## 5 Case Study and Experiments  
We designed two sets of experiments to evaluate (i) the effectiveness of the event monitoring method based on our previously developed event detection technique and (ii) the validity of the smart contract we developed for adaptive supply chain management by implementing a case study and the system simulations.  
Experimented with Event Related Index and Indictor for Event Monitoring of a Real World Case (COVID-19 Epidemic Outbreaks)  
In this work, we experimented with a vast amount of Twitter data to identify the validity of the developed social-sentiment method and the approach of smart contracts. We conducted an empirical study on a real-world case (i.e., COVID-19 epidemic outbreaked events) and used the events detected by our previously developed algorithms for experiments. With our developed online event detection algorithm, over 10 years of Twitter messages and detected events were collected as an experimental corpus [62; 63]. The event monitoring technique developed in this work is for detecting and understanding predicted changes in order to obtain meaningful signals of uncertainty and changes in reality, which is essentially required for adopting appropriate actions in the engineering of blockchain and smart contracts to avoid possible loss caused by supply chain disruption.  
Case Study: \"COVID-19 Spread on the Global Supply Chain on 2020\" Event  
Twitter offers a Streaming API that allows for the collection of publicly available tweets, and this technique was used for us to retrieve Twitter data. Along with our online event detection algorithm, we continuously collected new data through Twitter StreamAPI for carrying out the event detection task. For this case study, we collected COVID-19-related events detected by the developed Twitter-based event detection system.  
For reference, here are some relevant ground facts reported by the news, summarized as follows:  
* Jan 25: Production stop at suppliers in China;
* Feb 11: Port operations stop in China;
* Feb 25: Shortage in distribution centers worldwide;
* March 11: Re-start production in China;
* March 13: Extended quarantine measures in Europe and the USA.  
For continuous analysis, we utilized the timeline of critical events of coronavirus dispersal, which were found from the resulting detected events by our method starting from mid of February 2020 till April 2020. Samples of resulting COVID-19-related events detected by the developed Twitter-based event detection system are displayed on the world map, as shown in Figure 6. As the event situation of COVID-19 outbreaks develops and different countries/regions react differently to the events, there will be differences between what is being modeled and what is happening. The exponential growth at the beginning of the pandemic is quickly separated from the actual situation, which is why continuous monitoring is needed. Uncertainty in the development of events related to COVID-19 may mislead experiments on prediction accuracy. Therefore, during this epidemic, none of us can accurately predict the outcome of this pandemic. In this case, we have no idea what will happen, but using our event monitoring methods, we have a better chance of predicting it correctly than in the past. In this experiment, we demonstrated that the effectiveness of the event monitoring of COVID-19 outbreaks based on our developed event detection technique for event awareness.  
Nevertheless, we still need the function of smart contracts to automatically execute the designed supply chain actions after the emergency signal is detected. The smart contracts will only be executed according to what is written in the contract and cannot be tampered with. The implementation of smart contracts will be described later.