Predicting malaria outbreaks from sea surface temperature variability up to 9 months ahead in Limpopo, South Africa, using machine learning.
Predicting malaria outbreaks from sea surface temperature variability up to 9 months ahead in Limpopo, South Africa, using machine learning.
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DOI：
10.3389/fpubh.2022.962377
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发表时间：
2022
影响因子：
5.2
通讯作者：
Mabunda, Qavanisi E. E.
中科院分区：
文献类型：
--
作者：
Martineau, Patrick;Behera, Swadhin K. K.;Nonaka, Masami;Jayanthi, Ratnam;Ikeda, Takayoshi;Minakawa, Noboru;Kruger, Philip;Mabunda, Qavanisi E. E.
疟疾每年在全世界造成近50万人死亡，造成巨大的社会经济负担。尽管最近在了解气候对疟疾感染率的影响方面取得了进展，但对可预测性的气候来源仍然知之甚少，利用不足。仅当地天气变化就可以在1-2个月的短时间内提供预测能力，因为太短而无法充分规划干预措施。在这里，我们表明，热带气候变化和相关的海洋表面温度在太平洋和印度洋是有价值的预测疟疾在林波波，南非，提前三个赛季。疟疾暴发的气候前兆首先通过对从再分析和观测数据集获得的气候数据进行滞后回归分析来确定，这些数据与南非察宁疟疾研究所提供的1998-2020年每月疟疾病例计数数据有关。在分析的11个海表温度区域中，印度洋和西太平洋两个区域是最强有力的前兆。通过训练一套机器学习分类模型来预测疟疾病例数是高于还是低于历史中位数，并评估它们在提供疟疾发病率预警预测方面的技能，提前时间从1个月到一年不等。通过这个预测系统的发展，我们发现，过去的信息在西太平洋SST提供了令人印象深刻的预测技巧（~80%的准确率）长达三个赛季（9个月）提前。热带印度洋的SST变化也被发现提供了两个季节（6个月）的良好技能。与之前的预测系统相比，这一结果代表了有效预测提前时间的延长，与当前研究中使用的机器学习技术相比，之前的预测系统计算成本更高。它还表明了气候信息和预测框架的价值，在此制定的早期规划的干预措施，防止疟疾爆发。
Malaria is the cause of nearly half a million deaths worldwide each year, posing a great socioeconomic burden. Despite recent progress in understanding the influence of climate on malaria infection rates, climatic sources of predictability remain poorly understood and underexploited. Local weather variability alone provides predictive power at short lead times of 1–2 months, too short to adequately plan intervention measures. Here, we show that tropical climatic variability and associated sea surface temperature over the Pacific and Indian Oceans are valuable for predicting malaria in Limpopo, South Africa, up to three seasons ahead. Climatic precursors of malaria outbreaks are first identified via lag-regression analysis of climate data obtained from reanalysis and observational datasets with respect to the monthly malaria case count data provided from 1998–2020 by the Malaria Institute in Tzaneen, South Africa. Out of 11 sea surface temperature sectors analyzed, two regions, the Indian Ocean and western Pacific Ocean regions, emerge as the most robust precursors. The predictive value of these precursors is demonstrated by training a suite of machine-learning classification models to predict whether malaria case counts are above or below the median historical levels and assessing their skills in providing early warning predictions of malaria incidence with lead times ranging from 1 month to a year. Through the development of this prediction system, we find that past information about SST over the western Pacific Ocean offers impressive prediction skills (~80% accuracy) for up to three seasons (9 months) ahead. SST variability over the tropical Indian Ocean is also found to provide good skills up to two seasons (6 months) ahead. This outcome represents an extension of the effective prediction lead time by about one to two seasons compared to previous prediction systems that were more computationally costly compared to the machine learning techniques used in the current study. It also demonstrates the value of climatic information and the prediction framework developed herein for the early planning of interventions against malaria outbreaks.
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