an estimated 9599 premises across two States. Most of the geographic spread occurred within the first 10 days and was associated with the movement of infected horses prior to the implementation of movement controls. The outbreak was contained through a series of interventions that ultimately led to the eradication of equine influenza from Australia. During and immediately after the outbreak, intensive epidemiological investigations, laboratory and retrospective analytical studies were conducted culminating in a series of detailed reports and publications, and the collation of a highly detailed outbreak dataset. Further research into the factors that contributed to the spread of the outbreak and the effectiveness of measures implemented to control and contain it was considered important. The aim of this thesis was therefore to investigate the factors that contributed to the spread of the 2007 equine influenza outbreak in Australia and to develop statistical methods and tools useful for informing the surveillance and control of future emergency animal disease events. A case-control study was conducted to investigate premises-level risk factors, specifically whether compliance with advised biosecurity measures prevented the spread of equine influenza onto horse premises. Horse owners and managers on 200 properties across highly affected areas of New South Wales were interviewed. The proximity of premises to the nearest infected premises was the factor most strongly associated with case status. Case premises were more likely than control premises to be within 5 km and beyond 10km of an infected premises. Having a footbath in place on the premises before any horses were infected was associated with a nearly four-fold reduction in odds of infection (odds ratio = 0.27; 95% confidence interval: iv 0.09, 0.83). This protective association may have reflected overall premises biosecurity standards related to the fomite transmission of equine influenza: there was high correlation amongst several, generally protective, variables representing personal ‗barrier hygiene‘ biosecurity measures (hand-washing, changing clothes and shoes, and having a footbath in place). The movement of infected horses and local disease diffusion were known to be important mechanisms of spread early in this outbreak. A network analysis was conducted to investigate the relative contribution of each mechanism. The relationship between infected and susceptible horse premises (contact through animal movements and spatial proximity) was described by constructing a mixed transmission network. During the first 10 days of the 2007 equine influenza outbreak in Australia, horses on 197 premises were infected. A new likelihood-based approach was developed and it was estimated that 28.3% of early disease spread (prior to the implementation of horse movement restrictions) was through the movement of infected horses (95% CI: 25.6, 31.0%). Most local spread was estimated to have occurred within 5 km of infected premises. Based on a direct estimate of the shape of the spatial transmission kernel, the incidence beyond 15 km was very low. The median distance that infected horses were moved was 123 km (range 4–579 km). In an extension of the network analysis, novel methods were developed to delineate spatial clusters of infected premises and describe the sequence of cluster formation and the widespread dispersal experienced during the first 30 days of the outbreak. Premises identified as infected by the movement of infected horses were found to be critical to the seeding of infection in spatial clusters. Combined analysis of spatial and contact network data demonstrated that early in this outbreak local spread emanated outwards from the small number of infected premises in the contact network, up to a distance of around 15 km. A purely spatial method of modelling epidemic spread (kriging) was imprecise in describing the pattern of spread during this early phase of the outbreak (explaining only 13% of the variation in estimated date of onset of v infected premises), because early dissemination was dominated by network-based spread. Prior to this thesis, there was an abundance of anecdotal information regarding the role of meteorological factors and other environmental determinants in the spread of the 2007 equine influenza outbreak in Australia. A survival analysis was therefore conducted to empirically estimate the association between meteorological variables (wind, air temperature, relative humidity and rainfall) and time-to-infection in the largest cluster of the outbreak, in northwest Sydney. The equine influenza outbreak dataset was structured to enable generalised Cox regression modelling of the association between time-varying covariates representing premiseslevel meteorological conditions. The cumulative incidence in the northwest Sydney cluster was estimated to be 53.0% (95% CI: 51.4, 54.7%). Local spatial spread of equine influenza was found to be associated with relative humidity, air temperature and wind velocity. Meteorological conditions 3–5 days prior were strongly associated with hazard of influenza infection. Strong winds (>30 km hour-1) from the direction of nearby infected premises were associated with influenza infection, as was low relative humidity (<60%). A nonlinear relationship was observed with air temperature: the lowest hazard was on days when maximum daily air temperature was between 20–25 °C. Drawing on the findings of the above studies, a spatially-explicit stochastic epidemic model of equine influenza transmission was developed to investigate the underlying disease process, estimate the effectiveness of several control measures applied during the 2007 outbreak and to provide a dynamic modelling framework for rapid assessment of future equine influenza outbreaks in Australia. A reversible jump Markov chain Monte Carlo algorithm was used to estimate Bayesian posterior distributions of key epidemiological parameters based on data from two highly affected regions. A large amount of regional heterogeneity was observed in the underlying epidemic process, the estimated rate of decay of transmission by distance from infected premises, the intra-premises transmission rate and the effect of premises area. Model outputs were highly cross-correlated both temporally and spatially with data observed during vi the 2007 outbreak, and with outputs of a previous model. Pseudo-validation of the model against data, not used in its development, demonstrated of how it may be applied to develop rapid assessments of future outbreaks affecting horse populations in comparable regions to those studied. The study results documented in this thesis have elucidated the key factors underlying the spread of the 2007 equine influenza outbreak in Australia, and presented new methods of describing such rapidly spreading epidemics. The movement of infected horses, meteorological variables (air temperature, humidity and wind speed), on-farm biosecurity measures and intrinsic features of horse premises (proximity to other infected premises, numbers of horses held and premises area) were all important variables that influenced the spread of infection onto horse premises. These insights allow development of better policy and control programs in the event of a future equine influenza virus incursion

Stochastic programming and agent-based simulation approaches for epidemics control and logistics planning

This dissertation addresses the resource allocation challenges of fighting against infectious disease outbreaks. The goal of this dissertation is to formulate multi-stage stochastic programming and agent-based models to address the limitations of former literature in optimizing resource allocation for preventing and controlling epidemics and pandemics. In the first study, a multi-stage stochastic programming compartmental model is presented to integrate the uncertain disease progression and the logistics of resource allocation to control a highly contagious infectious disease. The proposed multi-stage stochastic program, which involves various disease growth scenarios, optimizes the distribution of treatment centers and resources while minimizing the total expected number of new infections and funerals due to an epidemic. Two new equity metrics are defined and formulated, namely infection and capacity equity, to explicitly consider equity for allocating treatment funds and facilities for fair resource allocation in epidemics control. The multi-stage value of the stochastic solution (VSS), demonstrating the superiority of the proposed stochastic programming model over its deterministic counterpart, is studied. The first model is applied to the Ebola Virus Disease (EVD) case in West Africa, including Guinea, Sierra Leone, and Liberia. In the following study, the previous model is extended to a mean-risk multi-stage vaccine allocation model to capture the influence of the outbreak scenarios with low probability but high impact. The Conditional Value at Risk (CVaR) measure used in the model enables a trade-off between the weighted expected loss due to the outbreak and expected risks associated with experiencing disastrous epidemic scenarios. A method is developed to estimate the migration rate between each infected region when limited migration data is available. The second study is applied to the case of EVD in the Democratic Republic of the Congo. In the third study, a new risk-averse multi-stage stochastic epidemics-ventilator-logistics compartmental stochastic programming model is developed to address the resource allocation challenges of mitigating COVID-19. This epidemiological logistics model involves the uncertainty of untested asymptomatic infections and incorporates short-term human migration. Disease transmission is also forecasted through deriving a new formulation of transmission rates that evolve over space and time with respect to various non-pharmaceutical interventions, such as wearing masks, social distancing, and lockdown. In the fourth study, a simulation-optimization approach is introduced to address the vaccination facility location and allocation challenges of the COVID-19 vaccines. A detailed agent-based simulation model of the COVID-19 is extended and integrated with a new vaccination center and vaccine-allocation optimization model. The proposed agent-based simulation-optimization framework simulates the disease transmission first and then minimizes the total number of infections over all the considered regions by choosing the optimal vaccine center locations and vaccine allocation to those centers. Specifically, the simulation provides the number of susceptible and infected individuals in each geographical region for the current time period as an input into the optimization model. The optimization model then minimizes the total number of estimated infections and provides the new vaccine center locations and vaccine allocation decisions for the following time period. Decisions are made on where to open vaccination centers and how many people should be vaccinated at each future stage in each region of the considered geographical location. Then these optimal decision values are imported back into the simulation model to simulate the number of susceptible and infected individuals for the subsequent periods. The agent-based simulation-optimization framework is applied to controlling COVID-19 in the states of New Jersey. The results provide insights into the optimal vaccine center location and vaccine allocation problem under varying budgets and vaccine types while foreseeing potential epidemic growth scenarios over time and spatial locations