Epidemic Analysis and Prediction of COVID-19 Using a Gaussian Doubling Times (Preprint)
Authors: Albahli Saleh 
Published: August 2020

BACKGROUND

Covid-19 has been turned into a pandemic, with its extensive spreading chains in many countries and in few countries with large spreading chains resulting in boost spreading in countries which includes, Italy, South Korea, Italy and Japan. We have shown that most of countries have likely the spread of covid-19 in early stages, before any necessary steps being taken up by governments of different countries. We make reliable estimates on key epidemic parameters and make predictions on the point of inflection and possible washout time from countries around the world. The estimates and predictions are based on the data gathered from John Hopkins Center and observed after April 21st and up to 27th June 2020. We use the normal distribution for simple and quick predictions of the coronavirus epidemic model and estimate the parameters of Gaussian curves using least square parameter curve fitting for various countries of different continents. The predictions rely on the possible outcomes of Gaussian time evolution with central lime theorem of statistics that well justified the predictions. The duration of this Gaussian distribution i.e. maximum time and width is determined through a statistical χ2 -fit for the purpose of doubling times after April 21,2020.

OBJECTIVE

Covid-19 has been turned into a pandemic, with its extensive spreading chains in many countries and in few countries with large spreading chains resulting in boost spreading in countries which includes, Italy, South Korea, Italy and Japan. We have shown that most of countries have likely the spread of covid-19 in early stages, before any necessary steps being taken up by governments of different countries. We make reliable estimates on key epidemic parameters and make predictions on the point of inflection and possible washout time from countries around the world. The estimates and predictions are based on the data gathered from John Hopkins Center and observed after April 21st and up to 27th June 2020. We use the normal distribution for simple and quick predictions of the coronavirus epidemic model and estimate the parameters of Gaussian curves using least square parameter curve fitting for various countries of different continents. The predictions rely on the possible outcomes of Gaussian time evolution with central lime theorem of statistics that well justified the predictions. The duration of this Gaussian distribution i.e. maximum time and width is determined through a statistical χ2 -fit for the purpose of doubling times after April 21,2020.

METHODS

Covid-19 has been turned into a pandemic, with its extensive spreading chains in many countries and in few countries with large spreading chains resulting in boost spreading in countries which includes, Italy, South Korea, Italy and Japan. We have shown that most of countries have likely the spread of covid-19 in early stages, before any necessary steps being taken up by governments of different countries. We make reliable estimates on key epidemic parameters and make predictions on the point of inflection and possible washout time from countries around the world. The estimates and predictions are based on the data gathered from John Hopkins Center and observed after April 21st and up to 27th June 2020. We use the normal distribution for simple and quick predictions of the coronavirus epidemic model and estimate the parameters of Gaussian curves using least square parameter curve fitting for various countries of different continents. The predictions rely on the possible outcomes of Gaussian time evolution with central lime theorem of statistics that well justified the predictions. The duration of this Gaussian distribution i.e. maximum time and width is determined through a statistical χ2 -fit for the purpose of doubling times after April 21,2020.

RESULTS

Covid-19 has been turned into a pandemic, with its extensive spreading chains in many countries and in few countries with large spreading chains resulting in boost spreading in countries which includes, Italy, South Korea, Italy and Japan. We have shown that most of countries have likely the spread of covid-19 in early stages, before any necessary steps being taken up by governments of different countries. We make reliable estimates on key epidemic parameters and make predictions on the point of inflection and possible washout time from countries around the world. The estimates and predictions are based on the data gathered from John Hopkins Center and observed after April 21st and up to 27th June 2020. We use the normal distribution for simple and quick predictions of the coronavirus epidemic model and estimate the parameters of Gaussian curves using least square parameter curve fitting for various countries of different continents. The predictions rely on the possible outcomes of Gaussian time evolution with central lime theorem of statistics that well justified the predictions. The duration of this Gaussian distribution i.e. maximum time and width is determined through a statistical χ2 -fit for the purpose of doubling times after April 21,2020.

CONCLUSIONS

Covid-19 has been turned into a pandemic, with its extensive spreading chains in many countries and in few countries with large spreading chains resulting in boost spreading in countries which includes, Italy, South Korea, Italy and Japan. We have shown that most of countries have likely the spread of covid-19 in early stages, before any necessary steps being taken up by governments of different countries. We make reliable estimates on key epidemic parameters and make predictions on the point of inflection and possible washout time from countries around the world. The estimates and predictions are based on the data gathered from John Hopkins Center and observed after April 21st and up to 27th June 2020. We use the normal distribution for simple and quick predictions of the coronavirus epidemic model and estimate the parameters of Gaussian curves using least square parameter curve fitting for various countries of different continents. The predictions rely on the possible outcomes of Gaussian time evolution with central lime theorem of statistics that well justified the predictions. The duration of this Gaussian distribution i.e. maximum time and width is determined through a statistical χ2 -fit for the purpose of doubling times after April 21,2020.