Mathematical Analysis of Fractal-Fractional Mathematical Model of COVID-19

In this work, we modified a dynamical system that addresses COVID-19 infection under a fractal-fractional-order derivative. The model investigates the psychological effects of the disease on humans. We establish global and local stability results for the model under the aforementioned derivative. Additionally, we compute the fundamental reproduction number, which helps predict the transmission of the disease in the community. Using the Carlos Castillo-Chavez method, we derive some adequate results about the bifurcation analysis of the proposed model. We also investigate sensitivity analysis to the given model using the criteria of Chitnis and his co-authors. Furthermore, we formulate the characterization of optimal control strategies by utilizing Pontryagin's maximum principle. We simulate the model for different fractal-fractional orders subject to various parameter values using Adam Bashforth's numerical method. All numerical findings are presented graphically. © 2023 by the authors.

Evolutionary game analysis of crowding risk control strategies during the COVID-19 Pandemic

To discuss the decision-making scheme of crowding risk management during the COVID-19 pandemic, this paper constructs an evolutionary game model based on the changes of pedestrian and government strategies, and simulates the strategy selection under different states. The results show that under the condition of pedestrian rationality, when the difference between the benefits and costs of the government's active response strategy is less than the benefits of inaction, the government will choose the strategy of inaction. If the benefit of rational action is less than the additional benefit of irrational action, pedestrians will choose irrational action. By establishing the replication dynamic equations of governments and pedestrians, the stability strategy of the system is analyzed. It is found that the values of R1, R2, R3, R4, R5, C1, C2, C3, C4, C5, C6, C7 will affect the strategy choices of the players, and how to measure the benefits and costs under different circumstances becomes the key to the problem. These findings provide a theoretical basis for the risk control decision of human crowding during the COVID-19 epidemic. © 2023 SPIE.

Effectiveness of a multicomponent intervention to face the COVID-19 pandemic in Rio de Janeiro's favelas: difference-in-differences analysis.

INTRODUCTION: Few community-based interventions addressing the transmission control and clinical management of COVID-19 cases have been reported, especially in poor urban communities from low-income and middle-income countries. Here, we analyse the impact of a multicomponent intervention that combines community engagement, mobile surveillance, massive testing and telehealth on COVID-19 cases detection and mortality rates in a large vulnerable community (Complexo da Maré) in Rio de Janeiro, Brazil. METHODS: We performed a difference-in-differences (DID) analysis to estimate the impact of the multicomponent intervention in Maré, before (March-August 2020) and after the intervention (September 2020 to April 2021), compared with equivalent local vulnerable communities. We applied a negative binomial regression model to estimate the intervention effect in weekly cases and mortality rates in Maré. RESULTS: Before the intervention, Maré presented lower rates of reported COVID-19 cases compared with the control group (1373 vs 1579 cases/100 000 population), comparable mortality rates (309 vs 287 deaths/100 000 population) and higher case fatality rates (13.7% vs 12.2%). After the intervention, Maré displayed a 154% (95% CI 138.6% to 170.4%) relative increase in reported case rates. Relative changes in reported death rates were -60% (95% CI -69.0% to -47.9%) in Maré and -28% (95% CI -42.0% to -9.8%) in the control group. The case fatality rate was reduced by 77% (95% CI -93.1% to -21.1%) in Maré and 52% (95% CI -81.8% to -29.4%) in the control group. The DID showed a reduction of 46% (95% CI 17% to 65%) of weekly reported deaths and an increased 23% (95% CI 5% to 44%) of reported cases in Maré after intervention onset. CONCLUSION: An integrated intervention combining communication, surveillance and telehealth, with a strong community engagement component, could reduce COVID-19 mortality and increase case detection in a large vulnerable community in Rio de Janeiro. These findings show that investment in community-based interventions may reduce mortality and improve pandemic control in poor communities from low-income and middle-income countries.

Learning Behavior and Perspective of Medical Students in India on COVID-19 Pandemic

Introduction: Medical colleges in India are opening gradually for resumption of medical education. Assessment of learning behavior and perspectives of students toward COVID-19 pandemic is essential for effective medical education as well as to assess their role in pandemic if need arises. Hence, the present study aims to assess learning behavior and perspective of medical students on COVID-19 pandemic. Materials and Methods: A descriptive cross-sectional study was conducted from September to December 2020 among 392 medical students at various medical colleges of the country. An online self-administered questionnaire was designed to assess learning behavior of medical students about infection prevention and control practices in COVID-19 adopted during the course of pandemic. Snowball sampling method was used for data collection. Results: On assessment of 392 medical students about preventive measures during COVID-19 pandemic, more than 80% of medical students were aware of the importance of facemask, social distancing, frequent hand washing, and use of digital tools such as Aarogya Setu app in surveillance. Majority of students were aware of the symptoms, testing, and treatment protocols along with importance of self-reporting. Most of the students were aware of various sources of getting scientific and relevant information about pandemic. While majority of students were aware of rationale of quarantine, appropriate waste management technique, and post-COVID precautions, there was a lack of knowledge about appropriate disinfection measures. Conclusions: Medical students are an asset to health care;learning behaviors adopted by them can certainly help to assist health-care system in COVID times.

Development of gait rehabilitation devices: a review of the literature

A "Stroke" is a neurological disease due to poor blood flowing to the brain, resulting in body cell death. It is ranked second as the most common cause of death globally. The "World Health Organization" estimates that about 15 million people suffer a stroke annually. Most stroke survivors have gait disorders, and most patients cannot walk without assistance. Physiotherapy is crucial for stroke patients to recover and maintain their mobility, functionality, and well-being. In the last 20 years, the replacement of physiotherapists with wearable robotics has become essential due to the developing technology, the need for economic growth, and the challenging health circumstances around the world, such as the COVID-19 pandemic recently. Lower Limb Exoskeleton (LLE) represents the solution for stroke patients under such circumstances, though its performance is a critical challenge paid attention to in the industry. This challenge has motivated the researchers to investigate the application of gait rehabilitation. This review presents and discusses the developments in the control system of LLE over the last decade. It also explores the limitations, new directions, and recommendations in LLE development according to the literature.

Analyzing the effects of quarantine, isolation, and vaccination on the spread of COVID-19 via a mathematical model

The main COVID-19 control strategies presently practiced are maintaining social distancing, quarantin-ing suspected exposures, and isolating infectious people. In this paper, a deterministic compartmental mathematical model is proposed considering these three control strategies. Based on the proposed model the effect of vaccination on the suppression of the disease is discussed. Critical vaccination rate and vaccinated population size relevant to disease suppression are determined based on the proposed mathematical model. Different forms of the most used key term in infectious disease modelling, reproduction number, are determined relevant to the proposed model. Sensitivity analysis of the reproduction numbers is done to identify model parameters mostly affecting the spread of the disease. Based on the reproduction number of the model disease controlling parameter regions are determined and graphical representations of those parameter regions are presented. Based on the results of the proposed mathematical model, it is observed that earlier implementation of the vaccination process is helpful to better control the disease. However, it takes considerable time to invent successful vaccinations for newly out-breaking diseases like COVID-19. Therefore, it took considerable time to start the vaccination process for COVID-19. It is observed that after starting a vaccination process at a particular rate it should continue until the vaccinated population reaches a critical size. © 2023, National Science Foundation. All rights reserved.

Contact Tracing and Epidemic Intervention via Deep Reinforcement Learning

The recent outbreak of COVID-19 poses a serious threat to people's lives. Epidemic control strategies have also caused damage to the economy by cutting off humans' daily commute. In this article, we develop an Individual-based Reinforcement Learning Epidemic Control Agent (IDRLECA) to search for smart epidemic control strategies that can simultaneously minimize infections and the cost of mobility intervention. IDRLECA first hires an infection probability model to calculate the current infection probability of each individual. Then, the infection probabilities together with individuals' health status and movement information are fed to a novel GNN to estimate the spread of the virus through human contacts. The estimated risks are used to further support an RL agent to select individual-level epidemic-control actions. The training of IDRLECA is guided by a specially designed reward function considering both the cost of mobility intervention and the effectiveness of epidemic control. Moreover, we design a constraint for control-action selection that eases its difficulty and further improve exploring efficiency. Extensive experimental results demonstrate that IDRLECA can suppress infections at a very low level and retain more than 95% of human mobility. © 2023 Copyright held by the owner/author(s). Publication rights licensed to ACM.

The role and contribution of civil society and community actors in COVID-19 prevention and control: the case of the COMVID COVID-19 movement in Burkina Faso.

Communities should play a crucial role in the fight against public health emergencies but ensuring their effective and sustained engagement remains a challenge in many countries. In this article, we describe the process of mobilising community actors to contribute to the fight against COVID-19 in Burkina Faso. During the early days of the pandemic, the national COVID-19 response plan called for the involvement of community actors, but no strategy had been defined for this purpose. The initiative to involve community actors in the fight against COVID-19 was taken, independently of the government, by 23 civil society organisations gathered through a platform called 'Health Democracy and Citizen Involvement (DES-ICI)'. In April 2020, this platform launched the movement 'Communities are committed to Eradicate COVID-19 (COMVID COVID-19)' which mobilised community-based associations organised into 54 citizen health watch units (CCVS) in Ouagadougou city. These CCVS worked as volunteers, performing door-to-door awareness campaigns. The psychosis created by the pandemic, the proximity of civil society organisations to the communities and the involvement of religious, customary and civil authorities facilitated the expansion of the movement. Given the innovative and promising nature of these initiatives, the movement gained recognition that earned them a seat on the national COVID-19 response plan. This gave them credibility in the eyes of the national and international donors, thus facilitating the mobilisation of resources for the continuity of their activities. However, the decrease in financial resources to offset the community mobilisers gradually reduced the enthusiasm for the movement. In a nutshell, the COMVID COVID-19 movement fostered dialogues and collaboration among civil society, community actors and the Ministry of Health, which plans to engage the CCVS beyond the COVID-19 response, for the implementation of other actions within the national community health policy.

A differential epidemic Model for Information, Misinformation, and disinformation in online Social Networks: CoVId-19 Vaccination

These days the online social network has become a huge source of data. People are actively sharing information on these platforms. The data on online social networks can be misinformation, information, and disinformation. Because online social networks have become an important part of our lives, the information on online social networks makes a great impact on us. Here a differential epidemic model for information, misinformation, and disinformation on online social networks is proposed. The expression for basic reproduction number has been developed. Again, the stability condition for the system at both infection-free and endemic equilibriums points has been discussed. The numerical simulation has been performed to validate the theoretical results. Data available on Twitter related to COVID-19 vaccination is used to perform the experiment. Finally, the authors discuss the control strategy to minimize the misinformation and disinformation related to vaccination. © 2022 Authors. All rights reserved.

Smart Health Care Monitoring and Indoor Occupancy Control System using Arduino

The recent COVID-19 pandemic has necessitated the need to develop effective COVID-19 pandemic control strategies. One of the crucial steps for individual protection is to stop the virus spread by the wearing face masks. The proposed method is developed to monitor the infected people in the crowded public areas like shopping centers, wedding hall, workplace, school or college. The abnormal temperature is detected by using sensor and the obtained signal will then be sent to the Arduino device connected to the controller. In order to stop the spread of COVID 19 viruses, this study intends to design and develop a novel system to automatically limit the room capacity based on temperature. The proposed Atmega328 microcontroller-based body temperature detection and a room capacity measuring device is connected with the android smart phone of the user. © 2022 IEEE.

Modelling presymptomatic infectiousness in COVID-19

This paper considers SEPIR, an extension of the well-known SEIR continuous simulation compartment model. Both models can be fitted to real data as they include parameters that can be estimated from the data. SEPIR deploys an additional presymptomatic infectious compartment, not modelled in SEIR but known to exist in COVID-19. This stage can also be fitted to data. We focus on how to fit SEPIR to a first wave of COVID. Both SEIR and SEPIR and the existing SEIR models assume a homogeneous mixing population with parameters fixed. Moreover, neither includes dynamically varying control strategies deployed against the virus. If either model is to represent more than just a single wave of the epidemic, then the parameters of the model would have to be time dependent. In view of this, we also show how reproduction numbers can be calculated to investigate the long-term overall outcome of an epidemic. © 2023 The Operational Research Society.

Time Series based Models for Corona Data Analytics

There is a great challenge to deal with prediction of an epidemic or pandemic in the future through artificial intelligence or state-of-art technology. This is evident in the case of pandemic happened from January 2020 which is a result of corona virus. In early stages of covid-19 caused by corona virus, the symptoms are not severe and mostly cured through self-medication. In this situation, estimating the real spread based on the reports from various hospitals might be misleading. There might be lot of variation in the reports based on different types of measurements performed, and the tests conducted on only the symptomatic patients. In spite of all these constraints, a huge amount of covid-19 related data is published since 3 years and also updated on a daily basis. This serves as a motivation to consider various mathematical models to predict the course of change in an epidemic and result in effective control strategies. The challenge is to predict the peak and end of the epidemic together with its evolution through available incomplete data and intrinsic complexity. In this paper, time series models are proposed to analyze corona spread data and analyzing its impact based on gender, age and geographical location. The proposed algorithm leverages machine learning models to predict number of corona cases in the future. An early detection of spread of corona would help in stopping community transmission and this serves a major motivation for this research. ARIMA model and Recurrent Neural Networks (RNN) based LSTM model perform way better than the machine learning models based on regression and decision trees. © 2022 IEEE.

How big of an impact do asymptomatic people have on the dynamics of an epidemic?

Asymptomatic carriers serve as a potential source of transmission of epidemic diseases. Exposed people who develop symptoms only get tested and remain isolated in their homes or sometimes in hospitals when needed. In contrast, the asymptomatic individuals go untested and spread the disease silently as they roam freely throughout their entire infectious lifetime. The work intends to explore the role of asymptomatic carriers in the transmission of epidemic diseases and investigate suitable optimal control strategies. We propose a SEIAQR compartmental model subdividing the total population into six different compartments. To illustrate the model's implication, we estimate the number of asymptomatic individuals using COVID-19 data during June 9–July 18, 2021 from Bangladesh. We then analyze the model to explore whether the epidemic subsides if the asymptomatic individuals are tested randomly and isolated. Finally, to gain a better understanding of the potential of this unidentified transmission route, we propose an optimal control model considering two different control strategies: personal protective measures and isolation of asymptomatic carriers through random testing. Our results show that simultaneous implementation of both control strategies can reduce the epidemic early. Most importantly, sustained effort in identifying and isolation of asymptotic individuals allows relaxation in personal protective measures. © 2023

What does equitable global health research and delivery look like? Tackling Infections to Benefit Africa (TIBA) partnership as a case study.

There is a current global push to identify and implement best practice for delivering maximum impact from development research in low-income and middle-income countries. Here, we describe a model of research and capacity building that challenges traditional approaches taken by western funders in Africa. Tackling Infections to Benefit Africa (TIBA) is a global health research and delivery partnership with a focus on strengthening health systems to combat neglected tropical diseases, malaria and emerging pathogens in Africa. Partners are academic and research institutions based in Ghana, Sudan, Rwanda, Uganda, Kenya, Tanzania, Zimbabwe, Botswana, South Africa and the UK. Fifteen other African countries have participated in TIBA activities. With a starting budget of under £7 million, and in just 4 years, TIBA has had a verified impact on knowledge, policy practice and capacity building, and on national and international COVID-19 responses in multiple African countries. TIBA's impact is shown in context-specific metrics including: strengthening the evidence base underpinning international policy on neglected tropical diseases; 77% of research publications having Africa-based first and/or last authors; postgraduate, postdoctoral and professional training; career progression for African researchers and health professionals with no net brain drain from participating countries; and supporting African institutions. Training in real-time SARS-CoV-2 viral genome sequencing provided new national capabilities and capacities that contributed to both national responses and global health security through variant detection and tracking. TIBA's experience confirms that health research for Africa thrives when the agenda and priorities are set in Africa, by Africans, and the work is done in Africa. Here, we share 10 actionable recommendations for researchers and funders from our lessons learnt.

Cost-effectiveness of a mathematical modeling with optimal control approach of spread of COVID-19 pandemic: A case study in Peru

COVID-19 pandemic affects 213 countries and regions around the world. Which the number of people infected with the virus exceeded 26 millions infected and more than 870 thousand deaths until september 04, 2020, in the world, and Peru among the countries most affected by this pandemic. So we proposed a mathematical model describes the dynamics of spread of the COVID-19 pandemic in Peru. The optimal control strategy based on the model is proposed, and several reasonable and suitable control strategies are suggested to the prevention and reduce the spread COVID-19 virus, by conducting awareness campaigns and quarantine with treatment. coronavirus 2019 (COVID-19). Pontryagin's maximum principle is used to characterize the optimal controls and the optimality system is solved by an iterative method. Finally, some numerical simulations are performed to verify the theoretical analysis using Matlab. © 2022

Determinants of vaccine acceptance, knowledge, attitude, and prevention practices against COVID-19 among governmental healthcare workers in Addis Ababa and Adama, Ethiopia: A cross-sectional study.

Background and Aims: COVID-19 vaccines are vital tools for infection prevention and control of the pandemic. However, coronavirus immunization requires acceptance among healthcare workforces and by the community. In Ethiopia, studies focused on determinants of vaccine acceptance, knowledge, attitude, and prevention practices (KAP) contrary to the novel coronavirus among healthcare staff are limited. Hence, closing this gap requires research. Methods: A cross-sectional study was conducted on 844 governmental healthcare workers. A stratified, simple random sampling technique was used to select the respondents. Data were collected using a structured questionnaire. Binary and multivariable logistic regression statistical models were used to analyze the data. Results: This study indicated that only 57.9% of the participants had good COVID-19 vaccine acceptance, meaning they took at least a dose of the vaccine themselves. We found that 65%, 60.9%, and 51.3% of the participants had good knowledge, prevention practices, and attitude against the pandemic. The novel coronavirus vaccine acceptance rate was 2.19 times more likely among females (adjusted odds ratio [AOR] = 2.19 with 95% confidence interval [CI]: 1.54-3.10) than among male participants. Further, respondents who did not report having any chronic diseases were 9.40 times higher to accept COVID-19 vaccines (AOR = 9.40 with 95% CI: 4.77, 18.53) than those who reported having a chronic condition. However, healthcare workers who had a habit of chewing khat at least once per week were 4% less likely to take the vaccine (AOR = 0.04 with 95% CI: 0.01, 0.32) than those who had no habit of chewing khat. Conclusion: Many core factors influencing COVID-19 vaccine acceptance were identified. A significant number of participants had poor vaccine acceptance, KAP against COVID-19. Therefore, the government should adopt urgent and effective public health measures, including public campaigns to enhance public trust in COVID-19 vaccines. In addition, continuous, timely, and practical training should be provided to healthcare workers.

Capacity building at points of entry during COVID-19 pandemic: harmonising training curriculum for Economic Community of West African States.

This paper describes the process for developing, validating and disseminating through a train-the-trainer (TOT) event a standardised curriculum for public health capacity building for points of entry (POE) staff across the 15-member state Economic Community of West African States (ECOWAS) that reflects both international standards and national guidelines.A five-phase process was used in developing the curriculum: phase (1) assessment of existing materials developed by the US Centers for Disease Control and Prevention (CDC), Africa CDC and the West African Economic and Monetary Union, (2) design of retained and new, harmonised content, (3) validation by the national leadership to produce final content, (4) implementation of the harmonised curriculum during a regional TOT, and (5) evaluation of the curriculum.Of the nine modules assessed in English and French, the technical team agreed to retain six harmonised modules providing materials for 10 days of intensive training. Following the TOT, most participants (n=28/30, 93.3%) indicated that the International Health Regulations and emergency management modules were relevant to their work and 96.7% (n=29/30) reported that the training should be cascaded to POE staff in their countries.The ECOWAS harmonised POE curriculum provides a set of training materials and expectations for national port health and POE staff to use across the region. This initiative contributes to reducing the effort required by countries to identify emergency preparedness and response capacity-building tools for border health systems in the Member States in a highly connected region.

A Brief Review of Safe Management of COVID-19 Related Death and its Control Strategies

Coronavirus disease 2019 (COVID-19) is a disease caused by a virus named Severe Acute Respiratory Syndrome Coronavirus 2 (SARS-CoV-2). With the increasing COVID-19 associated deaths, the infectivity and handling of the dead bodies associated with COVID-19 has become a worldwide concern in terms of spreading the infection further during handling of these bodies. As a precaution, measures should be undertaken to contain the spread of infection while handling COVID-19 associated deaths. This article reviews the current management of COVID-19 associated deaths and control strategies in various countries in order to guide medical examiners to handle the bodies including those autopsied. [ FROM AUTHOR]

A review on characteristics and mitigation strategies of indoor air quality in underground subway stations.

Due to the rapidly increasing ridership and the relatively enclosed underground space, the indoor air quality (IAQ) in underground subway stations (USSs) has attracted more public attention. The air pollutants in USSs, such as particulate matter (PM), CO2 and volatile organic compounds (VOCs), are hazardous to the health of passengers and staves. Firstly, this paper presents a systematic review on the characteristics and sources of air pollutants in USSs. According to the review work, the concentrations of PM, CO2, VOCs, bacteria and fungi in USSs are 1.1-13.2 times higher than the permissible concentration limits specified by WHO, ASHRAE and US EPA. The PM and VOCs are mainly derived from the internal and outdoor sources. CO2 concentrations are highly correlated with the passenger density and the ventilation rate while the exposure levels of bacteria and fungi depend on the thermal conditions and the settled dust. Then, the online monitoring, fault detection and prediction methods of IAQ are summarized and the advantages and disadvantages of these methods are also discussed. In addition, the available control strategies for improving IAQ in USSs are reviewed, and these strategies are classified and compared from different viewpoints. Lastly, challenges of the IAQ management in the context of the COVID-19 epidemic and several suggestions for underground stations' IAQ management in the future are put forward. This paper is expected to provide a comprehensive guidance for further research and design of the effective prevention measures on air pollutants in USSs so as to achieve more sustainable and healthy underground environment.