Abstract

Infected individuals must be aware of disease symptoms to seek care, so outreach and education programs are critical to disease control. However, public health organizations often only have limited resources for outreach and must carefully design campaigns to maximize effectiveness, potentially leveraging word-of-mouth information spread. We show how classic epidemiological models can be reformulated such that identifying an efficient disease control resource allocation policy in the context of information spread becomes a submodular maximization problem. This means that our framework can simultaneously handle multiple, interacting dynamic processes coupled through the likelihood of disease clearance, allowing our framework to provide insight into optimal resource allocation while considering social dynamics in addition to disease dynamics (e.g., knowledge spread and disease spread). We then demonstrate that this problem can be algorithmically solved and can handle stochasticity in input parameters by examining a numerical example of tuberculosis control in India.

摘要

感染者必须了解疾病症状才能寻求护理，因此外展和教育计划对于控制疾病至关重要。但是，公共卫生组织通常仅具有有限的外展资源，必须精心设计运动以最大程度地发挥作用，从而有可能利用口碑传播信息。我们展示了如何可以重新构造经典的流行病学模型，以便在信息传播的背景下确定有效的疾病控制资源分配策略成为子模块最大化问题。这意味着我们的框架可以同时处理通过疾病清除的可能性而耦合的多个相互作用的动态过程，从而使我们的框架能够提供最佳资源分配的见解，同时考虑疾病动态之外的社会动态（例如，知识传播和疾病传播）。然后，我们通过研究印度结核病控制的数值示例，证明了该问题可以通过算法解决，并且可以处理输入参数中的随机性。