Intervention to maximise the probability of epidemic fade-out.

The emergence of a new strain of a disease, or the introduction of an existing strain to a naive population, can give rise to an epidemic. We consider how to maximise the probability of epidemic fade-out - that is, disease elimination in the trough between the first and second waves of infection - in the Markovian SIR-with-demography epidemic model. We assume we have an intervention at our disposal that results in a lowering of the transmission rate parameter, ß, and that an epidemic has commenced. We determine the optimal stage during the epidemic in which to implement this intervention. This may be determined using Markov decision theory, but this is not always practical, in particular if the population size is large. Hence, we also derive a formula that gives an almost optimal solution, based upon the approximate deterministic behaviour of the model. This formula is explicit, simple, and, perhaps surprisingly, independent of ß and the effectiveness of the intervention. We demonstrate that this policy can give a substantial increase in the probability of epidemic fade-out, and we also show that it is relatively robust to a less than ideal implementation.

The probability of epidemic fade-out is non-monotonic in transmission rate for the Markovian SIR model with demography.

Epidemic fade-out refers to infection elimination in the trough between the first and second waves of an outbreak. The number of infectious individuals drops to a relatively low level between these waves of infection, and if elimination does not occur at this stage, then the disease is likely to become endemic. For this reason, it appears to be an ideal target for control efforts. Despite this obvious public health importance, the probability of epidemic fade-out is not well understood. Here we present new algorithms for approximating the probability of epidemic fade-out for the Markovian SIR model with demography. These algorithms are more accurate than previously published formulae, and one of them scales well to large population sizes. This method allows us to investigate the probability of epidemic fade-out as a function of the effective transmission rate, recovery rate, population turnover rate, and population size. We identify an interesting feature: the probability of epidemic fade-out is very often greatest when the basic reproduction number, R0, is approximately 2 (restricting consideration to cases where a major outbreak is possible, i.e., R0>1). The public health implication is that there may be instances where a non-lethal infection should be allowed to spread, or antiviral usage should be moderated, to maximise the chance of the infection being eliminated before it becomes endemic.

Determination of microsome and hepatocyte scaling factors for in vitro/in vivo extrapolation in the rat and dog.

1. In vivo clearance predictions from in vitro assays require scaling factors to relate the concentrations of hepatocytes or microsomal protein to the intact liver. 2. The aims were to measure the variability in scaling factors for Wistar rat and beagle dog for which the literature is particularly scarce and determine any sex differences. 3. Scaling factors were determined by comparing the cytochrome P450 (P450) content in hepatocytes or microsomes against the P450 content of fresh liver homogenate. The use of fresh homogenate is recommended as freezing can increase contamination and affect the P450 assay. 4. Mean(geo) hepatic microsomal concentrations in Wistar rats were 61 mg g(-1) liver (95% confidence interval (CI); 47-75 mg g(-1) liver) and in beagle dogs 55 mg g(-1) liver (95% CI = 48-62 mg g(-1) liver). Mean(geo) hepatocellularity was 163 x 10(6) cells g(-1) liver for Wistar rats (95% CI = 127-199 x 10(6) cells g(-1) liver) and 169 x 10(6) cells g(-1) liver (95% CI = 131-207 x 10(6) cells g(-1) liver) for beagle dogs. The data generated in this study indicate a consistency in scaling factors between rat and dog. No sex differences were observed.