Transmission Dynamics Of Coronavirus Disease 2019 Research Articles

Incidence and transmission dynamics of Coronavirus Disease 2019 (COVID-19) among fresh recruits in a Training Establishment of Indian Armed Forces: An observational study

Introduction: In times of COVID-19, when all institutions remained closed even post lock-down, the Airmen Training School of Indian Air Force proceeded with the recruitment of airmen. The present study intends to explore the incidence and transmission dynamics of COVID-19 among these freshly recruits who were quarantined in barracks with each barrack serving as one cohort. Material and Methods: In an observational study, 2978 male Air Force recruits, between the age group of 18-22 years, in two batches between 1st to 31st July 2020, were followed up in the quarantine period post recruitment. Since the quarantine was unconventional in terms of being in barracks, special and unique precautions such as repeated screening, delegation of specific toilets per billets, three ply mask distributions, Bio-Medical Waste Disposal etc were paid utmost attention. The COVID 19 cases were treated at the in-house isolation facility and the contacts with the symptomatics tested with RT-PCR or RAT before release from quarantine. All individuals were monitored with active surveillance and the data was meticulously maintained with zero attrition. Incidence, Secondary Attack Rate (SAR), demographic characteristics and others aspects of transmission dynamics were analyzed. Results: The incidence and SAR was observed to be 8.1% (71 primary cases) and 15.59% (170 secondary cases). 64.7% of cases were symptomatic with an average of 15 days of hospitalization. The rate positivity of testing with RTPCR or RAT was 11.44%. Severity was more marked in the first batch than the second batch of recruits. The overall R0 was found to be 2.39. Conclusion: The results of the study revealed some interesting characteristics of COVID-19 transmission dynamics. The cumulative incidence was found to be higher than national average. The variations between the recruits from different part of the country and the two batches could be attributed to the travel distance and mode of conveyance. The study also validates the mild nature of the infection in young adults. Fair conclusion on the implication of good quarantine and isolation measures and regular surveillance for symptoms for early diagnosis has also been compellingly indicated from these observations.

Predicting the effective reproduction number of COVID-19: inference using human mobility, temperature, and risk awareness

ObjectivesThe effective reproduction number (Rt) has been critical for assessing the effectiveness of countermeasures during the coronavirus disease 2019 (COVID-19) pandemic. Conventional methods using reported incidences are unable to provide timely Rt data due to the delay from infection to reporting. Our study aimed to develop a framework for predicting Rt in real time, using timely accessible data — i.e. human mobility, temperature, and risk awareness. MethodsA linear regression model to predict Rt was designed and embedded in the renewal process. Four prefectures of Japan with high incidences in the first wave were selected for model fitting and validation. Predictive performance was assessed by comparing the observed and predicted incidences using cross-validation, and by testing on a separate dataset in two other prefectures with distinct geographical settings from the four studied prefectures. ResultsThe predicted mean values of Rt and 95% uncertainty intervals followed the overall trends for incidence, while predictive performance was diminished when Rt changed abruptly, potentially due to superspreading events or when stringent countermeasures were implemented. ConclusionsThe described model can potentially be used for monitoring the transmission dynamics of COVID-19 ahead of the formal estimates, subject to delay, providing essential information for timely planning and assessment of countermeasures.

Mathematical modeling of COVID-19 transmission dynamics between healthcare workers and community

Corona-virus disease 2019 (COVID-19) is an infectious disease that has affected different groups of humankind such as farmers, soldiers, drivers, educators, students, healthcare workers and many others. The transmission rate of the disease varies from one group to another depending on the contact rate. Healthcare workers are at a high risk of contracting the disease due to the high contact rate with patients. So far, there exists no mathematical model which combines both public control measures (as a parameter) and healthcare workers (as an independent compartment). Combining these two in a given mathematical model is very important because healthcare workers are protected through effective use of personal protective equipment, and control measures help to minimize the spread of COVID-19 in the community. This paper presents a mathematical model named SWE IsIaHR; susceptible individuals (S), healthcare workers (W), exposed (E), symptomatic infectious (Is), asymptomatic infectious (Ia), hospitalized (H), recovered (R). The value of basic reproduction number R0 for all parameters in this study is 2.8540. In the absence of personal protective equipment ξ and control measure in the public θ, the value of R0≈4.6047 which implies the presence of the disease. When θ and ξ were introduced in the model, basic reproduction number is reduced to 0.4606, indicating the absence of disease in the community. Numerical solutions are simulated by using Runge–Kutta fourth-order method. Sensitivity analysis is performed to presents the most significant parameter. Furthermore, identifiability of model parameters is done using the least square method. The results indicated that protection of healthcare workers can be achieved through effective use of personal protective equipment by healthcare workers and minimization of transmission of COVID-19 in the general public by the implementation of control measures. Generally, this paper emphasizes the importance of using protective measures.

High variability in model performance of Google relative search volumes in spatially clustered COVID-19 areas of the USA

Objective: Incorporating spatial analyses and online health information queries may be beneficial in understanding the role of Google relative search volume (RSV) data as a secondary public health surveillance tool during pandemics. This study identified coronavirus disease 2019 (COVID-19) clustering and defined the predictability performance of Google RSV models in clustered and non-clustered areas of the USA.Methods: Getis-Ord General and local G statistics were used to identify monthly clustering patterns. Monthly country- and state-level correlations between new daily COVID-19 cases and Google RSVs were assessed using Spearman's rank correlation coefficients and Poisson regression models for January–December 2020.Results: Huge clusters involving multiple states were found, which resulted from various control measures in each state. This demonstrates the importance of state-to-state coordination in implementing control measures to tackle the spread of outbreaks. Variability in Google RSV model performance was found among states and time periods, possibly suggesting the need to use different frameworks for Google RSV data in each state. Moreover, the sign of correlation can be utilized to understand public responses to control and preventive measures, as well as in communicating risk.Conclusion: COVID-19 Google RSV model accuracy in the USA may be influenced by COVID-19 transmission dynamics, policy-driven community awareness and past outbreak experiences.

International trade as critical parameter of COVID-19 spread that outclasses demographic, economic, environmental, and pollution factors

The novel severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) that caused the Coronavirus Disease 2019 (COVID-19), generating high numbers of COVID-19 related infected individuals and deaths, is still circulating in 2021 with new variants of the coronavirus, such that the state of emergency remains in manifold countries. Currently, there is still a lack of a full understanding of the factors determining the COVID-19 diffusion that clarify the causes of the variability of infections across different provinces and regions within countries. The main goal of this study is to explain new and main determinants underlying the diffusion of COVID-19 in society. This study focuses on international trade because this factor, in a globalized world, can synthetize different drivers of virus spread, such as mobility patterns, economic potentialities, and social interactions of an investigated areas. A case study research is performed on 107 provinces of Italy, one of the first countries to experience a rapid increase in confirmed cases and deaths. Statistical analyses from March 2020 to February 2021 suggest that total import and export of provinces has a high association with confirmed cases over time (average r > 0.78, p-value <.001). Overall, then, this study suggests total import and export as complex indicator of COVID-19 transmission dynamics that outclasses other common parameters used to justify the COVID-19 spread, given by economic, demographic, environmental, and climate factors. In addition, this study proposes, for the first time, a time-dependent correlation analysis between trade data and COVID-19 infection cases to explain the relation between confirmed cases and social interactions that are a main source of the diffusion of SARS-CoV-2 and subsequent negative impact in society. These novel findings have main theoretical and practical implications directed to include a new parameter in modelling of the diffusion of COVID-19 pandemic to support effective policy responses of crisis management directed to constrain the impact of COVID-19 pandemic and similar infectious diseases in society.

Using preprints in evidence synthesis: Commentary on experience during the COVID-19 pandemic

Using preprints in evidence synthesis: Commentary on experience during the COVID-19 pandemic

Preliminary Evidence from a Cross-sectional Study on Epidemiology and Early Transmission Dynamics of COVID-19 in Karnataka State of India

Abstract Introduction Coronavirus disease 2019 (COVID-19) is an emerging infection and quickly disseminated around the world. This article studies the epidemiology and early transmission dynamics of COVID-19 in Karnataka, which would be useful for effective epidemic management and policy formulation. Materials and Methods All COVID-19 cases reported in the state of Karnataka, India, till June 12, 2020, are included in the study. The epidemiology and transmission dynamics of COVID-19 in Karnataka is studied through descriptive statistical analysis. Results The findings illustrate a gender-, age-, and region-based disparity in the susceptibility and fatality. There appears to be a male preponderance in the susceptibility, but a female preponderance in fatality. It is also found that the adults are more susceptible to the infection, while the elderly have the risk of high fatality. Further, infected individuals in the region with urbanization have a higher risk of fatality than other regions. The study shows that the chances of recovery for females are lower than males, and further, the chances of recovery are positively related to the age of the infected person. The chances of recovery are higher if the infected individual is younger and they diminish if the individual is older. The study also explores that the chances of recovery are affected by the patient’s geographical location. It is also noted that individuals who returned from foreign travel have better chances of recovery than the locally transmitted individuals. Conclusion Though the risk of susceptibility to COVID-19 infection is equal to all, the burden of getting infected and the burden of fatality is unequally distributed among different demographic categories. To manage the contagious spread of epidemic, to reduce fatality, and to increase the chances of recovery, targeted policy actions are suggested to benefit the vulnerable demographic categories.

Vaccination Prioritization Strategies for COVID-19 in Korea: A Mathematical Modeling Approach.

Coronavirus disease 2019 (COVID-19) vaccination has recently started worldwide. As the vaccine supply will be limited for a considerable period of time in many countries, it is important to devise the effective vaccination strategies that reduce the number of deaths and incidence of infection. One of the characteristics of COVID-19 is that the symptom, severity, and mortality of the disease differ by age. Thus, when the vaccination supply is limited, age-dependent vaccination priority strategy should be implemented to minimize the incidences and mortalities. In this study, we developed an age-structured model for describing the transmission dynamics of COVID-19, including vaccination. Using the model and actual epidemiological data in Korea, we estimated the infection probability for each age group under different levels of social distancing implemented in Korea and investigated the effective age-dependent vaccination strategies to reduce the confirmed cases and fatalities of COVID-19. We found that, in a lower level of social distancing, vaccination priority for the age groups with the highest transmission rates will reduce the incidence mostly, but, in higher levels of social distancing, prioritizing vaccination for the elderly age group reduces the infection incidences more effectively. To reduce mortalities, vaccination priority for the elderly age group is the best strategy in all scenarios of levels of social distancing. Furthermore, we investigated the effect of vaccine supply and efficacy on the reduction in incidence and mortality.

Effectiveness of Non-Pharmaceutical Intervention Against Local Transmission of COVID-19: An Individual-Based Modelling Study

Background: The outbreak of the novel coronavirus disease 2019 (COVID-19) has caused global transmission, and been spread all over the world. For those regions that are currently free of infected cases, it is an urgent issue to prevent and control the local outbreak of COVID-19 when there are sporadic cases. We aimed to evaluate the effects of non-pharmaceutical interventions, such as isolation, contact tracing, and physical distancing, against local transmission of COVID-19, and to forecast the epidemic dynamics after local outbreak of diseases under different control measures. Methods: For this computational modelling study, we developed an individual-based model (IBM) to simulate the transmission dynamics of COVID-19 from a microscopic perspective of individual-to-individual contacts and heterogenous among individuals. We simulated the effects of different levels of non-pharmaceutical interventions in controlling disease transmission after the appearance of sporadic cases. Through model simulations, we discussed the epidemic dynamics and evaluated the conditions of initiating different level responses in order to achieve better controls of the local transmission. Findings: Model simulations shown that isolation of infected cases and quarantine of close contacts alone would not eliminate the local transmission of COVID-19, and there is a risk of a second wave epidemics. Quarantine the second-layer close contacts can obviously reduce the size of outbreak. Moreover, to effectively eliminate the daily new infections in a short time, it is necessary to reduce the individual-to-individual contacts. Interpretation: IBM provides a numerical representation for the local transmission of infectious diseases, the model extends the compartmental models to include individual heterogeneity and the close contacts network. Through model simulation, we quantitatively analysis the transmission patterns of COVID-19 under different control measures. Combinations of self-isolation, quarantine of close contacts, and social distancing would be necessary to block the local transmission of severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2).Funding Statement: This work was supported by the National Natural Science Foundation ofChina under grant No.11971023 and No.11871371.Declaration of Interests: The authors declare that they do not have any commercial or associative interest that represents a conflict of interest in connection with the work submitted.

Transmission dynamics and quarantine control of COVID-19 in cluster community: A new transmission-quarantine model with case study for diamond princess

Coronavirus Disease 2019 (COVID-19) is a zoonotic illness which has spread rapidly and widely since December, 2019, and is identified as a global pandemic by the World Health Organization. The pandemic to date has been characterized by ongoing cluster community transmission. Quarantine intervention to prevent and control the transmission are expected to have a substantial impact on delaying the growth and mitigating the size of the epidemic. To our best knowledge, our study is among the initial efforts to analyze the interplay between transmission dynamics and quarantine intervention of the COVID-19 outbreak in a cluster community. In the paper, we propose a novel Transmission-Quarantine epidemiological model by nonlinear ordinary differential equations system. With the use of detailed epidemiologic data from the Cruise ship “Diamond Princess”, we design a Transmission-Quarantine work-flow to determine the optimal case-specific parameters, and validate the proposed model by comparing the simulated curve with the real data. First, we apply a general SEIR-type epidemic model to study the transmission dynamics of COVID-19 without quarantine intervention, and present the analytic and simulation results for the epidemiological parameters such as the basic reproduction number, the maximal scale of infectious cases, the instant number of recovered cases, the popularity level and the final scope of the epidemic of COVID-19. Second, we adopt the proposed Transmission-Quarantine interplay model to predict the varying trend of COVID-19 with quarantine intervention, and compare the transmission dynamics with and without quarantine to illustrate the effectiveness of the quarantine measure, which indicates that with quarantine intervention, the number of infectious cases in 7 days decrease by about 60%, compared with the scenario of no intervention. Finally, we conduct sensitivity analysis to simulate the impacts of different parameters and different quarantine measures, and identify the optimal quarantine strategy that will be used by the decision makers to achieve the maximal protection of population with the minimal interruption of economic and social development.

Associations between meteorology and COVID-19 in early studies: Inconsistencies, uncertainties, and recommendations

Meteorological variables, such as the ambient temperature and humidity, play a well-established role in the seasonal transmission of respiratory viruses and influenza in temperate climates. Since the onset of the novel coronavirus disease 2019 (COVID-19) pandemic, a growing body of literature has attempted to characterize the sensitivity of COVID-19 to meteorological factors and thus understand how changes in the weather and seasonality may impede COVID-19 transmission. Here we select a subset of this literature, summarize the diversity in these studies' scopes and methodologies, and show the lack of consensus in their conclusions on the roles of temperature, humidity, and other meteorological factors on COVID-19 transmission dynamics. We discuss how several aspects of studies' methodologies may challenge direct comparisons across studies and inflate the importance of meteorological factors on COVID-19 transmission. We further comment on outstanding challenges for this area of research and how future studies might overcome them by carefully considering robust modeling approaches, adjusting for mediating and covariate effects, and choosing appropriate scales of analysis.

Mathematical Modeling Predicts That Strict Social Distancing Measures Would Be Needed to Shorten the Duration of Waves of COVID-19 Infections in Vietnam.

Coronavirus disease 2019 (COVID-19) emerged in Wuhan, China in 2019, has spread throughout the world and has since then been declared a pandemic. As a result, COVID-19 has caused a major threat to global public health. In this paper, we use mathematical modeling to analyze the reported data of COVID-19 cases in Vietnam and study the impact of non-pharmaceutical interventions. To achieve this, two models are used to describe the transmission dynamics of COVID-19. The first model belongs to the susceptible-exposed-infectious-recovered (SEIR) type and is used to compute the basic reproduction number. The second model adopts a multi-scale approach which explicitly integrates the movement of each individual. Numerical simulations are conducted to quantify the effects of social distancing measures on the spread of COVID-19 in urban areas of Vietnam. Both models show that the adoption of relaxed social distancing measures reduces the number of infected cases but does not shorten the duration of the epidemic waves. Whereas, more strict measures would lead to the containment of each epidemic wave in one and a half months.

Mathematical modeling of transmission dynamics of COVID-19

<abstract> The emergence of coronavirus disease 2019 (COVID-19) demonstrates the importance of research on understanding and accurately modeling the transmission and spread of pandemic. In this paper, we consider a susceptible-exposed-infected-recovered-deceased (SEIRD) system of differential equations to describe relationship among the number of susceptible individuals, the number of exposed individuals who are transmitting the virus, the number of infected individuals among the exposed people, the number of recovered individuals from those infected, and the number of deaths from those infected in a town, state or country. Based on the empirical results of transmission process of COVID-19 in the United States from April 16th to June 30th, 2020, we consider a few cases of contact rate, incidence rate, recovery rate, and mortality rate to model the transmission and dynamics of the virus. Numerical analysis and analytical method are used to explore the dynamics and prediction of the pandemic. </abstract>

Effectiveness of non-pharmaceutical interventions against local transmission of COVID-19: An individual-based modelling study.

The outbreak of the novel coronavirus disease 2019 (COVID-19), caused by the novel severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2), has caused global transmission, and been spread all over the world. For those regions that are currently free of infected cases, it is an urgent issue to prevent and control the local outbreak of COVID-19 when there are sporadic cases. To evaluate the effects of non-pharmaceutical interventions against local transmission of COVID-19, and to forecast the epidemic dynamics after local outbreak of diseases under different control measures, we developed an individual-based model (IBM) to simulate the transmission dynamics of COVID-19 from a microscopic perspective of individual-to-individual contacts to heterogenous among individuals. Based on the model, we simulated the effects of different levels of non-pharmaceutical interventions in controlling disease transmission after the appearance of sporadic cases. Simulations shown that isolation of infected cases and quarantine of close contacts alone would not eliminate the local transmission of COVID-19, and there is a risk of a second wave epidemics. Quarantine the second-layer close contacts can obviously reduce the size of outbreak. Moreover, to effectively eliminate the daily new infections in a short time, it is necessary to reduce the individual-to-individual contacts. IBM provides a numerical representation for the local transmission of infectious diseases, and extends the compartmental models to include individual heterogeneity and the close contacts network. Our study suggests that combinations of self-isolation, quarantine of close contacts, and social distancing would be necessary to block the local transmission of COVID-19.

Mathematical modeling of COVID-19 epidemic with effect of awareness programs.

Severe acute respiratory syndrome coronavirus 2 (SARS-COV-2) is a novel virus that emerged in China in late 2019 and caused a pandemic of coronavirus disease 2019 (COVID-19). The epidemic has largely been controlled in China since March 2020, but continues to inflict severe public health and socioeconomic burden in other parts of the world. One of the major reasons for China’s success for the fight against the epidemic is the effectiveness of its health care system and enlightenment (awareness) programs which play a vital role in the control of the COVID-19 pandemic. Nigeria is currently witnessing a rapid increase of the epidemic likely due to its unsatisfactory health care system and inadequate awareness programs. In this paper, we propose a mathematical model to study the transmission dynamics of COVID-19 in Nigeria. Our model incorporates awareness programs and different hospitalization strategies for mild and severe cases, to assess the effect of public awareness on the dynamics of COVID-19 infection. We fit the model to the cumulative number of confirmed COVID-19 cases in Nigeria from 29 March to 12 June 2020. We find that the epidemic could increase if awareness programs are not properly adopted. We presumed that the effect of awareness programs could be estimated. Further, our results suggest that the awareness programs and timely hospitalization of active cases are essential tools for effective control and mitigation of COVID-19 pandemic in Nigeria and beyond. Finally, we perform sensitive analysis to point out the key parameters that should be considered to effectively control the epidemic.

Stability analysis of a fractional ordered COVID-19 model

Abstract The main purpose of this work is to study transmission dynamics of COVID-19 in Italy 2020, where the first case of Coronavirus disease 2019 (COVID-19) in Italy was reported on 31st January 2020. Taking into account the uncertainty due to the limited information about the Coronavirus (COVID-19), we have taken the modified Susceptible-Asymptomatic-Infectious-Recovered (SAIR) compartmental model under fractional order framework. We have formulated our model by subdividing infectious compartment into two sub compartments (reported and unreported) and introduced hospitalized class. In this work, we have studied the local and global stability of the system at different equilibrium points (disease free and endemic) and calculated sensitivity index for Italy scenario. The validity of the model is justified by comparing real data with the results obtained from simulations.

Dynamics of COVID-19 Transmission: Compartmental-based Mathematical Modeling

&#x0D; &#x0D; &#x0D; The current pandemic of coronavirus disease 2019 (COVID-19) caused by severe acute respiratory syndrome coronavirus 2 (SARS-Cov2) demands scientists all over the world to make their possible contributions in whatever way possible to control this disease. In such health emergency, mathematical epidemiologists are playing a pivotal role by constructing different mathematical and statistical models for predicting different future scenario and their impact on different intervention strategies to policy makers and health legislators. Compartmental-based models (CBM), are a type of transmission dynamic framework, which are one of the most studied models during this pandemic. This communication highlights the role CBM models play for the understanding of COVID-19 transmission dynamics.&#x0D; &#x0D; &#x0D;

Analysis and Dynamics of Fractional Order Mathematical Model of COVID-19 in Nigeria Using Atangana-Baleanu Operator

We propose a mathematical model of the coronavirus disease 2019 (COVID-19) to investigate the transmission and control mechanism of the disease in the community of Nigeria. Using stability theory of differential equations, the qualitative behavior of model is studied. The pandemic indicator represented by basic reproductive number R0 is obtained from the largest eigenvalue of the next-generation matrix. Local as well as global asymptotic stability conditions for the disease-free and pandemic equilibrium are obtained which determines the conditions to stabilize the exponential spread of the disease. Further, we examined this model by using Atangana–Baleanu fractional derivative operator and existence criteria of solution for the operator is established. We consider the data of reported infection cases from April 1, 2020, till April 30, 2020, and parameterized the model. We have used one of the reliable and efficient method known as iterative Laplace transform to obtain numerical simulations. The impacts of various biological parameters on transmission dynamics of COVID-19 is examined. These results are based on different values of the fractional parameter and serve as a control parameter to identify the significant strategies for the control of the disease. In the end, the obtained results are demonstrated graphically to justify our theoretical findings.

Prediction of the COVID-19 outbreak in China based on a new stochastic dynamic model

The current outbreak of coronavirus disease 2019 (COVID-19) has become a global crisis due to its quick and wide spread over the world. A good understanding of the dynamic of the disease would greatly enhance the control and prevention of COVID19. However, to the best of our knowledge, the unique features of the outbreak have limited the applications of all existing dynamic models. In this paper, a novel stochastic model was proposed aiming to account for the unique transmission dynamics of COVID-19 and capture the effects of intervention measures implemented in Mainland China. We found that: (1) instead of aberration, there was a remarkable amount of asymptomatic virus carriers, (2) a virus carrier with symptoms was approximately twice more likely to pass the disease to others than that of an asymptomatic virus carrier, (3) the transmission rate reduced significantly since the implementation of control measures in Mainland China, and (4) it was expected that the epidemic outbreak would be contained by early March in the selected provinces and cities in China.

Transmission dynamics of COVID-19 among index case family clusters in Beijing, China.

The outbreak of coronavirus disease-2019 (COVID-19) impacts public health dramatically around the world. The demographic characteristics, exposure history, dates of illness onset and dates of confirmed diagnosis were collected from the data of 24 family clusters from Beijing. The characteristics of the cases and the estimated key epidemiologic time-to-event distributions were described. The basic reproductive number (R0) was calculated. Among 89 confirmed COVID-19 patients from 24 family clusters, the median age was 38.0 years and 43.8% were male. The median of incubation period was 5.08 days (95% confidence interval (CI) 4.17-6.21). The median of serial interval was 6.00 days (95% CI 5.00-7.00). The basic reproductive number (R0) was 2.06 (95% CI 2.02-2.08). The median of onset-to-care-seeking days and the median of onset-to-hospital admission days were significantly reduced after 23 January 2020, which implied the enhanced public health awareness among families. With epidemic containment measures in place, the results can inform health authorities about possible extent of epidemic transmission within families. Furthermore, following initiation of interventions, public health measures are not only important for curbing the epidemic spread at the community level but also improve health seeking behaviour at the individual level.