Swiss Tropical and Public Health Institute, Kreuzstrasse 2, CH-4123 Allschwil, Switzerland.
University of Basel, Petersplatz 1, CH-4001 Basel, Switzerland.
Int J Environ Res Public Health. 2024 Nov 8;21(11):1487. doi: 10.3390/ijerph21111487.
Malaria remains a climate-driven public health issue in Burkina Faso, yet the interactions between climatic factors and malaria interventions across different zones are not well understood. This study estimates time delays in the effects of climatic factors on malaria incidence, develops forecasting models, and assesses their short-term forecasting performance across three distinct climatic zones: the Sahelian zone (hot/arid), the Sudano-Sahelian zone (moderate temperatures/rainfall); and the Sudanian zone (cooler/wet).
Monthly confirmed malaria cases of children under five during the period 2015-2021 were analyzed using Bayesian generalized autoregressive moving average negative binomial models. The predictors included land surface temperature (LST), rainfall, the coverage of insecticide-treated net (ITN) use, and the coverage of artemisinin-based combination therapies (ACTs). Bayesian variable selection was used to identify the time delays between climatic suitability and malaria incidence. Wavelet analysis was conducted to understand better how fluctuations in climatic factors across different time scales and climatic zones affect malaria transmission dynamics.
Malaria incidence averaged 9.92 cases per 1000 persons per month from 2015 to 2021, with peak incidences in July and October in the cooler/wet zone and October in the other zones. Periodicities at 6-month and 12-month intervals were identified in malaria incidence and LST and at 12 months for rainfall from 2015 to 2021 in all climatic zones. Varying lag times in the effects of climatic factors were identified across the zones. The highest predictive power was observed at lead times of 3 months in the cooler/wet zone, followed by 2 months in the hot/arid and moderate zones. Forecasting accuracy, measured by the mean absolute percentage error (MAPE), varied across the zones: 28% in the cooler/wet zone, 53% in the moderate zone, and 45% in the hot/arid zone. ITNs were not statistically important in the hot/arid zone, while ACTs were not in the cooler/wet and moderate zones.
The interaction between climatic factors and interventions varied across zones, with the best forecasting performance in the cooler/wet zone. Zone-specific intervention planning and model development adjustments are essential for more efficient early-warning systems.
疟疾仍然是布基纳法索受气候驱动的公共卫生问题，但不同区域气候因素与疟疾干预措施之间的相互作用仍不甚清楚。本研究旨在评估不同气候区（萨赫勒区[炎热/干旱]、苏丹萨赫勒区[温和气温/降雨]和苏丹区[凉爽/湿润]）气候因素对疟疾发病率的影响的时滞，开发预测模型，并评估其短期预测性能。
使用贝叶斯广义自回归移动平均负二项式模型分析了 2015 年至 2021 年期间五岁以下儿童确诊的每月疟疾病例。预测因子包括地表温度（LST）、降雨量、经杀虫剂处理的蚊帐（ITN）覆盖率和青蒿素为基础的联合疗法（ACT）覆盖率。使用贝叶斯变量选择来确定气候适宜性与疟疾发病率之间的时滞。进行了小波分析，以更好地了解不同时间尺度和气候区的气候因素波动如何影响疟疾传播动态。
2015 年至 2021 年期间，疟疾发病率平均为每 1000 人每月 9.92 例，凉爽/湿润区的发病高峰期在 7 月和 10 月，其他区在 10 月。在所有气候区，从 2015 年至 2021 年，疟疾发病率和 LST 存在 6 个月和 12 个月的周期性，而降雨量则存在 12 个月的周期性。在不同气候区，确定了气候因素影响的变化滞后时间。在凉爽/湿润区，预测效果最佳的时间提前期为 3 个月，炎热/干旱区和温和区为 2 个月。预测精度（以平均绝对百分比误差（MAPE）衡量）因区域而异：凉爽/湿润区为 28%，温和区为 53%，炎热/干旱区为 45%。在炎热/干旱区，ITN 并不具有统计学意义，而在凉爽/湿润区和温和区，ACT 则没有统计学意义。
气候因素与干预措施之间的相互作用因区域而异，在凉爽/湿润区的预测性能最佳。针对特定区域的干预措施规划和模型开发调整对于更有效的早期预警系统至关重要。
