Transmission Dynamics Of COVID-19 Research Articles

Analyzing the Dynamics of COVID-19 Transmission in Select Regions of the Philippines: A Modeling Approach to Assess the Impact of Various Tiers of Community Quarantines

The COVID-19 pandemic has significantly impacted communities worldwide, and effective management strategies are critical to reduce transmission rates and minimize the impact of the disease. In this study, we modeled and analyzed the COVID-19 transmission dynamics and derived relevant epidemiological values for three regions of the Philippines, namely, the National Capital Region (NCR), Davao City, and Baguio City, under different community quarantine implementations. The unique features and differences of these regions-of-interest were accounted for in simulating the disease spread and in estimating key epidemiological parameters fitted to the reported COVID-19 cases. Results support the robustness of the model formulated and provides insights into the effect of the government's implemented intervention protocols. With a forecasting feature, this modeling framework is beneficial for science-based decision support, policy making, and assessment for recent and future pandemics wherever regions-of-interest.

A vaccination-based COVID-19 model: Analysis and prediction using Hamiltonian Monte Carlo

Compartmental models have emerged as robust computational frameworks and have yielded remarkable success in the fight against COVID-19. This study proposes a vaccination-based compartmental model for COVID-19 transmission dynamics. The model reflects the specific stages of COVID-19 infection and integrates a vaccination strategy, allowing for a comprehensive analysis of how vaccination rates influence the disease spread. We fit this model to daily confirmed COVID-19 cases in Tennessee, United States of America (USA), from June 4 to November 26, 2021, in a Bayesian inference approach using the Hamiltonian Monte Carlo (HMC) algorithm. First, excluding vaccination dynamics from the model, we estimated key epidemiological parameters like infection, recovery, and disease-induced death rates. This analysis yielded a basic reproduction number (R0) of 1.5. Second, we incorporated vaccination dynamics and estimated the vaccination rate for three vaccines: 0.0051 per day for both Pfizer and Moderna and 0.0059 per day for Janssen. The fitted curves show reductions in the epidemic peak for all three vaccines. Pfizer and Moderna vaccines bring the peak down from 8,029 infected cases to 5,616 infected cases, while the Janssen vaccine reduces it, to 6,493 infected cases. Simulations of the model by varying the vaccination rate and vaccine efficacy were performed. A highly effective vaccine (95% efficacy) with a daily vaccination rate of 0.006 halved COVID-19 infections, reducing cases from 8,029 to around 4,000. The results also show that the model's prediction accuracy for new observations improves with the number of observed data used to train the model.

Deep learning infused SIRVD model for COVID-19 prediction: XGBoost-SIRVD-LSTM approach.

The global impact of the ongoing COVID-19 pandemic, while somewhat contained, remains a critical challenge that has tested the resilience of humanity. Accurate and timely prediction of COVID-19 transmission dynamics and future trends is essential for informed decision-making in public health. Deep learning and mathematical models have emerged as promising tools, yet concerns regarding accuracy persist. This research suggests a novel model for forecasting the COVID-19's future trajectory. The model combines the benefits of machine learning models and mathematical models. The SIRVD model, a mathematical based model that depicts the reach of the infection via population, serves as basis for the proposed model. A deep prediction model for COVID-19 using XGBoost-SIRVD-LSTM is presented. The suggested approach combines Susceptible-Infected-Recovered-Vaccinated-Deceased (SIRVD), and a deep learning model, which includes Long Short-Term Memory (LSTM) and other prediction models, including feature selection using XGBoost method. The model keeps track of changes in each group's membership over time. To increase the SIRVD model's accuracy, machine learning is applied. The key properties for forecasting the spread of the infection are found using a method called feature selection. Then, in order to learn from these features and create predictions, a model involving deep learning is applied. The performance of the model proposed was assessed with prediction metrics such as R 2, root mean square error (RMSE), mean absolute percentage error (MAPE), and normalized root mean square error (NRMSE). The results are also validated to those of other prediction models. The empirical results show that the suggested model outperforms similar models. Findings suggest its potential as a valuable tool for pandemic management and public health decision-making.

A data science pipeline applied to Australia's 2022 COVID-19 Omicron waves

The field of software engineering is advancing at astonishing speed, with packages now available to support many stages of data science pipelines. These packages can support infectious disease modelling to be more robust, efficient and transparent, which has been particularly important during the COVID-19 pandemic. We developed a package for the construction of infectious disease models, integrated it with several open-source libraries and applied this composite pipeline to multiple data sources that provided insights into Australia's 2022 COVID-19 epidemic. We aimed to identify the key processes relevant to COVID-19 transmission dynamics and thereby develop a model that could quantify relevant epidemiological parameters.The pipeline's advantages include markedly increased speed, an expressive application programming interface, the transparency of open-source development, easy access to a broad range of calibration and optimisation tools and consideration of the full workflow from input manipulation through to algorithmic generation of the publication materials. Extending the base model to include mobility effects slightly improved model fit to data, with this approach selected as the model configuration for further epidemiological inference. Under our assumption of widespread immunity against severe outcomes from recent vaccination, incorporating an additional effect of the main vaccination programs rolled out during 2022 on transmission did not further improve model fit. Our simulations suggested that one in every two to six COVID-19 episodes were detected, subsequently emerging Omicron subvariants escaped 30–60% of recently acquired natural immunity and that natural immunity lasted only one to eight months on average. We documented our analyses algorithmically and present our methods in conjunction with interactive online code notebooks and plots.We demonstrate the feasibility of integrating a flexible domain-specific syntax library with state-of-the-art packages in high performance computing, calibration, optimisation and visualisation to create an end-to-end pipeline for infectious disease modelling. We used the resulting platform to demonstrate key epidemiological characteristics of the transition from the emergency to the endemic phase of the COVID-19 pandemic.

Modeling and global stability analysis of COVID-19 dynamics with optimal control and cost-effectiveness analysis

In addressing the global challenges posed by COVID-19, this study introduces a mathematical model aimed at investigating the transmission dynamics of COVID-19 and forwarding strategies for controlling it. By employing Lyapunov functions, we perform a thorough stability analysis of both disease-free and endemic equilibria. We calibrated the model using daily COVID-19 data from early 2022 in Ethiopia, after vaccination initiation. A global sensitivity analysis confirmed the robustness of the model. In addition, we extended the model to address optimal control by incorporating vaccination, public health education, and treatment. Our findings highlight the effectiveness of individual control measures and reveal that vaccination, public health educational campaign and treatment is the most cost-effective method for mitigating COVID-19 spread.

Analysis of the impact of COVID-19 variants and vaccination on the time-varying reproduction number: statistical methods.

The COVID-19 pandemic has profoundly impacted global health systems, requiring the monitoring of infection waves and strategies to control transmission. Estimating the time-varying reproduction number is crucial for understanding the epidemic and guiding interventions. Probability distributions of serial interval are estimated for Pre-Delta and Delta periods. We conducted a comparative analysis of time-varying reproduction numbers, taking into account population immunity and variant differences. We incorporated the regional heterogeneity and age distribution of the population, as well as the evolving variants and vaccination rates over time. COVID-19 transmission dynamics were analyzed with variants and vaccination. The reproduction number is computed with and without considering variant-based immunity. In addition, values of reproduction number significantly differed by variants, emphasizing immunity's importance. Enhanced vaccination efforts and stringent control measures were effective in reducing the transmission of the Delta variant. Conversely, Pre-Delta variant appeared less influenced by immunity levels, due to lower vaccination rates. Furthermore, during the Pre-Delta period, there was a significant difference between the region-specific and the non-region-specific reproduction numbers, with particularly distinct pattern differences observed in Gangwon, Gyeongbuk, and Jeju in Korea. This research elucidates the dynamics of COVID-19 transmission concerning the dominance of the Delta variant, the efficacy of vaccinations, and the influence of immunity levels. It highlights the necessity for targeted interventions and extensive vaccination coverage. This study makes a significant contribution to the understanding of disease transmission mechanisms and informs public health strategies.

On Data Driven SIRD Model of Delta and Omicron Variants of COVID-19

The compartmental model stands as a cornerstone in quantitatively describing the transmission dynamics of diseases. Through a series of assumptions, this model can be formulated and subsequently validated against real-world conditions. Leveraging the abundance of COVID-19 data presently available, this study endeavors to reverse engineer the model construction process. Specifically, we analyse the compartmental model governing two notable variants of COVID-19: Delta and Omicron, utilizing empirical data. Employing the SINDy method, we extract parameters that define the model by effectively fitting the available data. To ensure robustness, the obtained model undergoes validation via comparison with real-world data through numerical integration. Additionally, we conduct fine-tuning in regularization techniques and input features to refine model selection. The constructed model then undergoes thorough analysis to gain qualitative insights and interpretations regarding the transmission dynamics of COVID-19.

Optimal region-specific social distancing strategies in a complex multi-patch model through reinforcement learning

Although non-pharmaceutical interventions such as social distancing have proven effective in curbing outbreaks, they also carry economic consequences. This poses a dilemma for policymakers striving to find a balance between disease control and economic burden. This delicate balance varies regionally, influenced by non-epidemiological factors such as population movements, socio-demographic characteristics, and the intricacies of social distancing policies. These factors interact in intricate ways, shaping the transmission dynamics of COVID-19. To address this complexity, we propose an innovative approach utilizing deep reinforcement learning (RL). This method assists in tailoring intervention policies for diverse regions, taking into account their unique dynamics. We incorporate South Korea’s social distancing policies and their economic impact into a RL framework with a multi-region epidemic model, offering a comprehensive solution. We integrate official mobility data and GDP specific to each region, employing the proximity policy optimization algorithm to determine the most appropriate region-specific social distancing policy. The algorithm’s reward function considers both outbreak control and economic impacts, providing policymakers with the flexibility to fine-tune the balance between these two factors according to their preferences. This adjustment can be performed across three distinct cost scenarios: High, Base, and Low-cost scenarios. In scenarios with High-costs, social distancing measures are aimed at regions with extensive connectivity and higher transmission rates. When costs are moderate, policies center around the period of peak prevalence, illustrating adaptable strategies in areas characterized by high transmission rates, budget limitations, and population mobility. In situations with Low-costs, these measures encompass most regions, excluding those with low transmission rates. The study’s results support focused interventions in specific regions to balance outbreak control and economic impact mitigation.

A Retrospective Study of COVID-19 Based on A Dual-Stage Model

This study proposed initial stage SEI1I2I3RD and final stage SEQ I1I2I3RD models to deeply analyze the transmission dynamics of COVID-19 at different stages and propose scientific prevention and control strategies. The initial stage SE I1I2I3RD model divides infected individuals into mild, moderate, and severe cases during the early stage of the epidemic, effectively predicting the number of new infections and cumulative recoveries. The final stage SEQ I1I2I3RD model builds on the initial stage SE I1I2I3RD model by adding a quarantine compartment, which reflects the impact of nucleic acid testing and quarantine measures on epidemic control. By introducing dynamic parameters and nonlinear processing, the final stage SEQ I1I2I3RD model significantly improves prediction accuracy. The study demonstrates that increasing the isolation rate, reducing the daily contact rate of infected persons, and improving the recovery rate of mildly infected persons are effective control strategies. Based on the model's predictions, policy recommendations such as enhancing public health education, strictly implementing isolation measures, restricting population movement, and optimizing medical resource allocation are proposed to effectively control the spread of the epidemic, reduce the burden on the healthcare system, and create favorable conditions for economic recovery.

Deterministic Double Dose Vaccination Model of COVID-19 Transmission Dynamics - Optimal Control Strategies with Cost-Effectiveness Analysis

In this study, we propose a deterministic double dose vaccination model of COVID-19 transmission dynamics optimal control with cost-effectiveness analysis. It is imperative for decision-makers and the government to prioritize the application of preventive and control measures for COVID-19 based on efficiency and costbenefit analysis. This is pivotal in resource-constrained regions where the disease is endemic. Thus, this work is mainly devoted with the development and analysis of an optimal control for COVID-19 dynamics with five timevarying functions; first dose vaccination, second dose vaccination, personal protection, testing or screening, and treatment. The model is qualitatively analysed with the overall goal to minimize the spread of COVID-19 and the costs related to control implementation with the aid of optimal control theory. The effect of adopting each control intervention in each of the three distinct groups which are created by classifying all conceivable combinations of at least three control interventions is demonstrated through the numerical simulations of the optimality system. Using the average cost-effectiveness ratio and incremental cost-effectiveness ratio techniques, the most economical control intervention is determined for each group. The study reveals that when the resources are readily available, application of the strategy that combines optimal first dose vaccination, personal protection, screening or testing and treatment is as efficient as implementing all the five optimal control interventions simultaneously as they both avert the same number of infections. However, in resource-limited communities when joint implementation of only three interventions is possible, the strategy combining personal protection, testing or screening and treatment is strongly recommended. Out of all the intervention options being considered, this strategy is also affirmed to be the most cost-effective overall. Economic evaluation of the control intervention strategies further suggests that combination of first dose vaccination, second dose vaccination, testing or screening and treatment is the most cost-effective strategy when implementation of only four interventions is strictly allowed.

Comprehensive analysis of COVID-19 transmission dynamics: mathematical modeling, stability analysis, and optimal control strategies

This paper presents a mathematical model for comprehensively analyzing the transmission dynamics of COVID-19. We investigate the model’s various properties, such as positivity, boundedness, and the existence and uniqueness of solutions. Additionally, we calculate the basic reproductive number, denoted as R 0, to gauge the epidemic’s potential spread. Furthermore, we conduct a stability analysis to understand the long-term behavior of the model. Furthermore, we devised an optimal control strategy to effectively curb disease transmission. Using graphical analysis, we assess the impact of secondary infection rates and quarantine rates across different population groups. Finally, we compare our proposed numerical scheme with the well-known RK-4 scheme, emphasizing the NSFD scheme’s ability to maintain positivity, unlike the RK-4 scheme. Our numerical simulations offer strong evidence supporting the theoretical findings, demonstrating the effectiveness of our results.

Modelling COVID-19 transmission dynamics in Laos under non-pharmaceutical interventions, vaccination, and replacement of SARS-CoV-2 variants

BackgroundUnderstanding how the COVID-19 pandemic evolved under control measures is crucial to tackle the SARS-CoV-2 virus spread. Laos, a country bordering China but with late occurrence and low burden of COVID-19 compared to its neighbouring countries, was used for a case study.MethodsA transmission model with disease reporting was proposed to investigate the impact of control measures on the SARS-CoV-2 virus spread in Laos from April 2021 to May 2022. It was assumed that the transmission rate changed with people’s behaviours, control measures and emerging variants; susceptibility decreased with vaccination and infection. Bayesian inference was used for model calibration to data of confirmed cases, deaths, and recoveries, and the deviance information criterion was used to select the best model variant.ResultsOur model including Non-pharmaceutical interventions (NPIs), behaviour change, vaccination, and changing variants well explained the three waves in Laos. The Alpha variant was estimated to have a basic reproduction number of 1.55 (95% CrI: 1.47–1.64) and was replaced by the Delta variant from September 2021 which was 1.88 (95% CrI: 1.77–2.01) times more transmissible; the Delta variant was replaced by Omicron variant from March 2022 which was 3.33 (95% CrI: 2.84–3.74) times more transmissible. The Delta variant was the most severe with a case fatality rate of 1.05% (95% CrI: 0.96–1.15%) while the Alpha variant and Omicron variant were much milder. The ascertainment rate was low and variable: first decreasing from 13.2 to 1.8% by 23 May 2021, and then increasing to 23.4% by 15 March 2022. Counterfactual simulations indicated that vaccination played strong roles in reducing infections even under the emergence of immune escape variants while behaviour change delayed but might not flatten the peak of outbreaks.ConclusionsThe three waves of Laos’ epidemics were due to the invasion of more transmissible and immune escape variants that affected the herd immunity built via vaccination and infection. Even with immunity waning and the escape of new variants, vaccination was still the major contributor to control COVID-19 and combining behaviour changes and vaccination would best suppress future outbreaks of COVID-19.

Impact assessment of self-medication on COVID-19 prevalence in Gauteng, South Africa, using an age-structured disease transmission modelling framework

ObjectiveTo assess the impact of self-medication on the transmission dynamics of COVID-19 across different age groups, examine the interplay of vaccination and self-medication in disease spread, and identify the age group most prone to self-medication.MethodsWe developed an age-structured compartmentalized epidemiological model to track the early dynamics of COVID-19. Age-structured data from the Government of Gauteng, encompassing the reported cumulative number of cases and daily confirmed cases, were used to calibrate the model through a Markov Chain Monte Carlo (MCMC) framework. Subsequently, uncertainty and sensitivity analyses were conducted on the model parameters.ResultsWe found that self-medication is predominant among the age group 15-64 (74.52%), followed by the age group 0-14 (34.02%), and then the age group 65+ (11.41%). The mean values of the basic reproduction number, the size of the first epidemic peak (the highest magnitude of the disease), and the time of the first epidemic peak (when the first highest magnitude occurs) are 4.16499, 241,715 cases, and 190.376 days, respectively. Moreover, we observed that self-medication among individuals aged 15-64 results in the highest spreading rate of COVID-19 at the onset of the outbreak and has the greatest impact on the first epidemic peak and its timing.ConclusionStudies aiming to understand the dynamics of diseases in areas prone to self-medication should account for this practice. There is a need for a campaign against COVID-19-related self-medication, specifically targeting the active population (ages 15-64).

Uncovering COVID-19 transmission tree: identifying traced and untraced infections in an infection network.

This paper presents a comprehensive analysis of COVID-19 transmission dynamics using an infection network derived from epidemiological data in South Korea, covering the period from January 3, 2020, to July 11, 2021. The network illustrates infector-infectee relationships and provides invaluable insights for managing and mitigating the spread of the disease. However, significant missing data hinder conventional analysis of such networks from epidemiological surveillance. To address this challenge, this article suggests a novel approach for categorizing individuals into four distinct groups, based on the classification of their infector or infectee status as either traced or untraced cases among all confirmed cases. The study analyzes the changes in the infection networks among untraced and traced cases across five distinct periods. The four types of cases emphasize the impact of various factors, such as the implementation of public health strategies and the emergence of novel COVID-19 variants, which contribute to the propagation of COVID-19 transmission. One of the key findings is the identification of notable transmission patterns in specific age groups, particularly in those aged 20-29, 40-69, and 0-9, based on the four type classifications. Furthermore, we develop a novel real-time indicator to assess the potential for infectious disease transmission more effectively. By analyzing the lengths of connected components, this indicator facilitates improved predictions and enables policymakers to proactively respond, thereby helping to mitigate the effects of the pandemic on global communities. This study offers a novel approach to categorizing COVID-19 cases, provides insights into transmission patterns, and introduces a real-time indicator for better assessment and management of the disease transmission, thereby supporting more effective public health interventions.

Modeling the Effects of Media Coverage on the COVID-19 Transmission Dynamics

Modeling the Effects of Media Coverage on the COVID-19 Transmission Dynamics

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

We develop a mathematical model for the SARAS-CoV-2 double variant transmission characteristics with variant 1 vaccination to address this novel aspect of the disease. The model is theoretically examined, and adequate requirements are derived for the stability of its equilibrium points. The model includes the single variant 1 and variant 2 endemic equilibria in addition to the endemic and disease-free equilibria. Various approaches are used for the global and local stability of the model. For both strains, we determine the basic reproductive numbers R1 and R2. To investigate the occurrence of the layers (waves), we expand the model to include some analysis based on the second-order derivative. The model is then expanded to its stochastic form, and numerical outcomes are computed. For numerical purposes, we use the nonstandard finite difference method. Some error analysis is also recorded.

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.

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.