Q 248. We have been doing forecasting for Coronavirus global new cases for several days on Linkedin and Forum -   https://www.benchmarksixsigma.com/forum/topic/36002-corona-forecasts-and-status/

 

Try to describe the model (equation) behind the forecasts for 26th and 27th March. The most accurate answer wins. 

 

 

Note for website visitors - Two questions are asked every week on this platform. One on Tuesday and the other on Friday.

• All questions so far can be seen here - https://www.benchmarksixsigma.com/forum/lean-six-sigma-business-excellence-questions/
• Please visit the forum home page at https://www.benchmarksixsigma.com/forum/ to respond to the latest question open till the next Tuesday/ Friday evening 5 PM as per Indian Standard Time. Questions launched on Tuesdaya are open till Friday and questions launched on Friday are open till Tuesday. 
• The best answer is always shown at the top among responses and the author finds honorable mention in our Business Excellence dictionary at https://www.benchmarksixsigma.com/forum/business-excellence-dictionary-glossary/ along with the related term

Excellent way of applying the teachings with the current affairs - using Time Series and Forecasting to forecast the number of new cases for Coronavirus.

 

Basis my research (and I am sure by now everyone knows), that pandemics follow an exponential growth. So, when governments say they want to flatten the curve, they basically mean that the exponential growth should be controlled.

 

Exponential growth happens when base grows not be addition or multiplication but by powers. A to the power of B is an example of exponential growth. E.g. let's assume 1.01 to the power of 2

Day 1 => 1.01 to power of 2 = 1.0201

Day 2 => 1.0201 to power of 2 = 1.04

Day 3 => 1.04 to power of 2 = 1.08

...

...

...

Day 10 => value becomes 26612.57

 

Initially the growth is relatively smaller, but as the time passes, the exponential growth results in very high numbers.

 

Regarding the forecasts for Coronavirus, I picked up the actuals data that was published. Picked up the data from 15th Mar as you are using that as the base value (as below)

After running the trend analysis in Minitab for Exponential Growth and using the same for forecasting, below are the results.

Growth Model for 26th March forecast

Using the above growth model, the forecasted value for 26th March = 60064

 

Doing the same analysis for 27th March, but this time added the actual figure for 26th March.

Growth model for 27th March

Using the above growth model, the forecasted value for 27th March = 69336

 

 

Corona virus infection pandemic is right now creating havoc in all parts of the world, with  infection that originated from China has travelled to 199 countries with a significant surge in the number of case in European countries like Italy, Spain, Germany, France and in a time frame of  March 20 th- March 30 th 2020, there has been a marked increase in the number of cases in the United States, with number of cases in USA, going beyond 150,000.

 

The growth pattern is presumed to be exponential type, with number of infected individuals increasing in proportion to the total number of present cases. 

This pattern can be exemplified by a task assigned to a person, the person tagging the task to three other persons. All the three persons assigning the tasks to three other persons and so on the pattern continues, so the number of involved persons rising from 1 to 3 to 27 to 81 to 243, so there is a rapid and exponential increase in the number of persons involved.

 

Exponential growth of  viral infections has led to predictions of new cases  day by day, which may fall short of(26 th March 2020) , nearly equal to or may surpass (27 th March 2020) the number of actual cases that are being observed in the data of worldometers.info.  The increase in the number of cases  than predicted  can be attributed to be due to many factors-  due to multiple modes of transmission of virus, including airborne route reported in some studies, relapse of cured  cases with re-infection by new mutant stains of virus. The decrease in number of cases, compared to predicted number of cases and flattening of curve  can be attributed to-   proactive measures of social distancing adapted actively by different countries, poor testing facilities of virus in different regions. The number of deaths  arising out of these cases is much difficult to predict, as it depends on vulnerability of the population, demographic data and  efficacy of healthcare system.

The exponential prediction does not take into account the complexity of pandemic and multiple  interactions of the variables and also does not account for the flattening of the curve due to social distancing. 

The equation used for exponential prediction is-

 

x(t) = x₀ × (1 + r) ^t

 where x(t) is the value at  a particular time t

x0 is the base value at time t=0.

r is the presumed  growth rate 

t is the time interval between time 0 and day of prediction.

 

 

• 
   

 

 

Imagesource:https://www.google.com/urlsa=i&url=https%3A%2F%2Fwww.vox.com%2F2020%2F3%2F15%2F21180342%2Fcoronavirus-covid-19-us-social-distancing&psig=AOvVaw39XxyUJPlQ32Vjm7sdLYLs&ust=1585689691869000&source=images&cd=vfe&ved=0CAIQjRxqFwoTCMjumc6Qw-gCFQAAAAAdAAAAABBA

 

 

Natwarlal has provided the correct logic and near accurate answer. He is the winner for today.