1_SnLaBtj20gduA5C54JCP0A[1].jpeg

With over 6 million confirmed cases worldwide, the global COVID-19 pandemic has had a tremendous impact on our day-to-day lives. The main reason for this is that — in order to minimize the spreading of the virus — governments from all over the world had to implement drastic measures. These include shutting down schools and shops, canceling large public events, and even going in a full-blown lockdown at some stage during this crisis.

Whereas these measures have helped to reduce the number of casualties tremendously, they come at a certain cost. Undoubtedly, shutting down an entire economy has significant consequences for all businesses that are operating within that economy. Most likely, this will result in a big wave of bankruptcies during the upcoming period.

Therefore, finding the sweet spot between reducing the number of casualties while trying to spare the economy has been a constant battle during negotiations. But how do experts know which measures are appropriate and which ones are not? How does one predict the outcome of a certain action, when a pandemic — on this scale — has never happened in history before? The answer lies in using predictive analytics and scenario planning with the help of powerful mathematical tools like Machine Learning and Artificial Intelligence. In what follows, we will discuss how scenario planning can reduce the amount of uncertainty during times of global pandemics.

What is Scenario Planning?

Planning — in general — has been of fundamental importance in the history of human beings. It allows us to gauge the probability of the occurrence of future events, and to take appropriate measures when needed. In short, scenario planning is a technique where people, organizations, or governments combine historical data with future certainties in order to estimate the probability of different scenarios and to make long-term plans accordingly. Whereas we have relied on human expertise for scenario planning during most of our history, human beings are in fact fundamentally bad in making such predictions due to the limited computing and memory capacity of our brains.

However, thanks to the ever-increasing computing power and the widespread availability of qualitative datasets, powerful techniques like artificial intelligence and machine learning have proven to be much more effective for solving complex problems like this. Machine learning — widely accepted to be a subpart of Artificial Intelligence — is a technique in which mathematical algorithms are used to detect patterns within large amounts of data. The patterns detected by the algorithm can then be used to make predictions for future observations, which closely aligns to what is being done during scenario planning.

Scenario planning during COVID-19

The global outbreak of the corona virus has proven to be a school example for situations in which scenario planning is both feasible and of utmost importance. The reason for this is that — from the beginning of the outbreak — global powers have been very keen in sharing information regarding new cases and deaths across international borders. A prime example of the vast amount of data that has been collected about the corona virus is shown in the John Hopkins COVID-19 dashboard. This dashboard provides governments and companies with a very granular overview regarding the latest COVID related numbers and statistics. In what follows, two cases will be discussed where this vast amount of data can be used in combination with scenario planning in order to reduce uncertainty for governments and businesses.

1. Detecting COVID-19 spreading and new outbreaks

All over the world, mathematicians and statisticians have bundled their powers to create strong and reliable forecasting methods to predict the spreading behavior of the corona virus. Initially, data on the virus spreading behavior was sparse, requiring scientists to make lots of assumptions regarding its transmissibility, incubation period, and survival rate. However, as data became more abundant and medical research progressed, assumptions in predictive models made way for accurate parameter approximations which improved model accuracies dramatically.

The result of this process is a predictive model with tweakable parameters that allows to investigate different scenarios of virus spreading. Parameters like population density, number of daily/weekly contacts, and several governments implemented measures can be used to model the spreading of the corona virus for individual countries or even cities. Moreover, predictive models like this provide an indication about the effect of a change in one of these parameters. This will become increasingly more important as more and more countries are entering the final phase of the COVID-19 epidemic. This means that countries from all over the world are lifting their full-blown lock-downs by implementing a gradual, phase-by-phase approach. By relying on the scenarios provided by predictive models, governments are able to indicate the effects of different phase out strategies, thereby helping them to select the most appropriate measures to lift or implement.

2. Scenario planning for business operations

The results of virus spreading, outbreak behavior, and the resulting measures taken by governments has a tremendous impact on business operations as well. For many industries, the past couple of months have been characterized by uncertainty regarding operational continuity and employee safety.

Whereas currently, most businesses are able to restart their operations like before, there is still a lot of economic uncertainty due to the high number of companies going out of business and the chances of going through a second outbreak. Like governments, businesses are able to utilize complex forecasting models for scenario planning in order to reduce the amount of uncertainty. For example, scenario planning allows businesses to prepare for a second lockdown by making strategic lay-offs in advance; or to prepare for a reopening of the economy by ramping up production and stocking up supplies.

Conclusion

Scenario planning — and thereby accounting for unexpected future events — has been one of the key reasons that caused human beings to thrive during the past centuries. Whereas scenario planning has been widely adopted within the field of business and economics, advances in the field of mathematical modeling have allowed the technology to be used for solving much more complex problems as well. The COVID-19 pandemic turned out to be an ideal case for testing out the use of such complex scenario planning methodologies on a worldwide scale — and they turn out to work. Countries that have adopted scenario planning algorithms for selecting appropriate measures turn out to have far fewer cases in comparison to countries that were reluctant in changing public behavior. Cases like this — once again — prove the power of scenario planning and reducing the amount of uncertainty in our day to day lives.