Mathematical models describing indirect contact transmission are an important component of infectious disease mitigation and risk assessment. A model that tracks microorganisms between compartments by coupled ordinary differential equations or a Markov chain is benchmarked against a mechanistic interpretation of the physical transfer of microorganisms from surfaces to fingers and subsequently to a susceptible person's facial mucosal membranes. The primary objective was to compare these models in their estimates of doses and changes in microorganism concentrations on hands and fomites over time. The abilities of the models to capture the impact of episodic events, such as hand hygiene, and of contact patterns were also explored. For both models, greater doses were estimated for the asymmetrical scenarios in which a more contaminated fomite was touched more often. Differing representations of hand hygiene in the Markov model did not notably impact estimated doses but affected pathogen concentration dynamics on hands. When using the Markov model, losses due to hand hygiene should be handled as separate events as opposed to time-averaging expected losses. The discrete event model demonstrated the effect of hand-to-mouth contact timing on the dose. Understanding how model design influences estimated doses is important for advancing models as reliable risk assessment tools.

描述间接接触传播的数学模型是传染病缓解和风险评估的重要组成部分。通过耦合常微分方程或马尔可夫链追踪隔室之间微生物的模型，以微生物从表面到手指，随后到易感者面部粘膜的物理转移的机械解释为基准。主要目的是比较这些模型对剂量的估计以及手上和污染物随时间的微生物浓度变化。还探讨了模型捕捉偶发事件（例如手卫生）和接触模式影响的能力。对于这两个模型，在不对称情况下估计了更大的剂量，在这种情况下，污染程度更高的污染物被更频繁地接触。马尔可夫模型中手卫生的不同表示并没有显着影响估计剂量，但影响手上的病原体浓度动态。使用马尔可夫模型时，因手部卫生造成的损失应作为单独的事件处理，而不是时间平均预期损失。离散事件模型证明了手口接触时间对剂量的影响。了解模型设计如何影响估计剂量对于将模型发展为可靠的风险评估工具非常重要。