SG: Density dependence and disease dynamics: moving towards a predictive framework

SG：密度依赖性和疾病动态：迈向预测框架

• 批准号: 2211287
• 负责人: Shweta Bansal
• 金额: $ 20万
• 依托单位: Georgetown University
• 依托单位国家: 美国
• 项目类别: Standard Grant
• 财政年份: 2022
• 资助国家: 美国
• 起止时间: 2022-07-15 至 2024-06-30
• 项目状态: 已结题
• 来源: https://www.nsf.gov/awardsearch/showAward?AWD_ID=2211287&HistoricalAwards=false
• 关键词: SG; Density; dependence; disease; dynamics

In humans and animals, a more active social life often means more exposure to disease. Likewise, living in high density areas increases one’s chance of encountering disease-causing microorganisms (“pathogens”), thereby increasing the likelihood of getting sick. Scientists don’t know, though, how often density drives the spread of infectious disease, and in what way. In many cases, animals in social groups may reduce their risk of infection – for example, by avoiding infected individuals or through improved nutrition – so perhaps population density may not matter very much. Understanding how density and disease relate to each other is necessary for predicting, modelling and controlling disease outbreaks (like COVID-19). Investigating whether pathogens restrict animals from forming more complex, denser societies will tell us how human disease burdens are likely to change as our societies become more urbanized. This is especially important in a densely populated world that is increasingly beset by novel infectious diseases.This research will investigate whether and how density drives greater infection within animal populations. Using a compiled collection of dozens of wildlife disease datasets, investigators will employ meta-analyses to ask 1) whether individuals in high-density areas have more pathogens; 2) whether specific interactions become more likely with higher host densities; and 3) whether these relationships can explain density effects for diseases spread by those interactions. In doing so, it will form the basis for predicting density-infection interactions, thereby improving general models of disease dynamics.This award reflects NSF's statutory mission and has been deemed worthy of support through evaluation using the Foundation's intellectual merit and broader impacts review criteria.

在人类和动物中，更活跃的社会生活往往意味着更多的疾病暴露。同样，生活在高密度地区增加了一个人遇到致病微生物（“病原体”）的机会，从而增加了生病的可能性。然而，科学家们不知道人口密度多久会推动传染病的传播，以及以何种方式。在许多情况下，社会群体中的动物可能会降低感染的风险--例如，通过避开受感染的个体或通过改善营养--因此，种群密度可能并不重要。了解密度和疾病之间的相互关系对于预测、建模和控制疾病爆发（如COVID-19）是必要的。调查病原体是否限制动物形成更复杂，更密集的社会将告诉我们，随着我们的社会变得更加城市化，人类疾病负担可能会如何变化。这在一个人口密集的世界中尤其重要，这个世界越来越受到新型传染病的困扰。这项研究将调查密度是否以及如何导致动物种群内的更大感染。研究人员将使用数十种野生动物疾病数据集的汇编集，采用荟萃分析来询问1）高密度地区的个体是否有更多的病原体; 2）特定的相互作用是否随着宿主密度的增加而变得更有可能;以及3）这些关系是否可以解释这些相互作用传播疾病的密度效应。在这样做的过程中，它将成为预测密度-感染相互作用的基础，从而改善疾病动力学的一般模型。该奖项反映了NSF的法定使命，并被认为是值得通过使用基金会的知识价值和更广泛的影响审查标准进行评估的支持。