Inclusion of environmentally themed search terms improves Elastic net regression nowcasts of regional Lyme disease rates.
Inclusion of environmentally themed search terms improves Elastic net regression nowcasts of regional Lyme disease rates.
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DOI：
10.1371/journal.pone.0251165
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发表时间：
2022
期刊：
影响因子：
3.7
通讯作者：
Petersen CA
中科院分区：
文献类型：
--
作者：
Kontowicz E;Brown G;Torner J;Carrel M;Baker KK;Petersen CA
莱姆病是美国最广泛报道的媒介传播疾病。95%的确诊病例发生在东北部和中西部北部(东北部和中西部地区共有25778例确诊病例/美国共有27203例确诊病例)。人类病例通常发生在春季和夏季的几个月里，当时受感染的若虫吃了一顿血。目前的联邦监测战略每年报告数据，导致国家数据报告滞后近一年。这些报告的滞后使公共卫生机构很难评估和规划目前莱姆病的负担。实施即时预测模型，利用历史数据预测当前趋势，为公共卫生机构评估当前莱姆病负担和及时做出基于优先事项的预算决策提供了一种手段。这项研究的目的是利用谷歌趋势和疾病控制和预防中心的监测报告中的免费数据来开发和比较即时预测模型的性能。我们为美国五个地区开发了两套弹性网络模型：1.只使用21个疾病症状和扁虱相关术语的每月比例命中率数据，2.使用通过Google Correlate识别的术语的每月比例命中率数据以及疾病症状和病媒术语。使用全项列表的弹性网络模型对美国五个地区中的四个地区具有高精度(均方根误差：0.74，平均绝对误差：0.52，R2：0.97)，与仅使用疾病症状和向量项的模型预测相比，精度提高了1.33倍，误差减少了0.5倍。被认为对模型性能很重要的许多术语都与环境有关。可以实施这些模型，以帮助地方和州公共卫生机构在联邦公共卫生报告机构报告滞后的时间内准确监测莱姆病负担。
Lyme disease is the most widely reported vector-borne disease in the United States. 95% of confirmed human cases are reported in the Northeast and upper Midwest (25,778 total confirmed cases from Northeast and upper Midwest / 27,203 total US confirmed cases). Human cases typically occur in the spring and summer months when an infected nymph Ixodid tick takes a blood meal. Current federal surveillance strategies report data on an annual basis, leading to nearly a year lag in national data reporting. These lags in reporting make it difficult for public health agencies to assess and plan for the current burden of Lyme disease. Implementation of a nowcasting model, using historical data to predict current trends, provides a means for public health agencies to evaluate current Lyme disease burden and make timely priority-based budgeting decisions. The objective of the study was to develop and compare the performance of nowcasting models using free data from Google Trends and Centers of Disease Control and Prevention surveillance reports. We developed two sets of elastic net models for five regions of the United States: 1. Using only monthly proportional hit data from the 21 disease symptoms and tick related terms, and 2. Using monthly proportional hit data from terms identified via Google correlate and the disease symptom and vector terms. Elastic net models using the full-term list were highly accurate (Root Mean Square Error: 0.74, Mean Absolute Error: 0.52, R2: 0.97) for four of the five regions of the United States and improved accuracy 1.33-fold while reducing error 0.5-fold compared to predictions from models using disease symptom and vector terms alone. Many of the terms included and found to be important for model performance were environmentally related. These models can be implemented to help local and state public health agencies accurately monitor Lyme disease burden during times of reporting lag from federal public health reporting agencies.
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DOI：
10.1145/2337542.2337557
发表时间：
2012-01-01
影响因子：
5
作者：
Lampos, Vasileios;Cristianini, Nello
通讯作者：
Cristianini, Nello
影响因子：
3.7
作者：
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影响因子：
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作者：
Mead, Paul S.
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Mead, Paul S.
影响因子：
2.4
作者：
Kapitany-Foveny, Mate;Ferenci, Tamas;Sulyok, Mihaly
通讯作者：
Sulyok, Mihaly
影响因子：
8.5
作者：
Mavragani, Amaryllis;Ochoa, Gabriela
通讯作者：
Ochoa, Gabriela