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COVID-19 multi-state epidemic forecast in India
CLINICAL IMPORTANCE: Novel coronavirus disease is spread worldwide with considerable morbidity and mortality and presents an enormous burden on worldwide public health. Due to the non-stationarity and complicated nature of novel coronavirus waves, it is challenging to model such a phenomenon. Few ma...
Autores principales: | , , |
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Formato: | Online Artículo Texto |
Lenguaje: | English |
Publicado: |
Indian National Science Academy
2023
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Materias: | |
Acceso en línea: | https://www.ncbi.nlm.nih.gov/pmc/articles/PMC9910244/ http://dx.doi.org/10.1007/s43538-022-00147-5 |
Sumario: | CLINICAL IMPORTANCE: Novel coronavirus disease is spread worldwide with considerable morbidity and mortality and presents an enormous burden on worldwide public health. Due to the non-stationarity and complicated nature of novel coronavirus waves, it is challenging to model such a phenomenon. Few mathematical models can be used because novel coronavirus data are generally not normally distributed. This paper describes a novel bio-system reliability approach, particularly suitable for multi-regional environmental and health systems, observed over a sufficient period of time, resulting in a reliable long-term forecast of novel coronavirus infection rate. Traditional statistical methods dealing with temporal observations of multi-regional processes do not have the advantage of dealing efficiently with extensive regional dimensionality and cross-correlation between infection rate and mortality. OBJECTIVE: To determine extreme novel coronavirus death rate probability at any time in any region of interest. Traditional statistical methods dealing with temporal observations of multi-regional processes do not have the advantage of dealing efficiently with extensive regional dimensionality and cross-correlation between different regional observations. DESIGN: Apply modern novel statistical methods directly to raw clinical data. SETTING: Multicenter, population-based, medical survey data based bio statistical approach. MAIN OUTCOME AND MEASURE: Due to the non-stationarity and complicated nature of novel coronavirus, it is challenging to model such a phenomenon. Few mathematical models can be used because novel coronavirus data are generally not normally distributed. This paper describes a novel bio-system reliability approach, particularly suitable for multi-country environmental and health systems, observed over a sufficient period of time, resulting in a reliable long-term forecast of extreme novel coronavirus death rate probability. CONCLUSIONS AND RELEVANCE: The suggested methodology can be used in various public health applications, based on their clinical survey data. |
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