New Cases Of COVID-19 Research Articles

Motivators and barriers to COVID-19 vaccine acceptance in Russia.

Vaccine hesitancy is one of the major roadblocks to ending the COVID-19 pandemic. However, little is known about individuals' motivators and barriers to vaccination in Russia. We aimed to determine which factors were associated with COVID-19 vaccine acceptance in Russia. We conducted a cross-sectional, online survey across Russia at the end of 2021, during a time with increasing new cases of COVID-19. We used the Health Belief Model to test which of the constructs were associated with vaccine acceptance and controlled for demographic variables in the multivariate logistic regression analysis among our analytic sample of 550 respondents. About one-fifth (18.5%) of our study respondents reported vaccine hesitancy. Our multivariate analysis showed that perceived susceptibility, perceived benefits, perceived barriers, self-efficacy, and friends and family supporting vaccination (cue to action) all contributed to the likelihood of whether or not an individual was accepting of the COVID-19 vaccine. Analysis of our open-ended questions showed that individuals also considered the following factors in making decisions to vaccinate for COVID-19: policy restrictions, less worry, social responsibility, lack of trust, conspiracy theories, concerns about side effects and contraindications. Our results demonstrated the Health Belief Model to be a useful framework for understanding COVID-19 vaccine acceptance in Russia. Our study results highlight the need to focus on health beliefs in order to develop interventions to improve vaccine acceptance.

Co-movement between COVID-19, oil price and American stock market during 2020: Fresh investigation from partial and multiple wavelet methods

The pandemic has brought economic and social challenges to society, policymakers, financial market investors and firms. The current study aims to shed light on the financial aspects of COVID-19 during the first wave in 2020. In doing so, the study aims to explore the association between COVID-19 cases, oil prices, and the stock market returns (Standard & Poor's). In this paper, the authors use the daily new cases of COVID-19, oil price and stock market return from 21 January 2020 to 10 April 2020. To check the nexus between studied factors, we apply Wavelet Coherence, Partial Wavelet Coherence and Multiple Wavelet Coherence methods. The graphical empirics of Wavelet Coherence Methods mentioned interesting outcomes; (i) COVID-19 and oil price tend to move in the same direction, with COVID-19 leading, (ii) COVID-19 affect stock market returns, and (iii) oil price and stock market returns have strong co-movement.

A fractional-order generalized Richards growth model and its implementation to COVID-19 data

In this article, we include the memory effect in the generalized Richards model (GRM), which is proposed in the form of a fractional-order GRM. The fractional-order GRM is developed by replacing the first-order derivative in the GRM with a fractional-order derivative and then taking care of model homogeneity of dimension. We consider two fractional-order differential operators: Caputo and Atangana–Baleanu in the Caputo sense (ABC). The proposed fractional-order models are then implemented to fit the COVID-19 data in East Java, Indonesia from March 25 until October 31, 2020. The fitting is performed by minimizing the sum of the squared residual between the numerical solutions of each fractional-order model and the daily data of a cumulative number of COVID-19 cases. The numerical solutions of both fractional-order models are determined by the predictor-corrector method. The performance of the two fractional models is measured by two performance metrics: coefficient of determination (R 2) and root mean square error (RMSE). By considering that the order of fractional derivative as an extra degree of freedom, we perform data fitting for several orders of fractional derivative and evaluate the two performance metrics. It is observed that the fractional-order model with the ABC operator generally has the best performance in calibrating and forecasting both the cumulative number of COVID-19 cases and daily new cases of COVID-19.

Clinical Outcomes After Admission of Patients With COVID-19 to Skilled Nursing Facilities

During the COVID-19 pandemic, stabilized COVID-19-positive patients were discharged to skilled nursing facilities (SNFs) to alleviate hospital crowding. These discharges generated controversy due to fears of seeding outbreaks, but there is little empirical evidence to inform policy. To assess the association between the admission to SNFs of COVID-19-positive patients and subsequent COVID-19 cases and death rates among residents. This cohort study analyzed survey data from the National Healthcare Safety Network of the Centers for Disease Control and Prevention. The cohort included SNFs in the US from June 2020 to March 2021. Exposed facilities (ie, with initial admission of COVID-19-positive patients) were matched to control facilities (ie, without initial admission of COVID-19-positive patients) in the same county and with similar preadmission case counts. Data were analyzed from June 2023 to February 2024. The week of the first observable admission of COVID-19-positive patients (defined as those previously diagnosed with COVID-19 and continued to require transmission-based precautions) during the study period. Weekly counts of new cases of COVID-19, COVID-19-related deaths, and all-cause deaths per 100 residents in the week prior to the initial admission. A stacked difference-in-differences approach was used to compare outcomes for 10 weeks before and 15 weeks after the first admission. Additional analyses examined whether outcomes differed in facilities with staff or personal protective equipment (PPE) shortages. A matched group of 264 exposed facilities and 518 control facilities was identified. Over the 15-week follow-up period, exposed SNFs had a cumulative increase of 6.94 (95% CI, 2.91-10.98) additional COVID-19 cases per 100 residents compared with control SNFs, a 31.3% increase compared with the sample mean (SD) of 22.2 (26.4). Exposed facilities experienced 2.31 (95% CI, 1.39-3.24) additional cumulative COVID-19-related deaths per 100 residents compared with control facilities, representing a 72.4% increase compared with the sample mean (SD) of 3.19 (5.5). Exposed facilities experiencing potential staff shortage and PPE shortage had larger increases in COVID-19 cases per 100 residents (additional 10.97 [95% CI, 2.76-19.19] cases and additional 14.81 [95% CI, 2.38-27.25] cases, respectively) compared with those without such shortages. This cohort study suggests that admission of COVID-19-positive patients into SNFs early in the pandemic was associated with preventable COVID-19 cases and mortality among residents, particularly in facilities with potential staff and PPE shortages. The findings speak to the importance of equipping SNFs to adhere to infection-control best practices as they continue to face COVID-19 strains and other respiratory diseases.

Non-linear effects of meteorological factors on COVID-19: An analysis of 440 counties in the americas

BackgroundIn the last three years, COVID-19 has caused significant harm to both human health and economic stability. Analyzing the causes and mechanisms of COVID-19 has significant theoretical and practical implications for its prevention and mitigation. The role of meteorological factors in the transmission of COVID-19 is crucial, yet their relationship remains a subject of intense debate. MethodsTo mitigate the issues arising from short time series, large study units, unrepresentative data and linear research methods in previous studies, this study used counties or districts with populations exceeding 100,000 or 500,000 as the study unit. The commencement of local outbreaks was determined by exceeding 100 cumulative confirmed cases. Pearson correlation analysis, generalized additive model (GAM) and distributed lag nonlinear model (DLNM) were used to analyze the relationship and lag effect between the daily new cases of COVID-19 and meteorological factors (temperature, relative humidity, solar radiation, surface pressure, precipitation, wind speed) across 440 counties or districts in seven countries of the Americas, spanning from January 1, 2020, to December 31, 2021. ResultsThe linear correlations between daily new cases and meteorological indicators such as air temperature, relative humidity and solar radiation were not significant. However, the non-linear correlations were significant. The turning points in the relationship for temperature, relative humidity and solar radiation were 5 °C and 23 °C, 74 % and 750 kJ/m2, respectively. ConclusionThe influence of meteorological factors on COVID-19 is non-linear. There are two thresholds in the relationship with temperature: 5 °C and 23 °C. Below 5 °C and above 23 °C, there is a positive correlation, while between 5 °C and 23 °C, the correlation is negative. Relative humidity and solar radiation show negative correlations, but there is a change in slope at about 74 % and 750 kJ/m2, respectively.

SARS-CoV-2 Omicron Subvariants Do Not Differ Much in Binding Affinity to Human ACE2: A Molecular Dynamics Study.

The emergence of the variant of concern Omicron (B.1.1.529) of severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) exacerbates the COVID-19 pandemic due to its high contagious ability. Studies have shown that the Omicron binds human ACE2 more strongly than the wild type. The prevalence of Omicron in new cases of COVID-19 promotes novel lineages with improved receptor binding affinity and immune evasion. To shed light on this open problem, in this work, we investigated the binding free energy of the receptor binding domain of the Omicron lineages BA.2, BA.2.3.20, BA.3, BA4/BA5, BA.2.75, BA.2.75.2, BA.4.6, XBB.1, XBB.1.5, BJ.1, BN.1, BQ.1.1, and CH.1.1 to human ACE2 using all-atom molecular dynamics simulation and the molecular mechanics Poisson-Boltzmann surface area method. The results show that these lineages have increased binding affinity compared to the BA.1 lineage, and BA.2.75 and BA.2.75.2 subvariants bind ACE2 more strongly than others. However, in general, the binding affinities of the Omicron lineages do not differ significantly from each other. The electrostatic force dominates over the van der Waals force in the interaction between Omicron lineages and human cells. Based on our results, we argue that viral evolution does not further improve the affinity of SARS-CoV-2 for ACE2 but may increase immune evasion.

Perbandingan Luaran Maternal dan Perinatal Kehamilan Terkonfirmasi Covid-19 antara Gelombang Pertama dan Kedua Pandemi

Background : New cases of COVID-19 continued to emerge due to the new variants. Pregnant women are more susceptible to severe infections.Objective : To compare the effect of COVID-19 infection on maternal and perinatal outcomes in the first and second waves.Method : An analytical observational study with a cross-sectional design was used. Samples were selected by consecutive sampling from the medical record data of RSUP Dr. Kariadi Semarang, Indonesia, with 47 cases during the first wave (1 August 2020 to 14 May 2021) and 47 cases during the second wave (16 May to 30 September 2021). Data were analysed using univariate, chi-square, fisher’s exact, and logistic regression tests with a significant value of p <0,05.Results and Discussion : Pneumonia, ICU admission, and oxygen consumptions were higher in the second than the first wave as 87.23% VS 70.21%; p = 0.044, 36.17% VS 14.89%; p = 0.018, 65.96% VS 12.77%; p < 0.0001 respectively. The severe COVID-19 infection and maternal mortality increased in the second wave (51,06% VS 14,89%; p = 0.009; 29,79% VS 8,51%; p = <0.001). There were no differences in perinatal outcomes between the first and second waves such as fetal distress, fetal growth retardation, low birth weight, nICU admission, and mortality (0.00% VS 8.51%; p = 0.117, 0.00% VS 4.26%; p = 0.495, 12.77% VS 25.53%; p = 0.116, 12.77% VS 21.28%; p = 0.272, 4.26% VS 8.51%; p = 0.677). The incidence of COVID-19 infection in neonates remained low in both waves at 17.02% VS 12.77%; p = 0.562.Conclusion : Even though a high number of severe diseases to maternal deaths were found during the second wave, neonatal COVID-19 infections remained low.

Analyzing public perception toward COVID-19 vaccines in Indonesia

The research is prompted by the dearth of studies addressing public perceptions of various COVID-19 vaccines in Indonesia using extensive datasets spanning and a wide timeframe. This study examined public perception toward COVID-19 vaccines in Indonesia using a dataset of tweets. We further detect whether there are any changes in sentiment toward each type of vaccine. The five most commonly used vaccines in Indonesia (AstraZeneca, Moderna, Pfizer, Sinopharm, and Sinovac) were analyzed for sentiment using a lexicon-based method: Valence Aware Dictionary and Sentiment Reasoner (VADER), with changes in sentiment detected using Pruned Exact Linear Time (PELT). The 280,826 tweets collected between 2021 and 2022, 39% were positive, 18% were negative, and 43% were neutral. While Indonesian citizens generally responded positively and neutrally to each vaccine, with Sinopharm and Pfizer receiving the highest sentiment scores and AstraZeneca receiving the lowest, some change points in sentiment were associated with real-world events. Jakarta had the highest number of tweets (22%), while Maluku had the highest sentiment score (0.498). A significant positive correlation was also found between the total number of tweets and two variables: new cases of COVID-19 (r=0.9, p=0.001) and total new deaths caused by COVID-19 (r=0.8, p=0.008). Overall, the discussion of COVID-19 vaccines is still ongoing, and Indonesian citizens tend to respond neutrally and positively regardless of location or time.

A Proposed Framework of Vulnerability Assessment and Penetration Testing (VAPT) in Cloud Computing Environments from Penetration Tester Perspective

Penetration testing is a process that focuses on finding security vulnerabilities in a target environment that could let an attacker penetrate the network or computer system or steal information. Due to the COVID-19 endemic, most employees still implement working from home or a hybrid approach, even though the number of new cases of COVID-19 is decreasing. However, working from home depends mainly on cloud computing applications that help employees efficiently accomplish their daily work. This situation also increased the number of data generated from various sources, so they may be exposed to different security risks. This research will propose a framework to conduct vulnerability assessment and penetration testing (VAPT) in cloud service models such as SaaS, PaaS, and IaaS from the perspective of penetration testers. This proposed framework is developed through the integration and mapping of existing frameworks and guidelines to conduct VAPT on testing components such as web applications, APIs, network testing, etc. In this proposed framework, the method of conducting VAPT for each cloud service model will be discussed in detail, from the planning and reconnaissance stage until the report is delivered to the cloud subscriber or cloud provider. An advantage of this proposed framework for the penetration tester is that there is still a lack of methods or guidelines for conducting VAPT that cover all the cloud service models in one comprehensive document

Sociodemographic inequalities in the incidence of COVID-19 in National Household Sample Survey cohort, Brazil, 2020.

To verify the association between sociodemographic factors and the time until the occurrence of new cases of COVID-19 and positive tests for SARS-CoV-2 in Brazil, during the period from May to November 2020, based on a cohort of Brazilians participating in the COVID-19 National Household Sample Survey. A concurrent and closed cohort was created using monthly data from the PNAD COVID-19, carried out via telephone survey. A new case was defined based on the report of the occurrence of a flu-like syndrome, associated with loss of smell or taste; and positivity was defined based on the report of a positive test, among those who reported having been tested. Cox regression models were applied to verify associations. The analyzes took into account sample weighting, calibrated for age, gender and education distribution. The cumulative incidence of cases in the overall fixed cohort was 2.4%, while that of positive tests in the fixed tested cohort was 27.1%. Higher incidences were observed in the North region, in females, in residents of urban areas and in individuals with black skin color. New positive tests occurred more frequently in individuals with less education and healthcare workers. The importance of prospective national surveys is highlighted, contributing to detailed analyzes of social inequalities in reports focused on public health policies.

Did Partial Lockdown Policy Effectively Reduce the First Wave of COVID-19 in Indonesia? A Regression Discontinuity Approach

One of the crucial issues in determining pandemic mitigation is deciding the optimal lockdown policy, whether a complete or partial lockdown could effectively reduce the spreading of COVID-19 without a vaccination policy, which could avoid global economic recession. The conventional pandemic model has limitations in predicting these linkages. The latest Causal Inference method that has been powerful in economic and health policy evaluation, such as Regressions Discontinuity Design (RDD), can contribute to this analysis. In contrast, RDD analysis in existing references is underused. This paper's objective is how the partial lockdown policy in Indonesia effects on spreading COVID-19 virus using RDD analysis. The results indicate that the partial lockdown policy reduced new cases of COVID-19 compared with having no partial lockdown policy. The results close to complete lockdown. This study uses panel data from official health, Waze, and other social variables. The pandemic model was developed from theoretical and empirical estimation. The spillover analysis was estimated using a Bayesian panel fixed-effect with Markov Chain Monte Carlo to relax the SUTVA problem. The DKI Jakarta has effectively reduced the spread of confirmed COVID-19 cases by about 10% to 22% from the baseline. The impact was less in West Java, at about 5%–18.5% from baseline. The government can control mobility during weekend periods and at the beginning of Ramadan. Policy enacted at the level of government (subdistrict level) most proximal to households had more influence on spillover effect reduction than those enacted at higher levels of government. The RDD analysis offers new implications to existing references for incorporating economic and pandemic variables for further research. Collaborating across the government level against the pandemic is paramount to controlling human mobility close to households.

Prophylaxis Against Covid-19: Analysis Of The Supply of Hydroxychloroquine and Ivermectin and Covid-19 Cases and Deaths

Introduction: The prophylaxis of the COVID-19 diseases remains questionable, regarding ivermectin, hydroxychloroquine and azithromycin action. Objective: This is an interrupted time series ecological study where a temporal analysis of the consumption of medications in the pre-pandemic and pandemic periods in the São Lourenço do Oeste (SLO) city was performed. Material and methods: Data were collected monthly from January 2018 to March 2021 directly from pharmacies/drug stores for medications, and from April 2020 to March 2021 for COVID-19. The temporal analysis was performed using the Prain Winsten test. Associations between variables (drugs and COVID-19 data) were made by Spearman's correlation test. Results: The supply of ivermectin (coefficient, 7.71 p=0.005) and hydroxychloroquine increased (coefficient 26.84, p=0.021) in the pandemic; and azithromycin remained stable (p? 0.05). There was a positive correlation between new cases of COVID-19 and the supply of azithromycin, hydroxychloroquine and ivermectin (Spearman rho 0.857, 0.760 and 0.741 p<0.01, respectively) and a positive correlation between deaths with azithromycin (Spearman rho 0.690, p<0.01) and hydroxychloroquine (Spearman rho 0.617, p<0.05). Conclusions: According to our data, it is possible to infer that the increased supply of ivermectin, azithromycin or hydroxychloroquine was not able to reduce new cases or deaths by COVID-19, showing no effectiveness as prophylaxis of this disease.

Long-term monitoring of COVID-19 prevalence in raw and treated wastewater in Salvador, the largest capital of the Brazilian Northeast

Wastewater-based epidemiology (WBE) becomes an interesting epidemiological approach to monitoring the prevalence of SARS-CoV-2 broadly and non-invasively. Herein, we employ for the first time WBE, associated or not with the PEG 8000 precipitation method, for the detection of SARS-CoV-2 in samples of raw or treated wastewater from 22 municipal wastewater treatment stations (WWTPs) located in Salvador, the fourth most populous city in Brazil. Our results demonstrate the success of the application of WBE for detecting SARS-CoV-2 in both types of evaluated samples, regardless of the usage of PEG 8000 concentration procedure. Further, an increase in SARS-CoV-2 positivity rate was observed in samples collected in months that presented the highest number of confirmed COVID-19 cases (May/2021, June/2021 and January/2022). While PEG 8000 concentration step was found to significantly increase the positivity rate in treated wastewater samples (p < 0.005), a strong positive correlation (r: 0.84; p < 0.002) between non-concentrated raw wastewater samples with the number of new cases of COVID-19 (April/2021–February/2022) was observed. In general, the present results reinforce the efficiency of WBE approach to monitoring the presence of SARS-CoV-2 in either low- or high-capacity WWTPs. The successful usage of WBE even in raw wastewater samples makes it an interesting low-cost tool for epidemiological surveillance.

Characteristic of upper respiratory tract microbiota in severe covid-19 adult patients in Indonesia

Introduction: Nowadays, new cases of COVID-19 have been found with severe cases and deaths. The upper respiratory microbiota has been associated with susceptibility to viral infection and disease severities. However, the association of respiratory tract microbiota with the modulation of COVID-19 outcomes still raises a question. Therefore, we examined the association between the diversity of upper respiratory microbiota and clinical manifestations of COVID-19. Methods: This research is an observational cross-sectional study. We characterized the microbiota in the upper airway using the 16S ribosomal RNA sequencing method in 74 COVID-19 patients aged 18-64 years. We also examined the association between COVID-19 severities with alpha and beta diversity of upper respiratory microbiota. The alpha and beta diversity was analyzed by QIIME and shown by R software, AMOVA and MRPP testing was done with R software. The t test and diagrams were performed using R software. Bivariate analysis was carried out by t-test and Chi-square test. Results: The top five phyla in the upper respiratory tract of Indonesian COVID-19 patients with age 18-64 years old were Firmicutes (32,3%), Bacteroidota (27,1%), Fusobacteriota (15,2%), Proteobacteria (15,1%) and Actinobacteria (7,1%). Shannon and ACE index analysis showed no decline of microbiota diversity in the upper airway with increased disease severity. However, there were significant beta diversity differences in the upper airway microbiota between mild and severe COVID-19. The Firmicutes phylum and Oribacterium genus abundance were significantly higher in severe COVID-19 than in mild. The quantity of the Fusobacteriota phylum, Neisseria, and Fusobacterium genera was significantly higher in mild COVID-19 than in severe. Conclusion: Our research supports the relationship between the severity of COVID-19 and the diversity of the microbiota in the upper airway in adults. Further studies are needed to examine how microbiota prevents COVID-19 severities.

Which Factors Were Related to the Number of COVID-19 Cases in the 2022/2023 Season Compared to the 2021/2022 Season in Europe?

The number of COVID-19 cases was greater in early autumn 2022 in contrast to in autumn 2021. Therefore, we decided to examine the factors that may have affected differences in the number of COVID-19 cases between the time periods 2021/2022 and 2022/2023 with consideration of the occurrence of influenza. In this cross-sectional study, we conducted a multiple factor analysis using data from publicly available databases for weeks 35-14 in 2022/2023 and 2021/2022 for Austria, Germany, Greece, Italy, and Slovenia. In the 2021/2022 season, the analyzed countries had similar profiles and were characterized by restrictions, health system policies, and SARS-CoV-2 variants, such as Alpha, Beta, Delta, Kappa, Eta, as well as Omicron sublineages (BA.1, BA.2), which were positively correlated with the number of new cases of COVID-19 per million people. However, in the 2022/2023 season, the analyzed countries were described by groups of variables corresponding to vaccination, influenza, the number of flights, and the Omicron SARS-CoV-2 subvariant. In summary, crucial factors correlated with the increasing of number of COVID-19 cases in the 2021/2022 season were the presence of dominant SARS-CoV-2 variants as well as the lifting of restrictions and strict health system policies.

An extreme value analysis of daily new cases of COVID-19 for sixteen countries in west Africa

We provide an extreme value analysis of daily new cases of COVID-19. We use data from Benin, Burkina Faso, Cabo Verde, Cote d’Ivoire, Gambia, Ghana, Guinea, Guinea-Bissau, Liberia, Mali, Mauritania, Niger, Nigeria, Senegal, Sierra Leone and Togo, covering a period of 37 months. Extreme values were defined as monthly maximums of daily new cases. The generalized extreme value distribution was fitted to them with two of its three parameters allowed to vary linearly or quadratically with respect to month number. Ten of the sixteen countries were found to exhibit significant downward trends in monthly maximums. The adequacy of fits was assessed by probability plots and the Kolmogorov-Smirnov test. The fitted models were used to derive quantiles of the monthly maximum of new cases as well as their limits when the month number is taken to infinity.

Mathematical and Statistical Model for Assessing the Impact of COVID-19 Waves on the Regional System (on the Example of the Kirov Region)

Since the f rst outbreak in China and the spread of COVID-19 in dif erent countries of the world, the study of mathematical models of the spread of the epidemic has begun and is intensively continuing. Such models are dynamic and of en based on dif erential or dif erence equations. As a rule, these models require an identif cation procedure to determine unknown parameters. But for a number of reasons, unambiguous identif cation of such parameters cannot be performed. For example, the preparation of statistical data for the identif cation procedure may be performed in various ways. T erefore, the preferred method of data preprocessing is to approximate them with the most appropriate functional dependence.T e study shows that epidemic curves may be represented by a superposition of several local waves — an outbreak of an epidemic in a particular region consists of many local waves and some of them may merge into one common wave. In this article, it is proposed to use analogs of the normal distribution density function to predict waves of new COVID-19 cases. T e purpose of the article was to develop a model of the dynamics of the total number of cases and new cases of COVID-19, taking into account the waves of the epidemic and the impact on the regional socioeconomic system.T e study was conducted on the basis of data on the incidence of COVID-19 in the Kirov region4 in 2020—2022. It is shown that the chosen model describes statistical data well and allows making realistic forecasts for the total number of diseases and new cases of diseases. T e results of the study may be used to develop preventive measures to prevent the spread of the disease and allow assessing the impact of the epidemiological situation on the socio-economic system of the region.

Prediction of Covid-19 Cases for Malaysia, Egypt, and USA using Deep Learning Models

Forecasting in pandemics and disasters is one of the means that contribute to reducing the damage of this pandemic, and the Corona virus is reportedly the most dangerous pandemic that the entire world is suffering from. As a result, we aim to use a deep learning algorithm to predict confirmed and new cases of Covid-19 in our study. This paper identifies the most essential deep learning techniques. Long short-term memory (LSTM) and gated recurrent unit (GRU) were shown to forecast verified Covid-19 fatalities in Malaysia, Egypt, and the U.S. using time series data from 1 January 2021 to 14 May 2022. The first section of this study examines a comparison of prediction models, while the second section examines how prediction and performance analysis may be enhanced using mean absolute error (MAE), mean absolute error percentage (MAPE), and root mean squared error (RMSE) Metrics. On the basis of the regression curves of two two-layer models, the data were split into training sets of 80% and test sets of 20%. The conclusion is that the outputs of the training model and the original data greatly converged. The findings of the study indicated that, for predicting Covid-19 cases, the GRU model in the three nations is superior than the LSTM model.

Cross-regional analysis of the association between human mobility and COVID-19 infection in Southeast Asia during the transitional period of "living with COVID-19".

In response to COVID-19, Southeast Asian (SEA) countries had imposed stringent lockdowns and restrictions to mitigate the pandemic ever since 2019. Because of a gradually boosting vaccination rate along with a strong demand for economic recovery, many governments have shifted the intervention strategy from restrictions to "Living with COVID-19" where people gradually resumed their normal activities since the second half of the year 2021. Noticeably, timelines for enacting the loosened strategy varied across Southeast Asian countries, which resulted in different patterns of human mobility across space and time. This thus presents an opportunity to study the relationship between mobility and the number of infection cases across regions, which could provide support for ongoing interventions in terms of effectiveness. This study aimed to investigate the association between human mobility and COVID-19 infections across space and time during the transition period of shifting strategies from restrictions to normal living in Southeast Asia. Our research results have significant implications for evidence-based policymaking at the present of the COVID-19 pandemic and other public health issues. We aggregated weekly average human mobility data derived from the Facebook origin and destination Movement dataset. and weekly average new cases of COVID-19 at the district level from 01-Jun-2021 to 26-Dec-2021 (a total of 30 weeks). We mapped the spatiotemporal dynamics of human mobility and COVID-19 cases across countries in SEA. We further adopted the Geographically and Temporally Weighted Regression model to identify the spatiotemporal variations of the association between human mobility and COVID-19 infections over 30 weeks. Our model also controls for socioeconomic status, vaccination, and stringency of intervention to better identify the impact of human mobility on COVID-19 spread. The percentage of districts that presented a statistically significant association between human mobility and COVID-19 infections generally decreased from 96.15% in week 1 to 90.38% in week 30, indicating a gradual disconnection between human mobility and COVID-19 spread. Over the study period, the average coefficients in 7 SEA countries increased, decreased, and finally kept stable. The association between human mobility and COVID-19 spread also presents spatial heterogeneity where higher coefficients were mainly concentrated in districts of Indonesia from week 1 to week 10 (ranging from 0.336 to 0.826), while lower coefficients were mainly located in districts of Vietnam (ranging from 0.044 to 0.130). From week 10 to week 25, higher coefficients were mainly observed in Singapore, Malaysia, Brunei, north Indonesia, and several districts of the Philippines. Despite the association showing a general weakening trend over time, significant positive coefficients were observed in Singapore, Malaysia, western Indonesia, and the Philippines, with the relatively highest coefficients observed in the Philippines in week 30 (ranging from 0.101 to 0.139). The loosening interventions in response to COVID-19 in SEA countries during the second half of 2021 led to diverse changes in human mobility over time, which may result in the COVID-19 infection dynamics. This study investigated the association between mobility and infections at the regional level during the special transitional period. Our study has important implications for public policy interventions, especially at the later stage of a public health crisis.

Molecular Docking of Bicycloproline Derivative Synthetic Compounds on Envelope Protein: Anti-SARS-CoV-2 Drug Discovery

Background: Although a SARS-CoV-2 vaccine is readily available, new cases of COVID-19 are still occurring. New drug discovery is needed to treat COVID-19. Protein E is one of the potential targets. Two synthetic compounds of bicycloproline derivatives have the potential to be developed. Objective: This study aimed to estimate the interaction of bicycloproline compounds to protein E in-silico. Methods: There were two bicycloproline-derived compounds, MI-09 and MI-30, used in docking. Remdesivir was used as a reference ligand. The crystal structure of the E protein was created using homology modeling, while the test compound was drawn using the Marvin Sketch. MOE 2022.02 and BDS 2021 were used for docking and visualization processes. Results: The pentamer of the SARS-CoV-2 E protein obtained a clash score (1.06); poor rotatomer (0.00%); favored rotamers (98.11%); Ramachandran favored (96.43%); Ramachandran outlier (1.78%); Rama Z-score (-1.08); and mol probity (1.04). Research shows promising inhibition potential of the MI-09 and MI-30. The MI-30 has the best binding energy of -10.3326 kcal/mol. Conclusion: The docking results show that MI-30 has potency as an inhibitor of protein E and can be developed in treating COVID-19. Further research is needed to confirm the result by in vitro and in vivo studies.