Author: Biqing Chen; Hao Liang; Xiaomin Yuan; Yingying Hu; Miao Xu; Yating Zhao; Binfen Zhang; Fang Tian; Xuejun Zhu
Title: Roles of meteorological conditions in COVID-19 transmission on a worldwide scale Document date: 2020_3_20
ID: 3svnvozz_36
Snippet: The copyright holder for this preprint . https://doi.org/10.1101/2020.03.16.20037168 doi: medRxiv preprint Using this full model for prediction in the replication datasets, we got a quite good prediction result for the national data all over the world (replication_world), with a case counts prediction significantly correlated with the real data (Pearson's correlation coefficient r 2 = 0.487, p = 0.003; Figure 5A ). When further reducing variables.....
Document: The copyright holder for this preprint . https://doi.org/10.1101/2020.03.16.20037168 doi: medRxiv preprint Using this full model for prediction in the replication datasets, we got a quite good prediction result for the national data all over the world (replication_world), with a case counts prediction significantly correlated with the real data (Pearson's correlation coefficient r 2 = 0.487, p = 0.003; Figure 5A ). When further reducing variables in the model to obtain a most parsimony and best fitted prediction model, we got better results. When visibility was removed from the model, the predicted values of the fitted model were more significantly correlated with the observed epidemiological data (r 2 = 0.624, p = 6.113e-05 for replication_world; r 2 = 0.287, p = 0.034 for replication_Italy; see Figure 5B & 6). This model, written as follows, was also best fitted compared to the full model and other 3-factor and 2-factor models, with the smallest AIC.
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