Access to special COVID-19 relief from distress grant and livelihood outcome of livestock farming households in Eastern Cape Province, South Africa

Unexpected events and shocks constitute greater threats to the attainment of zero hunger targets in Africa and the world over, and in the extreme case, lead to total collapse of the global food system and food supply chain. Consequently, this causes significant loss of critical income sources, renders individuals vulnerable, and further deteriorates households' livelihood outcome and welfare state. Therefore, the need for social protection programs to mitigate the impact of distress and unexpected events, as well as extreme occurrences cannot be over emphasized. This research used dataset from the 1499 households captured in the 2021 South African General Household Survey to investigate whether access to a special relief from distress grant has effect on the livestock farming households' food security status in Eastern Cape Province of South Africa. Descriptive statistics, cross-tabulation, a two-sample t-test, a food insecurity experience-based scale technique, and a fractional outcome model were used to analyze the datasets. Based on access to the grant, households in the non-beneficiary group are significantly distinguishable from the beneficiary counterparts, such that the beneficiary households out-performed the non-beneficiary households in the food break-even and food surplus categories. The findings further indicated the possibility of transition of the beneficiary households' population under the transitory food insecurity category to either the chronic food insecurity status or food break-even status, subject to the effectiveness of the food security policy to which they are exposed. The fractional outcome model also indicated that non -metropolitan resident households (p < 0.05), access to the special grant (p < 0.01), access to health facilities (p < 0.01), age of households' heads (p < 0.01), colored, indian and white population groups (both at p < 0.01), as well as access to remittance (p < 0.01) made significant contributions to the households' food security status. The Wald test indicated that access to the special relief grant had a significant effect on the households' food security status in the study area. The study therefore recommends accelerated investments in various social investment programs as sustained responses to expected and unexpected shocks and occurrences to be able to induce progress and realize more resilient food systems.

GIS-Based Analysis of the Spatial Distribution and Influencing Factors of Traditional Villages in Hebei Province, China

Traditional villages are a valuable cultural asset that occupy an important position in Chinese traditional culture. This study focuses on 206 traditional villages in Hebei Province and aims to explore their spatial distribution characteristics and influencing factors using ArcGIS spatial analysis. The analysis shows that traditional villages in Hebei Province were distributed in clusters during different historical periods, and eventually formed three core clusters in Shijiazhuang, Zhangjiakou and Xingtai-Handan after different historical periods. Moreover, the overall distribution of traditional villages in Hebei Province is very uneven, with clear regional differences, and most of them are concentrated in the eastern foothills of the Taihang Mountains. To identify the factors influencing traditional villages, natural environmental factors, socio-economic factors, and historical and cultural factors are considered. The study finds that socio-economic and natural environmental factors alternate in the spatial distribution of traditional villages in Hebei Province. The influence of the interaction of these factors increases significantly, and socio-economic factors have a stronger influence on the spatial distribution. Specifically, the spatial distribution of traditional villages in Hebei Province is influenced by natural environmental factors, while socio-economic factors act as drivers of spatial distribution. Historical and cultural factors act as catalysts of spatial distribution, and policy directions are external forces of spatial distribution. Overall, this study provides valuable insights into the spatial distribution characteristics and influencing factors of traditional villages in Hebei Province, which can be used to develop effective strategies for rural revitalisation in China.

Application of MSCs therapy in COVID-19

The year 2019 ended with the official report of an unknown pneumonia outbreak in Wuhan, Hubei Province, China. Subsequently, this novel pneumonia was named COVID-19, which mainly attacks the respiratory system, causing severe damage. Although vaccination has relieved the stress of combating pandemics around the world after one year, there are still unknowns and challenges that come with hope. In this regard, stem cell therapy has been proposed as an effective approach to treating COVID-19. Mesenchymal stem cells (MSCs) can potentially be used as a hopeful tool in the cell-based therapy due to their ability to regenerate and regulate immune response. Although research and clinical results have shown encouraging achievement in patients who were treated with MSCs, drawbacks and challenges still exist in the face of new opportunities. This review aims to introduce the challenges of the COVID-19 vaccine and the possible clinical use of MSC-based therapy. Through analysis of COVID-19 and MSC-based therapy, the author aims to find the possibilities and feasibility of using MSCs to treat acute respiratory diseases, such as COVID. As a result, the author finds that MSC treatment is very practical, and it shows significant potential to treat COVID-19. © 2023 SPIE.

The Effect of Provincial Policies on the COVID-19 Mortality Rate: Risk Analysis Study in Hamadan Province

Background: Governments adopt different policies and strategies to control and reduce the mortality rate of COVID-19. In order to investigate the effect of the adopted policies on the reduction of mortality caused by this disease, the policies implemented by the Regional Headquarter for the Control of COVID-19 Epidemic in Hamedan Province were evaluated. Materials and Methods: The required information was obtained from the Vice-Chancellor of Health of Hamadan University of Medical Sciences and the minutes of the meetings of the Headquarter for the Control of COVID-19 Epidemic in Hamadan Governorate. All the information obtained dates to the period from April to August 2021. A Bayesian network model was used in GeNIe software version 2.2 for the analysis of the information. Results: In this study, seven models were used to evaluate the impact of the adopted strategies. The first model included social distancing, including travel restriction and limiting gatherings, and the mortality rate was estimated to reach 4.72% by implementing the model. The second model includes observing personal hygiene, wearing masks, and vaccination, and the mortality rate was estimated to reach 4.92% by its implementation. The third model encompassed both travel restrictions and business closures, and the mortality rate reached 6.41% after its implementation. Models 4, 5, and 6, which are a combination of the first, second, and third models, have estimated the mortality rate to reach 1.95%, 2.77%, and 2.26%, respectively. In addition, model 7, which combines the above conditions, made the mortality rate reach 2.35%. In the present study, model 6 was selected as the most suitable model with five policies and RMES=0.03005. Conclusion: According to the results obtained in this study, the simultaneous implementation of five policies, including travel restrictions, business closures, personal hygiene, wearing masks and vaccination, can greatly reduce the risk of mortality. © 2022, Bushehr University of Medical Sciences. All rights reserved.

Response of PM2.5 pollution to meteorological and anthropogenic emissions changes during COVID-19 lockdown in Hunan Province based on WRF-Chem model.

In December 2019, the New Crown Pneumonia (the COVID-19) outbroke around the globe, and China imposed a nationwide lockdown starting as early as January 23, 2020. This decision has significantly impacted China's air quality, especially the sharp decrease in PM2.5 (aerodynamic equivalent diameter of particulate matter less than or equal to 2.5 µm) pollution. Hunan Province is located in the central and eastern part of China, with a "horseshoe basin" topography. The reduction rate of PM2.5 concentrations in Hunan province during the COVID-19 (24.8%) was significantly higher than the national average (20.3%). Through the analysis of the changing character and pollution sources of haze pollution events in Hunan Province, more scientific countermeasures can be provided for the government. We use the Weather Research and Forecasting with Chemistry (WRF-Chem, V4.0) model to predict and simulate the PM2.5 concentrations under seven scenarios before the lockdown (2020.1.1-2020.1.22) and during the lockdown (2020.1.23-2020.2.14). Then, the PM2.5 concentrations under different conditions is compared to differentiate the contribution of meteorological conditions and local human activities to PM2.5 pollution. The results indicate the most important cause of PM2.5 pollution reduction is anthropogenic emissions from the residential sector, followed by the industrial sector, while the influence of meteorological factors contribute only 0.5% to PM2.5. The explanation is that emission reductions from the residential sector contribute the most to the reduction of seven primary contaminants. Finally, we trace the source and transport path of the air mass in Hunan Province through the Concentration Weight Trajectory Analysis (CWT). We found that the external input of PM2.5 in Hunan Province is mainly from the air mass transported from the northeast, accounting for 28.6%-30.0%. To improve future air quality, there is an urgent need to burn clean energy, improve the industrial structure, rationalize energy use, and strengthen cross-regional air pollution synergy control.

Machine Learning Techniques on X-ray Images for Covid-19 Classification

The Public Health Commission of Hubei Province, China, at the end of 2019reported cases of severe and unknown pneumonia, marked by fever, malaise, dry cough, dyspnea, and respiratory failure, that occurred in the urban area of Wuhan, according to the World Health Organization (WHO). The lung infection, SARS-CoV-2, also known as COVID-19, was caused by a brand-new coronavirus (coronavirus disease 2019). Since then, infections have increased exponentially, and the WHO labeled the outbreak a worldwide emergency at the beginning of March 2020. Infected and asymptomatic individuals who can spread the virus are the main sources of it. The transmission occurs mainly by airthrough the air through the droplets, however indirect transmission is also possible, such as through contact with infected surfaces. It becomes essential to identify viral carriers as soon as possible in order to stop the spread of the disease and reduce morbidity and mortality. Imaging examinations, which are among the specific tests used to make the definite diagnosis, are crucial in the patient's management when COVID-19 is suspected. Numerous papers that use machine learning techniques discuss the use of X-ray chest radiographs as a component that aids in diagnosis and permits disease follow-up. The goal of this work is to supply the scientific community with information on the most widely used Machine Learning algorithms applied to chest X-ray images. © 2022 IEEE.

Environment and health co-benefits of vehicle emission control policy in Hubei, China

Vehicle exhaust has been important source of atmospheric pollution in China. In terms of the environmental effects of vehicle emission control policies (VECPs), changes in air pollutants and greenhouse gases (GHG) emissions are receiving increasing attention. Hubei has implemented many traffic controls to accelerate pollution abatement. However, few studies have reported how they would affect pollutant emissions in Hubei in the future, as most concentrate on assessments during COVID-19. Further, there has been little research on whether these controls bring observable health benefits. Thus, this study comprehensively evaluates the emission of major air pollutants (including NOx, CO, VOCs, PM2.5, PM10, and PMTSP) and GHGs (CO2, CH4, and N2O) from the transportation sector concerning different VECPs in Hubei during 2015–2050, together with health outcomes. It highlights that individual VECPs contribute differently to environmental and health benefits, encouraging innovation in mechanisms and technologies to mitigate atmospheric pollution while generating health benefits.

Effects of Covid-19 Pandemic on Surface Water Quality: A Case Study in Hong-Thai Binh River, Vietnam

The Covid-19 epidemic that appeared and broke out from the end of 2019 until now has contributed to limiting human production and emissions. As a result, the quality of the global environment tends to be good, including the water environment. The impact of the Covid-19 epidemic on the water environment has been studied by us in the Hong-Thai Binh river of Vietnam. Data at 30 water monitoring points on the Hong-Thai Binh in the period of 2018 - 2021 were used to calculate water pollution indexes including: Comprehensive Polluted Index (CPI), Organic Pollution Index (OPI) and Trace Metal Pollution Index (TPI). The results show that the average values of all three indicators CPI, OPI, TPI on the Hong-Thai Binh river in the period of during the Covid-19 (from 2020 to 2021) were higher than their values before (from 2018 to 2019). This implies that the blockade and social distancing activities implemented during Vietnam's Covid-19 epidemic have disrupted production activities and reduced the amount of waste discharged into the Hong-Thai Binh river. As a result the river water quality has been improved. To protect the water quality of the Hong-Thai Binh river in particular and the quality of the water environment in general, measures to strictly control waste sources and transform the economy from linear to circular are recommended solutions for Vietnamese government in the post-Covid-19.

A COMPARISON OF ESTIMATED LAND SURFACE TEMPERATURE FROM LANDSAT 8 BETWEEN DKI JAKARTA AND PEKAT VILLAGE

Land Surface Temperature (LST) shows the general temperature condition of environment and many factors may affect it such as weather, cloud coverage, and sun exposure time. During the Corona Virus pandemic, Indonesia implemented the Pemberlakuan Pembatasan Kegiatan Masyarakat (PPKM) system that restricted many activities and suspected of influencing LST in one or another way. PPKM system itself does not cover every province in Indonesia;thus, it is suspected that LST between areas either do or do not implement PPKM may differ. This paper aims to map and analyze LST between Jakarta Province and Pekat Village before and during implementation of PPKM using descriptive research method with a quantitative approach. The results show that there is a significant temperature change in Jakarta when the PPKM level changes transpires. Pekat Village also experiences temperature changes although it is not affected by PPKM system. Even though there are some data anomalies, the temperature changes are within expectation. Therefore, this concludes that PPKM brought some slight effects toward LST and its changes. © 2023 ICIC International. All rights reserved.

Post-COVID cognitive impairment among general population of Khyber Pakhtunkhwa: Pakistan

OBJECTIVE: To assess and compare the cognitive functioning of post-Covid patient with non-Covid person among the general population of Khyber Pakhtunkhwa province of Pakistan. METHODS: This descriptive cross-sectional study was conducted from January to May 2021. Sample of 500 community members, including both post-Covid and non-Covid, from population of Khyber Pakhtunkhwa, Pakistan. Male and female with age of 18-64 years were included, age less than 18 years, having any nervous system or psychiatric illness or previous known cognitive impairment were excluded from the study. Validated Cognitive Functioning Self Reporting Scale (CFSS) questionnaire was used. Data was analyzed through SPSS V.25. RESULTS: Out of 600 approached, 500 (83.3%) participants responded to online questionnaire. Mean age of participants was 45+or-22 years, Majority (n=174: 34.7%) of participants were from 31- 40 years' age group. Hypertension (n=33;6.6%) and Diabetes Mellitus (n=30;6%) were the most common comorbid conditions;while 378 (75.6%) had no comorbidities. Mean CFSS values was 38.4+or-11.5 and 42.2+or-12.04 for male (n=230) and female (n=270) participants respectively. Based on standard cut off values of CFSS, mild, moderate and severe cognitive impairment was observed in 46 (24.8%), 127 (68.6%) and 12 (6.4%) cases of post-COVID and 19 (20.4%), 73 (78.4%) and 1 (1.0%) cases of non-COVID participants having no comorbidities. CONCLUSION: Cognitive performance was moderately affected among post COVID patients as compared to non-Covid population, compromising daily executive functioning as well as other elements of mental health such as attention, memory, spatial functioning & inhibition.

Food Sources May Affect the Symptom Rates of COVID-19, an Epidemiological Analysis Based on the Public Data in Gansu Province, China, During the Summer Epidemic Cycle in 2022.

According to the public data collected from the Health Commission of Gansu Province, China, regarding the COVID-19 pandemic during the summer epidemic cycle in 2022, the epidemiological analysis showed that the pandemic spread stability and the symptom rate (the number of confirmed cases divided by the sum of the number of asymptomatic cases and the number of confirmed cases) of COVID-19 were different among 3 main epidemic regions, Lanzhou, Linxia, and Gannan; both the symptom rate and the daily instantaneous symptom rate (daily number of confirmed cases divided by the sum of daily number of asymptomatic cases and daily number of confirmed cases) in Lanzhou were substantially higher than those in Linxia and Gannan. The difference in the food sources due to the high difference of the population ethnic composition in the 3 regions was probably the main driver for the difference of the symptom rates among the 3 regions. This work provides potential values for prevention and control of COVID-19 in different regions.

Campaign-Style Paired Assistance: The Chinese Experience in Fighting the COVID-19 Pandemic

To cope with the COVID-19 pandemic, the Chinese government initiated a medical resource allocation and assistance mechanism that was characterized as a large-scale and regional mutual approach. Specifically, thirty provinces delivered medical resources (e.g., medical staff, medical supplies, and living materials) to "1+16” cities severely affected by the epidemic within a small amount of time, which solved the dilemma of medical collapse and governance "downtime” in epidemic areas, thereby changing the prevalence curve of the pandemic in China. "Campaign-style” targeted assistance can be interpreted based on the Chinese dual party-government model as well as the governance model of vertical accountability and horizontal competition, drawing from previous experience of normalized "designated assistance.” Consequently, paired assistance contributes to intergovernmental situations of decreasing divisibility and increasing cooperation. This study has the potential to bring insights to other countries around the world that are fighting the COVID-19 pandemic.

The COVID-19 Mortality Rate and its Related Factors in Fars Province

Background: The outbreak of COVID-19 has become the current crisis in most countries. Therefore, paying attention to the consequences and determinants of COVID-19. Mortality can lead to better control of the condition. This study aimed to investigate the COVID-19 mortality rate and its demographic and health determinants in Fars province. Methods: This research was conducted using a quantitative method. For this purpose, available data for selected counties in Fars province were analyzed. The COVID-19 mortality rate was considered a dependent variable. In addition, the variables of literacy rate, urbanization rate, elderly population ratio, unemployment rate, the ratio of the active hospital, ratio of pre-hospital emergency stations, the ratio of centers for primary health care, and the ratio of active hospital beds were considered independent variables. Results: Findings showed that the variables of the elderly population ratio, urbanization rate, and unemployment rate had a direct relationship with the COVID-19 mortality rate. The findings also indicated that the COVID-19 mortality rate in the 45-49 age range begins to accelerate and peaks between 95 and 99 years old. In addition, the literacy rate was inversely related to the COVID-19 mortality rate. The results also showed an inverse relationship between all the selected health variables and the dependent variable. Conclusion: Improving the economic situation, specifically reducing the unemployment rate, emphasizing public education of the people, as well as improving the medical and health facilities, can facilitate the response to pandemics. © 2023 Authors. All rights reserved.

Housing Squats in the Pandemic: Viale delle Province 198

The twin buildings located in Viale delle Province 198 used to be the administrative headquarters of the National Institute of Social Protection (INPS) in Rome, then left vacant since the acquisition by the real estate fund, Investire SGR. It was squatted in 2012 by hundreds of families in a condition of housing vulnerability with the political and logistical support of the Blocchi Precari Metropolitani, that are part of the local Housing Rights Movements. Given its layout and central location, Viale delle Province 198 has become a hub of autonomous infrastructures of the welfare and contentious politics from below, with a strong focus on healthcare. This vocation has been highlighted by the Covid-19 pandemic, whose unforeseen challenges compounded pre-existing patterns of exclusion. On the one hand, the activists, inhabitants and local social workers have engaged to consolidate the social innovations that have been devised since the onset of the pandemic. On the other hand, they and their solidarity networks are coalescing to cope with the repercussion of the eviction procedure that started during the summer 2022, and that would cause the dissolution of the autonomous infrastructures they have generated.

Causes of the unexpected slowness in reducing winter PM2.5 for 2014-2018 in Henan Province.

Toughest-ever clean air actions in China have been implemented nationwide to improve air quality. However, it was unexpected that from 2014 to 2018, the observed wintertime PM2.5 (particulate matter with an aerodynamic diameter of less than 2.5 µm) concentrations showed an insignificant decrease in Henan Province (HNP), a region in the west of the North China Plain. Emission controls seem to have failed to improve winter air quality in HNP, which has caused great confusion in formulating the next air improvement strategy. We employed a deweathering technique to decouple the impact of meteorological conditions. The results showed that the deweathered PM2.5 trend was -3.3%/yr in winter from 2014 to 2018, which had a larger decrease than the observed concentrations (-0.9%/yr), demonstrating that emission reduction was effective at improving air quality. However, compared with the other two megacity clusters, Beijing-Tianjin-Hebei (BTH) (-8.4%/yr) and Yangtze River Delta (YRD) (-7.4%/yr), the deweathered decreasing trend of PM2.5 for HNP remained slow. The underlying mechanism driving the changes in PM2.5 and its chemical components was further explored, using the Weather Research and Forecasting model coupled with Chemistry (WRF-Chem). Model simulations indicated that nitrate dominated the increase of PM2.5 components in HNP and the proportions of nitrate to total PM2.5 increased from 22.4% in January 2015 to 39.7% in January 2019. There are two primary reasons for this phenomenon. One is the limited control of nitrogen oxide emissions, which facilitates the conversion of nitric acid to particulate nitrate by ammonia. The other is unfavourable meteorological conditions, particularly increasing humidity, further enhancing nitrate formation through multiphase reactions. This study highly emphasizes the importance of reducing nitrogen oxide emissions owing to their impact on the formation of particulate nitrate in China, especially in the HNP region.

Changes of Air Pollutants in Urban Cities during the COVID-19 Lockdown-Sri Lanka

In response to the COVID-19 pandemic in early 2020, Sri Lanka underwent a nationwide lockdown that limited motor vehicle movement, industrial operations, and human activities. This study analyzes the impact of COVID-19 lockdown on carbon monoxide (CO), ozone (O3), nitrogen dioxide (NO2), sulfur dioxide (SO2), and particulate matter (PM10, PM2.5) concentrations in two urban cities (Colombo and Kandy) in Sri Lanka, by comparison of data from the lockdown period (March to May 2020) with its analogous period of 2019 and 2021. The results showed that the percentage change of daytime PM10, PM2.5, CO, and NO2 concentration during the lockdown in Colombo (Kandy) is –42.3% (–39.5%), –46% (–54.2%), –14.7% (–8.8%) and –82.2% (–80.9%), respectively. In both cities, the response of NO2 to the lockdown was the most sensitive. In contrast, daytime O3 concentration in Colombo (Kandy) has increased by 6.7% (27.2%), suggesting that the increase in O3 concentration was mainly due to a reduction in NOx emissions leading to lower O3 titration by NO. In addition, daytime SO2 concentration in Colombo has increased by 22.9%, while daytime SO2 concentration in Kandy has decreased by –40%. During the lockdown period, human activities were significantly reduced, causing significant reductions in industrial operations and transportation activities, further reducing emissions and improving air quality in two cities. The results of this study offer potential for local authorities to better understand the emission sources, assess the effectiveness of current air pollution control strategies, and form a basis for formulating better environmental policies to improve air quality and human health. © The Author(s).

Research on machine learning-based epidemic impact on Social Electricity Consumption model

This paper uses social electricity consumption data from 2015-2021 in a city in Hubei province, and uses some methods of artificial intelligence, for example, python function fitting and machine learning to construct an impact analysis and prediction model of the COVID-19 epidemic on Electricity Consumption. Through comparison with the effects of general linear regression and polynomial regression, a better model is developed which comprises four independent variables and uses polynomial regression. The model developed in this paper helps to quantify and measure the impact of the epidemic on society's electricity consumption, and ultimately enables users in the electricity industry to make convenient and rapid forecasts, helping them to make reasonable power supply plans, trading plans and dispatch plans, and to ensure safe and economic operation of the Electricity System. © 2022 ACM.

EFFECT OF LONG VACATION ON DAILY CASES OF COVID-19 DURING PARTIAL RESTRICTION IN JAKARTA, INDONESIA

Since the first case of COVID-19 was discovered in December 2020, the number of transmissions due to COVID-19 infection in the capital city of Jakarta has never decreased in the long term. The decrease in the number of daily positive cases through restrictions on social interaction implemented by the Government faced many obstacles during long holidays, hence it did not last long. In this research paper, we investigate the effect of long vocation on daily cases of COVID-19 by considering few factors including the level of surveillance through contact tracing and testing during social restrictions which are identified to be hampered by long holidays. Hence, based on the number of testing aspects in the time of the outbreak and serials of long holidays, a structural model was built to explain how these variables affect daily number of positive cases. The data for analysis taken at the first social restrictions period up to end of 2020 showed that daily positive cases consistently increasing. Time series structural model of the two-step approach shows that the time and number of testing variables significantly affect the number of daily positive cases, but long holidays did not directly affect the number of positive cases. In a sense, the effect of long holidays results in an increase in the quantity of the number of tests, which in turn will affect the number of daily positive cases. The forecast function for daily new cases obtained by the model performs relatively well in terms of the mean value of the squared prediction error, which gives a positivity rate around 7%. © 2023 the author(s).

Comparison of hematological and inflammatory markers to predict outcome in COVID-19 in 1st and 4th wave

OBJECTIVES: To compare the values of the hematological and inflammatory markers in 1st and 4th waves to predict the outcome of COVID-19 in a hospital-based study. METHODOLOGY: This comparative study was conducted in the Department of Hematology, Hayatabad Medical Complex Peshawar, from April 2020 to 20 August 2021. Tests of significance (Independent t-test/Mann Whitney U test) and Chi-square test were used. Relevant information was recorded on a pre-designed proforma prepared following the study's objectives. RESULTS: A total of 178 patients, 71 from (the 1st wave) and 107 from (the 4th wave) with known outcomes, were studied. A statistically significant difference exists between the groups (1st vs 4th wave) regarding hematological markers;neutrophil to lymphocyte ratio (NLR) (p=0.02), Absolute Neutrophilic count (ANC) (p=0.01) and platelet count (p=0.001). Similarly, significantly higher inflammatory markers values were recorded in the 1st wave compared with the 4th wave regarding inflammatory markers;CRP (p=0.002) and D-dimer (p=0.001). During the 1st wave, Total Leukocyte Count (TLC), ANC and d-dimer were the leading prognostic indicators to predict mortality/worst outcome in COVID-19 with an Area Under Curve (AUC) of 0.74, 0.70 and 0.7 on receiver operating characteristics (ROC) respectively. In 4th, the Area under the curve (AUC) of d-dimer was 0.84 to predict mortality. CONCLUSION: TLC, ANC, NLR, and low platelet count were the worst hematological markers in COVID-19 in the first wave, while d-dimer and CRP were the primary prognostic inflammatory markers. Unlikely in the 4th wave, the prognostic values of hematological markers were merely significant. The d-dimer values in both the waves proved to be reliable for predicting the severity and mortality of COVID-19.