內容簡介
這個外掛可做什麼？
使用這個外掛，你可以模擬一個大流行的結果，並使用自己的輸入參數。該外掛會產生圖表和表格，以更好地理解生成的結果。
請繼續模擬中國武漢爆發或大流行的SARS-CoV-2冠狀病毒如何在全球範圍內傳播，使用這個冠狀病毒傳播預測工具。你可以用它來測試自己的方案，輸入病毒的自己的參數，或使用CDC / WHO提供的真實數據，將爆發結果模擬（表格和圖表）添加到自己的個人網站中！
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COVID-19大流行期間的免費資源
嵌入演示疫情進展的表格和圖表：
你可以嵌入表格和圖表，帶有全球或地區大流行病情的進展預測。它也可以使用iframe自動嵌入WHO和Johns Hopkins的儀表板，以實時追蹤爆發的進展情況。目標是實現以下：
COVID-19冠狀病毒-病毒大流行預測工具將自動創建預測表格和圖表。
免費版中包括的功能：
顯示可能出現大流行的建模表和圖形
顯示建模期間易感人群的新感染病例和新死亡病例的圖表
顯示建模期間總感染病例和總死亡病例的圖表
顯示隨時間總人口下降的圖表
顯示隨時間死亡總數的圖表
顯示大流行運行詳細數據的表格（列詳細說明：日期，天數，新感染數，總感染數，新死亡數，總死亡數，活著的人口數量）
你可以設定自己的大流行參數，包括：開始人口，開始日期，初始感染數，免疫和無敵精英在掩體中，感染率，潛伏期，死亡率，死亡併發症，疾病燒盡率
可用Gutenberg區塊
還有更多很酷的功能！
外掛完整版中包括的功能：
嵌入Johns Hopkins研究所的官方疫情追踪儀表板
嵌入WHO的官方疫情追踪儀表板
允許你網站的訪問者輸入自己的數據來模擬大流行病情的結果
大流行總結圖表和表格
過去爆發事件的樣本數據，可在插件中使用（SARS-CoV-2，MERS，SARS，西班牙流感，普通流感）
詳細的文檔，帶有參數解釋
定期添加新更新和功能
高級支援
還有更多很酷的功能！
請檢查完整版的外掛：COVID-19冠狀病毒 – 病毒大流行預測工具WordPress外掛
外掛標籤
開發者團隊
② 後台搜尋「Coronavirus Spread Prediction Tools Free Version」→ 直接安裝（推薦）
原文外掛簡介
What Can You Do With This Plugin?
Using this plugin, you can simulate the outcome of a pandemic, using your own input parameters. The plugin generates charts and tables for better understanding of generated results.
Go ahead and simulate how the SARS-CoV-2 Coronavirus causing the COVID-19 disease (Wuhan China Outbreak or Pandemic) can spread over the entire globe, with this Coronavirus Spreading Prediction Tool. You can use it to test your own scenarios, enter your own parameters for the virus, or use real live data provided by the CDC / WHO to add an outbreak outcome simulation (tables and charts) to your own personal website!
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Free Stuff During The COVID-19 Pandemic
Embed Tables and Charts Showing Pandemic Progression:
You can embed tables and charts with the prediction of the evolution of the spreading of a global or regional pandemic. – It can also use iframes to automatically embed dashboards from WHO and Johns Hopkins, to track in real time the progress of the outbreak. The goal is to achieve the following:
COVID-19 Coronavirus – Viral Pandemic Prediction Tools will automatically create prediction tables and charts.
Features included in the free version:
Show modeling tables and graphs of possible outcomes of a pandemic
Graph for modeling new infected cases and new deaths for the period of the pandemic
Graph for modeling total infected cases and total deaths for the period of the pandemic
Graph for modeling total population decline, over time
Graph for modeling total death count, over time
Table with detailed data on the ongoing of the pandemic (columns detailing: date, day count, new infected count, total infected count, new death count, total death count, population alive count)
You can set your own pandemic parameters, including: starting population, start date, initial infected count, immune and unkillable elite in bunkers, infection rate, incubation period, mortality rate, mortality complicator, disease burnout rate
Gutenberg blocks available
And many more cool features!
Features included in the full version of the plugin:
Embed Official Pandemic Tracking Dashboard from Johns Hopkins Institute
Embed Official Pandemic Tracking Dashboard from WHO
Allow your web site’s visitors to enter their own data to simulate the outcome of pandemics
Pandemic summary charts and tables
Sample past outbreaks data, to use in the plugin (SARS-CoV-2, MERS, SARS, Spanish Flu, Common Flu)
Detailed documentation, with parameter explanations
New updates and features added regularly
Premium support
And many more cool features!
Check the full version of the plugin, here: COVID-19 Coronavirus – Viral Pandemic Prediction Tools WordPress Plugin
Parameter Explanations:
Starting Population – The starting population for the outbreak/pandemic. This can be the population of the globe, your country, city or even university.
Elite in Bunkers and Immune – This is the number of people that will never get sick – because they are naturally immune or they are hiding in bunkers or are fully isolated from the infected population.
Start Date – This is the date when the first patient is infected.
Initial Infections – The number of initial infections that occurred on the start date.
Infection Rate (R0) – The number of additional people that are infected by a single patient that already has the virus (during the incubation period).
Incubation Period (Days) – The average time from becoming infected to showing thee first symptoms.
Mortality Rate (Percentage) – How many people die of the virus as a percentage of those who become infected (an average death rate).
Mortality Complicator (Percentage) – This will increase the ‘Mortality Rate’ where there is a large number of new infected in a short period of time and is based on the increasing likelihood of mortality as more of the health care system becomes overwhelmed (more and more severe and critical cases that need medical help to survive, in a short period of time).
Virus Burnout Rate (Percentage) – This represents a reduction of the ‘Infection Rate’ over time – it represents the increased quarantine measures, progress with discovering a cure or vaccine and fewer healthy hosts to infect.
Documentation:
Please check the documentation of this plugin, here: CodeRevolution’s Documentation Portal.
Some more info on pandemic spreading and epidemiology:
Public health efforts depend heavily on predicting how diseases such as that caused by the 2019 novel coronavirus, now named COVID-19 by the World Health Organization, spread across the globe. During the early days of a new outbreak, when reliable data are still scarce, researchers turn to mathematical models that can predict where people who could be infected are going and how likely they are to bring the disease with them. These computational methods use known statistical equations that calculate the probability of individuals transmitting the illness.
How fast a disease spreads is determined by calculating a reproduction number, or R0. The WHO estimates the coronavirus R0 at somewhere between 1.4 and 2.5. WHO also reports that 12-21 percent of the people with the virus became critically ill, and 2-3 percent of those infected have died.
The R0 refers to the average number of people a sick person will infect. So, if a virus has an R0 of 2, patient zero will likely infect two people, and then those two people will each infect two more people. The cycle repeats. If an RO is greater than 1, the infection will likely continue to spread. If it is below 1, it is unlikely to spread further.
Footnotes:
This plugin was made by Szabi from CodeRevolution. He sells his premium plugins and scripts on Envato Market. Be sure to check his work @CodeRevolution. Be sure to check his blog and YouTube channel for updates and news.
Plugin Requirements
PHP 5.2 or higher
