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

在人类和动物中，更活跃的社交生活往往意味着更多地接触疾病。同样，生活在高密度地区会增加一个人遇到致病微生物(“病原体”)的机会，从而增加生病的可能性。然而，科学家们不知道密度多久会以何种方式驱动传染病的传播。在许多情况下，社会群体中的动物可能会降低它们感染的风险--例如，通过避开受感染的个体或通过改善营养--所以也许种群密度可能不是很重要。了解密度和疾病是如何相互关联的，对于预测、建模和控制疾病暴发是必要的(如新冠肺炎)。研究病原体是否会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，人类的疾病负担可能会发生怎样的变化。这在人口稠密的世界中尤其重要，因为新的传染病越来越多地困扰着这个世界。这项研究将调查密度是否以及如何在动物种群中导致更大的感染。使用几十个野生动物疾病数据集的汇编集合，研究人员将使用荟萃分析来询问：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通过这些相互作用传播的疾病的密度效应。这样做，它将成为预测密度-感染相互作用的基础，从而改进疾病动态的一般模型。这一奖项反映了NSF的法定使命，并通过使用基金会的智力优势和更广泛的影响审查标准进行评估，被认为值得支持。

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.