Morlighem Camille, Nnanatu Chibuzor Christopher, Aheto Justice Moses K, Linard Catherine
Department of Geography, University of Namur, Namur, 5000, Belgium.
ILEE, University of Namur, Namur, 5000, Belgium.
BMC Infect Dis. 2025 Aug 18;25(1):1031. doi: 10.1186/s12879-025-11412-5.
Significant efforts over the past decades have successfully reduced the global burden of malaria. However, progress has stalled since 2015. In low-transmission settings, the traditional distribution of malaria along vector suitability gradients is shifting to a new profile, with the emergence of hotspots where the disease persists. To support elimination in this context, it is essential that malaria risk maps consider not only environmental and climatic factors, but also societal vulnerabilities, in order to identify remaining hotspots and ensure that no contributing factors are overlooked. In this paper, we present an integrated approach to malaria risk mapping based on the decomposition of malaria risk into two components: 'hazard', which refers to the potential presence of infected vectors (e.g. influenced by rainfall and temperature), and 'vulnerability', which is the predisposition of the population to the burden of malaria (e.g. related to health care access and housing conditions). We focus on Senegal, which has a heterogeneous malaria epidemiological profile, ranging from high transmission in the south-east to very low transmission in the north, and which aims to eliminate malaria by 2030.
We combined data from several sources: the 2017 Demographic and Health Survey (DHS) (national coverage) and the 2020-21 Malaria Indicator Survey (MIS) (south-east regions), as well as remotely sensed, high-resolution covariate data. Using Bayesian geostatistical models, we predicted the prevalence of malaria in children under five years of age with a spatial resolution of 1 km.
Including vulnerability factors alongside hazard factors in the 2017 DHS data model improved the accuracy of predictive maps, achieving a median predictive R² of 0.64. Furthermore, models including only vulnerability factors outperformed those including only hazard factors. However, the models trained on the 2020-21 MIS data performed poorly, achieving a median R² of 0.13 at best for the model based on hazard factors, likely due to data collection during the dry season.
These findings highlight the importance of integrating both vulnerability and hazard factors into predictive maps. Future work could validate this approach further using routine malaria data from health management information systems, such as DHIS2.
在过去几十年中，人们付出了巨大努力，成功减轻了全球疟疾负担。然而，自2015年以来，进展陷入停滞。在低传播环境中，疟疾沿媒介适宜性梯度的传统分布正在转变为一种新的模式，出现了疾病持续存在的热点地区。为了在此背景下支持疟疾消除工作，疟疾风险地图不仅要考虑环境和气候因素，还要考虑社会脆弱性，这一点至关重要，以便识别剩余的热点地区，并确保不遗漏任何促成因素。在本文中，我们提出了一种综合的疟疾风险绘图方法，该方法基于将疟疾风险分解为两个组成部分：“危害”，指受感染媒介的潜在存在（例如受降雨和温度影响）；“脆弱性”，指人群对疟疾负担的易感性（例如与医疗保健可及性和住房条件有关）。我们重点关注塞内加尔，该国疟疾流行病学特征各异，从东南部的高传播到北部的极低传播，并且其目标是到2030年消除疟疾。
我们结合了多个来源的数据：2017年人口与健康调查（DHS）（全国范围）和2020 - 21年疟疾指标调查（MIS）（东南部地区），以及遥感高分辨率协变量数据。使用贝叶斯地理统计模型，我们预测了五岁以下儿童的疟疾患病率，空间分辨率为1公里。
在2017年DHS数据模型中纳入脆弱性因素以及危害因素提高了预测地图的准确性，预测R²中位数达到0.64。此外，仅包含脆弱性因素的模型优于仅包含危害因素的模型。然而，基于2020 - 21年MIS数据训练的模型表现不佳，基于危害因素的模型R²中位数最高仅为0.13，这可能是由于在旱季进行数据收集所致。
这些发现突出了将脆弱性和危害因素都纳入预测地图的重要性。未来的工作可以使用来自健康管理信息系统（如DHIS2）的常规疟疾数据进一步验证这种方法。
