2013
DOI: 10.1177/0037549713479052
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Analytic solution of the susceptible-infective epidemic model with state-dependent contact rates and different intervention policies

Abstract: We consider the susceptible-infective (SI) epidemiological model, a variant of the Kermack–McKendrick models, and let the contact rate be a function of the number of infectives, an indicator of disease spread during the course of the epidemic. We represent the resultant model as a continuous-time Markov chain. The result is a pure death (or birth) process with state-dependent rates, for which we find the probability distribution of the associated Markov chain by solving the Kolmogorov forward equations. This m… Show more

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Cited by 4 publications
(15 citation statements)
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“…In Yarmand and Ivy 64 the closed-from analytic solution of the stochastic SI model with state-dependent contact rates is given. In that paper, the impact of different intervention policies is related to the contact rate.…”
Section: Research Question and Methodologymentioning
confidence: 99%
See 1 more Smart Citation
“…In Yarmand and Ivy 64 the closed-from analytic solution of the stochastic SI model with state-dependent contact rates is given. In that paper, the impact of different intervention policies is related to the contact rate.…”
Section: Research Question and Methodologymentioning
confidence: 99%
“…In that paper, the impact of different intervention policies is related to the contact rate. Here we use the same model developed in Yarmand and Ivy 64 with the addition of the cost of each intervention policy and the reward associated with remaining uninfected. We consider both full and partial adherence to interventions among individuals which, as in Yarmand and Ivy, 64 are represented by different intervention policies.…”
Section: Research Question and Methodologymentioning
confidence: 99%
“…Examples of capability assessment strategies developed by the PERRCs included a toolkit to evaluate Medical Reserve Corps performance 34 and studies examining how to better use after-action reports [35][36][37] and exercises [38][39][40][41][42] for performance improvement. PERRC research pursued modeling of various PHP system phenomena, including mass evacuation, 43 the impact of different vaccine distribution strategies, 44,45 infectious disease progression in a population, 46,47 and effects of variation in mitigation strategies, such as the impact of different school closure approaches. 31,[45][46][47][48][49][50] Broad incorporation of modeling findings into policy and practice could reduce system performance variability by helping to standardize guidance for preparedness planning.…”
Section: Realization Of the Perrc Program Objectivesmentioning
confidence: 99%
“…PERRC research pursued modeling of various PHP system phenomena, including mass evacuation, 43 the impact of different vaccine distribution strategies, 44,45 infectious disease progression in a population, 46,47 and effects of variation in mitigation strategies, such as the impact of different school closure approaches. 31,[45][46][47][48][49][50] Broad incorporation of modeling findings into policy and practice could reduce system performance variability by helping to standardize guidance for preparedness planning.…”
Section: Realization Of the Perrc Program Objectivesmentioning
confidence: 99%
“…Finally, Yarmand 23,24 and Yarmand et al 25,26 used a compartmental simulation model to analyze the impact of different control policies on the spread of H1N1 and find cost-effective control policies. In a later work, Yarmand and Ivy 27 followed an analytic approach to model the spread of epidemics and impact of different intervention policies. Also Yarmand at al.…”
Section: Introductionmentioning
confidence: 99%