Transmission Dynamics Of COVID-19 Research Articles

Deterministic and Stochastic Nonlinear Model for Transmission Dynamics of COVID-19 with Vaccinations Following Bayesian-Type Procedure

Deterministic and Stochastic Nonlinear Model for Transmission Dynamics of COVID-19 with Vaccinations Following Bayesian-Type Procedure

SEIR Model on the Review Impact of Social Distancing for COVID-19 Transmission Dynamics

The COVID-19 pandemic has had a huge influence on world health, with many countries enacting lockdowns and social distancing measures to help reduce transmission. However, the efficiency of these efforts is questionable, particularly in locations like as North East Nigeria, where specific socioeconomic and demographic characteristics can alter virus transmission dynamics. In this paper, we assess the pandemic in North East Nigeria by modifying the SIR to SEIR compartmental model, which incorporates data from the National Centre for Disease Control. We exposed that social distancing techniques like face masks, hand washing, and avoiding big gatherings are more successful than lockdowns in slowing the spread of COVID-19 in the region. Our findings suggest that future pandemic response plans should concentrate on social distancing measures that are both effective and feasible while avoiding the economic and social disruptions caused by lockdowns. Our work emphasizes the significance of specialized approaches to pandemic control in varied situations, including North East Nigeria.

COVID-19 vaccination strategies in Africa: A scoping review of the use of mathematical models to inform policy.

Mathematical models are vital tools to understand transmission dynamics and assess the impact of interventions to mitigate COVID-19. However, historically, their use in Africa has been limited. In this scoping review, we assess how mathematical models were used to study COVID-19 vaccination to potentially inform pandemic planning and response in Africa. We searched six electronic databases: MEDLINE, Embase, Web of Science, Global Health, MathSciNet and Africa-Wide NiPAD, using keywords to identify articles focused on the use of mathematical modelling studies of COVID-19 vaccination in Africa that were published as of October 2022. We extracted the details on the country, author affiliation, characteristics of models, policy intent and heterogeneity factors. We assessed quality using 21-point scale criteria on model characteristics and content of the studies. The literature search yielded 462 articles, of which 32 were included based on the eligibility criteria. Nineteen (59%) studies had a first author affiliated with an African country. Of the 32 included studies, 30 (94%) were compartmental models. By country, most studies were about or included South Africa (n = 12, 37%), followed by Morocco (n = 6, 19%) and Ethiopia (n = 5, 16%). Most studies (n = 19, 59%) assessed the impact of increasing vaccination coverage on COVID-19 burden. Half (n = 16, 50%) had policy intent: prioritising or selecting interventions, pandemic planning and response, vaccine distribution and optimisation strategies and understanding transmission dynamics of COVID-19. Fourteen studies (44%) were of medium quality and eight (25%) were of high quality. While decision-makers could draw vital insights from the evidence generated from mathematical modelling to inform policy, we found that there was limited use of such models exploring vaccination impacts for COVID-19 in Africa. The disparity can be addressed by scaling up mathematical modelling training, increasing collaborative opportunities between modellers and policymakers, and increasing access to funding.

Understanding COVID-19 propagation: a comprehensive mathematical model with Caputo fractional derivatives for Thailand

This research introduces a sophisticated mathematical model for understanding the transmission dynamics of COVID-19, incorporating both integer and fractional derivatives. The model undergoes a rigorous analysis, examining equilibrium points, the reproduction number, and feasibility. The application of fixed point theory establishes the existence of a unique solution, demonstrating stability in the model. To derive approximate solutions, the generalized Adams-Bashforth-Moulton method is employed, further enhancing the study's analytical depth. Through a numerical simulation based on Thailand's data, the research delves into the intricacies of COVID-19 transmission, encompassing thorough data analysis and parameter estimation. The study advocates for a holistic approach, recommending a combined strategy of precautionary measures and home remedies, showcasing their substantial impact on pandemic mitigation. This comprehensive investigation significantly contributes to the broader understanding and effective management of the COVID-19 crisis, providing valuable insights for shaping public health strategies and guiding individual actions.

Modeling the transmission dynamics of COVID-19 with genetically resistant humans

The emergence of a coronavirus disease (COVID-19) pandemic caused by severe acute respiratory syndrome coronavirus (SARS-COV 2) has caused an unprecedented global societal and public health crisis. A mathematical model that incorporates an individual’s ability to resist COVID-19 infection is considered to study the dynamics of COVID-19. Detection and testing for COVID-19 in individuals who has ability to resist the infection, proper preventive measures, and timely medical care are essential in managing and controlling COVID-19 outbreak. These allows us to make quantifiable predictions about the effects of testing utilization on the COVID-19 prevalence. First, we analyze the disease-free state of the COVID-19 model, calculate the baseline control reproduction number, and obtain the local stability of disease-free equilibrium. The model is calibrated using data obtained from Nigeria Centre for Disease Control and key parameters of the model are estimated. Sensitivity analysis is carried out to investigate the influence of the parameters in curtailing the disease. Numerical simulations are then used to explore the behavior of the model solutions involving the novel variable (genetically resistant humans) and control measures. Our results suggest that strict intervention effort (early detection of genetically resistant individuals, testing and isolation of the infected individuals) is required for quick suppression of the disease.

Analyzing Co-Infection Dynamics: A Mathematical Approach Using Fractional Order Modeling and Laplace-Adomian Decomposition

The co-infection of HIV and COVID-19 is a pressing health concern, carrying substantial potential consequences. This study focuses on the vital task of comprehending the dynamics of HIV-COVID-19 co-infection, a fundamental step in formulating efficacious control strategies and optimizing healthcare approaches. Here, we introduce an innovative mathematical model grounded in Caputo fractional order differential equations, specifically designed to encapsulate the intricate dynamics of co-infection. This model encompasses multiple critical facets: the transmission dynamics of both HIV and COVID-19, the host’s immune responses, and the influence of treatment interventions. Our approach embraces the complexity of these factors to offer an exhaustive portrayal of co-infection dynamics. To tackle the fractional order model, we employ the Laplace-Adomian decomposition method, a potent mathematical tool for approximating solutions in fractional order differential equations. Utilizing this technique, we simulate the intricate interactions between these variables, yielding profound insights into the propagation of co-infection. Notably, we identify pivotal contributors to its advancement. In addition, we conduct a meticulous analysis of the convergence properties inherent in the series solutions acquired through the Laplace-Adomian decomposition method. This examination assures the reliability and accuracy of our mathematical methodology in approximating solutions. Our findings hold significant implications for the formulation of effective control strategies. Policymakers, healthcare professionals, and public health authorities will benefit from this research as they endeavor to curtail the proliferation and impact of HIV-COVID-19 co-infection.

Modeling the Impact of Misinformation on the Transmission Dynamics of COVID-19

The threat posed by the COVID-19 pandemic has been accompanied by an epidemic of misinformation, causing confusion and mistrust among the public. Misinformation about COVID-19 whether intentional or unintentional takes many forms, including conspiracy theories, false treatments, and inaccurate information about the origins and spread of the virus. Though the pandemic has brought to light the significant impact of misinformation on public health, mathematical modeling emerged as a valuable tool for understanding the spread of COVID-19 and the impact of public health interventions. However, there has been limited research on the mathematical modeling of the spread of misinformation related to COVID-19. In this paper, we present a mathematical model of the spread of misinformation related to COVID-19. The model highlights the challenges posed by misinformation, in that rather than focusing only on the reproduction number that drives new infections, there is an additional threshold parameter that drives the spread of misinformation. The equilibria of the model are analyzed for both local and global stability, and numerical simulations are presented. We also discuss the model’s potential to develop effective strategies for combating misinformation related to COVID-19.

Analyzing the COVID-19 Transmission Dynamics in Acre, Brazil: An Ecological Study.

The north region of Brazil is characterized by significant vulnerabilities, notably surpassing national poverty indicators. These disparities exacerbated the impact of respiratory illnesses on the healthcare system during the COVID-19 pandemic, particularly in areas with limited healthcare resources, inadequate infrastructure, and barriers to healthcare access. The crisis was further influenced by multiple lineages that emerged as significant virus variants associated with increased transmissibility. Within this context, our ecological study focused on analyzing the epidemiological evolution of COVID-19 in the state of Acre. We constructed time-series trends in incidence, lethality, and mortality from March 2020 to December 2022 using the Prais-Winsten regression model. Our findings revealed that in 2020, there was an increasing trend in incidence, while mortality and lethality continued to decrease (p < 0.05). In the following year, both incidence and mortality decreased, while lethality increased at a rate of 1.02% per day. By the end of 2022, trends remained stationary across all rates. These results underscore the importance of ongoing surveillance and adaptive public health measures to bolster the resilience of healthcare systems in remote and vulnerable regions. Indeed, continuous monitoring of the most predominant SARS-CoV-2 lineages and their dynamics is imperative. Such proactive actions are essential for addressing emerging challenges and ensuring effective responses to adverse situations.

Architectural design and epidemic prevalence: Insights from Hong Kong's fifth wave

Understanding spatiotemporal patterns of COVID-19 transmissions and their relationships with the built environment is crucial for effective pandemic prevention. While existing research has primarily focused on the locations inhabited or visited by COVID-19 cases, limited attention has been given to the spatiotemporal pattern of transmission locations and their associations with architectural design forms. This study addresses this research gap by examining the spatiotemporal distribution patterns of COVID-19 transmission locations and their associations with residential building forms in high density context of Hong Kong during the fifth wave of the outbreak. Employing information on n = 319 residential buildings with a restriction-testing declaration, we report that attributes of building blocks, especially design forms, and housing type were associated with transmission rates. Our findings revealed that public housing were more vulnerable to COVID-19 pandemic relative to private housing block. Relative to the L-shaped building form, Y- and X-shaped building forms had the lowest COVID-19 prevalence, adjusting for other factors. The study results provide valuable insights for urban designers, health authorities and policymakers regarding keys attributes of building forms and types that influence the transmission dynamics of COVID-19, exemplifying the role architectural design has on pandemic prevention. Evidence generated from this study can be tested in other high density contexts as we prepare to risk-proof our cities from future pandemics.

Household secondary attack rate amongst the susceptible contacts of COVID-19 cases and its epidemiological profile: A retrospective study from central Gujarat, India

ABSTRACT Introduction: Secondary attack rate (SAR) is a proportion of primary contacts developing the diseases within the incubation period upon exposure to a primary case among the total susceptible household contacts. The epidemiological profile and SAR will help in understanding the transmission dynamics of COVID-19 for further strengthening preventive and effective control measures. Objectives of the Study: The study was conducted with the primary objective to estimate the household SAR of COVID-19 cases at Mahisagar District, Gujarat, and to study the epidemiological profile of primary and secondary cases of COVID-19. Methodology: A retrospective study was conducted to estimate SAR among 245 confirmed cases of COVID-19 and 898 susceptible household contacts in Mahisagar district through telephonic interview and questionnaire by the Investigator team. Results: The household SAR was calculated from the current study in Mahisagar district, Gujarat, and was 13.9%. Among primary cases, 74.7% males were affected, and among secondary cases, 52.8% females were affected. The SAR among elderly was 23.9%, and the SAR among children was 3.9%. 74.1% household contacts had developed disease among symptomatic contacts as compared to asymptomatic household contacts. Conclusion: The household SAR in Mahisagar district, Gujarat, was found to be 13.9%. In primary cases, more males, and in secondary cases, more females were found to be affected. The household SAR was increased in elderly as compared to the younger age group. The SAR was more among the contacts of symptomatic cases than asymptomatic cases. Overall hospitalization in public hospitals was more than that in private hospitals.

Evaluating the Demand for Nucleic Acid Testing in Different Scenarios of COVID-19 Transmission: A Simulation Study.

The 2019 novel coronavirus (COVID-19) has been recognized as the most severe human infectious disease pandemic in the past century. To enhance our ability to control potential infectious diseases in the future, this study simulated the influence of nucleic acid testing on the transmission of COVID-19 across varied scenarios. Additionally, it assessed the demand for nucleic acid testing under different circumstances, aiming to furnish a decision-making foundation for the implementation of nucleic acid screening measures, the provision of emergency materials, and the allocation of human resources. Considering the transmission dynamics of COVID-19 and the preventive measures implemented by countries, we explored three distinct levels of epidemic intensity: community transmission, outbreak, and sporadic cases. Integrating the theory of scenario analysis, we formulated six hypothetical epidemic scenarios, each corresponding to possible occurrences during different phases of the pandemic. We developed an improved SEIR model, validated its accuracy using real-world data, and conducted a comprehensive analysis and prediction of COVID-19 infections under these six scenarios. Simultaneously, we assessed the testing resource requirements associated with each scenario. We compared the predicted number of infections simulated by the modified SEIR model with the actual reported cases in Israel to validate the model. The root mean square error (RMSE) was 350.09, and the R-squared (R2) was 0.99, indicating a well-fitted model. Scenario4 demonstrated the most effective prevention and control outcomes. Strengthening non-pharmaceutical interventions and increasing nucleic acid testing frequency, even under low testing capacity, resulted in a delayed epidemic peak by 78days. The proportion of undetected cases decreased from 77.83% to 31.21%, and the overall testing demand significantly decreased, meeting maximum demand even with low testing capacity. The initiation of testing influenced case detection probability. Under high testing capacity, increasing testing frequency elevated the detection rate from 36.40% to 77.83%. Nucleic acid screening proved effective in reducing the demand for testing resources under diverse epidemic prevention and control strategies. While effective interventions and nucleic acid screening measures substantially diminished the demand for testing-related resources, varying degrees of insufficient testing capacity may still persist. The nucleic acid detection strategy proves effective in promptly identifying and isolating infected individuals, thereby mitigating the infection peak and extending the time to peak. In situations with constrained testing capacity, implementing more stringent measures can notably decrease the number of infections and alleviate resource demands. The improved SEIR model demonstrates proficiency in predicting both reported and unreported cases, offering valuable insights for future infection risk assessments. Rapid evaluations of testing requirements across diverse scenarios can aptly address resource limitations in specific regions, offering substantial evidence for the formulation of future infectious disease testing strategies.

Modelling Transmission Dynamics of COVID-19 During Pre-Vaccination Period in Malaysia: A GUI-Based SEIRD Predictive Model using Streamlit

Modelling Transmission Dynamics of COVID-19 During Pre-Vaccination Period in Malaysia: A GUI-Based SEIRD Predictive Model using Streamlit

A mathematical model for the transmission of co-infection with COVID-19 and kidney disease

The world suffers from the acute respiratory syndrome COVID-19 pandemic, which will be scary if other co-existing illnesses exacerbate it. The co-occurrence of the COVID-19 virus with kidney disease has not been available in the literature. So, further research needs to be conducted to reveal the transmission dynamics of COVID-19 and kidney disease. This study aims to create mathematical models to understand how COVID-19 interacts with kidney diseases in specific populations. Therefore, the initial step was to formulate a deterministic Susceptible-Infected-Recovered (SIR) mathematical model to depict the co-infection dynamics of COVID-19 and kidney disease. A mathematical model with seven compartments has been developed using nonlinear ordinary differential equations. This model incorporates the invariant region, disease-free and endemic equilibrium, along with the positivity solution. The basic reproduction number, calculated via the next-generation matrix, allows us to assess the stability of the equilibrium. Sensitivity analysis is also utilised to understand the influence of each parameter on disease spread or containment. The results show that a surge in COVID-19 infection rates and the existence of kidney disease significantly enhances the co-infection risks. Numerical simulations further clarify the potential outcomes of treating COVID-19 alone, kidney disease alone, and co-infected cases. The study of the potential model can be utilised to maximise the benefits of simulation to minimise the global health complexity of COVID-19 and kidney disease.

Mathematical Model and Optimal Control of Covid-19 in Nigeria

The global pandemic, Covid-19, caused by corona virus disease is responsible for a significant number of deaths and huge economic losses in almost all the countries of the world, including Nigeria. In order to manage the spread of this disease in Nigeria, the Nigeria Center for Disease Control(NCDC) has proposed and implemented various control and preventive measures such as vaccination, use of alcohol-based hand sanitizers, social distancing, and others. The aim of this paper is to model the transmission dynamics of Covid-19 in Nigeria, and obtain, by using Pontryagin Maximum Principle, the combination of these control strategies for effective control of the disease in Nigeria. Numerical experiments with Nigeria Covid-19 data show the effectiveness optimal use of these preventive and control measures for Covid-19.

Modelling the Use of the Trace-Test-Isolate-Treat Strategy for Controlling the Spread of COVID-19

During the COVID-19 pandemic that ravaged the entire world between 2019 and 2021, the Trace-Test-Isolate-Treat Strategy was devised as an emergency way of managing the spread of the disease. As the name implies, the Trace-Test-Isolate-Treat Strategy involves identifying those who had contact with an infected person through contact tracing, and subsequent isolation and treatment if confirmed to be infected with the disease. This paper aims to model the transmission dynamics of COVID-19, with the Trace-Test-Isolate-Treat Strategy as a control strategy. To do this, we propose a simple nonlinear system of ordinary differential equations that models COVID-19 dynamics and incorporates the Trace-Test-Isolate-Treat strategy as a way of controlling the spread of the disease. The analysis of the model shows that the disease-free equilibrium is locally asymptotically stable if the reproduction number, R_eff is less than one. Furthermore, the model is shown to possess a unique and stable endemic equilibrium if, R_eff&gt;1. This confirms the global asymptotic stability of the disease-free equilibrium and the absence of backward bifurcation in the model. Numerical plots show the effectiveness of isolation and treatment of infected persons in reducing the spread of the disease.

Mechanistic insights of COVID-19 dynamics by considering the influence of neurodegeneration and memory trace

COVID-19 has been declared a global pandemic as it disturbs education, society, agriculture, the economy, poverty, death rate, social development, mental psychology, and many more. Neurodegenerative disease is a brain disorder associated with several pathological factors along with mental psychology. This paper introduces a mathematical model to inspect mechanistic insights into COVID-19 dynamics by considering the influence of neurodegeneration and memory trace. The analysis of the proposed model and the existence and uniqueness of the model are derived using the fixed-point criteria. A numerical experiment is presented to validate the theoretical results and examine the impact of various biological parameters, the influence of neurodegeneration, and memory trace on the transmission dynamics of COVID-19.

The role of vaccine status homophily in the COVID-19 pandemic: a cross-sectional survey with modelling

BackgroundVaccine homophily describes non-heterogeneous vaccine uptake within contact networks. This study was performed to determine observable patterns of vaccine homophily, as well as the impact of vaccine homophily on disease transmission within and between vaccination groups under conditions of high and low vaccine efficacy.MethodsResidents of British Columbia, Canada, aged ≥ 16 years, were recruited via online advertisements between February and March 2022, and provided information about vaccination status, perceived vaccination status of household and non-household contacts, compliance with COVID-19 prevention guidelines, and history of COVID-19. A deterministic mathematical model was used to assess transmission dynamics between vaccine status groups under conditions of high and low vaccine efficacy.ResultsVaccine homophily was observed among those with 0, 2, or 3 doses of the vaccine. Greater homophily was observed among those who had more doses of the vaccine (p < 0.0001). Those with fewer vaccine doses had larger contact networks (p < 0.0001), were more likely to report prior COVID-19 (p < 0.0001), and reported lower compliance with COVID-19 prevention guidelines (p < 0.0001). Mathematical modelling showed that vaccine homophily plays a considerable role in epidemic growth under conditions of high and low vaccine efficacy. Furthermore, vaccine homophily contributes to a high force of infection among unvaccinated individuals under conditions of high vaccine efficacy, as well as to an elevated force of infection from unvaccinated to suboptimally vaccinated individuals under conditions of low vaccine efficacy.InterpretationThe uneven uptake of COVID-19 vaccines and the nature of the contact network in the population play important roles in shaping COVID-19 transmission dynamics.

The Impact of Urban Scaling Structure on the Local-Scale Transmission of COVID-19: A Case Study of the Omicron Wave in Hong Kong Using Agent-Based Modeling

Superspreading events underscore the uneven distribution of COVID-19 transmission among individuals and locations. These heterogenous transmission patterns could stem from human mobility, yet the underlying mechanisms are still not fully understood. Here, we employ an agent-based model incorporating urban scaling structure to simulate fine-grained mobility and the human-to-human transmission process. Our results reveal that not only the quantity but also the scaling structure of mobility profoundly influences local transmission risk. Urban scaling structure is characterized by a widely found power-law scaling distribution of mobility volumes across different locations. By integrating this structure, our model fits reasonably well with empirical Omicron data at various spatial scales in Hong Kong. Further analyses show a positive association between the scaling index, representing the location’s importance within the structure, and local transmission risks among urban areas as well as the likelihood of becoming superspreaders among local visitors. This implies that urban scaling structure could offer the first-mover advantage to a minority of places and individuals to infect earlier and thus infect more. This study brings important insights for the transmission dynamics of COVID-19 and similar diseases, highlighting the role of urban scaling structure in influencing local transmission risks and superspreading events.

Impact of Lifestyle Factors on COVID-19 Transmission Dynamics in Kabwe, Lusaka and Kafue, Zambia

Impact of Lifestyle Factors on COVID-19 Transmission Dynamics in Kabwe, Lusaka and Kafue, Zambia

Centralized CNN–GRU Model by Federated Learning for COVID-19 Prediction in India

In 2019, the corona virus was found in Wuhan, China. The corona virus has traveled several countries in the world from the beginning of 2020. The early estimation of COVID-19 cases is one of the efficient approaches to control the pandemic. Many researchers had proposed the deep learning model for the efficient estimation of COVID-19 cases for different provinces in the world. The research work had not focused on the discussion of robustness in the model. In this study, centralized federated-convolutional neural network–gated recurrent unit (Fed-CNN–GRU) model is proposed for the estimation of active cases per day in different provinces of India. In India, the uneven transmission of COVID-19 virus was seen in 36 provinces due to the different geographical areas and population densities. So, the methodology of this study had focused on the development of single deep learning algorithm, which is robust and reliable to estimate the active cases of COVID-19 in different provinces of India. The concept of transfer and federated learning is involved to enhance the estimation of active cases of COVID-19 by the CNN–GRU model. The study had considered the active cases per day dataset for 36 provinces in India from 12 March, 2020 to 17 January, 2022. Based on the study, it is proven that the centralized CNN–GRU model by federated learning had captured the transmission dynamics of COVID-19 in different provinces with an enhanced result.