Oniyelu Dolapo Oluwaseun, Folorunsho Olaiya, Adewole Lawrence, Bakare Emmanuel Afolabi, Okoronkwo Chukwu, Eze Nelson
Department of Computer Science, Federal University Oye-Ekiti, Ekiti State, Nigeria.
International Centre for Applied Mathematical Modelling and Data Analytics (ICAMMDA), Federal University Oye-Ekiti, Ekiti State, Nigeria.
PLoS One. 2025 Aug 6;20(8):e0328888. doi: 10.1371/journal.pone.0328888. eCollection 2025.
Malaria in pregnancy (MIP) remains a global health challenge, affecting approximately 40% of pregnant women. Despite malaria control efforts by the Nigerian Government and its partners, regional disparities in health outcomes and malaria incidence trends among pregnant women remain under-studied. This study objectives were to assess MIP variability compared to general malaria cases, and forecast short-term MIP incidence over two years. This was achieved by analyzing malaria in pregnancy (MIP) variability across Nigeria from January 2015 to January 2025, using wavelet coherence, patterns of transmission cycles and selecting best modelling approach by comparing ARIMA and SARIMAX models to assess temporal trends before the forecast of short-term MIP incidence. Findings showed significant regional variability, with Cross River peaking in 2017 and 2019, while Enugu recorded its lowest trough in 2017. Malaria peaks in southern states remained lower than troughs in northern regions. Strong cross-correlations between MIP and general malaria transmission cycles were observed in Kebbi, Niger, Yobe, and Ondo, indicating persistent trends, while South-South and South-East exhibited weaker correlations, likely due to intervention fluctuations. SARIMAX models captured MIP trends more effectively, except Kebbi, where ARIMA fit better, and Niger, where SARIMAX exaggerated forecasts due to sensitivity to exogenous variables. Thus, SARIMAX was adopted for Cross River, Enugu, Ondo, and Yobe; while ARIMA was used for Kebbi and Niger States. It was discovered that Cross River and Enugu exhibited intervention-driven malaria fluctuations, Ondo, Niger, and Yobe displayed unstable or cyclical trends, reinforcing the importance of climate-sensitive forecasting models and seasonal interventions for improving malaria prediction accuracy. South-South and South-East need improved healthcare access, North-Central and North-West require seasonality forecasting, while North-East demands urgent control measures. Targeted malaria interventions are crucial to support achievement of the Nigeria's National Malaria Elimination Programme (NMEP) goals.
妊娠疟疾（MIP）仍然是一项全球性的健康挑战，影响着约40%的孕妇。尽管尼日利亚政府及其合作伙伴做出了疟疾防控努力，但孕妇健康结果和疟疾发病率趋势的地区差异仍未得到充分研究。本研究的目的是评估与一般疟疾病例相比的MIP变异性，并预测两年内的短期MIP发病率。这是通过分析2015年1月至2025年1月尼日利亚各地的妊娠疟疾（MIP）变异性来实现的，使用小波相干分析、传播周期模式，并通过比较ARIMA和SARIMAX模型来选择最佳建模方法，以评估短期MIP发病率预测前的时间趋势。研究结果显示出显著的地区变异性，克罗斯河州在2017年和2019年达到峰值，而埃努古州在2017年记录到最低谷。南部各州的疟疾峰值仍低于北部地区的谷值。在凯比州、尼日尔州、约贝州和翁多州观察到MIP与一般疟疾传播周期之间存在很强的交叉相关性，表明趋势持续存在，而南南地区和东南地区的相关性较弱，可能是由于干预波动所致。SARIMAX模型能更有效地捕捉MIP趋势，除了凯比州（ARIMA拟合更好）和尼日尔州（由于对外生变量敏感，SARIMAX夸大了预测）。因此，克罗斯河州、埃努古州、翁多州和约贝州采用SARIMAX模型；而凯比州和尼日尔州使用ARIMA模型。研究发现，克罗斯河州和埃努古州呈现出由干预驱动的疟疾波动，翁多州、尼日尔州和约贝州表现出不稳定或周期性趋势，这强化了气候敏感预测模型和季节性干预对于提高疟疾预测准确性的重要性。南南地区和东南地区需要改善医疗服务可及性，中北部和西北部需要进行季节性预测，而东北部则需要采取紧急控制措施。有针对性的疟疾干预对于支持实现尼日利亚国家疟疾消除计划（NMEP）的目标至关重要。
