To address this gap, we're using other sources such as satellite imagery, climate data and demographic information to estimate dengue risk. Specifically, we had success predicting the spread of dengue in Brazil at the regional, state and municipality level using these data streams as well as clinical surveillance data and Google search queries that used terms related to the disease. While our predictions aren't perfect, they show promise. Our goal is to combine information from each data stream to further refine our models and improve their predictive power.
为了弥补这一差距,我们正在利用卫星图像、气候数据和人口信息等其他来源来估计登革热风险。具体来说,我们成功地利用这些数据流、临床监测数据和使用与疾病有关的术语的谷歌搜索查询,预测了登革热在巴西的地区、州和市一级的蔓延。虽然我们的预测并不完美,但它们显示出了希望。我们的目标是将来自每个数据流的信息结合起来,以进一步完善我们的模型并提高它们的预测能力。
Similarly, to forecast the flu season, we have found that Wikipedia and Google searches can complement clinical data. Because the rate of people searching the internet for flu symptoms often increases during their onset, we can predict a spike in cases where clinical data lags.
同样,为了预测流感季节,我们发现维基百科和谷歌搜索可以补充临床数据。由于人们在互联网上搜索流感症状的比率在发病期间经常增加,我们可以预测到临床数据滞后的病例会出现激增。
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