[Malaysia] Understanding and managing the risk of water related diseases under hydrometeorological extremes

[马来西亚] 了解和管理极端水文气象条件下与水有关的疾病的风险

• 批准号: NE/S003053/1
• 负责人: Wouter Buytaert
• 金额: $ 49.54万
• 依托单位: Imperial College London
• 依托单位国家: 英国
• 项目类别: Research Grant
• 财政年份: 2019
• 资助国家: 英国
• 起止时间: 2019 至 无数据
• 项目状态: 已结题
• 来源: https://gtr.ukri.org/projects?ref=NE%2FS003053%2F1
• 关键词: Malaysia; Understanding; managing; risk; water

Globally, water-related diseases are a major obstacle to sustainable development (WHO, 2018). Many of these diseases, such as Cholera and Hepatitis A, have been successfully phased out in Malaysia. However, leptospirosis and malaria still affect Malaysians every year. The annual incidence rate of Leptospirosis is actually increasing, from 0.97 cases per 100,000 population in 2004 to 12.47 per 100,000 in 2012.It is well known that leptospirosis and malaria are strongly linked to environmental conditions, and humidity and temperature in particular. Although scientific understanding of this link is advancing at a rapid pace, it is still very difficult to build computational models that make quantitative forecasts of outbreaks. Yet such systems are indispensable for proactive disease management, and to optimise the allocation of resources for medical prevention and interventions.A major difficulty with predicting outbreaks of water-related diseases is the large number of driving factors, which span the environmental and socio-economic realms. Additionally, many of the processes that link the driving factors with disease outbreaks, are highly non-linear and difficult to represent in computational algorithms. This proposal therefore sets out to explore the use of artificial intelligence approaches to identify and model the physical and microbiological interactions that lead to conditions favouring disease occurrences, with the goal of developing an early warning system for disease outbreaks. The complexity and non-linearity in the processes makes AI methods such as the neural network approach highly promising as it is inherently suited to problems that are mathematically difficult to describe and highly non-linear.The scientific field of artificial intelligence is developing at a very rapid pace. This evolution is driven by the exponentially increasing amount of information available online (often referred to as the "big data" era), much of which is highly unstructured and diverse (e.g., data from social media such as twitter feeds and news posts). This has resulted in the development of many novel and powerful algorithms and routines. However, its exploration in the context of water-related diseases is still very limited. Therefore, we propose to leverage these breakthroughs, by testing and adapting these new methodologies to advance predictive modelling of the link between hydrometeorological extremes and water-related diseases. The proposed research combines extensive compilation, synthesis and integration of socio-demographic and infrastructural data alongside data of environmental extremes, with novel computational algorithms to "learn" from the datasets and leverage the outcomes to improve operational forecasting systems.We have assembled a world-leading consortium of scientists that combines expertise on hydrometeorological extremes, artificial intelligence and community health issues. We will use the Malaysian state of Negeri Sembilan as a case study, and will work in close collaboration with the State Department of Health. This will allow us to access historical records that include patients' demographic information. More recently, risk assessment have been conducted using questionnaires that includes assessment of water supply and drainage infrastructure. The epidemiological data will be complemented by environmental data from the Department of Meteorology and the Department of Irrigation and Drainage (which are either available for academic use for free or a small fee), and monthly water quality monitoring data from local District Offices.ReferencesWHO, 2018. http://www.who.int/water_sanitation_health/diseases-risks/diseases/diarrhoea

在全球范围内，与水有关的疾病是可持续发展的主要障碍（世卫组织，2018年）。其中许多疾病，如霍乱和甲型肝炎，已在马来西亚成功地逐步消除。然而，细螺旋体病和疟疾仍然每年影响马来西亚人。钩端螺旋体病的年发病率实际上在增加，从2004年的每10万人0.97例增加到2012年的每10万人12.47例。众所周知，钩端螺旋体病和疟疾与环境条件，特别是湿度和温度密切相关。虽然对这一联系的科学认识正在迅速发展，但仍然很难建立对疫情进行定量预测的计算模型。然而，这些系统对于积极主动的疾病管理以及优化医疗预防和干预资源的分配是必不可少的。预测与水有关的疾病爆发的一个主要困难是大量的驱动因素，这些因素跨越环境和社会经济领域。此外，许多将驱动因素与疾病爆发联系起来的过程是高度非线性的，难以用计算算法表示。因此，本提案着手探索使用人工智能方法，以确定导致有利于疾病发生的条件的物理和微生物相互作用并建立模型，目标是建立疾病爆发预警系统。由于过程的复杂性和非线性，使得人工智能方法（如神经网络方法）非常有前途，因为它本质上适合于数学上难以描述和高度非线性的问题。人工智能的科学领域正在以非常快的速度发展。这种演变是由在线可用的呈指数级增长的信息量（通常被称为“大数据”时代）驱动的，其中大部分是高度非结构化和多样化的（例如，来自社交媒体的数据，诸如Twitter馈送和新闻帖子）。这导致了许多新的和强大的算法和例程的发展。然而，在与水有关的疾病方面的探索仍然非常有限。因此，我们建议利用这些突破，通过测试和调整这些新方法来推进水文气象极端事件与水相关疾病之间联系的预测建模。该研究将社会人口和基础设施数据以及环境极端数据的广泛汇编、综合和整合与新的计算算法相结合，从数据集中“学习”，并利用结果来改善业务预报系统。我们组建了一个世界领先的科学家联盟，将水文气象极端、人工智能和社区健康问题的专业知识相结合。我们将以马来西亚内杰里·森比兰州为案例研究，并将与州卫生部密切合作。这将使我们能够访问包括患者人口统计信息在内的历史记录。最近，利用调查表进行了风险评估，其中包括对供水和排水基础设施的评估。流行病学数据将由气象局和灌溉和排水局的环境数据（可免费或收取少量费用用于学术用途）以及当地地区办事处的每月水质监测数据补充。http://www.who.int/water_sanitation_health/diseases-risks/diseases/diarrhoea

项目成果

Prediction model of Leptospirosis Occurrence for Seremban (Malaysia) using Meteorological Data

利用气象数据预测芙蓉（马来西亚）钩端螺旋体病发生率

• DOI: 10.30880/ijie.2019.11.04.007
• 发表时间: 2019
• 期刊: International Journal of Integrated Engineering
• 影响因子: 0.6
• 作者: Rahmat F

Exploratory Data Analysis and Artificial Neural Network for Prediction of Leptospirosis Occurrence in Seremban, Malaysia Based on Meteorological Data

• DOI: 10.3389/feart.2020.00377
• 发表时间: 2020-11-19
• 期刊: FRONTIERS IN EARTH SCIENCE
• 影响因子: 2.9
• 作者: Rahmat, Fariq;Zulkafli, Zed;Masrani, Afiqah
• 通讯作者: Masrani, Afiqah