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

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灭。然而，钩端螺旋体病和疟疾每年仍影响着马来西亚人。钩端螺旋体病的年发病率实际上正在增加，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。尽管对这一联系的科学理解正在迅速发展，但建立对疫情进行定量预测的计算模型仍然非常困难。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的资源分配是必不可少的。预测与水有关的疾病爆发的一个主要困难是，驱动因素众多，涉及环境和社会经济领域。此外，将驱动因素与疾病爆发联系起来的许多过程是高度非线性的，难以用计算算法表示。因此，该提案着手探索使用人工智能方法来识别和模拟导致有利于疾病发生的条件的物理和微生物相互作用，目标是开发疾病爆发的早期预警系统。过程中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信息的指数级增长（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twitter feed和新闻帖子等社交媒体的数据）。这导致了许多新颖而强大的算法和例程的发展。然而，其在与水有关的疾病背景下的探索仍然非常有限。因此，我们建议利用这些突破，测试和调整这些新方法，以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。拟议的研究结合了广泛的汇编、综合和整合社会人口和基础设施数据以及极端环境数据，并使用新颖的计算算法从数据集中“学习”，并利用结果来改进业务预测系统。我们组建了一个世界领先的科学家联盟，汇集了极端水文气象、人工智能和社区卫生问题方面的专业知识。我们将以马来西亚森美兰州为案例进行研究，并将与马来西亚卫生部密切合作。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，使用调查问卷进行了风险评估，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部和灌溉排水部提供的环境数据（这些数据可免费或收取少量费用供学术使用）和地方民政事务处提供的每月水质监测数据加以补充。ReferencesWHO, 2018年。http://www.who.int/water_sanitation_health/diseases-risks/diseases/diarrhoea

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