Models for short term malaria prediction in Sri Lanka.
Models for short term malaria prediction in Sri Lanka.
复制标题
斯里兰卡短期疟疾预测的模型。
DOI：
10.1186/1475-2875-7-76
复制
发表时间：
2008-05-06
期刊：
影响因子：
3
通讯作者：
Amerasinghe PH
中科院分区：
文献类型：
--
作者：
Briët OJ;Vounatsou P;Gunawardena DM;Galappaththy GN;Amerasinghe PH
斯里兰卡的疟疾不稳定，在空间和时间上都有剧烈波动。虽然现时的个案数目正在减少，但鉴于过往曾有疫情在有效的控制措施下死灰复燃的情况，因此，控制计划必须继续作好准备。由于有监测/诊断的疟疾病例的长期时间序列，因此可以研究预测模式，目的是建立一个预测系统，协助有效分配防治疟疾的资源。指数加权移动平均模型，自回归积分移动平均（ARIMA）模型与季节性成分，季节性乘法自回归积分移动平均（SARIMA）模型进行了比较，每月的时间序列的地区疟疾病例的能力，预测疟疾病例数1至4个月。增加协变量，如邻近地区的疟疾病例数或降雨量进行了评估，以提高选定的（季节性）ARIMA模型的预测能力。最佳预测模型和预测误差在不同地区之间差异很大。降雨量作为协变量的增加，改善了预测选定的（季节性）ARIMA模型适度在某些地区，但在其他地区的预测恶化。通过增加降雨量的改善更频繁地在更大的预测视野。斯里兰卡疟疾模式的异质性需要区域特定的预测模型。一个月前的预测误差最小为22%（其中一个地区）。通过将降雨量作为协变量添加到这些预测模型中，在短期预测中取得的适度改进可能不足以值得投资于降雨量数据常规处理的预测系统。
Malaria in Sri Lanka is unstable and fluctuates in intensity both spatially and temporally. Although the case counts are dwindling at present, given the past history of resurgence of outbreaks despite effective control measures, the control programmes have to stay prepared. The availability of long time series of monitored/diagnosed malaria cases allows for the study of forecasting models, with an aim to developing a forecasting system which could assist in the efficient allocation of resources for malaria control. Exponentially weighted moving average models, autoregressive integrated moving average (ARIMA) models with seasonal components, and seasonal multiplicative autoregressive integrated moving average (SARIMA) models were compared on monthly time series of district malaria cases for their ability to predict the number of malaria cases one to four months ahead. The addition of covariates such as the number of malaria cases in neighbouring districts or rainfall were assessed for their ability to improve prediction of selected (seasonal) ARIMA models. The best model for forecasting and the forecasting error varied strongly among the districts. The addition of rainfall as a covariate improved prediction of selected (seasonal) ARIMA models modestly in some districts but worsened prediction in other districts. Improvement by adding rainfall was more frequent at larger forecasting horizons. Heterogeneity of patterns of malaria in Sri Lanka requires regionally specific prediction models. Prediction error was large at a minimum of 22% (for one of the districts) for one month ahead predictions. The modest improvement made in short term prediction by adding rainfall as a covariate to these prediction models may not be sufficient to merit investing in a forecasting system for which rainfall data are routinely processed.
登录
查看更多内容
影响因子：
3
作者：
Briët OJ;Gunawardena DM;van der Hoek W;Amerasinghe FP
通讯作者：
Amerasinghe FP
影响因子：
64.8
作者：
Thomson, MC;Doblas-Reyes, FJ;Palmer, TN
通讯作者：
Palmer, TN
影响因子：
--
作者：
Myers, M F;Rogers, D J;Cox, J;Flahault, A;Hay, S I
通讯作者：
Hay, S I
影响因子：
3.3
作者：
Bouma, MJ;vanderKaay, HJ
通讯作者：
vanderKaay, HJ
影响因子：
2.4
作者：
Abeku, TA;De Vlas, SJ;Habbema, JDF
通讯作者：
Habbema, JDF