Prospective forecasts of annual dengue hemorrhagic fever incidence in Thailand, 2010-2014.
Prospective forecasts of annual dengue hemorrhagic fever incidence in Thailand, 2010-2014.
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DOI：
10.1073/pnas.1714457115
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发表时间：
2018-03-06
影响因子：
11.1
通讯作者：
Reich NG
中科院分区：
文献类型：
--
作者：
Lauer SA;Sakrejda K;Ray EL;Keegan LT;Bi Q;Suangtho P;Hinjoy S;Iamsirithaworn S;Suthachana S;Laosiritaworn Y;Cummings DAT;Lessler J;Reich NG
登革出血热是泰国公共卫生官员面临的一个主要问题。病例的数量和地点每年都有很大不同，这使得在登革热季节之前规划预防和治疗活动变得困难。我们开发的统计模型与生物学动机的协变量，使预测泰国每个省每年。从我们的模型的预测有更少的误差比基线模型的样本外数据。此外，根据雨季开始前发生的发病率模型进行的预测成功地按爆发风险对各省进行了排序。这些对登革出血热发病率的早期准确预测可以帮助公共卫生官员确定未来将资源分配到哪里。登革出血热（DHF）是登革病毒感染的一种严重表现，可导致严重出血，器官损伤甚至死亡，每年在泰国影响15，000至105，000人。虽然泰国所有省份大多数年份都至少发生一例登革出血热病例，但病例的区域分布每年都有变化。在登革热季节之前准确预测登革出血热爆发的地点可以帮助公共卫生官员优先考虑公共卫生活动。我们开发了统计模型，使用生物学上合理的协变量，每年4月观察，预测一年中剩余时间的累积DHF发病率。我们在训练阶段（2000-2009）进行交叉验证，以选择这些模型的协变量。基于季前发病率的简约模型在65%的省级年度预测中优于10年中位数，平均绝对误差降低了19%，并成功预测了测试期间（2010-2014年）的爆发（受试者工作特征曲线下的面积= 0.84）。我们发现，过去的发病率的功能，最强烈的模型性能，而环境协变量的重要性各不相同的区域。这项工作表明，在与政策相关的时间范围内准确预测登革热风险是可能的。
Dengue hemorrhagic fever poses a major problem for public health officials in Thailand. The number and location of cases vary dramatically from year to year, which makes planning prevention and treatment activities before the dengue season difficult. We develop statistical models with biologically motivated covariates to make forecasts for each Thai province every year. The forecasts from our models have less error than those of a baseline model on out-of-sample data. Furthermore, the forecasts from a model based on incidence occurring before the start of the rainy season successfully order provinces by outbreak risk. These early, accurate forecasts of dengue hemorrhagic fever incidence could help public health officials determine where to allocate their resources in the future. Dengue hemorrhagic fever (DHF), a severe manifestation of dengue viral infection that can cause severe bleeding, organ impairment, and even death, affects between 15,000 and 105,000 people each year in Thailand. While all Thai provinces experience at least one DHF case most years, the distribution of cases shifts regionally from year to year. Accurately forecasting where DHF outbreaks occur before the dengue season could help public health officials prioritize public health activities. We develop statistical models that use biologically plausible covariates, observed by April each year, to forecast the cumulative DHF incidence for the remainder of the year. We perform cross-validation during the training phase (2000–2009) to select the covariates for these models. A parsimonious model based on preseason incidence outperforms the 10-y median for 65% of province-level annual forecasts, reduces the mean absolute error by 19%, and successfully forecasts outbreaks (area under the receiver operating characteristic curve = 0.84) over the testing period (2010–2014). We find that functions of past incidence contribute most strongly to model performance, whereas the importance of environmental covariates varies regionally. This work illustrates that accurate forecasts of dengue risk are possible in a policy-relevant timeframe.
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影响因子：
3.2
作者：
Brady OJ;Johansson MA;Guerra CA;Bhatt S;Golding N;Pigott DM;Delatte H;Grech MG;Leisnham PT;Maciel-de-Freitas R;Styer LM;Smith DL;Scott TW;Gething PW;Hay SI
通讯作者：
Hay SI
影响因子：
7.7
作者：
Lowe, Rachel;Coelho, Caio A. S.;Rodo, Xavier
通讯作者：
Rodo, Xavier
影响因子：
5
作者：
Endy, TP;Chunsuttiwat, S;Ennis, FA
通讯作者：
Ennis, FA
影响因子：
2.7
作者：
Juliano, SA;O'Meara, GF;Cutwa, MM
通讯作者：
Cutwa, MM
DOI：
10.4269/ajtmh.1988.38.172
发表时间：
1988-01-01
影响因子：
3.3
作者：
BURKE, DS;NISALAK, A;SCOTT, RM
通讯作者：
SCOTT, RM