Some remarks on a mathematical model of COVID-19 pandemic with health care capacity

In this paper, a SEIR model proposed in the article "Dynamic analysis of mathematical model with health care capacity for COVID-19 pandemic” by S. Çakan (2020) is analysed. The model describes COVID-19 pandemic spread affected by healthcare capacity and is expressed by a system of delay differential equations. To prove the local stability of stationary states, S. Çakan uses linearization technique, though she does this as if the equations did not depend on the delay. Additionally, it is shown that the crucial argument used by S. Çakan to prove boundedness of the solutions is not correct, which implies that the proofs of global stability in the original article are not correct either. In this paper, improved proofs of local and global stability of the stationary states are provided. For local stability of the stationary states a standard linearization technique is used. Global stability of the stationary states is proved based on Lyapunov's functionals. Although the functionals are the same as those proposed by S. Çakan, additional properties of the solutions (in the case of disease-free stationary state) and the functional (in the case of the endemic stationary state) are proved. © 2022 Polish Mathematical Society. All rights reserved.

Prevention and Control of COVID-19 Infection and Property Management: Evidence from Wuhan, China

Property enterprise has contributed significantly to the prevention and control of COVID-19, and its functions received positive feedback from the urban residents via a survey. Detailed data on confirmed COVID-19 cases in 446 communities in Wuhan were collected and the property fee of each community was used to assess the quality of the property services provided. Both binary logit and ordered logit models were used to measure the impact of property fees on the pandemic prevention and control efficiency of each community. The results showed that a higher property fee corresponded to a better property service and a higher probability that the residential community would be free of COVID-19. Furthermore, where property fees were higher, pandemic prevention and control efficiency increased and the community achieved a lower pandemic risk level. In conclusion, the promotion of high-quality property services is conducive to community disease prevention and control in the case of a pandemic.

COVID-19 in children: Clinical and epidemiological data.

Coronavirus disease 2019 (COVID-19) is an acute infectious disease of the respiratory system caused by the new coronavirus 2 (SARS-CoV-2). COVID-19 affects children of all ages, even newborns and children under one year. There are no reliable data on risk factors of SARS-CoV-2 infection in children, although family cases are well documented. Most children have a mild clinical course with common symptoms such as fever, cough, fatigue, myalgia, vomiting, and diarrhea. Elevated markers of inflammation and radiological changes are less common and pronounced than in adults. There are no reliable data on the relationship between aggravating comorbid conditions in children and the severity of COVID-19.Copyright © 2021, Professionalnye Izdaniya. All rights reserved.

COVID-19 in children: Clinical and epidemiological data.

Coronavirus disease 2019 (COVID-19) is an acute infectious disease of the respiratory system caused by the new coronavirus 2 (SARS-CoV-2). COVID-19 affects children of all ages, even newborns and children under one year. There are no reliable data on risk factors of SARS-CoV-2 infection in children, although family cases are well documented. Most children have a mild clinical course with common symptoms such as fever, cough, fatigue, myalgia, vomiting, and diarrhea. Elevated markers of inflammation and radiological changes are less common and pronounced than in adults. There are no reliable data on the relationship between aggravating comorbid conditions in children and the severity of COVID-19.Copyright © 2021, Professionalnye Izdaniya. All rights reserved.

Influence of indoor environmental conditions on airborne transmission and lifetime of sneeze droplets in a confined space: a way to reduce COVID-19 spread.

Effects of indoor temperature (T∞) and relative humidity (RH∞) on the airborne transmission of sneeze droplets in a confined space were studied over the T∞ range of 15-30 °C and RH∞ of 22-62%. In addition, a theoretical evaporation model was used to estimate the droplet lifetime based on experimental data. The results showed that the body mass index (BMI) of the participants played an important role in the sneezing jet velocity, while the impact of the BMI and gender of participants was insignificant on the size distribution of droplets. At a critical relative humidity RH∞,crit of 46%, the sneezing jet velocity and droplet lifetime were roughly independent of T∞. At RH∞ < RH∞,crit, the sneezing jet velocity decreased by increasing T∞ from 15 to 30 °C, while its trend was reversed at RH∞ > RH∞,crit. The maximum spreading distance of aerosols increased by decreasing the RH∞ and increasing T∞, while the droplet lifetime increased by decreasing T∞ at RH∞ > RH∞,crit. The mean diameter of aerosolized droplets was less affected by T∞ than the large droplets at RH∞ < RH∞,crit, while the mean diameter and number fraction of aerosols were more influenced by RH∞ than the T∞ in the range of 46% ≤ RH∞ ≤ 62%. In summary, this study suggests suitable indoor environmental conditions by considering the transmission rate and lifetime of respiratory droplets to reduce the spread of COVID-19.

Disinfectant impacts on water quality and fishes during the fight against COVID-19 spread

The World Health Organization recommended disinfectant use as a way of tackling the global spread of corona virus pandemic (COVID-19). A total of 246,245,186 infection cases, 246,134,984 recoveries and 4,995,890 deaths have been reported across the world with 3,767,744 confirmed cases in over 160 countries. The spread of the virus was addressed by restricting human and vehicular movements, compelled use of sanitizers, social distancing, and wearing of masks. Chlorine, alcohol and bleach disinfectants, which contain different active compounds, were also used to combat the spread of the virus by applying them on surfaces. The indiscriminate use of disinfectants was reported to have disastrous effects on water quality, and on skin and organs of fish in the long run. The virus affected generally almost all spheres of life. To this end, disinfectants must be used at recommended rate of application, and the proper disposal of wastewater is also important, so as to limit transmission of diseases. Strict adherence to human activities based on the approved guidelines by the government and stakeholders in the health sector is essential for a healthy life. This paper therefore reviews the types of disinfectants, and their effects on water quality, fish species and sustainability of the environment. © 2022, Prince of Songkla University. All rights reserved.

Effect of indoor temperature on the velocity fields and airborne transmission of sneeze droplets: An experimental study and transient CFD modeling.

The spread of the COVID-19 pandemic through the airborne transmission of coronavirus-containing droplets emitted during coughing, sneezing, and speaking has now been well recognized. This study presented the effect of indoor temperature (T∞) on the airflow dynamics, velocity fields, size distribution, and airborne transmission of sneeze droplets in a confined space through experimental investigation and computational fluid dynamic (CFD) modeling. The CFD simulations were performed using the renormalization group k-ε turbulence model. The experimental shadowgraph imaging and CFD simulations showed the time evolution of sneeze droplet concentrations into the turbulent expanded puff, droplet cloud, and fully-dispersed droplets. Also, the predicted mean velocity of droplets was compared with the obtained experimental data to assess the accuracy of the results. In addition, the validated computational model was used to study the sneeze complex airflow behavior and airborne transmission of small, medium, and large respiratory droplets in confined spaces at different temperatures. The warm room showed more than ∼14 % increase in airborne aerosols than the room with a mild temperature. The study provides information on the effect of room temperature on the evaporation of respiratory droplets during sneezing. The findings of this fundamental study may be used in developing exposure guidelines by controlling the temperature level in indoor environments to reduce the exposure risk of COVID-19.

Human-Machine Interaction for Monitoring COVID-19 Internet Data in Russia and the World

We develop a human-machine interaction via dashboard for COVID-19 data visualization in the regions of Russia and the world. In particular, it includes an adaptive-compartmental multi-parametric model of the epidemic spread, which is a generalization of the classical SEIR models;and a module for visualizing and setting the parameters of this model according to epidemiological data, implemented in a dashboard. Data for testing have been collected since March 2020 on a daily basis from open Internet sources and placed on a “data farm” (an automated system for collecting, storing and pre-processing data from heterogeneous sources) hosted on a remote server. The combination of the proposed approach and its implementation in the form of a dashboard with the ability to conduct visual numerical experiments and compare them with real data allows most accurately tune the model parameters thus turning it into an intelligent system to support a decision-making. That is a small step towards Industry 5.0. © 2022, The Author(s), under exclusive license to Springer Nature Switzerland AG.

Pandemic Spread in Metropolitan Cities of India—Spatial Planning Factors

India has seen pandemic disasters several years in the past. The recent coronavirus disease of 2019 (COVID-19) is similar to severe acute respiratory syndrome (SARS) and other viruses earlier, except in its spatial spread and concentration within the city. This paper is an attempt to identify the risk spatial planning factors responsible for the pandemic spread in the metropolitan cities of the four worst affected states of India: Mumbai, Ahmedabad, New Delhi, and Chennai, and how the spread is getting concentrated in these cities among the low-income housing areas such as slums and squatter settlements. Based on COVID-19 spread in these areas, a total number of 14 spatial factors that induce spatial environment were identified. These factors were divided into three categories based on their spread. Further, a combination of geospatial overlay techniques was employed to assess the link between built-up density and the pandemic spread. This was done through geospatial overlay of slums over the risk zone maps obtained from the open sourced geospatial portals targeting hotspots in the study areas. The results revealed that higher built-up density areas such as slums are instrumental in steering a higher number of positive cases. Based on these parameters, a four-pronged planning approach is suggested to tackle the pandemic spread and hence to transform the future planning interventions in India. © 2022, The Author(s), under exclusive license to Springer Nature Switzerland AG.

Impact of Immobility and Mobility Activities on the Spread of Covid-19: Evidence from European Countries

To limit the spread of Covid-19, most countries in the world have put in place measures which restrict mobility. The co-presence of several people in the same place of work, shopping, leisure, or transport is considered a favourable vector for the transmission of the virus. However, this hypothesis remains to be verified in the light of the daily data available since the first wave of contamination. Does immobility reduce the spread of Covid-19 pandemics? Does mobility contribute to the increase in the number of infections for all activities? This paper applies several Pooled Mean Group?Autoregressive Distributed Lag (PMG-ARDL) models to investigate the impact of immobility and daily mobility activities on the spread of the Covid-19 pandemic in the European countries using daily data for the period 12 March 2020 to 31 August 2021. The results of the PMG-ARDL models show that immobility and higher temperatures play a significant role in reducing the Covid-19 pandemic. The increase in mobility activities (grocery, retail, use of transit) is also positively associated with the number of new Covid-19 cases. The combined analysis with the Granger test shows that the relationship between mobility and Covid-19 goes in both directions, with the exception of grocery shopping, visits to parks and commuting mobility. The former favours the spread of Covid-19, while the next two has no causal relationship with Covid-19. The results confirm the role of immobility in mitigating the spread of the pandemic, but call into question the drastic policies of systematically closing all places of activity.

Simulating COVID-19 Spreads in Indoor Space

It is known that the transmissibility of COVID-19 is higher in indoor space than outdoor. The fact that the indoor space is usually closed and has less factors to take into account than outdoor may facilitate the analysis of COVID-19 infection. However, few works have been done on the analysis on COVID-19 transmissibility in indoor space. In this paper, we discuss simulation methods to analyze the transmissibility in indoor space, particularly a simulation environment consisting of three components;indoor maps, positions and trajectories of persons in indoor space, and infection models of COVID-19 in indoor space. And we analyze the requirements and design issues of each component. Among three COVID-19 infection models, we developed a simulation tool for indoor person-person infection model. While only the person-person infection model has been implemented for the simulation, the other two models of COVID-19 are planned to be designed and implemented in the future. © 2022, Springer Nature Switzerland AG.

COVID-19 Infection May Not Produce Lasting Immunity in Non-vaccinated Young Adults: A Retrospective Review of COVID-19 Antibody Data

BackgroundDespite progress in achieving herd immunity through recovery from previous infection and vaccination efforts, the COVID-19 pandemic continues to be an imminent health concern. Exposure to the severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) viral antigen through infection or vaccination facilitates immune system efficacy against future infection, but it is currently unclear how long this immunity lasts. Therefore, understanding the necessary exposures to produce adequate antibody levels and the duration of this humoral response to prevent infection is imperative in updating guidelines for vaccination and ultimately ending this public health crisis. AimsThis study aimed to compare the presence of serum antibodies in younger and older age groups to determine how vaccination and previous infection compare as indicators of immunity against COVID-19. We also evaluated age to determine its role in antibody presence. We hope that this information will be helpful to the public to develop the best recommendations for vaccination guidelines concerning distinct demographics. ​Materials and methodsIn this retrospective data analysis, we evaluated saliva SARS-CoV-2 test results taken from 309 subjects (192F/117M;median age=53.4) during a community fair in Crawford County, PA. We sorted the subjects into groups based on age, reported infection with the COVID-19 virus, and vaccination status. We then performed a Chi-square analysis to compare the frequency of positive SARS-CoV-2 antibody tests within these groups.ResultsThe vaccinated but not previously-infected cohort (n=146, 81.5%) was significantly more likely to have antibodies than the unvaccinated infected cohort (n=55, 65.5%;p<0.0001). In the previously-infected, unvaccinated cohort, individuals who were 55 and older were more likely to have antibodies than younger individuals (p<0.0157), but no age-dependent difference was observed among vaccinated individuals.ConclusionsThe results suggest that vaccination provides a more durable immune response than recovery from infection, and there is an age-dependent humoral response following previous infection but not vaccination. Practically speaking, this information implies that despite popular misconception, individuals under the age of 55 must receive a COVID-19 vaccine despite the previous infection as they are significantly less likely to have antibodies following infection than their counterparts who are over the age of 55.

A city-level analysis of PM2.5 pollution, climate and COVID-19 early spread in Spain.

Purpuse: The COVID-19 outbreak has escalated into the worse pandemic of the present century. The fast spread of the new SARS-CoV-2 coronavirus has caused devastating health and economic crises all over the world, with Spain being one of the worst affected countries in terms of confirmed COVID-19 cases and deaths per inhabitant. In this situation, the Spanish Government declared the lockdown of the country. Methods: The variations of air pollution in terms of fine particulate matter (PM2.5) levels in seven representative cities of Spain are analyzed here considering the effect of meteorology during the national lockdown. The possible associations of PM2.5 pollution and climate with COVID-19 accumulated cases were also analyzed. Results: While the epidemic curve was flattened, the results of the analysis show that the 4-week Spanish lockdown significantly reduced the PM2.5 levels in only one city despite the drastically reduced human activity. Furthermore, no associations between either PM2.5 exposure or environmental conditions and COVID-19 transmission were found during the early spread of the pandemic. Conclusions: A longer period applying human activity restrictions is necessary in order to achieve significant reductions of PM2.5 levels in all the analyzed cities. No effect of PM2.5 pollution or weather on COVID-19 incidence was found for these pollutant levels and period of time. Supplementary Information: The online version contains supplementary material available at 10.1007/s40201-022-00786-2.

Investigating of climate factors associated with the number of COVID-19 incidences in Saudi Arabia

This study investigates the possible association between the climate variables of daily average temperature (°C), relative humidity (%), wind speed (mph), air pressure (mmHg), and the number of COVID-19 incidents in five main cities in Saudi Arabia. Furthermore, other non-climate factors that might influence the number of COVID-19 incidents, such as region, day type, and conducted number of COVID-19 tests (massive testing levels), are included in the model. A negative binomial regression model is applied to study the association between the climate and non-climate factors affecting COVID-19 cases for 75 days with an average temperature range of (18-36)°C. Results show significant findings that the only climate factor affecting the COVID-19 numbers is the average daily temperature. The regression model shows a significant positive association between average daily temperature and the COVID-19 incidents by increasing 6.1% in the number of COVID-19 cases for each extra 1°C average temperature increase.

A meta-analysis result: Uneven influences of season, geo-spatial scale and latitude on relationship between meteorological factors and the COVID-19 transmission.

Meteorological factors have been confirmed to affect the COVID-19 transmission, but current studied conclusions varied greatly. The underlying causes of the variance remain unclear. Here, we proposed two scientific questions: (1) whether meteorological factors have a consistent influence on virus transmission after combining all the data from the studies; (2) whether the impact of meteorological factors on the COVID-19 transmission can be influenced by season, geospatial scale and latitude. We employed a meta-analysis to address these two questions using results from 2813 published articles. Our results showed that, the influence of meteorological factors on the newly-confirmed COVID-19 cases varied greatly among existing studies, and no consistent conclusion can be drawn. After grouping outbreak time into cold and warm seasons, we found daily maximum and daily minimum temperatures have significant positive influences on the newly-confirmed COVID-19 cases in cold season, while significant negative influences in warm season. After dividing the scope of the outbreak into national and urban scales, relative humidity significantly inhibited the COVID-19 transmission at the national scale, but no effect on the urban scale. The negative impact of relative humidity, and the positive impacts of maximum temperatures and wind speed on the newly-confirmed COVID-19 cases increased with latitude. The relationship of maximum and minimum temperatures with the newly-confirmed COVID-19 cases were more susceptible to season, while relative humidity's relationship was more affected by latitude and geospatial scale. Our results suggested that relationship between meteorological factors and the COVID-19 transmission can be affected by season, geospatial scale and latitude. A rise in temperature would promote virus transmission in cold seasons. We suggested that the formulation and implementation of epidemic prevention and control should mainly refer to studies at the urban scale. The control measures should be developed according to local meteorological properties for individual city.

Analysis and Forecast of the COVID-19 Spreading Curve for the Resumption of In-person Classes

With the COVID-19 pandemic, maintaining social distancing is particularly important in daily life. In recently, indoor situations such as face-to-face teaching for university restart are tried to make feasible suggestions depend on the spread of the COVID-19. In this research, we analyze and forecast the COVID-19 spreading curve of the resumption of in-person classes at university by the graph structure with the spread weight of edges based on each student's relation. Our approach is based on the effectiveness of three distancing strategies designed to keep the curve flat and aid make the spread of the COVID-19 controllable. By detecting the possibility of student relation by three strategies, we can analyze the COVID-19 spreading curve by Graph Neural Network(GNN) and SIR model. The SIR model is a simple model that considers a population that belongs to one of the following states: Susceptible (S), Infected (I), and Recovered (R), and we calculate the contagion rate of the pathogen. In this article, we discuss two types of Open Group and Closed Group on university campuses and analyze face-to-face lectures, indoor social activities, and campus cafeterias. To verify the effectiveness of our two types of group, we simulated with the random infection curve by graph neural network model. The simulation analysis results show that our social distancing strategies can reduce the risk of COVID-19 transmission after school restarts. © 2022 IEEE.

Visualization Method for the Spreading Curve of COVID-19 in Universities using GNN

As the reopening of the university after the spread of COVID-19 on campus and we simulate and visualize the initial states spreading of COVID-19. In this research, we analyze and forecast the COVID-19 spreading curve of the resumption of in-person classes at university by the graph structure with the spread weight of edges based on each student's relation. Our approach is based on the effectiveness of three distancing strategies designed to keep the curve flat and aid make the spread of the COVID-19 controllable. By detecting the possibility of student relation based on three strategies, we can analyze the COVID-19 spreading curve by Graph Neural Network (GNN) and SIR model. In this article, we discuss two types of Open Group and Closed Group on university campuses and analyze face-to-face lectures, indoor social activities, and campus cafeterias. To verify the effectiveness of our two types of group, we simulated with the random infection curve by graph neural network model. At last, we visualized the COVID-19 spreading process and the results of diffusion prediction. © 2022 IEEE.

Mathematical Modelling of an Epidemic Based on Covid-19 Spread Functions

The study of the mechanisms of epidemic spread is an important way of controlling the disease. Reducing damage from a coronavirus epidemic is linked to the use of methods and tools for mathematical modelling of Covid-19 spread. Epidemic wave representations are used to characterize the spread of Covid-19, which is highly visual and informative. However, this "wave" representation places increased demands on Covid-19 spread models. For mathematical modelling of the spread of the Covid-19 epidemic, is considered the application of specific Covid-19 propagation functions, based on constrained growth functions. The Covid-19 spread functions show high accuracy in approximating statistical data, which demonstrates the good adequacy of these functions in principle. Application of the Covid-19 propagation functions makes it possible to quantitatively describe the basic concepts of the epidemic and conduct a comparative parametric analysis of the epidemic's spread and predict the development of the epidemic. Comparison of parameter values makes it possible to identify differences in indicators and growth rates, based on which the results of epidemic control can be assessed.

Data Science Models for Short-Term Forecast of COVID-19 Spread in Nigeria

The study presents data science models for a real-time forecast of COVID-19 size and spread in Nigeria. Firstly, an exploratory and comparative study of the disease spread in Nigeria and some other African nations are carried out. Then variants of support vector machine (SVM) using the Gaussian kernel and regression machine learning models suitable for modeling count data variables are built to estimate a 15-day prediction of infection cases. The data science models built in this research give a short-term forecast of the disease’s spread which is useful in better understanding the spread patterns of the disease as well as enabling future preparedness and better management of the disease by the government and relevant authorities. The research outcome can therefore serve as an effective decision support system. This work can also serve as an alternative to the mathematical-based epidemiological models for the forecast of COVID-19 spread because of their inherent advantages of learning from historical datasets and generalizing with new sets of data which promises better results. © 2022, The Author(s), under exclusive license to Springer Nature Switzerland AG.

Spatial analysis of COVID-19 spread in Europe using “center of gravity” concept

The COVID-19 global pandemic has affected all countries and become a real challenge for humanity. Scientists are intensively studying the specifics of the disease caused by this virus and the impact of restrictive measures on the economy, environment and other aspects of life. We present an approach to spatial modeling and analysis of the COVID19 spreading process using the concept of the “center of gravity”. Based on weekly data on this disease in all European countries, the trajectories of the center of gravity of new cases and deaths during the pandemic have been calculated. These two trajectories reflect the dominant role of certain countries or regions of Europe during different stages of the pandemic. It is shown that the amplitude of the trajectory of the center of gravity in the longitudinal direction was quite high (about 1,500 km) in comparison with the amplitude of the trajectory in the latitudinal direction (500 km). Using an approximation of the weekly data, the delays between the peaks of new cases and mortality for different countries were calculated, as well as the delays in comparison with the countries that first reached the peaks of morbidity and mortality. The trajectories of the center of gravity are also calculated for the regions of Ukraine as an example of analysis at the national scale. These results provide an opportunity to understand the spatial specifics of the spread of COVID-19 on the European continent and the roles of separate countries in these complex processes. © 2022 Lviv Polytechnic National University.