In this paper, we present an extension of our novel microsimulation technique, as applied to biological infection spread, to estimate the underlying causal parameters driving an infectious process. The underlying simulation framework, Simulacron, was developed in order to understand the development and course of, response to and recovery from single and multiple threats on community populations. Such threats include a range of natural (such as disease spread in communities, fire, flood etc.) and manmade events (such as terrorism, including the use of biological agents, money laundering, smuggling as well as accidents etc.). These threats can cause serious disruption to modern society and the optimal approaches to prevention, mitigation, response and recovery are little understood. Furthermore, assumptions currently used cannot otherwise be readily tested.

The confrontation in Syria during 2013 is an ongoing cause for concern regarding the potential use of chemical and biological weapons. There have been reports of the use of chemical weapons including Sarin (BBC, 2013a) which UN chemical weapons inspectors are investigating (BBC, 2013b). If chemical weapons have been used by either side, then the potential use of biological weapons cannot be disregarded. In addition to stockpiles of chemical weapon (BBC 2013c), Syria is thought to have stockpiles of a number of biological agents including anthrax, plague, tularaemia, botulinium, smallpox and cholera (Gordon, 2007). Some groups sympathetic to Al Qaeda might also have access to some of these through their terrorist networks. Because these weapons can have a substantial impact beyond the immediate conflict zone, there are serious questions about how best to respond efficiently to their use and manage their impacts.One such concern is whether the response to an attack involving a single agent would be the same as when more than one agent is used. There are indications that infectious diseases which promote cytokine response can have a protecting effect on infection with a second disease (Graham et al., 2007, Barton et al, 2007. Plague affects the innate immune system by suppressing cytokine responses (Li et al., 2008) while smallpox activates the cytokine response (Fenner et al, 1988). Such an interaction is therefore possible with smallpox and plague in people who are infected with both diseases. While there are a number of papers on the management of both smallpox and plague (Halloran, (2002), Rani et al, (2004)), there are few, if any, which discuss infection by both agents simultaneously or the likely confounding factors that will affect outcomes in their infection control after the attack. In this paper we explore the application of microsimulation modelling of a simultaneous attack on a civilian population using plague and smallpox as an example of a simultaneous coinfection through its effect on the spread of disease and number of deaths As a basis for analysis, we have developed simulations involving a population of 1250 people based on NSW statistics for households and work. The structure of a community model of social mixing is briefly discussed, over which a multi-infection model is imposed that accounts for varying infectivity in different stages of each disease as well as confinement to home as each disease progresses. A number of simulations were run assuming 10% immunity to both diseases, to establish a baseline for each disease in the community. Further simulations were used to model the delay of the introduction of plague compared to smallpox between 0 and 35 days respectively. The strength of the immunological interaction by smallpox on plague deaths was also investigated. Each scenario was repeated 10 times to assess the variability.Our model showed the outcome is complex as the number of deaths is dependent on the delay in the release of plague and varies according to the number of people progressi...