Trend estimation and short-term forecasting of COVID-19 cases and deaths worldwide

被引:5
|
作者
Krymova, Ekaterina [1 ,2 ]
Bejar, Benjamin [1 ,2 ]
Thanou, Dorina [3 ]
Sun, Tao [1 ,2 ]
Manetti, Elisa [4 ]
Lee, Gavin [1 ,2 ]
Namigai, Kristen [4 ]
Choirat, Christine [1 ,2 ]
Flahault, Antoine [4 ]
Obozinski, Guillaume [1 ,2 ]
机构
[1] Ecole Polytech Fed Lausanne, Swiss Data Sci Ctr, CH-1015 Lausanne, Switzerland
[2] Swiss Fed Inst Technol, CH-1015 Lausanne, Switzerland
[3] Ecole Polytech Fed Lausanne, Ctr Intelligent Syst, CH-1015 Lausanne, Switzerland
[4] Univ Geneva, Fac Med, Inst Global Hlth, CH-1202 Geneva, Switzerland
关键词
COVID-19; forecasting; trend estimation; seasonal decomposition;
D O I
10.1073/pnas.2112656119
中图分类号
O [数理科学和化学]; P [天文学、地球科学]; Q [生物科学]; N [自然科学总论];
学科分类号
07 ; 0710 ; 09 ;
摘要
Since the beginning of the COVID-19 pandemic, many dashboards have emerged as useful tools to monitor its evolution, inform the public, and assist governments in decision-making. Here, we present a globally applicable method, integrated in a daily updated dashboard that provides an estimate of the trend in the evolution of the number of cases and deaths from reported data of more than 200 countries and territories, as well as 7-d forecasts. One of the significant difficulties in managing a quickly propagating epidemic is that the details of the dynamic needed to forecast its evolution are obscured by the delays in the identification of cases and deaths and by irregular reporting. Our forecasting methodology substantially relies on estimating the underlying trend in the observed time series using robust seasonal trend decomposition techniques. This allows us to obtain forecasts with simple yet effective extrapolation methods in linear or log scale. We present the results of an assessment of our forecasting methodology and discuss its application to the production of global and regional risk maps.
引用
收藏
页数:8
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