Spread Of COVID-19 Research Articles

Assessing the impact of socio-economic and environmental factors on COVID-19 spread in urban neighborhoods: evidence from Urmia, Iran

ABSTRACT The majority of the world’s population has been living in cities for the past two decades, making these areas particularly vulnerable to various stressors, including natural and man-made disasters. A growing literature suggests that socio-economic and environmental indicators significantly affect the epidemic. As a result, this research aims to investigate the effect of these indicators on the rate of infection with COVID-19 in the neighborhoods of Urmia City in Iran. This study uses Moran’s analysis, cluster and outlier analysis to identify high-risk and low-risk areas, as well as weighted spatial regression analysis. The results indicate that the number of employees, the number of elderly people, construction density, and road density have a significant relationship in predicting the epidemic. Based on the study of the effect of variables on the number of COVID-19 patients in Urmia City in Iran, the predicted values for the number of coronavirus patients indicate that the risk of contracting COVID-19 in the southern and part of the western areas is higher than in the northern and northeastern areas.

Analysis of the incidence of influenza before and after the COVID-19 pandemic in South Korea.

Influenza outbreaks of varying size occur every year, but during the COVID-19 pandemic, many countries experienced influenza at lower levels. However, following the relaxation of COVID-19 prevention measures in 2022, the incidence of influenza began to increase gradually. Thus, this study compared the occurrence of influenza from week 36 of 2017, before the COVID-19 outbreak, until 2023. The analysis was conducted using influenza-like illness occurrence data available from the Korea Disease Control and Prevention Agency infectious disease website. Additionally, to examine the changes in COVID-19 and influenza occurrence during the pandemic, COVID-19 incidence data from 20 January 2020 to 31 August 2023 were obtained from the KDCA Coronavirus Disease 19 homepage. During the COVID-19 pandemic, which corresponds to the 2020/2021 and 2021/2022 influenza seasons, there was no seasonal influenza epidemic, and the incidence rates were below the usual outbreak levels. However, in the 2022/2023 season, when the spread of COVID-19 had eased, a seasonal pattern similar to that observed before the COVID-19 pandemic was noted. Furthermore, correlation analysis between the rates of influenza-like illness and COVID-19 incidence showed no significant correlation during the entire period. However, a significant correlation emerged in 2023 (r = 0.393, p <0.05). These results suggest that influenza was suppressed during the COVID-19 pandemic but returned to typical seasonal patterns after the COVID-19 prevention policies were eased. The positive correlation observed between the incidences of COVID-19 and influenza in 2023 indicates that COVID-19, no longer a novel pandemic-causing infectious disease, may have transitioned to an endemic pattern similar to seasonal influenza.

Local effects of non-pharmaceutical interventions on mitigation of COVID-19 spread through decreased human mobilities in Japan: a prefecture-level mediation analysis.

To control the COVID-19 epidemic, the Japanese government and the local governments have repeatedly implemented non-pharmaceutical interventions (NPIs) throughout 2020-2022. Using Bayesian state-space mediation models, we examined the effect of repeated NPIs on infection spread mitigation, mediated by human mobility changes in each prefecture during three epidemic phases: from April 1, 2020 to February 28, 2021; from March 1, 2021 to December 16, 2021; and from December 17, 2021 to December 31, 2022. In the first phase, controlling downtown populations at nighttime was effective in mitigating the infection spread in almost all prefectures. In the second and third phases, the effect was not clear, especially in metropolitan prefectures. Controlling visitors from the central prefectures of metropolitan areas was effective in mitigating infection spread in the surrounding prefectures during all phases. These results suggest that the local spread of infection can be mitigated by focusing on nighttime human mobility control in downtown areas before the epidemic spreads widely and transmission routes become more diverse, and that the geospatial spread of infection can be prevented by controlling the flows of people from large cities to other areas.

Safety and Immunogenicity of Intranasal Razi Cov Pars as a COVID-19 Booster Vaccine in Adults: Promising Results from a Groundbreaking Clinical Trial

Protective antibodies in the upper respiratory tract prevent the spread of COVID-19 in the community. Intranasal vaccines could raise the specific secretory IgA and IgG levels. This is a single-center, randomized, double-blind, placebo-controlled clinical trial to evaluate the safety and immunogenicity of Razi Cov Pars (RCP) intranasal recombinant protein subunit COVID-19 vaccine as a booster in adults. We compared specific IgG and IgA levels in the intranasal RCP group (n = 97) versus placebo (n = 96) in serum, saliva, and nasal mucosal secretions on days 0 and 14 and reported their Geometric Mean Ratios (GMR) and 95% confidence intervals (CI). We showed significant increases in IgA and IgG anti-RBD in the nasal mucosa in the RCP group, but their increase was not detectable in the serum and saliva. Anti-spike IgA in the nasal mucosa also increased in the RCP group compared to the placebo. This increase against the COVID-19 variant Omicron was also similar to that of the Wuhan. We detected no serious adverse reactions or anaphylaxis and all adverse events resolved completely during the follow-up period and were similar in both groups. Intranasal RCP is safe, stimulates the respiratory mucosal immunity, and could be a booster on various COVID-19 vaccines and be effective against new virus variants.

Review of domestic test systems for detecting current or past SARS‑CoV‑2 infection using enzyme‑linked immunosorbent assay

Introduction. Rapid and reliable assessment of circulating antibody levels to SARS-CoV-2 remains the current challenge for monitoring and preventing the spread of COVID-19. As the risk of emergence and dissemination of new viral strains continues to grow, epidemiologic research is also becomes increasingly important. The enzyme-linked immunosorbent assay (ELISA) is useful to assess the vaccination effectiveness and the need for revaccination, the proportion of post-disease and vaccinated individuals, and to monitor population immunity. This article provides an overview of the local ELISA kits currently available for the detection of antibodies against SARS-CoV-2 viral proteins, with a comparison to the certified kit by Mediagnost Ltd. (Germany).The aim. Laboratory testing of seven domestic reagent kits for the determination of antibodies to SARS-CoV-2 viral proteins by ELISA in comparison with the certified test system “Anti-SARS-CoV-2 ELISA E 111-IVD” (Mediagnost Ltd., Reutlingen, Germany).Materials and methods. The study included 80 serum samples from conditionally healthy donors: experimental group (n=70, serum samples collected after 2019) and control group (n=10, serum samples collected before 2018). The experimental group is comprised of non-infected and post-disease individuals; a proportion of those were vaccinated against COVID-19.Results and conclusions. The data obtained indicate high reproducibility, sensitivity (100 %), and specificity for all domestic ELISA kits in detecting both post-disease (90–95 %) and vaccinated volunteers (100 %). The main quality criteria of the obtained results were comparable to those for the certified test system, “Anti-SARS-CoV-2 ELISA E 111-IVD” (Mediagnost Ltd., Germany). All kits detecting IgG to the receptor-binding domain (RBD) and to the S-glycoprotein were effective for the reliable assessment of antibody production after the vaccination. Kits for the detection of antibodies to the RBD and/or viral nucleocapsid are useful for identifying unvaccinated post-disease individuals.

A multiple behaviour temporal network analysis for health behaviours during COVID-19.

The aim of this study was to examine the temporal dynamics of multiple health behaviours (physical activity, alcohol consumption, healthy eating, cigarette consumption, recreational drug use, vaping), and pandemic-related health behaviours (e.g., hand washing, physical distancing) using network psychometrics. The International COVID-19 Awareness and Responses Evaluation (iCARE) study is an international multi-wave observational cohort study of public awareness, attitudes, and responses to public health policies implemented to reduce the spread of COVID-19 on people around the world. A sub-sample of longitudinal data from Canadians (n = 254) was analysed across four waves (February-July 2020). We used temporal network models to fit temporal networks, contemporaneous networks, and between-subject networks from items within the iCARE survey. Positive temporal associations were observed between physical activity and healthy eating, and a bidirectional relationship was evident between outdoor mask use and vaping. A contemporaneous network revealed positive associations between consumption behaviours (vaping, cigarette use, alcohol use, and recreational drug use), and negative associations between physical activity and drug use, and healthy eating and cigarette use. Health behaviours are interconnected and can be modelled as networks or behavioural systems. The application of temporal network analysis to the study of multiple health behaviours is well suited to address key research questions in the field such as 'how do multiple health behaviours co-vary with one another over time'. Future research using time series data and measuring affective and cognitive mediators of behaviour, in addition to health behaviours, has the potential to contribute valuable hypothesis-generating insights.

Can Massive Religious Festival Celebrations Encourage a Faster Spread of a Pandemic? The Case of COVID-19 in Israel.

This paper attempts to evaluate how massive religious festivalcelebrations can encourage the faster spread of any pandemic according to our problem statement, such as the case of COVID-19. For example, we evaluate Israel's three major religions, namely Judaism, Christianity, and the Islamic festival celebrations, respectively. Firstly, we have the traditional Jewish festivities such asHanukkah, Yom Kippur, Sukkot, and Rosh Hashanah. In the Christian's traditional festivities celebrations, we identified Christmas, Easter Day, and All Saints Day. Finally, the Muslim festivities ofMuharram and the Birthday of theHoly Prophet Mohamad. Thepurpose of thisstudy wasto evaluate if these nine massive religious festival celebrations are the main reasons for the large spread of COVID-19 in Israel directly or indirectly. In fact, we propose a new methodology to evaluate the impact of any massive religious festivalcelebration andthe fast spread of any pandemic everywhere and anytime. The new indicator is entitled "The National Spread Levels of Infectious Diseases Risk from Massive Religious Festivities Index" (National-SLIDRMRF-Index). Finally, the major finding in this research is that any massive religious events can generate an exponential number of COVID-19 cases constantly. Therefore, this research concluded that we urgentlyneed a standardized index to monitor and control the expansion of any pandemic such as COVID-19 among different religious groups in the same country. At the same time, we give different policy recommendations to the Israeli government to constantlykeep major controls and measures ofdifferent religious events inJerusalem.

비대면 상담서비스 운영 변화와 과제: 국내 사이트를 중심으로

Objectives This study aims to identify operational changes and challenges in the development of non-face- to-face counseling by comparing the past and present usage of contactless counseling in the context of the rapid increase in the use of contactless counseling due to the spread of COVID-19 and the development of information technology. Methods For comparison with the past, we used the survey methodology from a previous study (Kim, Kye-hyun, &amp; Lim, Eun-mi, 2000) and analyzed 113 counseling sites, including five items (‘service target’, ‘service operator’, ‘counseling method’, ‘counseling content’, and ‘usage fee’) and two additional items (‘counselor’s expertise’ and ‘guidance on using non-face-to-face counseling’) as survey indicators. Results First, we found that counseling methods, which were limited in the early days of non-face-to-face coun-seling, are now being diversified through the development of technology and the expansion of media use. Second, we observed a new way of connecting counselors by establishing platforms for non-face-to-face counseling at large portal sites and private counseling organizations, not limited to the free services provided by public in-stitutions in the past. Third, although guidance for non-face-to-face counseling is currently provided, there is a lack of information on the expertise and confidentiality of counselors. Conclusions This study aims to identify the current status of non-face-to-face counseling in Korea by comparing the past and present operation status of non-face-to-face counseling in Korea, and to identify development tasks. It is expected to provide various implications on the need to strengthen the awareness, professionalism, and eth-ical considerations of non-face-to-face counseling.

Estimation of Epidemiological Parameters for COVID-19 Cases in Burkina Faso Using African Vulture Optimization Algorithm (AVOA)

In this paper, we present a mathematical model that accounts for virus transmission through deceased individuals to simulate the dynamics of COVID-19 spread in Burkina Faso. The existence and uniqueness of the model’s solution have been proven. The basic reproduction number was calculated using the Jacobian determinant method. The stability of the disease-free and endemic equilibrium points was studied. To estimate the model parameters, we used African Vulture Optimization Algorithm (AVOA). We then used this algorithm to estimate the parameters of the developed model using daily reported COVID-19 cases in Burkina Faso from March 11 to April 20, 2020. The results obtained show that the proposed model is more realistic for simulating the spread of COVID-19 in Burkina Faso.

Modeling Emergency Traffic Using a Continuous-Time Markov Chain

This paper aims to propose a novel call for help traffic (SOS) and study its impact over Machine-to-Machine (M2M) and Human-to-Human (H2H) traffic in Internet of Things environments, specifically during disaster events. During such events (e.g., the spread COVID-19), SOS traffic, with its predicted exponential increase, will significantly influence all mobile networks. SOS traffic tends to cause many congestion overload problems that significantly affect the performance of M2M and H2H traffic. In our project, we developed a new Continuous-Time Markov Chain (CTMC) model to analyze and measure radio access performance in terms of massive SOS traffic that influences M2M and H2H traffic. Afterwards, we validate the proposed CTMC model through extensive Monte Carlo simulations. By analyzing the traffic during an emergency case, we can spot a huge impact over the three traffic types of M2M, H2H and SOS traffic. To solve the congestion problems while keeping the SOS traffic without any influence, we propose to grant the SOS traffic the highest priority over the M2M and H2H traffic. However, by implementing this solution in different proposed scenarios, the system becomes able to serve all SOS requests, while only 20% of M2M and H2H traffic could be served in the worst-case scenario. Consequently, we can alleviate the expected shortage of SOS requests during critical events, which might save many humans and rescue them from being isolated.

Attitude, Behavior, and Practices of Infection Control (Hand Washing, Social Distancing and Wearing Mask) among Communities During COVID-19 Outbreak in Areas, (Orangi, Baldia, Movach, Site, Garden, Nazimabad, Qayyumabad) of Karachi, Pakistan

Background Novel corona virus infectious disease covid-19 was first time reported in Wuhan city of China at the end of December 2019.[1] Which proliferated rapidly in China and then around the globe in 209 countries of USA, Europe, Australia, and Asia as well as Pakistan. The death toll around the world has been reached to more than 42 million and 2 billion have been affected so far,[3] in Pakistan the death toll reached to 23 thousand and affected population reached to 10 million and still numbers increasing rapidly.[5] Government taken Various measures in Pakistan for the control of covid-19. Despite of having insufficient funds Pakistan also taken accurate quantify like planned special hospitals, laboratories for testing, isolation facilities, mindfulness drive and lock down to minimize spread virus. Despite the government strict policy towards COVID-19 there are many people who are unable to follow the measures which add into the rapid spread of virus. the aim of the study is to assess the attitude, behavior, and practice among the resident of Karachi specifically those who are residing in SITE area, Orangi Town, Baldia Town, Movach Goth, Nazimabad, Garden, Qayyumabad and Gulshan-e-Iqbal to know the public response towards following SOPs.  Method A self-administered questionnaire was designed, based on determined attitude, behavior, and practices among the resident of Karachi during COVID-19 outbreak. Questionnaire were prepared at google form and accessible online for the participants. The questionnaire was processed to the residents of Karachi, between the duration from 12, June to 10, July 2021. The collected data was analyzed with help of SPSS version 26.0 (IBM) to identify the attitude behavior and practices of people during COVID-19 outbreak. The responses were analyzed based on age, gender, level of education, and income, to identify statistical correlation by utilizing chi square test.  Results The study included 318 participants, out of which 193(61%) males and 125(39%) were female. The majority (291;92%) of the participant was familiar with the covid 19, (94%; 299) of the participant were concerned about the health of their families, (95%;303) agreed that self- care and protection can reduce the possibility to get infected by COVID-19 infection, a vast majority (98%; 313) participants were following cough etiquettes.  Conclusion All of the study participants were from Karachi and they have reasonably good attitude, behavior and practices towards COVID-19 precautions, but still there must be given further awareness to cope with the social and psychological impact of the COVID-19 on public health, and also awareness session should be taken into account to reduce the spread of the infection COVID-19.

Repeated time-series cross-validation: A new method to improved COVID-19 forecast accuracy in Malaysia

Forecasting COVID-19 cases is challenging, and inaccurate forecast values will lead to poor decision-making by the authorities. Conversely, accurate forecasts aid Malaysian government authorities and agencies (National Security Council, Ministry of Health, Ministry of Finance, Ministry of Education, and Ministry of International Trade and Industry) and financial institutions in formulating action plans, regulations, and legal acts to control COVID-19 spread in the country. Therefore, this study proposes Repeated Time-Series Cross-Validation, a new data-splitting strategy to identify the best forecasting model that is capable of producing the lowest error measures value and a high percentage of forecast accuracy for COVID-19 prediction in Malaysia. Some of the highlights of the proposed method are:•A total of 21 models, five data partitioning sets, and four error measures to improve the forecast accuracy of daily COVID-19 cases in Malaysia.•The best model selected produces the lowest error measure value for the Root Mean Squared Error (RMSE), Mean Absolute Error (MAE), Mean Absolute Percentage Error (MAPE), and Mean Absolute Scaled Error (MASE).•The average 8-day forecast accuracy is 90.2 %. The lowest and highest forecast accuracy was 83.7 % and 98.7 %.

A Deep Learning Algorithm to Detect Covid-19 from Lung X-ray Images

Covid-19 Predictive Model Using Deep Learning algorithm on X-Ray images of different patients. The individuals have been infected with the virus in the recent years due to large population spreading of covid- 19 virus has increased, A real-time reverse transcriptase- polymerase chain reaction (RT-PCR) kit serves to identify the illness. This project provides another solution by implementing a model that distingue, the covid and normal patients by using lung X-Ray images. In this project we consider the following three dataset such as coronavirus pneumonia (NCP) contaminated by SARS-COV-2, common pneumonia (CP) and normal control to implement the deep learning model during predicting covid-19. These model gives better accuracy and efficiency in anticipating covid-19 infection.

Catching-Up in Islamabad, Pakistan: Identifying and Reaching Zero-Dose Children Following the COVID-19

Abstract Background Routine Immunisation (RI) is critical in mitigating child morbidity and mortality while preventing the spread of infectious diseases. Nevertheless, the COVID-19 pandemic has resulted in considerable disruptions, which have given rise to an unprecedented number of zero-dose (ZD) infants. It has presented formidable obstacles for healthcare systems on an international scale. Methods The study employed a retrospective analysis of data obtained from thirteen Supplementary Immunisation Activities (SIAs) that were carried out from 2019 to 2021 to investigate the fluctuations in the documented number of children with ZD. Health authority stakeholder interviews yielded valuable insights about vaccination strategies and obstacles. Results An enormous increase in the number of documented ZD minors were identified during the study period. The number of documented ZD minors rose from 3,827 in 2019 to 4,271 in 2020, and then again to 9,620 in 2021. Notwithstanding diligent attempts to immunise ZD children, the vaccination rate persisted at a less-than-ideal 81.24% of documented ZD children in 2021. Conclusions As a result of the COVID-19 pandemic, pre-existing obstacles to accessing ZD infants in Islamabad were further intensified. The lessons learned emphasise the significance of community engagement, improved outreach strategies, and fortified healthcare infrastructure in guaranteeing comprehensive immunisation coverage. It is imperative to confront these challenges to reduce the likelihood of subsequent epidemics and protect children’s health. Key messages • The spread of COVID-19 among ZD neonates in Islamabad accelerated, prompting calls for improved vaccination programmes. • The suboptimal ZD vaccination rate in 2021, despite concerted efforts, underscores the criticality for enhanced strategies.

“I stayed on the street, as if there was no COVID-19”: enacting practices of care with youth at risk of violence and social exclusion in Portugal

As the COVID-19 pandemic unfolded, a world of carelessness was made visible. Public health guidelines against the spread of COVID-19 (e.g., school closures, staying home, social distancing) have substantially affected youth’s health and well-being and highlighted the need for context-specific understandings of infection, risk, and care. Our research aims to contribute to a deeper understanding of the impacts of COVID-19 on boys and girls serving educational measures and placed in the custody of the juvenile justice system in Portugal during the pandemic. Sustained by the use of ethnographic principles and methods along with participatory techniques, this article uncovers complex entanglements between the public health measures to mitigate contagion and the dynamics that exacerbated socio-spatial dynamics of social exclusion and isolation, educational and (mental) health inequities, and lived and practiced forms of violence by youth-serving tutelary measures in Portugal’s six educational centers. Following Tronto’s feminist ethical–political proposal of care, I argue that COVID-19 became a lens to access youth care needs — self-care, care for others, care as essential work of nurturing affective trajectories and solidarities and promoting positive and non-violent relationships. By caring for youth stories, the engagement of researchers and professionals in this action-oriented research aimed to promote the enactment of practices of care with youth in the Portuguese detention centers as a way to positively affect their inhabited present, promote healthier lives and nurture the construction of caring and non-violent post-pandemic futures.

Environment, climate, socio-economics &amp; restrictions impact on COVID-19 in Northen Italy

Abstract Up to now, studies on environmental, climatic, socio-economic factors, and non-pharmacological interventions (NPI) show diverse associations, often contrasting, with COVID-19 spread or severity. Most studies used large-scale, aggregated data, with limited adjustment for individual factors, and most of them focused on viral spread rather than severe outcomes. Moreover, evidence simultaneously evaluating variables belonging to different exposure domains is scarce, and none analysing their collective impact on an individual level. Our population-based retrospective cohort study aimed to assess the comprehensive role played by exposure variables belonging to four different domains, environmental, climatic, socio-economic, and non-pharmacological interventions (NPI), on individual COVID-19-related risk of hospitalization and death, analysing data from all patients (n.68472) tested positive to a SARS-CoV-2 swab in Modena Province (Northern Italy) between February 2020 and August 2021. Using adjusted Cox proportional hazard models, we estimated the risk of severe COVID-19 outcomes, investigating dose-response relationships through restricted cubic spline modelling for hazard ratios. Several significant associations emerged: long-term exposure to air pollutants (NO2, PM10, PM2.5) was linked to hospitalization risk in a complex way and showed an increased risk for death; while humidity was inversely associated; temperature showed a U-shaped risk; wind speed showed a linear association with both outcomes. Precipitation increased hospitalization risk but decreased mortality. Socio-economic and NPI indices showed clear linear associations, respectively negative and positive, with both outcomes. Our findings offer insights for evidence-based policy decisions, improving precision healthcare practices, and safeguarding public health in future pandemics. Refinement of pandemic response plans by healthcare authorities could benefit significantly. Key messages • Evaluating simultaneously four exposure domains on COVID-19 outcomes informs public health policy for future pandemic management. • The comprehension of risk factors associated with severe outcomes, such as hospitalization and mortality, is pivotal for planning responses to similar health emergencies in the future.

Using Social Network Analysis during COVID-19 contact tracing in Libya, 2020

Abstract Background Social network analysis (SNA) may provide valuable insights into individual-level transmission dynamics, allowing for targeted contact tracing interventions. We used SNA to understand the variability in SARS-CoV-2 transmission at the case level during the first wave in Libya. Methods We analyzed the spread of COVID-19 in Libya from March to August 2020 using contact tracing data from 287 confirmed cases. We created a network to visualize the transmission patterns, representing each case as a node and connections as links. We focused on direct contacts identified through contact tracing and calculated network measures such as out-degree centrality, in-degree centrality, and betweenness centrality. Gephi software was used for data visualization and understanding the network structure. Results Of the 287 COVID-19 cases, 264 (92%) had nodes with zero outdegrees, indicating that they did not contribute to infection transmission. The remaining 23 (8%) had non-zero out-degree measures and transmitter infection to other cases, 10 (4%) of whom were super-spreaders (outdegree≥5) causing 70% of the transmission. Six (2%) had no epidemiological contact, indicated by zero in-degree centrality measures; 63% had zero betweenness centrality measures, suggesting they did not act as intermediaries connecting separate transmission chains within the network. The network contained nine clusters of connected nodes, with the largest having 91 (24%) cases; the mean path length between cases was 1.846, and the network diameter was 4. Conclusions The study revealed distinct clusters of connected cases, along with the presence of super-spreaders. This network structure underscores the critical role of early identification and isolation of individuals with high transmission potential to effectively contain outbreaks. This revised conclusion directly references the findings of nine clusters and super-spreaders, demonstrating how these results support the importance of early intervention. Key messages • Social network analysis identifies clusters and super-spreaders in COVID-19 transmission, emphasizing the need for targeted interventions. • Connected clusters and super-spreaders highlight the importance of contact tracing and early isolation for effective control of COVID-19 outbreaks.

Urban Community Participation In Dealing With The Covid-19 Pandemic Through Environment-Based On Covid-19 Handling Policies In The City Of Mataram

The city of Mataram is an area that has a high number of Covid-19 cases in West Nusa Tenggara Province. Covid-19 has seriously impacted all aspects of life, from health, economy, society, culture, and tourism to religious rituals. This study aims to analyze the policies for handling Covid-19 in Mataram City. Second, analyze the form of community participation in efforts to deal with Covid-19 in Mataram City. This study uses the participation theory of Cohen and Uphoff as an analytical tool. This research is qualitative research with a descriptive approach. In- depth interviews, observation, and documentation employed data collection techniques. The results of this study indicate that this environment-based policy for handling Covid-19 effectively suppresses the spread of Covid-19 in Mataram City. This policy is implemented by involving the participation of all elements of government, local leaders, and the community. Community participation in handling Covid-19 starts with high public awareness of Covid-19, then implementing health protocols. Community participation is also seen in the formulation of policies for handling Covid-19, the movement to join a Task Force (Satgas), carrying out self- help movements to help each other, and being involved in evaluating the handling of Covid-19 in the City of Mataram.

Tracing applications for the control of virus dispersal - COVID-19 experience

Abstract Tracing apps have been promoted as an effective tool for the monitoring and control of the spread of COVID-19. Although the correlation between the use of mobile tracking apps and reducing the spread of the virus has been demonstrated, the success of these apps depends on a number of factors, such as the number of app users, the rate at which new systems get adopted, as well as concerns related to anonymity and privacy. The aim of the research was to analyse the pattern of adaptation and use of a COVID-19 tracing app in the context of Polish society. The research used a qualitative approach, data were collected through focus group and individual interviews. The sample consisted of 34 respondents, aged 23-61, with different educational levels and backgrounds. The study followed two phases. First, a model with a set of questions was proposed on the basis of the unified theory of technology acceptance and use. Secondly, using the set of questions, interviews were conducted. In order to examine the data, thematic analysis was applied. To summarise the results of the survey, the respondents have clear expectations towards the app, which are not fulfilled. They are afraid of new technologies. However, they use them frequently and willingly without thinking about the security of their personal data. They expect applications to provide reliable information, but at the same time do not want to enter necessary data that they consider sensitive. They are negative about government actions without losing trust in government applications. It can also be speculated that a lack of information and understanding of the app may have contributed to some of the objections. In conclusion, we need to better understand users and be able to accommodate often conflicting characteristics in order to design more effective tracking applications in the future. Key messages • Technology offers ways to successfully prevent viruses from spreading. • Getting the technology right, based on customer behaviour, is key to the effectiveness of tracking applications.

Chest x-ray images: transfer learning model in COVID-19 detection.

This research aims to develop an effective algorithm for diagnosing COVID-19 in chest X-rays using the transfer learning method and support vector machines. In total, data was collected from 10 clinics, including both large city hospitals and smaller medical institutions. This ensured a diverse range of geographical and demographic information in the sample. An extensive data set was collected, including 10,000 chest X-ray images. 5000 images represent normal cases, 3993 images represent pneumonia cases, and 1007 images represent COVID-19 cases. Machine learning methods were applied to develop a classification model, and the results were compared with seven state-of-the-art models and a lightweight CNN architecture. The results showed that the proposed method achieves high accuracy values (Accuracy): 0.95 for COVID-19, 0.89 for pneumonia, and 0.92 for normal images (p < 0.05). Comparison with other models demonstrates statistically significant superiority of our method in accuracy across all three classes. The EfficientNet-B0 model surpasses our method only in accuracy for normal images with p < 0.01, confirming the advantages of our method. Our method demonstrates high sensitivity values (Sensitivity): 0.96 for COVID-19, 0.88 for pneumonia, and 0.93 for normal images (p < 0.05), outperforming most of the compared models. Correlation analysis showed Pearson coefficients of 0.92, 0.89, and 0.94 for COVID-19, pneumonia, and normal images, respectively, confirming a high degree of consistency between predicted and true class labels. In addition, the model was validated on external datasets to assess its generalizability. This validation confirmed its high level of effectiveness in a variety of clinical settings. This study confirms the importance of applying machine learning methods in medical applications and opens new perspectives for early diagnosis of infectious diseases. The practical application of the obtained results can enhance the efficiency of diagnosis and control the spread of COVID-19, as well as contribute to the development of innovative methods in medical practice.