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The COVID-19 pandemic, also known as the coronavirus pandemic, is a global pandemic of coronavirus disease 2019 (COVID-19) caused by severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2). The novel virus was first identified in an outbreak in Wuhan, the capital of Hubei, China, in December 2019, before it spread to other areas of Asia, and then worldwide in early 2020.
- 6,956,160 (reported), 17.5–31.4 million (estimated)
- Worldwide
- Coronavirus disease 2019 (COVID-19)
- Severe acute respiratory syndromecoronavirus 2 (SARS‑CoV‑2)
The timeline of the COVID-19 pandemic lists the articles containing the chronology and epidemiology of SARS-CoV-2, [1] the virus that causes the coronavirus disease 2019 ( COVID-19) and is responsible for the COVID-19 pandemic . The first human cases of COVID-19 occurred in Wuhan, People's Republic of China, on or about 16 November 2019. [2]
In statistics, a normal distribution or Gaussian distribution is a type of continuous probability distribution for a real-valued random variable. The general form of its probability density function is. The parameter is the mean or expectation of the distribution (and also its median and mode ), while the parameter is its standard deviation.
Coronavirus disease 2019 (COVID-19) is a contagious disease caused by the coronavirus SARS-CoV-2. The first known case was identified in Wuhan, China, in December 2019.[7] The disease quickly spread worldwide, resulting in the COVID-19 pandemic. The symptoms of COVID‑19 are variable but often include fever,[8] fatigue, cough, breathing ...
The Beta function (in the denominator of the beta distribution) approaches infinity, for both parameters approaching zero, α, β → 0. Therefore, p−1 (1− p) −1 divided by the Beta function approaches a 2-point Bernoulli distribution with equal probability 1/2 at each end, at 0 and 1, and nothing in between, as α, β → 0.
Definitions Generation and parameters Let be a standard normal variable, and let and be two real numbers, with >.Then, the distribution of the random variable = + is called the log-normal distribution with parameters and .These are the expected value (or mean) and standard deviation of the variable's natural logarithm, not the expectation and standard deviation of itself.
For instance, consider a call center which receives, randomly, an average of λ = 3 calls per minute at all times of day. If the calls are independent, receiving one does not change the probability of when the next one will arrive. Under these assumptions, the number k of calls received during any minute has a Poisson probability distribution ...
