COVID-19's impact on the global economy. Mathematics & Economics.

IMPACTS OF COVID-19 ON GLOBAL ECONOMY
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Abstract
Coronavirus (COVID-19) is an infectious respiratory disease that plagued the world. The condition started as normal flu and spread to become a worldwide pandemic. Many people died from the disease, whose symptoms include difficulty in breathing and high body temperatures. Further, the pandemic posed a significant risk to people’s health, leading to business closings and total lockdowns in certain countries. COVID-19 deaths have increased at an alarming rate, with a recorded loss of more than 1.3 million lives worldwide. The paper examines and investigates how the global economy has been affected by the pandemic. The exercise will include factors contributing largely to the economy and how they have been affected. These factors include job losses, the increased unemployment rate, and the seasonal fluctuation of financial markets. Further, we will look at commodity prices, stock market indices, or the financial markets' performance together with the daily COVID-19 cases that will be used to forecast the state of the global economy.
IMPACTS OF COVID-19 ON GLOBAL ECONOMY
Introduction
The World Health Organization (WHO) describes a pandemic as “an epidemic occurring worldwide or over a very wide area, crossing international boundaries and usually affects a large number of people." The COVID-19 epidemic has affected many countries, especially America, Europe, and Asian-Pacific countries. Africa is the least affected by the pandemic based on current statistics. The outbreak has stressed humanity, and its symptoms and effects on the human body are not fully known. WHO advised people to observe social distance and wear a mask in public to minimize risks of infection. On the other hand, governments induced forceful lockdown and enforcement of social distancing to protect its citizens. The post-COVID-19 era will determine the global economy's stability if managed well as fiscal dues expected to rise compared to past years.
In the year 2003, Severe Acute Respiratory Syndrome (SARS) broke out. According to WHO, 8098 cases were reported worldwide, with 774 deaths. The organization took drastic measures to stop the spread, which is closely compared to COVID-19. The two diseases, SARS and COVID-19, have a common genome and characteristics. Although SARS lasted for less than a year, the financial markets were affected, and the business recorded losses. The Spanish flu impacts are also similar because of the devastating effects on the world economy, leaving more than 50 million people dead. The period between 1918 and 1919 horrible because more than 50 million people was affected, many succumbing to death. Across all world nations, schools were closed, businesses shut, and millions lost their jobs. Huge business losses were recorded in both domestic and international markets. This Spanish flue case is similar to COVID-19 impacts.
There will be a significant impact on the economy across the world, as many businesses deal with the ravages of COVID-19 in the market. Pandemics always influence financial markets. In a scenario such as a case between mid-February and April, financial markets decreased sharply. This was after a twelve-year gap where the decline was experienced in 2008. Between the early months of this year and the mid-year, investors from local and international markets sold off their shares due to fear of a collapsing market.
COVID-19 has created an unprecedented wave-based on the fact that the vaccine is yet to be discovered. Each crisis leaves significantly different challenges on the supply and demand. These demands resulted in a downward curve of gross domestic products (GDP) at either the national or international levels. Forecasting the market is challenging through the process of financial analysis due to periodic variations of prices. Applying simulation and risk analysis in this paper will help examine how well the models and data can be evaluated to develop better solutions and better policies to reduce these losses.
The bigger problem experienced is the risk of a second wave indicating that those who had earlier recovered can also get the disease. This has left many countries worrying whether to reopen the economy or continue shutting it down with the fear of experiencing huge losses previously recorded. This scenario's case fatality rate will be obtained from the post and pre-cases of the COVID-19 cases in different significant countries. Therefore, the paper looks at stock exchanges and financial markets' performance since the pandemic stroke the global market. We will also look at the financial stimulus for different countries and compare them across the globe.
Data Sources
The COVID-19 dataset was downloaded from the European Centre for Disease Prevention and Control (ECDC), and the stock exchange data was sourced from Lionbridge. The paper also used publicly available data sets from Yahoo.
Methods and Results
The research is based on the secondary data collected to investigate the impacts of COVID-19 on the economy. We will use time series to model the effects of COVID-19 on the global economy over time. The time will be in months, analyzing a series of events that have happened to the economy since the pandemic started. Risk analysis will be conducted to see how different projects have been mitigated and impacted by the covid19 crisis globally. In this paper, we will use COVID-19 as the risk factor or the risk event while undertaking risk analysis. Since we will have known our risk event, we will analyze both the qualitative and quantitative impacts posed by the risk event. We start by measuring uncertainties such as financial markets and trade fiscal policies to estimate the effects of uncertainties. This will, therefore, lead us to create a risk model to be evaluated.
On the matter of time series analysis, a moving average method will be used to cater to the time series periods using the lags as the months preceding the current month using quarterly data. The data are daily weighted market returns from early February to late October. In order to smooth the data from noise, as stated above, a method of moving average will be incorporated to produce a valid time series model. The seasonally adjusted monthly unemployment increased sharply from the data, indicating that people lost their jobs unprecedently. The 20.98% of people losing their jobs due to the closure of businesses globally led the nation’s government to come up with financial stimulus such as caring for the job lost by providing less stressful meas...