About: Predicting SARS-CoV-2 Infection Trend Using Technical Analysis Indicators

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An Entity of Type : schema:ScholarlyArticle, within Data Space : wasabi.inria.fr associated with source document(s)

Attributes	Values
type	Academic Article research paper schema:ScholarlyArticle
isDefinedBy	Covid-on-the-Web dataset
has title	Predicting SARS-CoV-2 Infection Trend Using Technical Analysis Indicators
Creator	Paroli, Marino Sirinian, Maria
Source	Medline; PMC
abstract	OBJECTIVES: Coronavirus disease 2019 (COVID-19) pandemic is a global health emergency caused by severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2). This study aimed to evaluate whether technical analysis (TA) indicators, commonly used in the financial market to spot security price trend reversals, might be proficiently used also to anticipate a possible increase of SARS-Cov-2 spread. METHODS: Analysis was performed on datasets from Italy, Iran, and Brazil. TA indicators tested were: (1) the combined use of a faster (3-d) and a slower (20-d) simple moving averages (SMA), (2) the moving average converge/divergence (MACD), and (3) the divergence in the direction of the number of new daily cases trend and the corresponding MACD histogram. RESULTS: We found that the use of both fast/slow SMAs and MACD provided a reliable signal of trend inversion of SARS-Cov-2 spread. Results were consistent for all the 3 countries considered. The trend reversals signaled by the indicators were always followed by a sustained trend persistence until a new signal of reversal appeared. CONCLUSIONS: TA indicators tested here proved to be reliable tools to identify in the short mid-term a subsequent change of direction of viral spread trend either downward, upward, or sideward.
has issue date	2020-07-17(xsd:dateTime)
bibo:doi	10.1017/dmp.2020.254
bibo:pmid	32674742
has license	cc-by
sha1sum (hex)	2e2c66b5a905f8e3815e4a5d299023a416fe75ca
schema:url	https://doi.org/10.1017/dmp.2020.254
resource representing a document's title	Predicting SARS-CoV-2 Infection Trend Using Technical Analysis Indicators
has PubMed Central identifier	PMC7411445
has PubMed identifier	32674742
schema:publication	Disaster medicine and public health preparedness
resource representing a document's body	covid:2e2c66b5a905f8e3815e4a5d299023a416fe75ca#body_text
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