Alex Chernyakhovsky

Abstract Seasonal migration of waterfowl, in which avian influenza viruses are enzootic, plays a strong role in the ecology of the disease and has been implicated in several zoonotic epidemics and pandemics. Recent investigations have established that with just 1 mutation current avian influenza viral strains gain the ability to be readily transmitted between humans. These investigations further motivate the need for detailed analysis, in addition to satellite surveillance, of migratory patterns and its influence on the ecology of the disease to aid design and assessment of prophylaxis and containment strategies for emergent epidemics. Accordingly, this paper proposes a novel methodology for generating a global agent-based stochastic epidemiological model involving detailed migratory patterns of waterfowl. The methodology transforms Geographic Information Systems (GIS) data containing global distribution of various species of waterfowl to generate metapopulation for agents that model collocated flocks of birds. Generic migratory flyways are suitably adapted to model migratory flyways for each waterfowl metapopulation. Migratory characteristics of various species are used to determine temporal attributes for the flyways. The resulting data is generated in XML format compatible with our simulation-based epidemiological analysis environment called SEARUMS. Case studies conducted using SEARUMS and the generated models for high-risk waterfowl species indicate good correlation between simulated and observed viral dispersion patterns, demonstrating the effectiveness of the proposed methodology.

Abstract Humanity is facing an increasing number of highly virulent and communicable diseases such as influenza. Combating such global diseases requires in-depth knowledge of their epidemiology. The only practical method for discovering global epidemiological knowledge and identifying prophylactic strategies is simulation. However, several interrelated factors, including increasing model complexity, stochastic nature of diseases, and short analysis timeframes render exhaustive analysis an infeasible task. An effective approach to alleviate the aforementioned issues and enable efficient epidemiological analysis is to manually steer bio-simulations to scenarios of interest. Selective steering preserves causality, inter-dependencies, and stochastic characteristics in the model better than “seeding”, i.e., manually setting simulation state. Accordingly, we have developed a novel Eco-modeling and bio-simulation environment called SEARUMS. The bio-simulation infrastructure of SEARUMS permits a human-in-the-loop to steer the simulation to scenarios of interest so that epidemics can be effectively modeled and analyzed. This article discusses mathematical principles underlying SEARUMS along with its software architecture and design. In addition, the article also presents the bio-simulations and multi-faceted case studies conducted using SEARUMS to elucidate its ability to forecast timelines, epicenters, and socio-economic impacts of epidemics. Currently, the primary emphasis of SEARUMS is to ease global epidemiological analysis of avian influenza. However, the methodology is sufficiently generic and it can be adapted for other epidemiological analysis required to effectively combat various diseases.

Abstract SEARUMS is an Eco-modeling, bio-simulation, and analysis environment to study the global epidemiology of Avian Influenza. Originally developed in Java, SEARUMS enables comprehensive epidemiological analysis, forecast epicenters, and time lines of epidemics for prophylaxis; thereby mitigating disease outbreaks. However, SEARUMS-based simulations were time consuming due to the size and complexity of the models. In an endeavor to reduce time for simulation, we have redesigned the infrastructure of SEARUMS to operate as a Time Warp synchronized, parallel and distributed simulation. This paper presents our parallelization efforts along with empirical evaluation of various design alternatives that were explored to identify the ideal parallel simulation configuration. Our experiments indicate that the redesigned environment called SEARUMS++ achieves good scalability and performance, thus meeting a mission-critical objective.

Abstract The World Health Organization has activated a global preparedness plan to improve response to avian influenza outbreaks, control outbreaks, and avoid an H5N1 pandemic. The effectiveness of the plan will greatly benefit from identification of epicenters and temporal analysis of outbreaks. Accordingly, we have developed a simulation-based methodology to analyze the spread of H5N1 using stochastic interactions between waterfowl, poultry, and humans. We have incorporated our methodology into a user friendly, extensible software environment called SEARUMS. SEARUMS is an acronym for Studying the Epidemiology of Avian Influenza Rapidly Using Modeling and Simulation. It enables rapid scenario analysis to identify epicenters and timelines of H5N1 outbreaks using existing statistical data. The case studies conducted using SEARUMS have yielded results that coincide with several past outbreaks and provide non-intuitive inferences about global spread of H5N1. This article presents the methodology used for modeling the global epidemiology of avian influenza and discusses its impacts on human and poultry morbidity and mortality. The results obtained from the various case studies and scenario analyses conducted using SEARUMS along with verification experiments are also discussed. The experiments illustrate that SEARUMS has considerable potential to empower researchers, national organizations, and medical response teams with timely knowledge to combat the disease, mitigate its adverse effects, and avert a pandemic.

Abstract Humanity is facing an increasing number of highly virulent and communicable diseases such as avian influenza. Researchers believe that avian influenza has potential to evolve into one of the deadliest pandemics. Combating these diseases requires in-depth knowledge of their epidemiology. An effective methodology for discovering epidemiological knowledge is to utilize a descriptive, evolutionary, ecological model and use bio-simulations to study and analyze it. These types of bio-simulations fall under the category of computational evolutionary methods because the individual entities participating in the simulation are permitted to evolve in a natural manner by reacting to changes in the simulated ecosystem. This work describes the application of the aforementioned methodology to discover epidemiological knowledge about avian influenza using a novel eco-modeling and bio-simulation environment called SEARUMS. The mathematical principles underlying SEARUMS, its design, and the procedure for using SEARUMS are discussed. The bio-simulations and multi-faceted case studies conducted using SEARUMS elucidate its ability to pinpoint timelines, epicenters, and socio-economic impacts of avian influenza. This knowledge is invaluable for proactive deployment of countermeasures in order to minimize negative socioeconomic impacts, combat the disease, and avert a pandemic.

Abstract Effectively combating avian influenza using constrained resources requires strategic planning and preemptive deployment of countermeasures. However, success of such proactive approaches is contingent on accurate and rapid forecasting of epicenters and time lines of outbreaks using epidemiological analyses. A pragmatic and effective methodology for such epidemiological analysis is computer-based simulation. However, effectively using simulations requires a sophisticated, efficient, and user-friendly software environment for modeling, simulation, and analysis. Accordingly, we have developed an userfriendly, extensible, and portable software environment called SEARUMS in Java. SEARUMS enables modeling, simulation, and epidemiological analysis of avian influenza including prediction of time lines, epicenters, and economic impacts of disease outbreaks. The analysis is performed using a composable, individual agent-based, spatially explicit model seeded with real world statistical data. Simulations are performed using a high performance, multi-threaded discrete event kernel built into SEARUMS. This paper presents the design and implementation of SEARUMS. The design rationale and implementation issues are discussed along with some of the lessons learned. The paper also presents experiments conducted to validate SEARUMS and verify its scalability.