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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 probability theory and 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 ...
18.2–33.5 million [6] (estimated) 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] Most scientists believe the SARS-CoV-2 virus entered into human populations through natural zoonosis, similar to the SARS-CoV-1 and MERS ...
- 6,960,770 (reported), 17.6–31.4 million (estimated)
- SARS-CoV-2
- 2–14 days (typically 5), after infection
- Symptomatic and supportive
In probability theory and statistics, the beta distribution is a family of continuous probability distributions defined on the interval [0, 1] or (0, 1) in terms of two positive parameters, denoted by alpha (α) and beta (β), that appear as exponents of the variable and its complement to 1, respectively, and control the shape of the distribution.
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 ...
Example of samples from two populations with the same mean but different variances. The red population has mean 100 and variance 100 (SD=10) while the blue population has mean 100 and variance 2500 (SD=50) where SD stands for Standard Deviation. In probability theory and statistics, variance is the expected value of the squared deviation from the mean of a random variable.
