Understanding zoonotic disease risk using dynamic ecological models

在气候变化、栖息地破坏、人口增长和全球化的推动下，自然世界预计将在下一个世纪经历重大转变。许多疾病，如埃博拉病毒，鼠疫和炭疽，是在人们接触野生动物时感染的，这些疾病被称为“人畜共患病”。自然界和人类世界中的过程决定了携带人畜共患病的物种目前被发现的位置，这些潜在过程的任何变化都将导致携带疾病的物种可以生活的地方的差异，因此，人们可以从他们那里感染疾病的地点。我将创建第一个全面但通用的一套高度优先的非洲人畜共患病的生态学和流行病学模型，重点关注那些对贫困和脆弱人类社区的生计有重大影响的疾病。我的建模方法将捕捉宿主物种经历的环境条件的季节性和年度差异，然后确定物种可以在地球仪中移动以应对环境变化的不同物理路线。在对真实的病例数据进行测试后，我的建模框架将首次允许研究人员和政策制定者同时致力于最大限度地减少可能感染各种不同人畜共患病的人数，然后预测气候和土地使用变化将如何影响未来的政策决定。我的工作有可能在未来减少疾病负担，从而减少整个非洲的人类痛苦。

The natural world is expected to undergo a significant transformation over the next century, driven by climate change, habitat destruction, human population increase and greater globalisation. Many diseases, such as Ebola, Plague and Anthrax, are caught when people come into contact with wild animals and these diseases are called 'zoonoses'. Processes within the natural and human world dictate where the species that carry zoonoses are currently found, and any changes to these underlying processes will lead to differences in where disease-carrying species can live, and therefore, the locations where people can catch diseases from them. I will create the first, comprehensive but general model of the ecology and epidemiology of a set of high priority African zoonoses, focusing on those diseases that have a major impact on the livelihoods of poor and vulnerable human communities. My modelling approach will capture the seasonal and annual differences to the environmental conditions that host species experience and then determine the different physical routes by which species can then move around the globe to respond to environmental change. After testing against real disease case data, my modelling framework will, for the first time, allow researchers and policy makers to simultaneously aim to minimise the number of people who can contract a wide set of very different zoonoses, and then predict how climate and land-use change will impact these policy decisions in the future. My work has the potential to reduce disease burden and consequently levels of human suffering across Africa in the future.