Forecasting the novel coronavirus COVID-19

By Fotios Petropoulos, Spyros Makridakis. PLoS ONE, 2020

549 citations recorded by the source corpus.

What will be the global impact of the novel coronavirus (COVID-19)? Answering this question requires accurate forecasting the spread of confirmed cases as well as analysis of the number of deaths and recoveries. Forecasting, however, requires ample historical data. At the same time, no prediction is certain as the future rarely repeats itself in the same way as the past. Moreover, forecasts are influenced by the reliability of the data, vested interests, and what variables are being predicted. Also, psychological factors play a significant role in how people perceive and react to the danger from the disease and the fear that it may affect them personally. This paper introduces an objective approach to predicting the continuation of the COVID-19 using a simple, but powerful method to do so. Assuming that the data used is reliable and that the future will continue to follow the past pattern of the disease, our forecasts suggest a continuing increase in the confirmed COVID-19 cases with sizable associated uncertainty. The risks are far from symmetric as underestimating its spread like a pandemic and not doing enough to contain it is much more severe than overspending and being over careful when it will not be needed. This paper describes the timeline of a live forecasting exercise with massive potential implications for planning and decision making and provides objective forecasts for the confirmed cases of COVID-19.

Subfields: Management Science and Operations Research

View Paper: https://www.semanticscholar.org/paper/fa0209bbb35fc57d51b11065347cb275da8c90a2

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In Management Science and Operations Research

Citations in the corpus

Real-time forecasts and risk assessment of novel coronavirus (COVID-19) cases: A data-driven analysis (2020, background): “To answer the first question, we study classical and modern forecasting techniques for which the prediction accuracy largely depend on the availability of data [28] .”
Uncertainty as a Form of Transparency: Measuring, Communicating, and Using Uncertainty (2020, background): “In this setting, forecasts are being disseminated to governments, organizations, and individuals for policy, resource allocation, and personal-risk judgments and behavior [48, 108, 146].”
COVID-19 Future Forecasting Using Supervised Machine Learning Models (2020, methodology): “Considering the best performance given by ES model in all the three forecasting cases among all the four models, the model has been used for further analysis with interval prediction [7].”
Modeling and Forecasting of COVID-19 Growth Curve in India (2020, methodology): “Petropoulos and Makridakis (2020) suggested the use of exponential smoothing method to model the trend of the virus, globally.”