4. Discussion
This research is one of the first of its kind to integrate exponential growth modelling with machine learning techniques. COVID-19 pandemic which started in Wuhan, China is spreading at a rapid rate across the world and understanding early signs of containment of the disease through proper policies, interventions, and behavioural changes are very important moving forward. The research presents machine learning models based on variables centred towards infrastructure, environment, policies, and the infection itself to predict if there are any early signs of containment in the country. For the purpose, disease data from 42 leading countries in COVID-19 infections were taken and exponential growth modelling was used to see if the countries showed signs of containment. Then with the sign of the containment of the infection as a dependent variable, supervised machine learning predictive models including logistic regression, decision tree, random forest, and support vector machine were developed. This research can directly of use to countries and policymakers to understand if the interventions they take is effective or not in containing infections. Table 5 shows a step of steps taken by countries as an attempt to contain the COVID-19 infection from spreading.
This research identifies a group of countries that have successfully or showing signs of containing COVID-19 since infection form the exponential growth modelling in stage I of the research. Logistic regression results prove the need for infrastructure and the percentage of lockdown days as significant factors to contain infections. This research also proves that environmental factors like temperature and humidity of the countries do not significantly affect spreading patterns or contain COVID-19 infections. Decision tree analysis also shows that early signs of containment are possible if the number of lockdown days is at least 33.7% of the days since the first contact to contain the infection. If that is not the case, countries show recovery signs if the lockdown is at least 10 days or more. For countries on a lower lockdown period of lesser than 10 days, the number of deaths per million population plays a significant role in containing the infection. This variable is indirectly related to the health care infrastructure of countries like beds, physician, ventilators, ICUs etc. The machine learning models random forest and support vector machines were able to classify the countries with respect to their signs of initial containment with an accuracy of 92.9 and 76.2 percentages respectively proving decision trees to be the best machine learning algorithm in pandemic situations.
While almost all countries practised lockdowns to contain the virus, certain countries have also taken some unique measures to contain the infection. China is one of the very first countries in the world to contain and control COVID-19. China used policy changes in terms of lockdowns, travel restrictions, infrastructure development, and machine learning to properly predict and flatten the infection curve over time and has almost resumed normal lives. Studying the transmission dynamics of the COVID-19 virus in different settings and continuously measuring the ongoing progress and impact lead to the containment (WHO, 2020b). Austria enforced strict rules on social distancing, closure of schools and colleges, the closing of entertainment and grouping places and this has led to showing initial signs of containment. The country passed a special act called COVID-19 Act which has proven effective to contain the infection [62]. The number of hospital beds per 1000 population of Austria was also on the higher side facilitating early recovery. Chile has implemented sanitary barriers and intense screening mechanisms to track and quarantine the infected [63]. Despite the tough quarantine measures, Denmark closed down schools and also announced lockdown in March. Employers were also instructed to not cut down the salaries of the employees on quarantine thereby inducing social distancing and hence containing the infection[64]. Japan, South Korea, and Singapore did not announce any lockdowns. South Korea used processes that led to early detection of the COVID-19 and quarantining the infected making virus spreads impossible. They also predicted the movement of viruses and tactical interventions were taken to minimize spread [65] . Singapore had a ready infrastructure with isolation wards in place during the SARS outbreak and was readily equipped which led to early containment of COVID-19. Strong community engagement messages and communications from the government was also a reason to contain the virus in Singapore [66]. Most other countries that showed early signs of recovery rigorously followed lockdowns, social distancing, travel restrictions, and rigorous testing to contain infections. Another reason for the countries like Japan, Korea and Austria to contain the infection was their availability of health care infrastructure to address the infections.
Table 5. Actions and Policies of Government to Contain COVID-19