Data from: A mechanistic underpinning for sigmoid dose-dependent infection

Theoretical models of environmentally transmitted diseases often assume that transmission is a constant process, which scales linearly with pathogen dose. Here we question the applicability of such an assumption and propose a sigmoidal form for the pathogens infectivity response. In our formulation, this response arises under two assumptions: 1) multiple invasion events are required for a successful pathogen infection and 2) the host invasion state is reversible. The first assumption reduces pathogen infection rates at low pathogen doses, while the second assumption, due to host immune function, leads to a saturating infection rate at high doses. The derived pathogen dose:infection rate -relationship was tested against an experimental data on host mortality rates across different pathogen doses. Compared to two simpler alternatives, the sigmoidal function gave a better fit to patterns in host mortality rate (process), as well as host mortality (endpoint). Combining these alternative approaches made us more confident to conclude that the proposed model for disease transmission is theoretically sound, provides a good description of the data at hand, and is likely to be useful in developing more reliable models for infectious diseases.

环境传播性疾病的理论模型通常假设传播过程为恒定过程，且其速率与病原体剂量呈线性比例关系。对此，我们质疑该假设的适用性，并提出了病原体感染性响应的S形函数（sigmoidal function）形式。在本研究的建模框架下，该响应基于两项假设推导得出：1）成功感染病原体需经历多次入侵事件；2）宿主的入侵状态具有可逆性。第一项假设会降低低病原体剂量下的感染速率，而第二项假设则因宿主免疫功能的调控作用，会在高剂量下使感染速率趋于饱和。所推导得到的病原体剂量-感染速率关系，通过不同病原体剂量下的宿主死亡率实验数据进行了对照验证。相较于两种更为简化的替代模型，S形函数能够更好地拟合宿主死亡率（过程层面）以及宿主死亡率（终点层面）的变化规律。综合上述不同建模方法的分析结果，我们更有把握得出以下结论：所提出的疾病传播模型在理论上严谨合理，能够很好地适配当前的实验数据，且有望为开发更可靠的传染性疾病模型提供有力支撑。