Forecasting on Covid-19 infection waves using a rough set filter driven moving average models

Abstract

The pandemic outbreak of severe acute respiratory syndrome caused by the Coronavirus 2 disease in 2019, also known as SARS-COV-2 and COVID-19, has claimed over 5.6 million lives till now. The highly infectious nature of the Covid-19 virus has resulted into multiple massive upsurges in counts of new infections termed as 'waves.' These waves consist of numerous rising and falling counts of Covid-19 infection cases with changing dates that confuse analysts and researchers. Due to this confusion, the detection of emergence or drop of Covid waves is currently a subject of intensive research. Hence, we propose an algorithmic framework to forecast the upcoming details of Covid-19 infection waves for a region. The framework consists of a displaced double moving average ( $\delta$ DMA) algorithm for forecasting the start, rise, fall, and end of a Covid-19 wave. The forecast is generated by detection of potential dates with specific counts called 'markers.' This detection of markers is guided by decision rules generated through rough set theory. We also propose a novel 'corrected moving average' ( $\chi$ SMA) technique to forecast the upcoming count of new infections in a region. We implement our proposed framework on a database of Covid-19 infection specifics fetched from 12 countries, namely: Argentina, Colombia, New Zealand, Australia, Cuba, Jamaica, Belgium, Croatia, Libya, Kenya, Iran, and Myanmar. The database consists of day-wise time series of new and total infection counts from the date of first case till 31st January 2022 in each of the countries mentioned above. The $\delta$ DMA algorithm outperforms other baseline techniques in forecasting the rise and fall of Covid-19 waves with a forecast precision of 94.08%. The $\chi$ SMA algorithm also surpasses its counterparts in predicting the counts of new Covid-19 infections for the next day with the least mean absolute percentage error (MAPE) of 36.65%. Our proposed framework can be deployed to forecast the upcoming trends and counts of new Covid-19 infection cases under a minimum observation window of 7 days with high accuracy. With no perceptible impact of countermeasures on the pandemic until now, these forecasts will prove supportive to the administration and medical bodies in scaling and allotment of medical infrastructure and healthcare facilities.

Keywords: Covid-19; Forecast; Moving average; Pandemic; Rough set.

Fig. 1

Timeline of Covid-19 pandemic .

Fig. 2

The general structure of a wave.

Fig. 3

Type and percentage of research literature referenced in this work.

Fig. 4

Conceptual distinction of single (SMA), double (DMA) and displaced ($\delta$MA) moving averages.

Fig. 5

Flowchart of decision rule generation process by use of rough sets.

Fig. 6

Architecture of the proposed approach.

Fig. 7

Interpretation of the first Covid-19 wave in Australia.

Fig. 8

Timeline graph of first wave of new Covid-19 infection cases in Australia.

Fig. 9

Flowchart of marker detection from $\delta$DMA(14) trend of a Covid-19 wave.

Fig. 10

Computation of upcoming infection count by $\chi$SMA(7) function.

Fig. 11

Timeline graph of new Covid-19 infection cases in Argentina.

Fig. 12

Timeline graph of new Covid-19 infection cases in Colombia.

Fig. 13

Timeline graph of new Covid-19 infection cases in New Zealand.

Fig. 14

Timeline graph of new Covid-19 infection cases in Australia.

Fig. 15

Timeline graph of new Covid-19 infection cases in Cuba.

Fig. 16

Timeline graph of new Covid-19 infection cases in Jamaica.

Fig. 17

Timeline graph of new Covid-19 infection cases in Belgium.

Fig. 18

Timeline graph of new Covid-19 infection cases in Croatia.

Fig. 19

Timeline graph of new Covid-19 infection cases in Libya.

Fig. 20

Timeline graph of new Covid-19 infection cases in Kenya.

Fig. 21

Timeline graph of new Covid-19 infection cases in Iran.

Fig. 22

Timeline graph of new Covid-19 infection cases in Myanmar.