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Mathematical Models for Understanding the Effect of Long-Range Interactions and Intervention Measures on the Spread of Epidemics

用于理解远程相互作用和干预措施对流行病传播影响的数学模型

基本信息

批准号：
1812148
负责人：
Shirshendu Chatterjee
金额：
$ 15万
依托单位：
CUNY City College
依托单位国家：
美国
项目类别：
Continuing Grant
财政年份：
2018
资助国家：
美国
起止时间：
2018-08-15 至 2022-07-31
项目状态：
已结题

来源：
https://www.nsf.gov/awardsearch/showAward?AWD_ID=1812148&HistoricalAwards=false
关键词：
Mathematical Models Understanding Effect Long

项目摘要

Developing efficient and cost-effective intervention and monitoring strategies for preventing the spread of infection in the plant and human world requires an understanding of the underlying mechanisms by which infection spreads spatially and temporally, and of the quantitative effect of different intervention strategies. In the case of plant pathogens, long-distance dispersal phenomena and spatial heterogeneity of infection spreading can lead to failure of disease-control strategies that are based on naive models. Furthermore, such weak control strategies are often uneconomical and use copious amounts of pesticides on crops. In the case of human-contagious diseases, better intervention strategies can be developed if quantitative effects of quarantine are known. By analyzing spatial epidemic models with long-distance dispersal and certain intervention strategies, this research project aims to provide new insights into the mechanistic underpinning of dispersal on features of plant epidemics, and into the effects of quarantine strategies on restraining the spread of contagious infection among humans. To guarantee that the results are relevant to epidemiology, a part of the project will be carried out in collaboration with ecologists and epidemiologists. The involvement of undergraduate and graduate students in this research will enhance their ability to work at the interface between mathematics and biology. This project addresses challenges in developing more effective and economical intervention strategies for preventing the spread of infection in the plant and human world. Most of the epidemic models that have been analyzed rigorously to investigate the role of space in infection spreading are nearest-neighbor in nature. However, there are many plant diseases where the infection spreads via long-distance dispersals. To understand the effect of long-distance dispersal on infection spreading, several complex agent-based models have been analyzed using simulation and some heuristic methods in the theoretical biology literature. These non-rigorous treatments of the epidemic models sometimes lead to erroneous conclusions. One of the primary goals of this project is to understand rigorously the effect of long-range interactions on the spread of infections and associated features. In this project, the investigator plans to study several aspects of long-range first-passage percolation models by adding "long-range interaction" features to the standard (nearest-neighbor) first-passage percolation models. The investigator also plans to study intervention strategies for SIS models, which are frequently used for modeling spread of recurring disease among humans, in one dimension and on complex heterogeneous graphs. The rigorous analyses of these models will require the development of novel mathematical techniques. These techniques are expected to lead to a deeper understanding of a broad class of spatial stochastic epidemic models.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.

制定有效和具有成本效益的干预和监测策略，以防止感染在植物和人类世界的传播，需要了解感染在空间和时间上传播的潜在机制，以及不同干预策略的数量效应。就植物病原体而言，远距离传播现象和感染传播的空间异质性可能导致基于幼稚模型的疾病控制策略失败。此外，这种薄弱的控制策略往往不经济，并在作物上使用大量农药。在人类传染病的情况下，如果知道隔离的数量影响，就可以制定更好的干预战略。本研究旨在通过分析具有长距离传播和特定干预策略的空间流行病模型，为植物流行病特征的传播机制基础和隔离策略对抑制传染病在人类中的传播的作用提供新的见解。为了保证结果与流行病学相关，该项目的一部分将与生态学家和流行病学家合作进行。本科生和研究生参与这项研究将提高他们在数学和生物学之间的界面工作的能力。该项目解决了在制定更有效和更经济的干预战略以防止感染在植物和人类世界传播方面的挑战。为研究空间在感染传播中的作用而严格分析的大多数流行病模型本质上都是最近邻模型。然而，有许多植物病害是通过远距离传播传播的。为了了解长距离传播对感染传播的影响，理论生物学文献中采用模拟和启发式方法分析了几种复杂的基于agent的模型。这些对流行病模型的不严格的处理有时会导致错误的结论。该项目的主要目标之一是严格理解远程相互作用对感染传播和相关特征的影响。在这个项目中，研究者计划通过在标准（最近邻）第一通道渗透模型中加入“远程相互作用”特征来研究远程第一通道渗透模型的几个方面。研究者还计划研究SIS模型的干预策略，SIS模型经常用于在一维和复杂的异构图上模拟人类复发性疾病的传播。对这些模型的严格分析需要发展新的数学技术。这些技术有望使人们对广泛的空间随机流行病模型有更深入的了解。该奖项反映了美国国家科学基金会的法定使命，并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，被认为值得支持。

项目成果

期刊论文数量（4）

专著数量（0）

科研奖励数量（0）

会议论文数量（0）

专利数量（0）

Restricted Percolation Critical Exponents in High Dimensions

DOI：
10.1002/cpa.21938
发表时间：
2018-10
期刊：
Communications on Pure and Applied Mathematics
影响因子：
3
作者：
S. Chatterjee;Jack Hanson
通讯作者：
S. Chatterjee;Jack Hanson

A General Framework for Spatio-Temporal Modeling of Epidemics With Multiple Epicenters: Application to an Aerially Dispersed Plant Pathogen

多震中流行病时空模型的通用框架：在空中传播的植物病原体中的应用

DOI：
10.3389/fams.2021.721352
发表时间：
2021
期刊：
Frontiers in Applied Mathematics and Statistics
影响因子：
1.4
作者：
Ojwang', Awino M.;Ruiz, Trevor;Bhattacharyya, Sharmodeep;Chatterjee, Shirshendu;Ojiambo, Peter S.;Gent, David H.
通讯作者：
Gent, David H.

The effect of avoiding known infected neighbors on the persistence of a recurring infection process