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In the previous section I described how each media driver or extension (for the remainder of this section I shall refer to these merely as drivers) that registers for a set of local drives also has to register for a set of DMedia objects at the same time. This media set must contain just one primary media object. This object is responsible for controlling the overall state of the media (for example, whether power is applied, whether the partition information has been determined and so on). The DPrimaryMediaBase class, which is derived from DMedia, provides this functionality. The driver that performs drive registration is responsible for creating the primary media object itself, which it then passes over to

If denotes the infection rate and denotes the removal rate of infected individuals, then assuming a homogeneous mixing model (ie, each of the susceptibles can get in contact with any of the infectives), it is simple to observe that in time t, there are xy t new infections and y t removals Therefore, the basic differential equations that describe the rate of change of susceptibles, infectives, and recovered individuals are given by dX(t) = XY, dt dY (t) = XY Y, dt dZ(t) = Y dt The above equations can be solved either approximately or precisely based on some boundary conditions, such as, at the start of the epidemic, when t = 0, (X, Y, Z) can take the values (x0 , y0 , 0) Note that, in particular, if y0 is very small, x0 is approximately equal to N.

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It also follows that if the relative removal rate, = / , is greater than x0 , only then can an epidemic start to build up as this condition will result in [dY (t)/dt]t=0 > 0, ie Y (t) will have a positive slope Therefore, the relative removal rate = x0 gives a threshold density of susceptibles On the other hand, the S-I-S model does not have the recovered subset Z(t), and those who are infected fall back into the susceptible subset S(t) after their infectivity duration.

TLocDrv objects required:

Mortality rates are typically between 50% and 90% with the time to death ranging from 14 to 21 days (as reviewed9,10) The US Centers for Disease Control and Prevention (CDC) has classi ed loviruses as Category A biological weapons11 The Working Group on Civilian Biodefense has cited some of the features of such agents as high morbidity/ mortality, ease of person-to-person transmission, environmental stability, and the potential to cause public anxiety12 Filoviruses have allegedly been evaluated for their potential to be used as biowarfare agents by the former Soviet Union13 Perhaps most daunting is the fact that, currently, there are no effective vaccines, nor therapeutics in use to prevent lovirus infection.

An important aspect that is of particular interest in epidemiological studies is the phenomenon of phase transition of the spreading process that is dependent on a threshold value of the epidemic parameter; that is, if the epidemic parameter is above the threshold, the infection will spread out and become persistent; on the contrary, if the parameter is below the threshold, the infection will die out Identi cation of this threshold value is critical in the study of how an epidemic spreads and how it can be controlled Apart from modeling technique based on the continuous differential rate equation, the study of epidemics has often been performed by treating the population as a network graph, with the nodes representing each individual and the edges their interaction.

DMedia objects required:

This is mainly due to the fact that few laboratories have the proper containment facilities necessary to work with these agents, and investigators have had dif culty in obtaining samples from the limited number of con rmed human infections following the rst recognition of the disease (<2000)14 To overcome these obstacles, researchers have adapted the loviruses to lower species, such as mice and guinea pigs, for the study of disease pathogenesis and treatment ef cacies When evaluating such studies, it should be kept in mind that viral adaptation can cause sequence changes in the virus as well as alterations to pathogenesis, and these occurrences have in fact been reported15 18 Researchers have also bene ted from the isolation of viral cDNAs, which has enabled them to generate expression systems to study isolated viral gene products and their roles in the virus life cycle.

This form of analysis [3] has mainly been used in scenarios where the end result of the epidemic spread is more important than the temporal dynamics of the propagation Several works have spawned from this formulation [3 8], where the spread of diseases have been studied by modeling the social network as a scalefree topology Several other works also exist that model the spread of computer viruses [9, 10] Epidemic Theory has found special attention in the design and modeling of several phenomena and protocols in sensor networks wherever there is a scope of information distribution on a large scale, preferably from a small number of sources to a large number of recipients Among the popular phenomena in sensor and ad hoc networks.

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