| Περίληψη: | The number of the daily COVID-19 cases* comprises a time series, and time series forecasting is the act of modeling to predict its future values based on previous data. Although most forecasting methodologies focus on value-oriented results, local optima, there is some research on time-oriented methods that focus on predicting the time that the local optima will occur (Androulakis & Lisgara, 2007; Lisgara, Karolidis, & Androulakis, 2010a, 2010b, 2012). These techniques regard time series as an objective function subject to the factors affecting its values and apply nonlinear optimization techniques to approach the time that future minimum/maximum will happen.
In this work, the technique provided by Lisgara, Karolidis & Androulakis, (Lisgara et al., 2010b) is exploited to predict the timing the COVID-19 time series will reach its maximum. Then, value-oriented methods are applied to extract an approximation of the expected number of cases until the predicted time period. For this study were used all avalable data until March the 21st (Dong, Du, & Gardner, 2020).
The COVID-19 time series consists of the daily number of cases; at the beginning, its rate seems to grow exponentially, then the rate gradually slows down and merely the time series reaches its maximum value. Similarly, the rate decreases exponentially and eventually the series reaches its end-time (zero cases). The rate’s path is defined as the cycle of the phenomenon.
It was observed that the cycle’s time extension would result to a lower maximum value, meaning less daily cases and, consequently, less daily serious cases. Also, it was also observed that such an extension would result to delaying the outbreak’s peak, meaning that the cases number would spread in a longer period of time. However, timing-control is vital to ensure that the healthcare system would not exceed its capacity.
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