Factors affecting COVID-19 cases before epidemic peaks
Objective: The COVID-19 pandemic has disrupted people's normal life as a result of strict policies applied to slow down the pandemic. To find out how extensive the virus spread is, most countries increase their daily testing rates. Method: This simple modelling work uses stringency index and da...
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| Main Authors: | , , |
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| Format: | Book |
| Published: |
Elsevier,
2021-01-01T00:00:00Z.
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| Online Access: | Connect to this object online. |
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| Summary: | Objective: The COVID-19 pandemic has disrupted people's normal life as a result of strict policies applied to slow down the pandemic. To find out how extensive the virus spread is, most countries increase their daily testing rates. Method: This simple modelling work uses stringency index and daily testing (including the lagged version up to the previous 14 days) to predict daily COVID-19 cases in India and Indonesia. A Stepwise Multiple Regression (SWMR) subroutine is used in this modelling to select factors based on a 0.01 significant level affecting daily COVID-19 cases before the epidemic peaks. Result: The models have high predictability close to 94% (Indonesia) and 99% (India). Increasing number of daily COVID-19 cases in Indonesia is associated with the country's increased testing capacity. On the other hand, stringency indices play more important role in determining India's daily COVID-19 cases. Cloclusion: Our finding shows that one question remains to be answered as to why testing and strict policy differ in determining daily cases in both Asian countries. |
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| Item Description: | 0213-9111 10.1016/j.gaceta.2021.10.007 |