COVI-19 ANALYSIS (Research Paper Sample)

COVID-19 Data Analysis in Thailand A 2020 – 2021 COVID-19 Statistical Analysis Student’s Name Institution Affiliation Course Name Professor’s name Date: 28/ 09/ 20224 ABSTRACT The topic of this study stems from my experience living during the COVID-19 epidemic in Thailand and witnessing how various public health measures, such as lockdowns, affected its spread. I've always been interested in how mathematical models may forecast real-world occurrences, particularly during times of crisis, such as the epidemic. But this is not it. The underlying reason is that I lost my cousin to COVID. Because of his carelessness with the virus's propagation, he unintentionally coughed it up initially, making me even more motivated to research its spread. After modifying the SEIR model's parameters (such as changing infection rates during lockdowns), forecasts on the virus's spread and comparisons with various predictions with actual data from Thailand were made. To determine how interventions (the "predators") impact the size of the infected population (the "prey"), investigations on predator-prey dynamics were calculated, and the results were graphed in this study. In the analysis, various points of view were taken into account, such as examining how regional variations within Thailand or different reactions from the government might have affected the results. Table of Contents TOC \o "1-3" \h \z \u ABSTRACT PAGEREF _Toc178331511 \h 2Introduction PAGEREF _Toc178331512 \h 4Aims and Objectives PAGEREF _Toc178331513 \h 5Criterion A: Presentation PAGEREF _Toc178331514 \h 5Criterion B: Mathematical Communication PAGEREF _Toc178331515 \h 6Criterion C: Personal Engagement PAGEREF _Toc178331516 \h 7Data Collected PAGEREF _Toc178331517 \h 7Findings PAGEREF _Toc178331518 \h 9Criterion D: Reflection PAGEREF _Toc178331519 \h 11Discussion PAGEREF _Toc178331520 \h 12Criterion E: Use of Mathematics PAGEREF _Toc178331521 \h 12References PAGEREF _Toc178331522 \h 14 Table of Figures TOC \h \z \c "Figure" Figure 1: Covid-19 results in Thailand PAGEREF _Toc178331733 \h 8 Figure 2: Active cases in Thailand PAGEREF _Toc178331734 \h 8 Figure 3: A graph on the surge of COVID-19 in Thailand PAGEREF _Toc178331735 \h 9 Table of Tables TOC \h \z \c "Table" Table 1: Table for Thailand COVID-19 results in PAGEREF _Toc178331934 \h 7 Introduction Thailand was affected by the COVID-19 epidemic. The Thai government was able to keep the number of COVID-19 infections low until September 2020, thanks to an early shutdown and an excellent contact tracing effort. While the government's initiatives halted the spread of the epidemic in Thailand, they resulted in the loss of employment, revenue, enterprises, food security for families, and children's education. Unfortunately, succeeding waves and developing new varieties have provided a significant economic burden to the country, with the number of COVID-19 cases surging to more than 2,000 per day in May 2021, excellent. The COVID-19 pandemic in Thailand is a part of the worldwide coronavirus disease 2019 (COVID-19) pandemic caused by severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2). Thailand was the first country to report a case outside China on January 13, 2020. As of April 2, 2022, the country has reported a cumulative total of 3,684,755 confirmed cases, with 25,318 deaths from the disease, and currently ranked fourth in the number of cases in Southeast Asia, behind Vietnam, Indonesia, and Malaysia, aiding in further stringent containment measures. This paper evaluates and analyzes data from the COVID-19 crisis. The COVID-19 pandemic was a calamity that saw several lives diminished. The aftermath depicted a result of demise, depression, and despair in the affected populations. A SEIR (Susceptible, Exposed, Infected, and Recovered Populations) model is addressed in this paper. The geographical location of the data and the place of interest is Thailand. In this case, other data is also incorporated for predator-prey dynamics where the predator is the virus, and the prey is the population. Aims and Objectives This paper aims to probe into the causes, influences, and effects of COVID-19 on different populations around the globe. Thailand is the location of interest in this particular study, but global data will be utilized to ascertain the accuracy of the number of deaths and effects of the COVID-19 epidemic. Criterion A: Presentation The epidemic analysis is strict about the number of deaths caused and the number of families affected. The SEIR model was chosen because of its viability in showing the effect of the pandemic altogether. The model addresses the following parameters, which are specific to Thailand: * Transmission rates – this variable entails the number of cases reported within the government of interest and shows the transmission rate that affects the latter statement. * Exposure time – The exposure time variable shows the probability of the populations under coverage getting the virus. * Recovery rates – This variable shows the data specific to those who got free after the COVID-19 virus election. It depicts the results of the efforts to treat the virus by medication or vaccination. The analysis requires detailed analysis to evaluate the three parameters/variables effectively. Criterion B: Mathematical Communication The World Bank funded a rapid phone survey conducted by Gallup Poll between April 27 and June 15, 2021, to monitor the social and economic effects of the COVID-19 pandemic in Thailand. The survey included interviews with approximately 2,000 adults aged 18 and up. The survey instrument addressed employment, household income sources, food and food security access, social protection and coping mechanisms, education, health services, and COVID-19 vaccines. There are three sections to the questionnaire. * Part 1: demographic data, such as the province, age, gender, occupation, income, level of education, religion, and government compensation. * Part 2: A 4-point rating scale comprising 48 items with responses of none, low, moderate, and high; the questionnaire examines the effects of the COVID-19 pandemic (the consequences of the spread of SARS-CoV-2) and policy measures (the consequences of the actions taken by governments, the ministry of public health, and other organizations to mitigate the spread of COVID-19). * Part 3: The depression anxiety stress questionnaire asks the respondents whether they have experienced any symptoms during the past week. This survey comprised 21 items total, with a 4-point rating system from 0 (did not apply to me at all) to 3 (applied to me very much or most of the time). Its purpose was to gauge the intensity of a variety of symptoms that are typical of stress, anxiety, and depression. Five response levels were distinguished: normal, mild, moderate, severe, and highly severe (Raude J. (2020)). Criterion C: Personal Engagement Data Collected Using stratified sampling, a cross-sectional study was conducted among 2,500 residents of five Thai provinces between October 2020 and January 2021. The provinces of Bangkok, Chonburi, Chiang Mai, Nakhon Ratchasima, and Yala saw a discernible increase in COVID-19 cases during the study period, so those residents were chosen as study participants. Participants had to be at least eighteen years old, proficient in Thai, and willing to participate in the study. Those who couldn't provide information for this study or were seriously ill during the study period were excluded. The following table shows the data on Thai deaths and recoveries from COVID-19 cases. Table SEQ Table \* ARABIC 1: Table for Thailand COVID-19 results Figure SEQ Figure \* ARABIC 1: Covid-19 results in Thailand Figure SEQ Figure \* ARABIC 2: Active cases in Thailand Figure SEQ Figure \* ARABIC 3: A graph on the surge of COVID-19 in Thailand The results show a surge. According to Chulalongkorn University political science professor Thitian Pongsudhirak, "this has exposed the worst side of Thailand: an intersection of corruption, government incompetence, and a lack of vision going forward at the expense of public health." Thailand's COVID-19 caseload is vanishingly tiny compared to other nations of similar size because of the country's medical authorities and the populace's disciplined reaction. In the Thai context, however, the figures are concerning: since the epidemic started, the cumulative cases have more than quadrupled to roughly 11,000 during the past month, with 67 fatalities. This is after local transmissions were brought to zero in mid-2020. Findings From March 2020 to June 2021, national employment was constant at 68%. However, significant differences were detected among locations and demographic groupings. Employment fell by eight percentage points in urban regions and the capital city but grew by eight percentage points in rural areas and the northern region, as many people who lost their employment owing to the epidemic returned to agriculture. Overall, more than half of respondents were affected by job losses, temporary work stops, decreased working hours, or lower pay. Between March 2020 and the present, over 70% of the households surveyed reported a decrease in their income; of these, about 80% belonged to low-income groups, rural areas, and the southern region. Because over half of non-farm businesses and farming activities saw a significant reduction in their income, these sectors were also severely impacted by income declines. The households that suffered the most from income losses were those in low-income groups and those in the southern region. Any household up to 60% of low-income and child-rearing households reported running out of food. Throughout the crisis, households used a variety of coping strategies. The most popular ones were reducing food and non-food consumption, relying on government assistance, saving money, and pursuing new sources...