Travel behavior adjustment based epidemic spreading model and prediction for COVID-19

Owing to the continuous variant of the COVID-19 virus, the present epidemic may persist for a long time, and each breakout displays strongly region/time-dependent characteristics. Predicting each specific burst is the basic task for the corresponding strategies. However, the refinement of prevention and control measures usually means the limitation of the existing records of the evolution of the spread, which leads to a special difficulty in making predictions. Taking into account the interdependence of people' s travel behaviors and the epidemic spreading, we propose a modified logistic model to mimic the COVID-19 epidemic spreading, in order to predict the evolutionary behaviors for a specific bursting in a megacity with limited epidemic related records. It continuously reproduced the COVID-19 infected records in Shanghai, China in the period from March 1 to June 28, 2022. From December 7, 2022 when Mainland China adopted new detailed prevention and control measures, the COVID-19 epidemic broke out nationwide, and the infected people themselves took "ibuprofen” widely to relieve the symptoms of fever. A reasonable assumption is that the total number of searches for the word "ibuprofen” is a good representation of the number of infected people. By using the number of searching for the word "ibuprofen” provided on Baidu, a famous searching platform in Mainland China, we estimate the parameters in the modified logistic model and predict subsequently the epidemic spreading behavior in Shanghai, China starting from December 1, 2022. This situation lasted for 72 days. The number of the infected people increased exponentially in the period from the beginning to the 24th day, reached a summit on the 31st day, and decreased exponentially in the period from the 38th day to the end. Within the two weeks centered at the summit, the increasing and decreasing speeds are both significantly small, but the increased number of infected people each day was significantly large. The characteristic for this prediction matches very well with that for the number of metro passengers in Shanghai. It is suggested that the relevant departments should establish a monitoring system composed of some communities, hospitals, etc. according to the sampling principle in statistics to provide reliable prediction records for researchers. © 2023 Chinese Physical Society.

Earth's magnetic field spatial-temporal changes in relation to the SARS-CoV-2 virus spreading

The results of a study on the possible connection between the spread of the SARS-CoV-2 virus and the Earth's magnetic field based on the analysis of a large array digital data for 95 countries of the world are presented. The dependence of the spatial SARS-CoV-2 virus spread on the magnitude of the BIGRF Earth's main magnetic field modular induction values was established. The maximum diseases number occurs in countries that are located in regions with reduced (25. 0-30. 0 μT) and increased (48. 0-55. 0 μT) values, with a higher correlation for the first case. The spatial dependence of the SARS-CoV-2 virus spreading on geomagnetic field dynamics over the past 70 years was revealed. The maximum diseases number refers to the areas with maximum changes in it, both in decrease direction (up to - 6500 nT) and increase (up to 2500 nT), with a more significant correlation for countries located in regions with increased geomagnetic field. © 2022 EAGE. All Rights Reserved.

*Artificial saliva aerosol source and detection system for spreading analysis in indoor environments

In the context of the Corona pandemic the investigation of aerosol spreading is utmost important as the virus is transported by the aerosol particles exhaled by an infected person. Thus, a new aerosol generation and detection system is set up and validated. The system consists of an aerosol source generating a particle size distribution mimicking typical human exhalation with particles sizes between 0.3-2.5 µm and an array of Sensirion SPS30 particulate matter sensors. An accuracy assessment of the SPS30 sensors is conducted using a TSI OPS3330, a high-precision optical particle sizer. Low deviations of ±5 % of the particle concentration measured with the SPS30 with respect to the OPS are reported for concentrations below 2'500/cm3 and +10% for particle densities up to 25'000/cm3. As an application example the system is employed in a short distance single-aisle research aircraft Dornier 728 (Do728) located at DLR Göttingen, to investigate the large-scale aerosol-spreading. With this measurement system spreading distance from an index passenger extending one seat row to the front and two seat rows to the back is determined. © 2022 17th International Conference on Indoor Air Quality and Climate, INDOOR AIR 2022. All rights reserved.

An Improved Computer Vision based Face Mask Detection and Alerting System

A global healthcare crisis has been declared as a result of the covid-19 nandemic's extensive snread. The coronavirus spreads mostly by the release of droplets from an infected person's irritated nose and throat. The risk of spreading disease is highest in public gathering places. Wearing a facial mask in public is one of the greatest ways, according to the World Health Organization, to avoid getting an infectious disease. This research work proposes an approach to human face mask detection using TensorFlow and OpenCV. Whether or not a character is wearing a mask is indicated by an enclosing field drawn around their head. An alert email will be sent to a person whose face is in the database if they make a call without a mask worn. © 2023 IEEE.

Spatially explicit effective reproduction numbers from incidence and mobility data.

Current methods for near real-time estimation of effective reproduction numbers from surveillance data overlook mobility fluxes of infectors and susceptible individuals within a spatially connected network (the metapopulation). Exchanges of infections among different communities may thus be misrepresented unless explicitly measured and accounted for in the renewal equations. Here, we first derive the equations that include spatially explicit effective reproduction numbers, ℛk(t), in an arbitrary community k. These equations embed a suitable connection matrix blending mobility among connected communities and mobility-related containment measures. Then, we propose a tool to estimate, in a Bayesian framework involving particle filtering, the values of ℛk(t) maximizing a suitable likelihood function reproducing observed patterns of infections in space and time. We validate our tools against synthetic data and apply them to real COVID-19 epidemiological records in a severely affected and carefully monitored Italian region. Differences arising between connected and disconnected reproduction numbers (the latter being calculated with existing methods, to which our formulation reduces by setting mobility to zero) suggest that current standards may be improved in their estimation of disease transmission over time.

Spread of SARS-CoV-2 Infection in Adult Populations in Cameroon: A Repeated Cross-Sectional Study Among Blood Donors in the Cities of Yaoundé and Douala.

Over a period of about 9 months, we conducted three serosurveys in the two major cities of Cameroon to determine the prevalence of SARS-COV-2 antibodies and to identify factors associated with seropositivity in each survey. We conducted three independent cross-sectional serosurveys of adult blood donors at the Central Hospital in Yaoundé (CHY), the Jamot Hospital in Yaoundé (JHY) and at the Laquintinie Hospital in Douala (LHD) who consented in writing to participate. Before blood sampling, a short questionnaire was administered to participants to collect their sociodemographic and clinical characteristics. We included a total of 743, 1202, and 1501 participants in the first (January 25-February 15, 2021), second (May 03-28, 2021), and third (November 29-December 31, 2021) surveys, respectively. The adjusted seroprevalence increased from 66.3% (95% CrI 61.1-71.3) in the first survey to 87.2% (95% CrI 84.0-90.0) in the second survey, and 98.4% (95% CrI 96.8-99.7) in the third survey. In the first survey, study site, participant occupation, and comorbid conditions were associated with SARS-CoV-2 seropositivity, whereas only study site remained associated in the second survey. None of the factors studied was significantly associated with seropositivity in the third survey. Together, the data suggest a rapid initial spread of SARS-CoV-2 in the study population, independent of the sociodemographic parameters assessed.

Smart Social Distancing Robot for COVID Safety

Social distancing plays an indispensable part during the ongoing pandemic. In this period, maintaining social distancing standards between people has turned into essential insurance to dial back the spreading of COVID-19. We present an original technique to recognize matches consequently of people in a jam-packed situation individuals do not maintain the social distance restriction, which calls for about 3 ft of space between them. This project assists in restricting the spread of the coronavirus by noticing the distance between disease-spreading people. Presently, it is absurd to expect to station an individual 24 × 7 at each line to screen social separating distance violations. For instance—banks, public offices, malls, schools, theatres, and so forth typically see long lines for hours consistently. To ensure social distancing in lines, this robot aids in monitoring the social distancing. Accordingly, this robot aids in maintaining the social distance between the crowd in a public environment to assist and forestall the spread of the virus. This robot serves to be an economical solution in public places where the gathering of people is significantly high. With appropriate obstacle detection, and crowd monitoring the official are also kept updated due to the Wi-Fi and IoT technology incorporated into a robot. This robot is expected to serve as a good solution in this pandemic time. © 2023, The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd.

Blockchain-based vaccination certificates management

With the explosion of COVID-19 cases and the government's needs to control virus spreading, the development of effective and robust systems for managing vaccination certificates to restrict citizens' activities has been in the centre of many governments. This paper proposes a system that allows for the update of the status of certificates and bases its function on a specific form of logs stored on Blockchains and a set of rules for the interpretation of these logs. Also an outline of a proof of concept implementation of the system in Ethereum together with a cost and security analysis are provided in the paper. The proposed architecture provides several benefits with the most prominent one being the suspension of certificates in case an already vaccinated individual is found positive. In existing certificate management systems a vaccinated individual that is tested positive still holds a valid vaccination certificate during the self-isolation period. This vulnerability allows infected individuals to commute freely and thus facilitates the spread of the pandemic. The proposed solution is not limited to COVID-19 related certificates, but rather it could be deployed in any kind of digital certificate. © 2022 ACM.

How containment policy and medical service impact COVID-19 transmission: A cross-national comparison among China, the USA, and Sweden.

As COVID-19 shows a heterogeneous spreading process globally, investigating factors associated with COVID-19 spreading among different countries will provide information for containment strategy and medical service decisions. A significant challenge for analyzing how these factors impact COVID-19 transmission is assessing key epidemiological parameters and how they change under different containment strategies across different nations. This paper builds a COVID-19 spread simulation model to estimate the core COVID-19 epidemiological parameters. Then, the correlation between these core COVID-19 epidemiological parameters and the times of publicly announced interventions is analyzed, including three typical countries, China (strictly containment), the USA (moderately control), and Sweden (loose control). Results show that the recovery rate leads to a distinct COVID-19 transmission process in the three countries, as all three countries finally have similar and close to zero spreading rates in the third period of COVID-19 transmission. Then, an epidemic fundamental diagram between COVID-19 "active infections" and "current patients" is discovered, which could plan a country's COVID-19 medical capacity and containment strategies when combined with the COVID-19 spreading simulation model. Based on that, the hypothetical policies are proved effectively, which will give support for future infectious diseases.

Using Different Surface Energy Models to Assess the Interactions between Antiviral Coating Films and phi6 Model Virus.

High molecular weight chitosan (HMWCh), quaternised cellulose nanofibrils (qCNF), and their mixture showed antiviral potential in liquid phase, while this effect decreased when applied to facial masks, as studied in our recent work. To gain more insight into material antiviral activity, spin-coated thin films were prepared from each suspension (HMWCh, qCNF) and their mixture with a 1:1 ratio. To understand their mechanism of action, the interactions between these model films with various polar and nonpolar liquids and bacteriophage phi6 (in liquid phase) as a viral surrogate were studied. Surface free energy (SFE) estimates were used as a tool to evaluate the potential adhesion of different polar liquid phases to these films by contact angle measurements (CA) using the sessile drop method. The Fowkes, Owens-Wendt-Rabel-Kealble (OWRK), Wu, and van Oss-Chaudhury-Good (vOGC) mathematical models were used to estimate surface free energy and its polar and dispersive contributions, as well as the Lewis acid and Lewis base contributions. In addition, the surface tension SFT of liquids was also determined. The adhesion and cohesion forces in wetting processes were also observed. The estimated SFE of spin-coated films varied between mathematical models (26-31 mJ/m2) depending on the polarity of the solvents tested, but the correlation between models clearly indicated a significant dominance of the dispersion components that hinder wettability. The poor wettability was also supported by the fact that the cohesive forces in the liquid phase were stronger than the adhesion to the contact surface. In addition, the dispersive (hydrophobic) component dominated in the phi6 dispersion, and since this was also the case in the spin-coated films, it can be assumed that weak physical van der Waals forces (dispersion forces) and hydrophobic interactions occurred between phi6 and the polysaccharide films, resulting in the virus not being in sufficient contact with the tested material during antiviral testing of the material to be inactivated by the active coatings of the polysaccharides used. Regarding the contact killing mechanism, this is a disadvantage that can be overcome by changing the previous material surface (activation). In this way, HMWCh, qCNF, and their mixture can attach to the material surface with better adhesion, thickness, and different shape and orientation, resulting in a more dominant polar fraction of SFE and thus enabling the interactions within the polar part of phi6 dispersion.

Digital cities and the spread of COVID-19: Characterizing the impact of non-pharmaceutical interventions in five cities in Spain.

Mathematical modeling has been fundamental to achieving near real-time accurate forecasts of the spread of COVID-19. Similarly, the design of non-pharmaceutical interventions has played a key role in the application of policies to contain the spread. However, there is less work done regarding quantitative approaches to characterize the impact of each intervention, which can greatly vary depending on the culture, region, and specific circumstances of the population under consideration. In this work, we develop a high-resolution, data-driven agent-based model of the spread of COVID-19 among the population in five Spanish cities. These populations synthesize multiple data sources that summarize the main interaction environments leading to potential contacts. We simulate the spreading of COVID-19 in these cities and study the effect of several non-pharmaceutical interventions. We illustrate the potential of our approach through a case study and derive the impact of the most relevant interventions through scenarios where they are suppressed. Our framework constitutes a first tool to simulate different intervention scenarios for decision-making.

Volcanic Ash as a Precursor for SARS-CoV-2 Infection Among Susceptible Populations in Ecuador: A Satellite Imaging and Excess Mortality-Based Analysis.

The global coronavirus disease 2019 (COVID-19) pandemic has altered entire nations and their health systems. The greatest impact of the pandemic has been seen among vulnerable populations, such as those with comorbidities like heart diseases, kidney failure, obesity, or those with worse health determinants such as unemployment and poverty. In the current study, we are proposing previous exposure to fine-grained volcanic ashes as a risk factor for developing COVID-19. Based on several previous studies it has been known since the mid 1980s of the past century that volcanic ash is most likely an accelerating factor to suffer from different types of cancer, including lung or thyroid cancer. Our study postulates, that people who are most likely to be infected during a severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) widespread wave will be those with comorbidities that are related to previous exposure to volcanic ashes. We have explored 8703 satellite images from the past 21 y of available data from the National Oceanic and Atmospheric Administration (NOAA) database and correlated them with the data from the national institute of health statistics in Ecuador. Additionally, we provide more realistic numbers of fatalities due to the virus based on excess mortality data of 2020-2021, when compared with previous years. This study would be a very first of its kind combining social and spatial distribution of COVID-19 infections and volcanic ash distribution. The results and implications of our study will also help countries to identify such aforementioned vulnerable parts of the society, if the given geodynamic and volcanic settings are similar.

A dynamic design strategy of epidemic spreading parameters based on data driven

When modeling and fitting various kinds of epidemic outbreaks, the value of parameters has always been an important practical problem for many scholars. In the existing studies, most of the authors select a fixed parameter by referring to the relevant literature or combined with medical experiments. With the help of Euler difference transformation and the characteristics of the solution of linear equations, we innovatively propose a dynamic update strategy of epidemic diffusion parameters based on data-driven in this study in order to overcome the above limitation. The method can help decision-makers to calculate the optimal parameters of epidemic spread by combining the real-time update data. A case study is conducted with the COVID-19 data of Wuhan. The results show that the dynamic parameter update strategy designed in this paper can effectively improve the accuracy of the evolution prediction of epidemic outbreaks, which provides an important decision support for the accurate allocation of government emergency resources. © 2023 Northeast University. All rights reserved.

Human Mobility Driven Modeling of an Infectious Disease

In conventional disease models, disease properties are dominant parameters (e.g., infection rate, incubation pe-riod). As seen in the recent literature on infectious diseases, human behavior - particularly mobility - plays a crucial role in spreading diseases. This paper proposes an epidemiological model named SEIRD+m that considers human mobility instead of modeling disease properties alone. SEIRD+m relies on the core deterministic epidemic model SEIR (Susceptible, Exposed, Infected, and Recovered), adds a new compartment D - Dead, and enhances each SEIRD component by human mobility information (such as time, location, and movements) retrieved from cell-phone data collected by SafeGraph. We demonstrate a way to reduce the number of infections and deaths due to COVID-19 by restricting mobility on specific Census Block Groups (CBGs) detected as COVID-19 hotspots. A case study in this paper depicts that a reduction of mobility by 50 % could help reduce the number of infections and deaths in significant percentages in different population groups based on race, income, and age. © 2022 IEEE.

A mathematical-based epidemic model to prevent and control outbreak of corona virus 2019 (COVID-19)

COVID-19 is the seventh member of the Coronaviridae family and this virus will spread quickly in humans, birds and other animals. Human infections are the major source of spreading this virus, it causes mainly respiratory and neurological diseases. In the month of December 2019 there were an increased number of patients reported to hospitals in Wuhan, China. They identified this virus as a novel Corona virus, named as COVID-19. Due to this uncontrollable virus two major challenges are faced by mankind. First, abnormal growth of COVID-19 cases is leading to insufficient medical resources and second, emergency protocols (such as lockdowns) are imposed as preventive measures. we provide a preliminary evolutionary graph theory based mathematical model was designed for control and prevention of COVID-19. In the proposed model, well known technique of social distancing with different variations are implemented. Lockdown by many countries leads to the decrease of Gross Domestic Product (GDP) and increase in mental problems in citizens. These two problems can be solved by the administration of anti virus in some form to the public as a counterpart to the virus. This model works more effectively with high percolation of antiviral nodes in a population and over a period of time. © 2022 Scrivener Publishing LLC.

IMPACT of the COVID-19 PANDEMIC on PRIMITIVE MELANOMA DIAGNOSES at the IDI-IRCCS of ROME

Background: Due to the COVID-19 pandemic, some planned medical activities have been postponed, for both national directives and out of concern of the patients who were afraid to go to hospitals.1 In our study we tried to evaluate if the pandemic has had any detrimental effect on melanoma diagnosis both in 2020 and 2021. Method(s): We collected all consecutive primary melanoma from the Pathology Registry of IDI-IRCCS of Rome (Breslow, ulceration and other main histological features). During year 2020 we divided the COVID-19 Italian pandemic into three phases: pre-lockdown (1 January- 9 March), lockdown (10 March-3 May), post-lockdown (4 May-6 June). We compared these data with the same period of year 2021. Result(s): In the year 2020 mean number of melanoma diagnoses per day were as follows: 2.3 in the pre-lockdown phase, 0.6 during the lockdown and 1.3 after the lockdown (in 20182019, we had 2.3/day). Mean Breslow thickness was 0.88 (95% CI, 0.501.26) pre-lockdown and 1.96 (95% CI, 1.162.76) post-lockdown. Proportion of ulceration was 5.9% (95% CI, 2.411.7%) pre-lockdown and 23.5% (95% CI 10.841.2%) post-lockdown. During the same period of year 2021 we observed a constant number of new melanoma cases, with a daily number similar to the 2020 pre-lockdown period. Overall, we observed a higher number of nodular melanoma and superficial spreading melanoma with nodule compared to 2020 pre-lockdown period. The proportion of in situ melanoma in 2021 (about 28%) is constant and it is very close to the observed values for 2018 (23.8%), 2019 (26.4%) and 2020 (25%). Conclusion(s): Our data support the hypothesis that during the COVID-19 lockdown period of year 2020, melanoma diagnoses may have been delayed. In 2020 a significant increase has been observed for men (from 0.96 to 2.70) but not for women (0.79 to 1.44), and in patients 50 years old or older. Regarding the year 2021, our data support the hypothesis that the number of new melanoma diagnoses returned to the pre-lockdown period, but the higher Breslow thickness and the largest number of thicker melanomas (nodular and superficial spreading with nodule) suggest it could be caused by the postponed prevention during the previous year. The constant proportion of in situ melanoma indicate that more health-conscious people were more likely to defy the 2020- 2021 lockdown limitations than people who might have been underestimating the severity of their lesions.

Smart Social Distancing Robot for COVID Safety

Social distancing plays an indispensable part during the ongoing pandemic. In this period, maintaining social distancing standards between people has turned into essential insurance to dial back the spreading of COVID-19. We present an original technique to recognize matches consequently of people in a jam-packed situation individuals do not maintain the social distance restriction, which calls for about 3 ft of space between them. This project assists in restricting the spread of the coronavirus by noticing the distance between disease-spreading people. Presently, it is absurd to expect to station an individual 24 × 7 at each line to screen social separating distance violations. For instance—banks, public offices, malls, schools, theatres, and so forth typically see long lines for hours consistently. To ensure social distancing in lines, this robot aids in monitoring the social distancing. Accordingly, this robot aids in maintaining the social distance between the crowd in a public environment to assist and forestall the spread of the virus. This robot serves to be an economical solution in public places where the gathering of people is significantly high. With appropriate obstacle detection, and crowd monitoring the official are also kept updated due to the Wi-Fi and IoT technology incorporated into a robot. This robot is expected to serve as a good solution in this pandemic time. © 2023, The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd.

Automated diagnosis of COVID stages using texture-based Gabor features in variational mode decomposition from CT images

COVID-19 is a deadly and fast-spreading disease that makes early death by affecting human organs, primarily the lungs. The detection of COVID in the early stages is crucial as it may help restrict the spread of the progress. The traditional and trending tools are manual, time-inefficient, and less accurate. Hence, an automated diagnosis of COVID is needed to detect COVID in the early stages. Recently, several methods for exploiting computed tomography (CT) scan pictures to detect COVID have been developed;however, none are effective in detecting COVID at the preliminary phase. We propose a method based on two-dimensional variational mode decomposition in this work. This proposed approach decomposes pre-processed CT scan pictures into sub-bands. The texture-based Gabor filter bank extracts the relevant features, and the student's t-value is used to recognize robust traits. After that, linear discriminative analysis (LDA) reduces the dimensionality of features and provides ranks for robust features. Only the first 14 LDA features are qualified for classification. Finally, the least square- support vector machine (SVM) (radial basis function) classifier distinguishes between COVID and non-COVID CT lung images. The results of the trial showed that our model outperformed cutting-edge methods for COVID classification. Using tenfold cross-validation, this model achieved an improved classification accuracy of 93.96%, a specificity of 95.59%, and an F1 score of 93%. To validate our proposed methodology, we conducted different relative experiments with deep learning and traditional machine learning-based models like random forest, K-nearest neighbor, SVM, convolutional neural network, and recurrent neural network. The proposed model is ready to help radiologists identify diseases daily. © 2023 Wiley Periodicals LLC.

A New UAV-Based Social Distance Detector for COVID-19 Outbreaks Reduction, Using IoT, Computer Vision and Deep Learning Technologies

Nowadays, we are living in a dangerous environment and our health system is under the threatened causes of Covid19 and other diseases. The people who are close together are more threatened by different viruses, especially Covid19. In addition, limiting the physical distance between people helps minimize the risk of the virus spreading. For this reason, we created a smart system to detect violated social distance in public areas as markets and streets. In the proposed system, the algorithm for people detection uses a pre-existing deep learning model and computer vision techniques to determine the distances between humans. The detection model uses bounding box information to identify persons. The identified bounding box centroid's pairwise distances of people are calculated using the Euclidean distance. Also, we used jetson nano platform to implement a low-cost embedded system and IoT techniques to send the images and notifications to the nearest police station to apply forfeit when it detects people's congestion in a specific area. Lastly, the suggested system has the capability to assist decrease the intensity of the spread of COVID-19 and other diseases by identifying violated social distance measures and notifying the owner of the system. Using the transformation matrix and accurate pedestrian detection, the process of detecting social distances between individuals may be achieved great confidence. Experiments show that CNN-based object detectors with our suggested social distancing algorithm provide reasonable accuracy for monitoring social distancing in public places, as well. © 2022 Lavoisier. All rights reserved.

The Effects of Social Media on Spreading Panic during COVID-19 Pandemic in Bangladesh: A Study

Now-a-days, people are linked to social media from the moment they wake up till going to bed. Social media attempt to disseminate the information as quickly as possible. Since the first identified Covid-19 patient in Bangladesh, there has always been a sense of dread among the people. This influence people's mental health conditions miserably. The study is aimed to observe the fact that social media influences people's mental condition and the transmission of COVID-19 fear in Bangladesh. Using an online questionnaire, 385 social media users were selected through convenient sampling. Significant variables were found out through ordinal logistic regression. The study shows, most of the participants were aged 15 to 25 years (n= 294, 76.4%), lived in urban (n=263, 68.3%) and 75.3% (n=290) of them used "Facebook" for gathering news related to COVID-19. Most of them had psychological effects (42.9%) due to the panic created by misinformation on social media and 82.6% (n= 318) felt the necessity of setting up filters for social media. The results show, using social media every day during COVID-19, having physical psyche effects of social media, reading mostly health news (COVID-19), spreading fear causing information about COVID-19 had higher significant effect on spreading fear among people. Social media had an impact on spreading fear and a significant negative influence on people's mental health during Covid-19. Filters need to be set up and people should verify before sharing any news in this pandemic.