Total Number Of COVID-19 Cases Research Articles

A Statistical Review of the Impact of COVID-19 on the World

The COVID-19 epidemic has caused an unparalleled era of disruption, significantly impacting public health, the environment, the economy, and civilizations globally. This data analysis offers a thorough examination of COVID-19's worldwide effects. Through statistical methods, it explores the epidemic's spread, mechanics of transmission, and social repercussions. In addition, it carries out quantitative research to comprehend how the pandemic affects society's behavior, healthcare systems, and the environment. To understand the substantial repercussions of the pandemic, the review combines many datasets and presents data from a reliable source. Through the analysis of both quantitative and qualitative data, this review focuses on the pandemic's effects from several perspectives, such as public health measurements, societal shifts, and environmental changes. The findings demonstrate the horrifying cost of human deaths and describe how society has adapted to new issues and routines. The study additionally examines the data's graphical and tabular format, which comprises the total number of COVID-19 cases and fatalities that have been confirmed in nine different nations.

COVID-19 Immunity in the Cohort of IRCCS San Raffaele Hospital Employees after BNT162b2 Vaccination: A Retrospective Observational Study.

The COVID-19 pandemic represents the most severe health and socioeconomic crisis of our century. It began with the first reports in China, in the Wuhan region in December 2019, and quickly spread worldwide, causing a new Severe Acute Respiratory Syndrome Coronavirus 2 (SARS-CoV-2). Among the population most at risk of infection and developing severe forms of the disease are the elderly and healthcare workers, who are more exposed to infected individuals. On December 11, 2020, the Food and Drug Administration approved the emergency use of the BNT162b2 vaccine, the first mRNA vaccine in history. Since then, the total number of vaccine doses administered has exceeded 12 billion. Italy was the first European country to be affected by the pandemic, recording the highest number of total COVID-19 cases (25,695,311) and, after the first 70 days, had the highest crude mortality rate (141.0 per 100,000). In this study, we analyze the rate of SARS-CoV-2 infection among healthcare workers at the San Raffaele Scientific Institute in Milan before and after receiving the BNT162b2 vaccine. Retrospective observational cohort study. The study analyzed the immunization status of 858 employees of the San Raffaele Scientific Institute in Milan, including doctors, healthcare workers, and administrative staff. The analysis is based on previous studies on the same cohort and is integrated with extrapolation and additional analysis of data from the Preventive Medicine Service's Biobank dataset of the same hospital to estimate the infection rate, duration of the disease, and antibody levels recorded in the personnel before and after receiving the double BNT162b2 vaccination. The analysis confirms the positive impact achieved by the introduction of mRNA vaccination in reducing the SARSCoV- 2 infection rate and increasing antibody levels in healthcare workers. Although the BNT162b2 vaccination may not provide complete protection against SARS-CoV-2, it appears to be able to reduce the number of infections, particularly the more severe and symptomatic forms often detected in individuals with various risk factors and comorbidities, making them more vulnerable. Healthcare workers, who have extensive contact with patients and record the greatest decrease in the infection rates, represent the population that receives the most benefit from vaccination. The evidence suggests that vaccinations are essential in protecting high-risk groups, such as healthcare workers, from SARS-CoV-2 infection. Providing adequate vaccination coverage to healthcare workers limits the spread of infections and decreases the severity of disease manifestations, while also reducing their duration.

An analysis of COVID-19 vaccination campaign in Ukraine.

The Coronavirus disease (COVID-19) pandemic is still an important problem of concern in Ukraine. The rapid deployment of the vaccination program is a key event for the formation of herd immunity and helps to prevent negative outcomes, overloading the public health system. The object of the retrospective-archival study was a depersonalized database of open panels on the management of the COVID-19 situation from the Ministry of Health of Ukraine. The total number of COVID-19 cases in Ukraine as of 25 December 2022 amounted to 5 314 388 individuals (12.83% of the population), and COVID-19-related deaths reached 110 029 individuals (0.26% of the population). The overall number of COVID-19 vaccinated persons in Ukraine as of 16 January 2022, with one dose was 36 198 per 100 000 persons (36% of the population), and with two doses, it was 34 703 per 100 000 persons (35% of the population). It was shown a decrease in the number of COVID-19 vaccinated persons among men and persons over the age of 70. In the dynamics of COVID-19 vaccination with one and two doses, an increase in the number of persons vaccinated with Pfizer/BioNTech and CoronaVac was noted. Despite the significant increase in the morbidity and mortality rates of COVID-19, the coverage of vaccination among the population remained insufficient in Ukraine. The effective COVID-19 vaccination strategy should include appropriate management and ensuring the public health system capacity, implementation of information technologies to address logistics issues, and clear communication strategies to maintain public trust.

Gaining further insights into the COVID-19 pandemic in Australia: Evidence using capture-recapture methods

ObjectiveWe re-examined the reported number of COVID-19 cases in Australia and across its states during the first wave of the pandemic. We provided estimates of the total number of cases, adjusted for under-reporting. MethodsPublicly available data sourced from Australian governments at federal, state and territory levels included records on cumulative confirmed COVID-19 cases and cumulative deaths occurring in Australia and across its states on a daily basis. Lower bound and upper bound estimates of the total number of COVID-19 cases in Australia and across its states, that included the undetected cases that have not been recorded, were estimated. ResultsOn January 25, 2020, Australia recorded its first 4 cases of COVID-19 and the first death occurred on March 3, 2020. On April 1, 2020, 4864 cases had been reported with 21 deaths. Our estimation showed that on April 1, 2020, the minimum and maximum number of COVID-19 cases in Australia were in fact 10,160 (95 % CI: 9781–10,538) and 21,748 (95 % CI: 21,607–22,014) respectively. We estimated that the total number of cases were at least twice and at most four times the observed cases recorded. These differences were also found at the state level where in New South Wales there was a minimum and maximum of 207 and 447 cases in total for every 100 reported cases, while in Victoria these figures were much lower at 157 and 265 respectively for every 100 reported cases. ConclusionCase ascertainment during the pandemic is known to have been underestimated due to difficulties in testing and contact tracing, amongst others. Capture-recapture methods provided a measure of the gap between the official number of cases recorded and the actual number during the first wave of the pandemic.

Decreasing the Risk for Transmission of SARS-CoV-2 During In-Person Visitation Within a State Prison System.

To decrease the introduction of COVID-19, in-person visitation programs were temporarily limited at California Department of Corrections and Rehabilitation facilities. After consultation with the California Department of Public Health, and in accordance with Centers for Disease Control and Prevention guidelines, in-person visitation was reintroduced on April 10, 2021, with COVID-19 mitigation strategies. To assess the risk of visitation as a mode of entry for COVID-19 into a prison setting, data were gathered demonstrating the number of visitors who were not allowed into the prison due to a positive COVID-19 test prior to visitation. The number of patients who tested positive for COVID-19 after interaction with visitors was also assessed. Between April 10 and June 27, 2021, 33,212 visitors participated in the in-person visiting program. Eight visitors received positive test results prior to entry. Between January 1 and October 1, 2021, 19,025 patients received a total of 93,135 visitations. Of these, 66 patients received positive test results from post-visit testing. These data suggest that when appropriate mitigation strategies are implemented, the visitation process is not a significant contributor to COVID-19 entry into the prison facility when compared with the total number of COVID-19 cases among patients.

The MODWT-ARIMA Model in Forecasting The COVID-19 Cases

The Maximal Overlap Discrete Wavelet Transform-Autoregressive Integrated Moving Average (MODWT-ARIMA) is a forecasting method that uses the ARIMA model generated from MODWT data. The purpose of this study is to analyze an investigation into the MODWT-ARIMA model with regard to the total number of COVID-19 cases in DKI Jakarta. For this study, daily data on cases of Covid-19COVID-19 in DKI Jakarta were obtained. The model is trained with data from April 3, 2022, to June 11, 2022 (referred to as the "in-sample"), and the outcomes of the prediction are tested with data from June 12, 2022, to June 18, 2022 (referred to as the "out-sample"). These data exhibit trends and are organizedorganised into four data series using MODWT. The ARIMA modelling technique is applied to each of the produced sequences. When using the MODWT-ARIMA approach, the RMSE value obtained from the in-sample data is found to be lower than the RMSE value obtained from the out-sample data. In light of the findings, Iit t became clear that MODWT-ARIMA is better suited for estimation than prediction. The fact that the RMSE value for the data acquired from the in-sample is lower than the RMSE value for the data collected from the out-sample demonstrates that this is the case. 

Public interest in different types of masks and its relationship with pandemic and policy measures during the COVID-19 pandemic: a study using Google Trends data.

Google Trends data have been used to investigate various themes on online information seeking. It was unclear if the population from different parts of the world shared the same amount of attention to different mask types during the COVID-19 pandemic. This study aimed to reveal which types of masks were frequently searched by the public in different countries, and evaluated if public attention to masks could be related to mandatory policy, stringency of the policy, and transmission rate of COVID-19. By referring to an open dataset hosted at the online database Our World in Data, the 10 countries with the highest total number of COVID-19 cases as of 9th of February 2022 were identified. For each of these countries, the weekly new cases per million population, reproduction rate (of COVID-19), stringency index, and face covering policy score were computed from the raw daily data. Google Trends were queried to extract the relative search volume (RSV) for different types of masks from each of these countries. Results found that Google searches for N95 masks were predominant in India, whereas surgical masks were predominant in Russia, FFP2 masks were predominant in Spain, and cloth masks were predominant in both France and United Kingdom. The United States, Brazil, Germany, and Turkey had two predominant types of mask. The online searching behavior for masks markedly varied across countries. For most of the surveyed countries, the online searching for masks peaked during the first wave of COVID-19 pandemic before the government implemented mandatory mask wearing. The search for masks positively correlated with the government response stringency index but not with the COVID-19 reproduction rate or the new cases per million.

Disability-adjusted life years due to COVID-19 in Sri Lanka: a retrospective cross-sectional study

ObjectivesTo estimate the health burden of COVID-19 in Sri Lanka using disability-adjusted life years (DALYs) and to investigate how the burden varies across age groups and sex.Methods and analysisA retrospective...

Robust T cell responses to the Pfizer/BioNTech vaccine compared to infection and evidence of attenuated CD8+ T cell responses due to COVID-19

Abstract The COVID-19 pandemic has resulted in rapid development of several novel vaccine platforms, including the mRNA-based Pfizer/BioNTech vaccine. As of October 2022, more than 3.8 billion Pfizer/BioNTech vaccines were administrated to people in 180 countries. Meanwhile, the total number of COVID-19 cases has reached 627 million globally. We recently developed a new antigen-specific T cell staining tool— ”spheromer”that is able to label T cells more efficiently and capture a broader swath of the repertoire than any other available platform. Using spheromers loaded with SARS-CoV-2 peptides, we applied a panel covers 49 epitopesspanning the entire genome to characterize the magnitude and durability of antigen-specific T cell responses at single-epitope resolution. Using very sensitive “spheromer” peptide-MHC multimer reagents, we analyzed healthy subjects receiving two doses of the Pfizer/BioNTech BNT162b2 vaccine. Vaccination resulted in very robust peripheral blood responses to predicted SARS-CoV-2 spike peptides bound to either class-I or class-II MHC alleles. These peaked at 9.8% and 7.5% for the dominant CD4+ (HLA-DRB1*15:01/S191) and CD8+ (HLA-A*02/S691) T cell epitopes. Interestingly, the antigen-specific CD4+ and CD8+ T cell responses were asynchronous, with the peak CD4+ T cell responses occurring one week post the second vaccination (boost), whereas CD8+ T cells didn’t peak until two weeks later. These responses were much higher than in COVID-19 patients. We also found that prior SARS-CoV-2 infectiondecreased the CD8+ T cell response post vaccination by 2.4 to 41.4-fold at peak, and that the remaining cells were less responsive, suggesting that the virus can profoundly damage an individuals’ CD8+ T cell response. 1. Howard Hughes Medical Institute and NIAID grant AI057229 to Mark.M.Davis; 2. Bill and Melinda Gates Foundation grant OPP1113682, Center for Human Systems Immunology, to Mark.M.Davis; 3. U01 AI140498 to to Mark.M.Davis.

Global landscape of SARS-CoV-2 mutations and conserved regions

BackgroundAt the end of December 2019, a novel strain of Severe Acute Respiratory Syndrome Coronavirus 2 (SARS-CoV-2) disease (COVID-19) has been identified in Wuhan, a central city in China, and then spread to every corner of the globe. As of October 8, 2022, the total number of COVID-19 cases had reached over 621 million worldwide, with more than 6.56 million confirmed deaths. Since SARS-CoV-2 genome sequences change due to mutation and recombination, it is pivotal to surveil emerging variants and monitor changes for improving pandemic management.Methods10,287,271 SARS-CoV-2 genome sequence samples were downloaded in FASTA format from the GISAID databases from February 24, 2020, to April 2022. Python programming language (version 3.8.0) software was utilized to process FASTA files to identify variants and sequence conservation. The NCBI RefSeq SARS-CoV-2 genome (accession no. NC_045512.2) was considered as the reference sequence.ResultsSix mutations had more than 50% frequency in global SARS-CoV-2. These mutations include the P323L (99.3%) in NSP12, D614G (97.6) in S, the T492I (70.4) in NSP4, R203M (62.8%) in N, T60A (61.4%) in Orf9b, and P1228L (50.0%) in NSP3. In the SARS-CoV-2 genome, no mutation was observed in more than 90% of nsp11, nsp7, nsp10, nsp9, nsp8, and nsp16 regions. On the other hand, N, nsp3, S, nsp4, nsp12, and M had the maximum rate of mutations. In the S protein, the highest mutation frequency was observed in aa 508–635(0.77%) and aa 381–508 (0.43%). The highest frequency of mutation was observed in aa 66–88 (2.19%), aa 7–14, and aa 164–246 (2.92%) in M, E, and N proteins, respectively.ConclusionTherefore, monitoring SARS-CoV-2 proteomic changes and detecting hot spots mutations and conserved regions could be applied to improve the SARS‐CoV‐2 diagnostic efficiency and design safe and effective vaccines against emerging variants.

Covid-19 Affects The US Economy Uncertainty?

The economic and social costs of the COVID-19 pandemic affect society, individual investors, policy makers and all financial market participants. The COVID-19 pandemic has created shock waves in financial markets, as well as commodity prices, including oil, and the real economy. This study analyzes the short-term and long-term relations among the number of total COVID-19 cases, USA stock price index, crude oil price and economic policy uncertainty index for USA, based on daily figures between 31.12.2019 and 30.11.2020. The results of the study indicate that there are short-term and long-term cointegration between economic policy uncertainty index determinants in USA. Long-term elasticities project that a 1% increase in the number of COVID-19 cases would lead to an increase of 0.086% in USA economic policy uncertainty index, and an increase of 1% in crude oil price would lead to a decrease of 0.410% in USA economic policy uncertainty index and an increase of 1% in USA stock price index would lead to a 3.167% decrease in USA economic policy uncertainty index.

Culture of Honor and COVID-19: Regional Mitigation Efforts in the United States During the Pandemic

As the COVID-19 pandemic struck in early 2020, states in the United States (U.S.) varied widely in the arrival of the first case of the virus, the official response of government officials and citizens, and the overall impact of the virus. The Culture of Honor, a reputation-based system more prevalent in the U.S. South and West, is linked with lawlessness or mistrust of the law where it exists, fierce independence, and condones aggressive behaviors, particularly in response to threat. The current project explored the influence of the Culture of Honor on perceptions of the legitimacy of COVID-19 messaging, government actions and citizens responses, and overall impact of the pandemic at the state level. It was predicted that the overall numbers of citizens infected by COVID-19 would be higher in culture of honor states, that any actions taken by government officials would be strict and perceived negatively by citizens residing in culture of honor states, and that citizens would be unlikely to follow any imposed government restrictions. Publicly available data were compiled for each of the U.S. states including total number of COVID-19 cases, total deaths, how the elected officials managed any coordinated response (such as issuing executive orders, any stay-at-home or lockdown orders), and coded evidence of citizens violating any of these orders such as by protesting or showing up at public locations that were currently closed. I am deeply indebted to Luke Conway for providing access to an individual-level dataset collected via Amazon Mechanical Turk (MTurk) that included 978 people from across the U.S. who had responded to items related to these variables of interest. Analyses indicated that individuals in US states identified as Culture of Honor had seen greater impact to their states of the COVID-19 pandemic as of 2020 in several ways, including higher total number of citizens who tested positive and also died from COVID-19, less support for government action to mitigate the spread, and stronger distrust of information shared by their state authorities about the pandemic. These findings indicate that cultural values including the Culture of Honor could impede state-level attempts to thwart the spread of contagious diseases and other measures intended to protect individuals.

Tax capacity assessment for COVID-19 resource mobilisation: evidence from an Indian state

COVID-19 pandemic has impacted mankind in a way that the modern world has never seen. With trade and commerce being impacted, the administrations of emerging economies are struggling to raise resources to fight the pandemic. The emerging economies are forced to have a hard look at their tax mobilisation efforts to fund the COVID-19 vaccine programs for the poor and needy. India ranks second in the total number of COVID-19 cases in the world and the country is facing the daunting task of protecting its citizens from the deadly disease. The paper analyses the tax capacity for the state of Kerala, India which is reporting the highest number of cases in India as of January 2021. Stochastic frontier analysis (SFA) is leveraged for determining the variables impacting tax capacity and technical inefficiency of the selected state. Two SFA models are constructed to identify the impact of the variables. The study reveals that the potential for additional resource mobilisation leveraging taxation is limited due to the humongous challenges faced by the tax capacity variables coupled with the strong presence of technical inefficiency.

Spatio-Temporal Spread of Covid-19 in Children Aged 0-11 Years at Central Java Province Indonesia Using Geographical Weight Regression

During the COVID-19 pandemic, children under the age of 12 are the most vulnerable age group to health concerns. The goal of this study was to conduct a spatiotemporal analysis of the distribution of COVID-19 cases in Central Java children using the GWR (Geographically Weighted Regression) approach. The data source is the Central Java Provincial Health Office, and the study objects are 35 cities and districts in Central Java province. The data obtained are the number of COVID-19 cases in children aged 0-11 years, the total number of Covid-19 cases, the number of PCR tests per day, the number of vaccinations and the number of health care facilities per city and district per month from March 2020 to November 2021. Hotspot analysis and the GWR approach were used to examine data in semesters 1–4 (S1–S4). From S1 to S4, the number of COVID-19 cases in children increased. Areas that became hotspots for more than two semesters were Semarang City, Semarang Regency, Banyumas, Cilacap, Kendal, and Demak. According to the GWR analysis in S1-S4, the total number of COVID-19 cases, PCR tests per day, vaccinations, and health care facilities all affect the number of COVID-19 patients in children by more than 75%. The total number of COVID-19 cases has a significant impact on the number of COVID-19 cases in children but the number of health care facilities has no effect. The results of the GWR prediction of COVID-19 cases in children show that the cities of Semarang and Banyumas became areas with a larger number of COVID-19 cases in two semesters. According to the hotspot and GWR analysis, the cities of Semarang and Banyumas are regions to be on the lookout for in the spread of COVID-19 cases in S1-S4.

Behaviour of Face Masks Search for Covid-19 Prevention in Indonesia: Trend and Policy Analysis

The studies on the Google searching behavior of face masks can be counted as evidence of how far the netizens’ enthusiasm for increasing their knowledge of the proper use of face masks. This current study aimed to analyze the trends in online searching behavior for face masks to prevent Covid-19 in Indonesia. This was conducted quantitatively utilizing Google search trends regarding the comparison between face mask types, including cloth masks, medical masks, and N95 masks in Indonesia after the first case of Covid-19. The mapping of sub-regions with the largest Google Trends filter category was also illustrated in this study. A time-trend analysis was conducted from Google data by describing several policy implementations, including social distancing and transition periods. A correlation analysis was performed by comparing the number of searches for face masks and the total number of Covid-19 cases. It was found that searching for all types of face masks increased in the early stage of the pandemic in Indonesia, while the trend later decreased. After implementing the social distancing policy, the results suggest that searches for cloth masks ranked highest. The increasing number of Covid-19 confirmed cases reduced the tendency of searching behavior for face masks in the category of cloth masks (r2 = -0.1730), medical masks (r2 = -0.1736), and N95 masks (r2 = -0.4329) at p-value <0.05. This study illustrates that the existence of a mandatory policy to use masks will increase mask-seeking behavior in Indonesia.

COVID-19 Progression: A County-Level Analysis of Vaccination and Case Fatality in Mississippi, USA.

The COVID-19 pandemic has created a severe upheaval in the U.S., with a particular burden on the state of Mississippi, which already has an exhausted healthcare burden. The main objectives of this study are: (1) to analyze the county-level COVID-19 cases, deaths, and vaccine distribution and (2) to determine the correlation between various social determinants of health (SDOH) and COVID-19 vaccination coverage. We analyzed COVID-19-associated data and county-level SDOH factors in 82 counties of Mississippi. The cumulative COVID-19 and socio-demographic data variables were grouped into feature and target variables. The statistical and exploratory data analysis (EDA) was conducted using Python 3.8.5. The correlation between the target and feature variables was performed by Pearson Correlation analysis. The heat Map Correlation Matrix was visually presented to illustrate the correlation between each pair of features and each target variable. Results indicated that people of Asian descent had the highest vaccination coverage of 77% fully vaccinated compared to 52%, 46%, 42% and 25% for African Americans, Whites, Hispanics, and American Indians/Alaska Natives, respectively. The county-level vaccination rate was significantly higher among the minority populations than the White population. It was observed that COVID-19 cases and deaths were positively correlated with per capita income and negatively correlated with the percentage of persons without a high school diploma (age 25+). This study strongly demonstrates that different SDOH factors influence the outcome of the COVID-19 vaccination rate, which also affects the total number of COVID-19 cases and deaths. Vaccine promotion should be given to all populations regardless of race and ethnicity to achieve uniform acceptance. Therefore, statewide policy recommendations focusing on specific community needs should help achieve health equity in COVID-19 vaccination management.

On computational analysis of nonlinear regression models addressing heteroscedasticity and autocorrelation issues: An application to COVID-19 data

This paper develops a method for nonlinear regression models estimation that is robust to heteroscedasticity and autocorrelation of errors. Using nonlinear least squares estimation, four popular growth models (Exponential, Gompertz, Verhulst, and Weibull) were computed. Some assumptions on the errors of these models (independence, normality, and homoscedasticity) being violated, the estimates are improved by modeling the residuals using the ETS method. For an application purpose, this approach has been used to predict the daily cumulative number of novel coronavirus (COVID-19) cases in Africa for the study period, from March 13, 2020, to June 26, 2021. The comparison of the proposed model to the competitors was done using statistical metrics such as MAPE, MAE, RMSE, AIC, BIC, and AICc. The findings revealed that the modified Gompertz model is the most accurate in forecasting the total number of COVID-19 cases in Africa. Moreover, the developed approach will be useful for researchers and policymakers for predicting purpose and for better decision making in different fields of its applications.

Impact of COVID-19 Outbreak on Retinopathy of Prematurity Screening

Objective: To evaluate the impact of COVID-19 outbreak on screening of premature babies for retinopathy of prematurity (ROP).Methods: Medical records of infants who underwent ROP screening at Ordu University, Training and Research Hospital were reviewed, retrospectively. Sixty premature babies who were not brought into their follow-up visits and reported to the Child, Adolescent, Woman and Reproductive Health (CEKUS) unit between March 2020 and March 2021 were included. The patients were divided into 4 groups according to the timing of CEKUS reports; Group 1: March-May 2020, Group 2: June-August 2020, Group 3: September-November 2020, and Group 4: December 2020-February 2021.Results: A total number of 60 babies were reported to the CEKUS unit between March 2020 and March 2021. While 18% of the patients were consulted from our neonatal intensive care unit (NICU), 82% of the infants were referred from other NICUs. The parents of only 17 (28%) of all patients were living in Ordu, additionally. Compared to the total number of patients reported to the CEKUS unit in the last year before the onset of the pandemic, a decrease in compliance with the appointments was observed after the announcement of the first COVID-19 case in Turkey (p<0.001). Adherence to the appointments increased in the period when COVID-19 patients decreased. When the COVID-19 cases began to rise again significantly after November (Group 4), the number of CEKUS reports increased correspondingly. However, there was no significant correlation between the number of CEKUS reports and the total number of COVID-19 cases in the groups (p = 0.600, r = 0.400).Conclusion: In addition to the difficulty of screening for ROP since the onset of the COVID-19 outbreak, the follow up of babies who are not brought in has become a serious problem. A decrease in adherence to appointments was observed after the beginning of the COVID-19 pandemic. An institution such as our CEKUS unit may help the ophthalmologists and neonatologists to complete all screening sessions. Most babies that were not brought in to ROP screening, were also the ones referred from other NICUs and those who lived in neighboring cities, in our study. Therefore, increasing the number of ROP units may also prevent the non-adherence of the parents.

Human mobility and infection from Covid-19 in the Osaka metropolitan area

Controlling human mobility is thought to be an effective measure to prevent the spread of the COVID-19 pandemic. This study aims to clarify the human mobility types that impacted the number of COVID-19 cases during the medium-term COVID-19 pandemic in the Osaka metropolitan area. The method used in this study was analysis of the statistical relationship between human mobility changes and the total number of COVID-19 cases after two weeks. In conclusion, the results indicate that it is essential to control the human mobility of groceries/pharmacies to between −5 and 5% and that of parks to more than −20%. The most significant finding for urban sustainability is that urban transit was not found to be a source of infection. Hence governments in cities around the world may be able to encourage communities to return to transit mobility, if they are able to follow the kind of hygiene processes conducted in Osaka.

A Statistical Non-Parametric data analysis for COVID-19 incidence data

Background:The impact of COVID-19 on the Global scale is tremendously drastic. There are several types of research going on across the world simultaneously to understand and overcome this dire pandemic outbreak. This paper is purely a statistical study on a distinct set of datasets regarding COVID-19 in India. The motivation of this study is to provide an insight into the rapid growth of confirmed COVID-19 cases in India. Methods:The rapid growth of COVID-19 cases in India started in March 2020. The main objective of this paper is to provide a solid statistical model for the policymaker to handle this kind of pandemic situation in the near future with nonlinear data. In this paper, the data was got from 1st April to 29th November 2020. To come up with a solid statistical model, various nonlinear data such as confirmed COVID-19 cases, maximum temperature, minimum temperature, the total population (state-wise), the total area in km2 (state-wise), and the total rural and urban population count (state-wise) have been analyzed. In this paper, six different Generalized Additive Models (GAM) was identified after a thorough analysis of other researchers’ (Xie and Zhu, 2020; Prata et al., 2020) findings. Results:In all perspectives, the results were identified and analyzed. The GAM model regarding total COVID-19 confirmed cases, total population, and the total rural population provides the best average fit of R2 value of 0.934. As the population value is quite high, the author has concise it using logarithm to provide the best p-value of 0.000542 and 0.001407 for a relation between the total number of COVID-19 cases regarding the total population and total rural population respectively.