Transmission Dynamics Of Coronavirus Disease 2019 Research Articles

Epidemiology and transmission dynamics of COVID-19 in two Indian states.

Although most cases of coronavirus disease 2019 (COVID-19) have occurred in low-resource countries, little is known about the epidemiology of the disease in such contexts. Data from the Indian states of Tamil Nadu and Andhra Pradesh provide a detailed view into severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) transmission pathways and mortality in a high-incidence setting. Reported cases and deaths have been concentrated in younger cohorts than would be expected from observations in higher-income countries, even after accounting for demographic differences across settings. Among 575,071 individuals exposed to 84,965 confirmed cases, infection probabilities ranged from 4.7 to 10.7% for low-risk and high-risk contact types, respectively. Same-age contacts were associated with the greatest infection risk. Case fatality ratios spanned 0.05% at ages of 5 to 17 years to 16.6% at ages of 85 years or more. Primary data from low-resource countries are urgently needed to guide control measures.

Optimal Control of Fractional Order COVID-19 Epidemic Spreading in Japan and India 2020

In Japan, the first case of Coronavirus disease 2019 (COVID-19) was reported on 15th January 2020. In India, on 30th January 2020, the first case of COVID-19 in India was reported in Kerala and the number of reported cases has increased rapidly. The main purpose of this work is to study numerically the epidemic peak for COVID-19 disease along with transmission dynamics of COVID-19 in Japan and India 2020. Taking into account the uncertainty due to the incomplete information about the coronavirus (COVID-19), we have taken the Susceptible-Asymptomatic-Infectious-Recovered (SAIR) compartmental model under fractional order framework for our study. We have also studied the effects of fractional order along with other parameters in transfer dynamics and epidemic peak control for both the countries. An optimal control problem has been studied by controlling social distancing parameter.

A study on COVID-19 transmission dynamics: stability analysis of SEIR model with Hopf bifurcation for effect of time delay

This paper deals with a general SEIR model for the coronavirus disease 2019 (COVID-19) with the effect of time delay proposed. We get the stability theorems for the disease-free equilibrium and provide adequate situations of the COVID-19 transmission dynamics equilibrium of present and absent cases. A Hopf bifurcation parameter τ concerns the effects of time delay and we demonstrate that the locally asymptotic stability holds for the present equilibrium. The reproduction number is brief in less than or greater than one, and it effectively is controlling the COVID-19 infection outbreak and subsequently reveals insight into understanding the patterns of the flare-up. We have included eight parameters and the least square method allows us to estimate the initial values for the Indian COVID-19 pandemic from real-life data. It is one of India’s current pandemic models that have been studied for the time being. This Covid19 SEIR model can apply with or without delay to all country’s current pandemic region, after estimating parameter values from their data. The sensitivity of seven parameters has also been explored. The paper also examines the impact of immune response time delay and the importance of determining essential parameters such as the transmission rate using sensitivity indices analysis. The numerical experiment is calculated to illustrate the theoretical results.

COVID-19: What do we know?

• Evidence regarding the provision of orthodontic care during the COVID-19 pandemic is examined.

Semiparametric Bayesian inference for the transmission dynamics of COVID-19 with a state-space model

The outbreak of Coronavirus Disease 2019 (COVID-19) is an ongoing pandemic affecting over 200 countries and regions. Inference about the transmission dynamics of COVID-19 can provide important insights into the speed of disease spread and the effects of mitigation policies. We develop a novel Bayesian approach to such inference based on a probabilistic compartmental model using data of daily confirmed COVID-19 cases. In particular, we consider a probabilistic extension of the classical susceptible-infectious-recovered model, which takes into account undocumented infections and allows the epidemiological parameters to vary over time. We estimate the disease transmission rate via a Gaussian process prior, which captures nonlinear changes over time without the need of specific parametric assumptions. We utilize a parallel-tempering Markov chain Monte Carlo algorithm to efficiently sample from the highly correlated posterior space. Predictions for future observations are done by sampling from their posterior predictive distributions. Performance of the proposed approach is assessed using simulated datasets. Finally, our approach is applied to COVID-19 data from six states of the United States: Washington, New York, California, Florida, Texas, and Illinois. An R package BaySIR is made available at https://github.com/tianjianzhou/BaySIR for the public to conduct independent analysis or reproduce the results in this paper.

Seasonal variations in Nigeria: understanding COVID-19 transmission dynamics and immune responses

There is a global rise in the emergence of infectious diseases and the enigmatic coronavirus disease 2019 (COVID-19) being the most recent one. It is ravaging the world with little understanding of its etiology and factors affecting its transmission dynamics. Meanwhile, seasonal variations in weather are major factors impacting infectious disease transmission patterns. Developing countries are likely to be most affected by weather changes that could impact on the spread and control of infectious diseases. In Nigeria, weather conditions alternate between rainy and dry seasons. Conditions such as rainfall, humidity and sunlight had been reported to influence host susceptibility to infectious diseases. On the other hand, these weather elements have a direct or indirect impact on the level of vitamin D availability. More so, vitamin C and D supplementation were highlighted to enhance immunity in COVID-19 patients. Thus, understanding the impact of weather changes in transmission dynamics and boosting immune response to COVID-19 through vitamin supplementation in infected patients may be crucial as the rainy season fully sets in.

Impact of returning population migration after the Chinese Spring Festival on the COVID-19 epidemic

The outbreak of the novel coronavirus disease 2019 (COVID-19) and its spread throughout the China have caused a huge impact on China and the international community. And now it becomes a worldwide infectious disease which poses a major threat to the lives of people around the world. What is worth noting about China is five million people left Wuhan before the Spring Festival, which caused the nationwide spreading of COVID-19 epidemic. Then, it raises a question of concern, should the return of migrant workers and students after the Spring Festival cause an increase in the epidemic? In this study, we use the discrete stochastic model (DSM) to study the transmission dynamics of COVID-19. The DSM is different from the classical continuous variable ordinary differential equations, and has two characteristics. First, on account of few patients at the beginning of the COVID-19 and random fluctuations during the transmission process are prominent; the DSM can better reflect the initial transmission characteristics than the continuous variable deterministic ordinary differential equation model. Second, the DSM can easily track the changes in the epidemic situation, and well reflect the infectious rate varies with time due to different prevention and control measures, and then gradually estimate the development of the epidemic situation. Meanwhile, based on the facts that there are successive time lags among epidemic infection, symptom onset, and diagnosis confirmation, the Erlang probability density distribution, which is frequent used in the queuing theory, has been applied to the calculation of numbers of epidemic daily outbreaks and daily infections respectively from the corrected numbers of patients diagnosed and confirmed reported every day since the outbreak of the epidemic in Hubei Province. The number of symptom onset patients we calculated agrees well with recent statistics made by the Chinese Center for Disease Control and Prevention (CDC), showing the feasibility of our method. The calculation results indicate that in the rising stage of the epidemic, although the number of newly diagnostic confirmed patients reported before the day of lock down of Wuhan city was only 180 daily, the number of newly infected people may have reached about 2500, and the cumulative number of infected may reach 33000. More than 5500 people may have rushed out of Hubei Province uncontrollably, thus causing a national epidemic of COVID-19. However, it is in the epidemic decline stage mow. Even if Hubei’s daily confirmed diagnosis remains at a high level of more than 1700 people a day, the number of newly infected people may be less than 800 nowadays, and most of whom may already be concerned about quarantine. The number of infections in other provinces and cities is much lower than that in Hubei. Therefore, as long as the epidemic situation in different regions is distinguished, return trips could be arranged at reduced people density, attentive epidemic prevention of transportation, and well preparation at the receiving cities. Rebound of the epidemic is quite unlikely. Although, the possibility of an epidemic rebound is small, only in conditions of slack thinking and strict measures are carried out. In accordance with the transmission of the world epidemic, more attention must be paid to the inspection of the influx of foreign infected people.

Characteristics and transmission dynamics of COVID-19 in healthcare workers at a London teaching hospital

BackgroundHealthcare worker (HCW)-associated coronavirus disease 2019 (COVID-19) is of global concern due to the potential for nosocomial spread and depletion of staff numbers. However, the literature on transmission routes and risk factors for COVID-19 in HCWs is limited.AimTo examine the characteristics and transmission dynamics of severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) in HCWs in a university teaching hospital in London, UK.MethodsStaff records and virology testing results were combined to identify staff sickness and COVID-19 rates from March to April 2020. Comparisons were made with staff professional groups, department of work, and ethnicity.FindingsCOVID-19 rates in our HCWs largely rose and declined in parallel with the number of community cases. White and non-White ethnic groups among our HCWs had similar rates of infection. Clinical staff had a higher rate of laboratory-confirmed COVID-19 than non-clinical staff, but total sickness rates were similar. Doctors had the highest rate of infection, but took the fewest sickness days. Critical care had lower rates than the emergency department (ED), but rates in the ED declined when all staff were advised to use personal protective equipment (PPE).ConclusionSustained transmission of SARS-CoV-2 among our hospital staff did not occur, beyond the community outbreak, even in the absence of strict infection control measures in non-clinical areas. Current PPE appears to be effective when used appropriately. Our findings emphasize the importance of testing both clinical and non-clinical staff groups during a pandemic.

Reconstruction of the full transmission dynamics of COVID-19 in Wuhan.

As countries in the world review interventions for containing the pandemic of coronavirus disease2019 (COVID-19), important lessons can be drawn from the study of the full transmission dynamics of its causative agent-severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2)- in Wuhan (China), where vigorous non-pharmaceutical interventions have suppressed the local outbreak of this disease1. Here we use a modelling approach to reconstruct the full-spectrum dynamics of COVID-19 in Wuhan between 1January and 8March2020 across 5periods defined by events and interventions, on the basis of 32,583laboratory-confirmed cases1. Accounting for presymptomatic infectiousness2, time-varying ascertainment rates, transmission rates and population movements3, we identify two key features of the outbreak: high covertness and high transmissibility. We estimate 87% (lower bound, 53%) of the infections before 8March2020 were unascertained (potentially including asymptomatic and mildly symptomatic individuals); and a basic reproduction number (R0) of 3.54 (95% credible interval 3.40-3.67) in the early outbreak, much higher than that of severe acute respiratory syndrome (SARS) and Middle East respiratory syndrome (MERS)4,5. We observe that multipronged interventions had considerable positive effects on controlling the outbreak, decreasing the reproduction number to 0.28 (95% credible interval 0.23-0.33) and-by projection-reducing the total infections in Wuhan by 96.0% as of 8March2020. We also explore the probability of resurgence following the lifting of all interventions after 14consecutive days of no ascertained infections; we estimate this probability at 0.32 and 0.06 on the basis of models with 87% and 53% unascertained cases, respectively-highlighting the risk posed by substantial covert infections when changing control measures. These results have important implications when considering strategies of continuing surveillance and interventions to eventually contain outbreaks of COVID-19.

Genetic gateways to COVID-19 infection: Implications for risk, severity, and outcomes.

The dynamics, such as transmission, spatial epidemiology, and clinical course of Coronavirus Disease‐2019 (COVID‐19) have emerged as the most intriguing features and remain incompletely understood. The genetic landscape of an individual in particular, and a population in general seems to play a pivotal role in shaping the above COVID‐19 dynamics. Considering the implications of host genes in the entry and replication of SARS‐CoV‐2 and in mounting the host immune response, it appears that multiple genes might be crucially involved in the above processes. Herein, we propose three potentially important genetic gateways to COVID‐19 infection; these could explain at least in part the discrepancies of its spread, severity, and mortality. The variations within Angiotensin‐converting enzyme 2 (ACE2) gene might constitute the first genetic gateway, influencing the spatial transmission dynamics of COVID‐19. The Human Leukocyte Antigen locus, a master regulator of immunity against infection seems to be crucial in influencing susceptibility and severity of COVID‐19 and can be the second genetic gateway. The genes regulating Toll‐like receptor and complement pathways and subsequently cytokine storm induced exaggerated inflammatory pathways seem to underlie the severity of COVID‐19, and such genes might represent the third genetic gateway. Host‐pathogen interaction is a complex event and some additional genes might also contribute to the dynamics of COVID‐19. Overall, these three genetic gateways proposed here might be the critical host determinants governing the risk, severity, and outcome of COVID‐19. Genetic variations within these gateways could be key in influencing geographical discrepancies of COVID‐19.

Contact Tracing Assessment of COVID-19 Transmission Dynamics in Taiwan and Risk at Different Exposure Periods Before and After Symptom Onset

The dynamics of coronavirus disease 2019 (COVID-19) transmissibility are yet to be fully understood. Better understanding of the transmission dynamics is important for the development and evaluation of effective control policies. To delineate the transmission dynamics of COVID-19 and evaluate the transmission risk at different exposure window periods before and after symptom onset. This prospective case-ascertained study in Taiwan included laboratory-confirmed cases of COVID-19 and their contacts. The study period was from January 15 to March 18, 2020. All close contacts were quarantined at home for 14 days after their last exposure to the index case. During the quarantine period, any relevant symptoms (fever, cough, or other respiratory symptoms) of contacts triggered a COVID-19 test. The final follow-up date was April 2, 2020. Secondary clinical attack rate (considering symptomatic cases only) for different exposure time windows of the index cases and for different exposure settings (such as household, family, and health care). We enrolled 100 confirmed patients, with a median age of 44 years (range, 11-88 years), including 44 men and 56 women. Among their 2761 close contacts, there were 22 paired index-secondary cases. The overall secondary clinical attack rate was 0.7% (95% CI, 0.4%-1.0%). The attack rate was higher among the 1818 contacts whose exposure to index cases started within 5 days of symptom onset (1.0% [95% CI, 0.6%-1.6%]) compared with those who were exposed later (0 cases from 852 contacts; 95% CI, 0%-0.4%). The 299 contacts with exclusive presymptomatic exposures were also at risk (attack rate, 0.7% [95% CI, 0.2%-2.4%]). The attack rate was higher among household (4.6% [95% CI, 2.3%-9.3%]) and nonhousehold (5.3% [95% CI, 2.1%-12.8%]) family contacts than that in health care or other settings. The attack rates were higher among those aged 40 to 59 years (1.1% [95% CI, 0.6%-2.1%]) and those aged 60 years and older (0.9% [95% CI, 0.3%-2.6%]). In this study, high transmissibility of COVID-19 before and immediately after symptom onset suggests that finding and isolating symptomatic patients alone may not suffice to contain the epidemic, and more generalized measures may be required, such as social distancing.

The effectiveness of quarantine of Wuhan city against the Corona Virus Disease 2019 (COVID-19): A well-mixed SEIR model analysis.

A novel coronavirus pneumonia, first identified in Wuhan City and referred to as COVID-19 by the World Health Organization, has been quickly spreading to other cities and countries. To control the epidemic, the Chinese government mandated a quarantine of the Wuhan city on January 23, 2020. To explore the effectiveness of the quarantine of the Wuhan city against this epidemic, transmission dynamics of COVID-19 have been estimated. A well-mixed "susceptible exposed infectious recovered" (SEIR) compartmental model was employed to describe the dynamics of the COVID-19 epidemic based on epidemiological characteristics of individuals, clinical progression of COVID-19, and quarantine intervention measures of the authority. Considering infected individuals as contagious during the latency period, the well-mixed SEIR model fitting results based on the assumed contact rate of latent individuals are within 6-18, which represented the possible impact of quarantine and isolation interventions on disease infections, whereas other parameter were suppose as unchanged under the current intervention. The present study shows that, by reducing the contact rate of latent individuals, interventions such as quarantine and isolation can effectively reduce the potential peak number of COVID-19 infections and delay the time of peak infection.

Prediction of COVID-19 transmission dynamics using a mathematical model considering behavior changes in Korea.

OBJECTIVESSince the report of the first confirmed case in Daegu on February 18, 2020, local transmission of coronavirus disease 2019 (COVID-19) in Korea has continued. In this study, we aimed to identify the pattern of local transmission of COVID-19 using mathematical modeling and predict the epidemic size and the timing of the end of the spread.METHODSWe modeled the COVID-19 outbreak in Korea by applying a mathematical model of transmission that factors in behavioral changes. We used the Korea Centers for Disease Control and Prevention data of daily confirmed cases in the country to estimate the nationwide and Daegu/Gyeongbuk area-specific transmission rates as well as behavioral change parameters using a least-squares method.RESULTSThe number of transmissions per infected patient was estimated to be about 10 times higher in the Daegu/Gyeongbuk area than the average of nationwide. Using these estimated parameters, our models predicts that about 13,800 cases will occur nationwide and 11,400 cases in the Daegu/Gyeongbuk area until mid-June.CONCLUSIONSWe mathematically demonstrate that the relatively high per-capita rate of transmission and the low rate of changes in behavior have caused a large-scale transmission of COVID-19 in the Daegu/Gyeongbuk area in Korea. Since the outbreak is expected to continue until May, non-pharmaceutical interventions that can be sustained over the long term are required.

Diffusion of COVID-19 Outbreaks: The Interaction between Air Pollution-to-Human and Human-to-Human Transmission Dynamics in Hinterland Regions with Cold Weather and Low Average Wind Speed

What is COVID-19? Coronavirus disease 2019 (COVID-19) is viral infection that generates a severe acute respiratory syndrome with serious pneumonia that may result in progressive respiratory failure and death. What are the goals of this investigation? This study explains the geo-environmental determinants of the accelerated diffusion of COVID-19 in Italy that is generating a high level of deaths and suggests general lessons learned for a strategy to cope with future epidemics similar to COVID-19 to reduce viral infectivity and negative impacts in economic systems and society. What are the results of this study? The main results are: o The accelerate and vast diffusion of COVID-19 in North Italy has a high association with air pollution. o Hinterland cities have average days of exceeding the limits set for PM10 (particulate matter 10 micrometers or less in diameter) equal to 80 days, and an average number of infected more than 2,000 individuals as of April 1st, 2020, coastal cities have days of exceeding the limits set for PM10 equal to 60 days and have about 700 infected in average. o Cities that average number of 125 days exceeding the limits set for PM10, last year, they have an average number of infected individual higher than 3,200 units, whereas cities having less than 100 days (average number of 48 days) exceeding the limits set for PM10, they have an average number of about 900 infected individuals. o The results reveal that accelerated transmission dynamics of COVID-19 is due to mainly to mechanisms of air pollution-to-human transmission rather than human-to-human transmission. o The finding here suggests that to minimize future epidemic similar to COVID-19, the max number of days per year in which cities can exceed the limits set for PM10 or for ozone, considering their meteorological condition, is less than 50 days. After this critical threshold, the analytical output here suggests that environmental inconsistencies because of the combination between air pollution and meteorological conditions (with high moisture%, low wind speed and fog) trigger a take-off of viral infectivity (accelerated epidemic diffusion) with damages for health of population, economy and society. What is a socioeconomic strategy to prevent future epidemics similar to COVID-19? Considering the complex interaction between air pollution, meteorological conditions and biological characteristics of viral infectivity, lessons learned for COVID-19 have to be applied for a proactive socioeconomic strategy to cope with future epidemics, especially an environmental policy based on reduction of air pollution mainly in hinterland zones of countries, having low wind speed, high percentage of moisture and fog that create an environment that can damage immune system of people and foster a fast transmission of viral infectivity similar to the COVID-19. This study must conclude that a strategy to prevent future epidemics similar to COVID 19 has also to be designed in environmental and sustainability science and not only in terms of biology.

A Computational Model for Estimating the Progression of COVID-19 Cases in the US West and East Coast Population Regions.

The ongoing coronavirus disease 2019 (COVID-19) pandemic is of global concern and has recently emerged in the US. In this paper, we construct a stochastic variant of the SEIR model to estimate a quasi-worst-case scenario prediction of the COVID-19 outbreak in the US West and East Coast population regions by considering the different phases of response implemented by the US as well as transmission dynamics of COVID-19 in countries that were most affected. The model is then fitted to current data and implemented using Runge-Kutta methods. Our computation results predict that the number of new cases would peak around mid-April 2020 and begin to abate by July provided that appropriate COVID-19 measures are promptly implemented and followed, and that the number of cases of COVID-19 might be significantly mitigated by having greater numbers of functional testing kits available for screening. The model is also sensitive to assigned parameter values and reflects the importance of healthcare preparedness during pandemics.

Transmission dynamics of COVID-19 in Algeria: The impact of physical distancing and face masks.

We propose an SIR epidemic model taking into account prevention measures against coronavirus disease 2019 (COVID-19) such as wearing masks and respecting safety distances. We look for the conditions to avoid a second epidemic peak in the phase of release from confinement. We derive equations for the critical levels of mask efficiency, mask adoption (fraction of population wearing masks) and fraction of population engaging in physical distancing that lower the basic reproduction number ℜ0 to unity. Conclusions: For ℜ0 = 2.5, if at least 40% of people wear masks with efficiency 50%, and at least 20% of the population without masks (or anti-maskers) respect physical distancing measures, the effective reproduction number can be reduced to less than 1 and COVID-19 infections would plummet. The model predicts also that if at least half of the people respecting physical distancing, COVID-19 outbreaks with ℜ0 of about 3, would be theoretically extinguished without wearing masks. The results of this study provide an alternative explanation for the spread of the disease, and suggest some valuable policy recommendations about the control strategies applied to mitigate disease transmission.

Inferring key epidemiological parameters and transmission dynamics of COVID-19 based on a modified SEIR model

This study aims to establish a model-based framework for inferring key transmission characteristics of the newly emerging outbreak of the coronavirus disease 2019 (COVID-19), especially the epidemic dynamics under quarantine conditions. Inspired by the shifting therapeutic levels and capacity at different stages of the COVID-19 pandemic, we propose a modified SEIR model with a two-phase removal rate of quarantined hosts undergoing continuously tunable transition. We employ the Markov Chain Monte Carlo (MCMC) approach for inferring and forecasting the epidemiological dynamics from the publicly available surveillance reports. The effectiveness of a short-term prediction is illustrated by adopting the data sets from 10 demographic regions including Chinese mainland and South Korea. In the surveillance period, the average R0 ranges from 1.74 to 3.28, and the median of the mean latent period does not exceed 10 days across the surveillance regions.