Estimation of the Transmission Risk of the 2019-nCoV and Its Implication for Public Health Interventions

On a Coupled Time-Dependent SIR Models Fitting with New York and New-Jersey States COVID-19 Data

This article describes a simple Susceptible Infected Recovered (SIR) model fitting with COVID-19 data for the month of March 2020 in New York (NY) state. The model is a classical SIR, but is non-autonomous; the rate of susceptible people becoming infected is adjusted over time in order to fit the available data. The death rate is also secondarily adjusted. Our fitting is made under the assumption that due to limiting number of tests, a large part of the infected population has not been tested positive. In the last part, we extend the model to take into account the daily fluxes between New Jersey (NJ) and NY states and fit the data for both states. Our simple model fits the available data, and illustrates typical dynamics of the disease: exponential increase, apex and decrease. The model highlights a decrease in the transmission rate over the period which gives a quantitative illustration about how lockdown policies reduce the spread of the pandemic. The coupled model with NY and NJ states shows a wave in NJ following the NY wave, illustrating the mechanism of spread from one attractive hot spot to its neighbor.

Transmission dynamics of COVID-19 in Wuhan, China: effects of lockdown and medical resources

Due to the strong infectivity of COVID-19, it spread all over the world in about three months and thus has been studied from different aspects including its source of infection, pathological characteristics, diagnostic technology and treatment. Yet, the influences of control strategies on the transmission dynamics of COVID-19 are far from being well understood. In order to reveal the mechanisms of disease spread, we present dynamical models to show the propagation of COVID-19 in Wuhan. Based on mathematical analysis and data analysis, we systematically explore the effects of lockdown and medical resources on the COVID-19 transmission in Wuhan. It is found that the later lockdown is adopted by Wuhan, the fewer people will be infected in Wuhan, and nevertheless it will have an impact on other cities in China and even the world. Moreover, the richer the medical resources, the higher the peak of new infection, but the smaller the final scale. These findings well indicate that the control measures taken by the Chinese government are correct and timely.

Biosafety and biosecurity approaches to restrain/contain and counter SARS-CoV-2/COVID-19 pandemic: a rapid-review

Emergence and reemergence of infectious diseases pose significant public health risks that are continuously haunting human civilization in the past several decades. Such emerging pathogens should be considered as a high threat to humans, animals, and environmental health. The year 2020 was welcomed by another significant virus from family Coronaviridae called severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) that caused the coronavirus disease 2019 (COVID-19). The disease was first reported in the city of Wuhan, Hubei province, China. Within a short time, this disease attained the status of the Public Health Emergency of International Concern. Presently, COVID-19 has spread to more than 150 countries, therefore, the World Health Organization (WHO) called it a pandemic. The Chinese government, along with WHO, other health agencies, and many nations, are monitoring the current situation closely to analyze the impact of SARS-CoV-2/COVID-19 on humans, animals, and environmental health. In the context of the current situation, biosafety and biosecurity measure that focus on One Health aspects of the disease outbreaks and the SARS-CoV-2 spread are of great importance to restrain this pathogen. Along with these efforts, standard precaution and control measures should also be taken at personal and community level to prevent the spreading of any contagion diseases, including COVID-19. Researchers are putting their very high efforts to develop suitable vaccines and therapeutics/drugs to combat COVID-19. This review aims to highlight the importance of biosafety, biosecurity, One Health approach, and focusing on recent developments and the ways forward to prevent and control COVID-19 in a useful way.

Spread and Impact of COVID-19 in China: A Systematic Review and Synthesis of Predictions From Transmission-Dynamic Models

Background: Coronavirus disease 2019 (COVID-19) was first identified in Wuhan, China, in December 2019 and quickly spread throughout China and the rest of the world. Many mathematical models have been developed to understand and predict the infectiousness of COVID-19. We aim to summarize these models to inform efforts to manage the current outbreak. Methods: We searched PubMed, Web of science, EMBASE, bioRxiv, medRxiv, arXiv, Preprints, and National Knowledge Infrastructure (Chinese database) for relevant studies published between 1 December 2019 and 21 February 2020. References were screened for additional publications. Crucial indicators were extracted and analysed. We also built a mathematical model for the evolution of the epidemic in Wuhan that synthesised extracted indicators. Results: Fifty-two articles involving 75 mathematical or statistical models were included in our systematic review. The overall median basic reproduction number (R0) was 3.77 [interquartile range (IQR) 2.78-5.13], which dropped to a controlled reproduction number (Rc) of 1.88 (IQR 1.41-2.24) after city lockdown. The median incubation and infectious periods were 5.90 (IQR 4.78-6.25) and 9.94 (IQR 3.93-13.50) days, respectively. The median case-fatality rate (CFR) was 2.9% (IQR 2.3-5.4%). Our mathematical model showed that, in Wuhan, the peak time of infection is likely to be March 2020 with a median size of 98,333 infected cases (range 55,225-188,284). The earliest elimination of ongoing transmission is likely to be achieved around 7 May 2020. Conclusions: Our analysis found a sustained Rc and prolonged incubation/ infectious periods, suggesting COVID-19 is highly infectious. Although interventions in China have been effective in controlling secondary transmission, sustained global efforts are needed to contain an emerging pandemic. Alternative interventions can be explored using modelling studies to better inform policymaking as the outbreak continues.

SEIR modeling of the COVID-19 and its dynamics

In this paper, a SEIR epidemic model for the COVID-19 is built according to some general control strategies, such as hospital, quarantine and external input. Based on the data of Hubei province, the particle swarm optimization (PSO) algorithm is applied to estimate the parameters of the system. We found that the parameters of the proposed SEIR model are different for different scenarios. Then, the model is employed to show the evolution of the epidemic in Hubei province, which shows that it can be used to forecast COVID-19 epidemic situation. Moreover, by introducing the seasonality and stochastic infection the parameters, nonlinear dynamics including chaos are found in the system. Finally, we discussed the control strategies of the COVID-19 based on the structure and parameters of the proposed model.

Influencing factors of COVID-19 spreading: a case study of Thailand

Aim

A novel corona virus disease 2019 (COVID-19) was declared as pandemic by WHO as global level and local levels in many countries. The movement of people might be one influencing factor, this paper aims to report the situation COVID-19 and spreading in Thailand, including influencing factors of spreading and control.

Subject and method

Infected, confirmed COVID-19 data were obtained from the official website of the Department of Disease Control, Ministry of Public Health. Tourist data was downloaded from Ministry of Tourism and Sports. Researchers analyzed the situation from the first found case in Thailand until 15 April 2020 with the timeline of important influencing factors. Correlation coefficients of tourist data and infected case was calculated by person correlation coefficient.

Results

The number of infected cases was significant associated (correlation coefficient > 0.7) with economic factor, namely; number of visitors, generated income from both Thai and foreigner tourist (p value <0.01). The influencing factors of slow increased rate were the enforcement and implementation of both central and local government regulation, the strength of the Thai health care system, the culture and social relation, the partnership among various governmental and private sectors.

Conclusion

We found that the number of tourist and their activities were significant associated with number of infected, confirmed COVID-19 cases. The public education and social supporting were the key roles for regulation enforcement and implementation.

Estimation of the serial interval and basic reproduction number of COVID-19 in Qom, Iran, and three other countries: A data-driven analysis in the early phase of the outbreak

The outbreak of COVID‐19 was first reported from China, and on 19 February 2020, the first case was confirmed in Qom, Iran. The basic reproduction number (R₀) of infection is variable in different populations and periods. This study aimed to estimate the R₀ of COVID‐19 in Qom, Iran, and compare it with that in other countries. For estimation of the serial interval, we used data of the 51 confirmed cases of COVID‐19 and their 318 close contacts in Qom, Iran. The number of confirmed cases daily in the early phase of the outbreak and estimated serial interval were used for R₀ estimation. We used the time‐varying method as a method with the least bias to estimate R₀ in Qom, Iran, and in China, Italy and South Korea. The serial interval was estimated with a gamma distribution, a mean of 4.55 days and a standard deviation of 3.30 days for the COVID‐19 epidemic based on Qom data. The R₀ in this study was estimated to be between 2 and 3 in Qom. Of the four countries studied, the lowest R₀ was estimated in South Korea (1.5–2) and the highest in Iran (4–5). Sensitivity analyses demonstrated that R₀ is sensitive to the applied mean generation time. To the best of the authors' knowledge, this study is the first to estimate R₀ in Qom. To control the epidemic, the reproduction number should be reduced by decreasing the contact rate, decreasing the transmission probability and decreasing the duration of the infectious period.

Modeling the transmission dynamics and the impact of the control interventions for the COVID-19 epidemic outbreak

In this paper we develop a compartmental epidemic model to study the transmission dynamics of the COVID-19 epidemic outbreak, with Mexico as a practical example. In particular, we evaluate the theoretical impact of plausible control interventions such as home quarantine, social distancing, cautious behavior and other self-imposed measures. We also investigate the impact of environmental cleaning and disinfection, and government-imposed isolation of infected individuals. We use a Bayesian approach and officially published data to estimate some of the model parameters, including the basic reproduction number. Our findings suggest that social distancing and quarantine are the winning strategies to reduce the impact of the outbreak. Environmental cleaning can also be relevant, but its cost and effort required to bring the maximum of the outbreak under control indicate that its cost-efficacy is low.

The effect of large-scale anti-contagion policies on the COVID-19 pandemic

Governments around the world are responding to the coronavirus disease 2019 (COVID-19) pandemic¹, caused by severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2), with unprecedented policies designed to slow the growth rate of infections. Many policies, such as closing schools and restricting populations to their homes, impose large and visible costs on society; however, their benefits cannot be directly observed and are currently understood only through process-based simulations^2-4. Here we compile data on 1,700 local, regional and national non-pharmaceutical interventions that were deployed in the ongoing pandemic across localities in China, South Korea, Italy, Iran, France and the United States. We then apply reduced-form econometric methods, commonly used to measure the effect of policies on economic growth^5,6, to empirically evaluate the effect that these anti-contagion policies have had on the growth rate of infections. In the absence of policy actions, we estimate that early infections of COVID-19 exhibit exponential growth rates of approximately 38% per day. We find that anti-contagion policies have significantly and substantially slowed this growth. Some policies have different effects on different populations, but we obtain consistent evidence that the policy packages that were deployed to reduce the rate of transmission achieved large, beneficial and measurable health outcomes. We estimate that across these 6 countries, interventions prevented or delayed on the order of 61 million confirmed cases, corresponding to averting approximately 495 million total infections. These findings may help to inform decisions regarding whether or when these policies should be deployed, intensified or lifted, and they can support policy-making in the more than 180 other countries in which COVID-19 has been reported⁷.

Modelling scenarios of the epidemic of COVID-19 in Canada

BACKGROUND

Severe acute respiratory syndrome virus 2 (SARS-CoV-2), likely a bat-origin coronavirus, spilled over from wildlife to humans in China in late 2019, manifesting as a respiratory disease. Coronavirus disease 2019 (COVID-19) spread initially within China and then globally, resulting in a pandemic.

OBJECTIVE

This article describes predictive modelling of COVID-19 in general, and efforts within the Public Health Agency of Canada to model the effects of non-pharmaceutical interventions (NPIs) on transmission of SARS-CoV-2 in the Canadian population to support public health decisions.

METHODS

The broad objectives of two modelling approaches, 1) an agent-based model and 2) a deterministic compartmental model, are described and a synopsis of studies is illustrated using a model developed in Analytica 5.3 software.

RESULTS

Without intervention, more than 70% of the Canadian population may become infected. Non-pharmaceutical interventions, applied with an intensity insufficient to cause the epidemic to die out, reduce the attack rate to 50% or less, and the epidemic is longer with a lower peak. If NPIs are lifted early, the epidemic may rebound, resulting in high percentages (more than 70%) of the population affected. If NPIs are applied with intensity high enough to cause the epidemic to die out, the attack rate can be reduced to between 1% and 25% of the population.

CONCLUSION

Applying NPIs with intensity high enough to cause the epidemic to die out would seem to be the preferred choice. Lifting disruptive NPIs such as shut-downs must be accompanied by enhancements to other NPIs to prevent new introductions and to identify and control any new transmission chains.

Emerging coronavirus diseases and future perspectives

Coronavirus related infectious diseases seems to be biggest challenge of 21 century that have been constantly emerging and threating public health around the globe. Coronavirus disease-19 (COVID-19) that was detected as cause of respiratory tract infection in China by end the December 2019 impelled World Health Organization to declare in January 2020 public health emergency of international concern and consequently pandemic in March 2020. Over a past six months COVID-19 pandemic has wrapped up all continents except Antarctica. Scientists around the globe are finding way to tackle and reduce the ultimate risk and size of pandemic with lower morbidity and mortality rates. In this context, technologies such as sequencing, Crispr and artificial intelligence are playing vital role in diagnosis and management of infectious disease in contrast to conventional methods. Despite of this, there is a need to have rapid and early diagnostic tools and systems that recognize infectious disease in asymptotic condition. Here we provide an overview on the recent CoV outbreak and contribution of technologies with the emphasis on the future management for detection of such infectious diseases.

[Quarantine Alone or in Combination with Other Public Health Measures to Control COVID-19: A Rapid Review (Review)]

BACKGROUND

COVID-19 (coronavirus disease 2019) is a new, rapidly emerging zoonotic infectious disease, that was reported to the World Health Organization for the first time on 31 December 2019. Currently, no effective pharmacological interventions or vaccines are available to treat or prevent COVID-19, therefore nonpharmacological public health measures are more in focus.

OBJECTIVES

The aim was to assess the effects of quarantine - alone or in combination with other measures - during coronavirus outbreaks.

METHODS

Because of the current COVID-19 pandemic, WHO commissioned a rapid review. To save time, the method of systematic reviews was slightly and with caution modified. This publication is a summary of the most important aspects of the rapid review, translated into German by members of the WHO Collaborating Centre at the Danube University Krems (Austria).

RESULTS

Overall, 29 studies were included. Ten modeling studies focused on COVID-19, 4 observational studies and 15 modeling studies focused on SARS and MERS. The modeling studies consistently reported a benefit of the simulated quarantine measures. For example, the models estimated that quarantine of people exposed to confirmed or suspected cases of COVID-19 prevented between 44 and 81% of the cases that would otherwise have happened and 31 to 63% of the deaths, when compared to no such measures. In regard to costs, the earlier the quarantine measures are implemented, the greater the cost savings will be.

CONCLUSION

Our confidence in the evidence is very limited. This is mainly because the COVID-19 studies based their models on the limited data that have been available in the early weeks of the pandemic and made different assumptions about the virus. The studies of SARS and MERS are not completely generalizable to COVID-19. Despite only having limited evidence, all the studies found quarantine to be important for controlling the spread of severe coronavirus diseases. Looking to the coming months, in order to maintain the best possible balance of measures, decision makers must continue to constantly monitor the outbreak situation and the impact of the measures they implement.

Real-time estimation of the reproduction number of the novel coronavirus disease (COVID-19) in China in 2020 based on incidence data

Background

Since the first appearance in Wuhan, China in December 2019, the novel coronavirus disease (COVID-19) has posed serious threats to the public health in many Chinese places and overseas. It is essential to quantify the transmissibility on real-time basis for designing public health responses.

Methods

We estimated the time-varying reproduction numbers in China, Hubei province and Wuhan city by using the renewable equation determined by the serial interval (SI) of COVID-19. We compare the average reproduction numbers in different periods of time to explore the effectiveness of the public health control measures against the COVID-19 epidemic.

Results

We estimated the reproduction numbers at 2.61 (95% CI: 2.47–2.75), 2.76 (95% CI: 2.54–2.95) and 2.71 (95% CI: 2.43–3.01) for China, Hubei province and Wuhan respectively. We found that the reproduction number largely dropped after the city lockdown. As of February 16, the three reproduction numbers further reduced to 0.98, 1.14 and 1.41 respectively.

Conclusions

The control of COVID-19 epidemic was effective in substantially reducing the disease transmissibility in terms of the reproduction number in China reduced to 0.98 as of February 16. At the same time, the reproduction number in Wuhan was probably still larger than 1, and thus the enhancement in the public health control was recommended to maintain.

Epidemic update of COVID-19 in Hubei Province compared with other regions in China

AIMS & BACKGROUND

The COVID-19 outbreak spread in China and is a threat to the world. The aims of this study to help health workers better understand the epidemic of the COVID-19 and provide different control strategies toward Hubei Province and other regions in China.

METHODS

A comprehensive search of the Chinese Center for Disease Control and Prevention official websites and announcements was performed between 20 Jan 2019 and 29 Feb 2020. The relevant data of the distribution of the infection on each reported day were obtained.

RESULTS& FINDINGS

Up to 29 Feb 2020, 79,824 confirmed cases with the COVID-19 including 66,907 in Hubei Province and 12,377 in other administrative regions were reported. The SARS-COV-2 showed faster epidemic trends compared with the 2003-SARS-CoV. A total of 2,870 deaths have been reported nationwide among 79,824 confirmed cases, with a mortality of 3.6%. The mortality of the COVID-19 was significantly higher in Hubei Province than that in other regions(4.1% versus 0.84%). Since 1 Feb 2020 the number of discharged cases exceeded the number of the dead. By 29 Feb 2020, the number of discharged patients was 41,625, which exceeded the number of hospitalized patients, and the trend has further increased.

CONCLUSIONS

The infection of the SARS-COV-2 is spreading and increasing nationwide, and Hubei Province is the main epidemic area, with higher mortality. The outbreak is now under initial control especially in other regions outside of Hubei Province. Due to the different epidemic characteristics between Hubei Province and other regions, we should focus on different prevention and control strategies.

Epidemiological Trends of Coronavirus Disease 2019 in China

Background: The Coronavirus Disease 2019 (COVID-19) epidemic broke out in Wuhan, China, and it spread rapidly. Since January 23, 2020, China has launched a series of unusual and strict measures, including the lockdown of Wuhan city to contain this highly contagious disease. We collected the epidemiological data to analyze the trend of this epidemic in China. Methods: We closely tracked the Chinese and global official websites to collect the epidemiological information about COVID-19. The number of total and daily new confirmed cases of COVID-19 in China was presented to illustrate the trend of this epidemic. Results: On January 23, 2020, 835 confirmed COVID-19 cases were reported in China. On February 6, 2020, there were 31,211 cases. By February 20, 2020, the number reached as high as 75,993. Most cases were distributed in and around Wuhan, Hubei province. Since January 23, 2020, the number of daily new cases in China except Hubei province reached a peak of 890 on the eleventh day and then it declined to a low level of 34 within two full-length incubation periods (28 days), and the number of daily new cases in Hubei also started to decrease on the twelfth day, from 3,156 on February 4, 2020 to 955 on February 15, 2020. Conclusion: The COVID-19 epidemic has been primarily contained in China. The battle against this epidemic in China has provided valuable experiences for the rest of the world. Strict measures need to be taken as earlier as possible to prevent its spread.

Estimation of reproduction numbers of COVID-19 in typical countries and epidemic trends under different prevention and control scenarios

The coronavirus disease 2019 (COVID-19) has become a life-threatening pandemic. The epidemic trends in different countries vary considerably due to different policy-making and resources mobilization. We calculated basic reproduction number (R₀) and the time-varying estimate of the effective reproductive number (R_t) of COVID-19 by using the maximum likelihood method and the sequential Bayesian method, respectively. European and North American countries possessed higher R₀ and unsteady R_t fluctuations, whereas some heavily affected Asian countries showed relatively low R₀ and declining R_t now. The numbers of patients in Africa and Latin America are still low, but the potential risk of huge outbreaks cannot be ignored. Three scenarios were then simulated, generating distinct outcomes by using SEIR (susceptible, exposed, infectious, and removed) model. First, evidence-based prompt responses yield lower transmission rate followed by decreasing R_t. Second, implementation of effective control policies at a relatively late stage, in spite of huge casualties at early phase, can still achieve containment and mitigation. Third, wisely taking advantage of the time-window for developing countries in Africa and Latin America to adopt adequate measures can save more people’s life. Our mathematical modeling provides evidence for international communities to develop sound design of containment and mitigation policies for COVID-19.

Predicting intervention effect for COVID-19 in Japan: state space modeling approach

Japan has observed a surge in the number of confirmed cases of the coronavirus disease (COVID-19) that has caused a serious impact on the society especially after the declaration of the state of emergency on April 7, 2020. This study analyzes the real time data from March 1 to April 22, 2020 by adopting a sophisticated statistical modeling based on the state space model combined with the well-known susceptible-infected-recovered (SIR) model. The model estimation and forecasting are conducted using the Bayesian methodology. The present study provides the parameter estimates of the unknown parameters that critically determine the epidemic process derived from the SIR model and prediction of the future transition of the infectious proportion including the size and timing of the epidemic peak with the prediction intervals that naturally accounts for the uncertainty. Even though the epidemic appears to be settling down during this intervention period, the prediction results under various scenarios using the data up to May 18 reveal that the temporary reduction in the infection rate would still result in a delayed the epidemic peak unless the long-term reproduction number is controlled.

[Study on the epidemic development of COVID-19 in Hubei province by a modified SEIR model]

OBJECTIVE

To establish a SEIR epidemic dynamics model that can be used to evaluate the COVID-19 epidemic, and to predict and evaluate the COVID-19 epidemic in Hubei province using the proposed model.

METHODS

COVID-19 SEIR transmission dynamics model was established, which took transmission ability in latent period and tracking quarantine interventions into consideration. Based on the epidemic data of Hubei province from January 23, 2020 to February 24, 2020, the parameters of the newly established modified SEIR model were fitted. By using Euler integral algorithm to solve the modified SEIR dynamics model, the epidemic situation in Hubei province was analyzed, and the impact of prevention and control measures such as quarantine and centralized treatment on the epidemic development was discussed.

RESULTS

The theoretical estimation of the epidemic situation by the modified SEIR epidemic dynamics model is in good agreement with the actual situation in Hubei province. Theoretical analysis showed that prevention and control quarantine and medical follow-up quarantine played an important inhibitory effect on the outbreak of the epidemic.The centralized treatment played a key role in the rapid decline in the number of infected people. In addition, it is suggested that individuals should improve their prevention awareness and take strict self-protection measures to curb the increase in infected people.

CONCLUSIONS

The modified SEIR model is reliable in the evaluation of COVID-19 epidemic in Hubei province, which provides a theoretical reference for the decision-making of epidemic interventions.

[Replication and transmission mechanisms of highly pathogenic human coronavirus]

The three known highly pathogenic human coronaviruses are severe acute respiratory syndrome coronavirus (SARS-CoV), Middle East respiratory syndrome coronavirus (MERS-CoV), and severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2). Human highly pathogenic coronaviruses are composed of non-structural proteins, structural proteins, accessory proteins and ribonucleic acid. Viral particles recognize host receptors via spike glycoprotein (S protein), enter host cells by membrane fusion, replicate in host cells through large replication-transcription complexes, and promote proliferation by interfering with and suppressing the host's immune response. Highly pathogenic human coronaviruses are hosted by humans and vertebrates. Viral particles are transmitted through droplets, contact and aerosols or likely through digestive tract, urine, eyes and other routes. This review discusses the mechanisms of replication and transmission of highly pathogenic human coronaviruses providing basis for future studies on interrupting the transmission and pathogenicity of these pathogenic viruses.

Analysis of COVID-19 transmission in Shanxi Province with discrete time imported cases

Since December 2019, an outbreak of a novel coronavirus pneumonia (WHO named COVID-19) swept across China. In Shanxi Province, the cumulative confirmed cases finally reached 133 since the first confirmed case appeared on January 22, 2020, and most of which were imported cases from Hubei Province. Reasons for this ongoing surge in Shanxi province, both imported and autochthonous infected cases, are currently unclear and demand urgent investigation. In this paper, we developed a SEIQR difference-equation model of COVID-19 that took into account the transmission with discrete time imported cases, to perform assessment and risk analysis. Our findings suggest that if the lock-down date in Wuhan is earlier, the infectious cases are fewer. Moreover, we reveal the effects of city lock-down date on the final scale of cases: if the date is advanced two days, the cases may decrease one half (67, 95% CI: 66-68); if the date is delayed for two days, the cases may reach about 196 (95% CI: 193-199). Our investigation model could be potentially helpful to study the transmission of COVID-19, in other provinces of China except Hubei. Especially, the method may also be used in countries with the first confirmed case is imported.

Marine Predators Algorithm for Forecasting Confirmed Cases of COVID-19 in Italy, USA, Iran and Korea

The current pandemic of the new coronavirus, severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2), or COVID-19, has received wide attention by scholars and researchers. The vast increase in infected people is a significant challenge for each country and the international community in general. The prediction and forecasting of the number of infected people (so-called confirmed cases) is a critical issue that helps in understanding the fast spread of COVID-19. Therefore, in this article, we present an improved version of the ANFIS (adaptive neuro-fuzzy inference system) model to forecast the number of infected people in four countries, Italy, Iran, Korea, and the USA. The improved version of ANFIS is based on a new nature-inspired optimizer, called the marine predators algorithm (MPA). The MPA is utilized to optimize the ANFIS parameters, enhancing its forecasting performance. Official datasets of the four countries are used to evaluate the proposed MPA-ANFIS. Moreover, we compare MPA-ANFIS to several previous methods to evaluate its forecasting performance. Overall, the outcomes show that MPA-ANFIS outperforms all compared methods in almost all performance measures, such as Root Mean Squared Error (RMSE), Mean Absolute Error (MAE), Mean Absolute Percentage Error (MAPE), Root Mean Squared Relative Error (RMSRE), and Coefficient of Determination(R2). For instance, according to the results of the testing set, the R2 of the proposed model is 96.48%, 98.59%, 98.74%, and 95.95% for Korea, Italy, Iran, and the USA, respectively. More so, the MAE is 60.31, 3951.94, 217.27, and 12,979, for Korea, Italy, Iran, and the USA, respectively.

Phase-adjusted estimation of the COVID-19 outbreak in South Korea under multi-source data and adjustment measures: a modelling study

Based on the reported data from February 16, 2020 to March 9, 2020 in South Korea including confirmed cases, death cases and recovery cases, the control reproduction number was estimated respectively at different control measure phases using Markov chain Monte Carlo method and presented using the resulting posterior mean and 95% credible interval (C_rI). At the early phase from February 16 to February 24, we estimate the basic reproduction number R₀ of COVID-19 to be 4.79(95% C_rI 4.38 - 5.2). The estimated control reproduction number dropped rapidly to R_c ≈ 0.32(95% C_rI 0.19 - 0.47) at the second phase from February 25 to March 2 because of the voluntary lockdown measures. At the third phase from March 3 to March 9, we estimate R_c to be 0.27 (95% C_rI 0.14 - 0.42). We predict that the final size of the COVID-19 outbreak in South Korea is 9661 (95% C_rI 8660 - 11100) and the whole epidemic will be over by late April. It is found that reducing contact rate and enhancing the testing speed will have the impact on the peak value and the peak time.

Analysis of the Transmissibility Change of 2019-Novel Coronavirus Pneumonia and Its Potential Factors in China from 2019 to 2020

BACKGROUND

Recently, a large-scale novel coronavirus pneumonia (NCP) outbreak swept China. As of Feb. 9, 2020, a total of 40,260 patients have been diagnosed with NCP, and 23,589 patients were suspected to have infected by the 2019 novel coronavirus (COVID-19), which puts forward a great challenge for public health and clinical treatment in China. Until now, we are in the high-incidence season of NCP. Thus, the analysis of the transmissibility change of NCP and its potential factors may provide a reliable reference for establishing effective prevention and control strategies.

METHOD

By means of the method of calculating the instantaneous basic reproduction number R 0t proposed by Cori et al. (2013), we use R 0t to describe the transmissibility change of COVID-19 in China, 2019-2020. In addition, the Baidu Index (BDI) and Baidu Migration Scale (BMS) were selected to measure the public awareness and the effect of Wuhan lockdown (restricted persons in Wuhan outflow from the epidemic area) strategy, respectively. The Granger causality test (GCT) was carried out to explore the association between public awareness, the effect of the Wuhan lockdown strategy, and the transmissibility of COVID-19.

RESULTS

The estimated averaged basic reproduction number of NCP in China was 3.44 with 95% CI (2.87, 4.0) during Dec. 8, 2019, to Feb. 9, 2020. The instantaneous basic reproduction numbers (R 0t ) have two waves and reaching peaks on Jan. 8 and Jan. 27, respectively. After reaching a peak on Jan. 27, R 0t showed a continuous decline trend. On Feb. 9, R 0t has fallen to 1.68 (95% CI: 1.66, 1.7), but it is still larger than 1. We find a significantly negative association between public awareness and the transmissibility change of COVID-19, with one unit increase in cumulative BDI leading to a decrease of 0.0295% (95% CI: 0.0077, 0.051) R 0t . We also find a significantly negative association between the effect of the Wuhan lockdown strategy and the transmissibility change of COVID-19, and a one unit decrease in BMS may lead to a drop of 2.7% (95% CI: 0.382, 4.97) R 0t .

CONCLUSION

The current prevention and control measures have effectively reduced the transmissibility of COVID-19; however, R 0t is still larger than the threshold 1. The results show that the government adopting the Wuhan lockdown strategy plays an important role in restricting the potential infected persons in Wuhan outflow from the epidemic area and avoiding a nationwide spread by quickly controlling the potential infection in Wuhan. Meanwhile, since Jan. 18, 2020, the people successively accessed COVID-19-related information via the Internet, which may help to effectively implement the government's prevention and control strategy and contribute to reducing the transmissibility of NCP. Therefore, ongoing travel restriction and public health awareness remain essential to provide a foundation for controlling the outbreak of COVID-19.

Three months of COVID-19: A systematic review and meta-analysis

The pandemic of 2019 novel coronavirus (SARS‐CoV‐2019), reminiscent of the 2002‐SARS‐CoV outbreak, has completely isolated countries, disrupted health systems and partially paralyzed international trade and travel. In order to be better equipped to anticipate transmission of this virus to new regions, it is imperative to track the progress of the virus over time. This review analyses information on progression of the pandemic in the past 3 months and systematically discusses the characteristics of SARS‐CoV‐2019 virus including its epidemiologic, pathophysiologic, and clinical manifestations. Furthermore, the review also encompasses some recently proposed conceptual models that estimate the spread of this disease based on the basic reproductive number for better prevention and control procedures. Finally, we shed light on how the virus has endangered the global economy, impacting it both from the supply and demand side.

De-Escalation by Reversing the Escalation with a Stronger Synergistic Package of Contact Tracing, Quarantine, Isolation and Personal Protection: Feasibility of Preventing a COVID-19 Rebound in Ontario, Canada, as a Case Study

Since the beginning of the COVID-19 pandemic, most Canadian provinces have gone through four distinct phases of social distancing and enhanced testing. A transmission dynamics model fitted to the cumulative case time series data permits us to estimate the effectiveness of interventions implemented in terms of the contact rate, probability of transmission per contact, proportion of isolated contacts, and detection rate. This allows us to calculate the control reproduction number during different phases (which gradually decreased to less than one). From this, we derive the necessary conditions in terms of enhanced social distancing, personal protection, contact tracing, quarantine/isolation strength at each escalation phase for the disease control to avoid a rebound. From this, we quantify the conditions needed to prevent epidemic rebound during de-escalation by simply reversing the escalation process.

Forecasting COVID-19-Associated Hospitalizations under Different Levels of Social Distancing in Lombardy and Emilia-Romagna, Northern Italy: Results from an Extended SEIR Compartmental Model

The outbreak of coronavirus disease 2019 (COVID-19) was identified in Wuhan, China, in December 2019. As of 17 April 2020, more than 2 million cases of COVID-19 have been reported worldwide. Northern Italy is one of the world’s centers of active coronavirus cases. In this study, we predicted the spread of COVID-19 and its burden on hospital care under different conditions of social distancing in Lombardy and Emilia-Romagna, the two regions of Italy most affected by the epidemic. To do this, we used a Susceptible-Exposed-Infectious-Recovered (SEIR) deterministic model, which encompasses compartments relevant to public health interventions such as quarantine. A new compartment L was added to the model for isolated infected population, i.e., individuals tested positives that do not need hospital care. We found that in Lombardy restrictive containment measures should be prolonged at least until early July to avoid a resurgence of hospitalizations; on the other hand, in Emilia-Romagna the number of hospitalized cases could be kept under a reasonable amount with a higher contact rate. Our results suggest that territory-specific forecasts under different scenarios are crucial to enhance or take new containment measures during the epidemic.

Coronavirus Disease 2019 (COVID-19) in Neonates and Children From China: A Review

At the end of 2019, a novel coronavirus began to spread in Wuhan, Hubei Province, China. The confirmed cases increased nationwide rapidly, in part due to the increased population mobility during the Chinese Lunar New Year festival. The World Health Organization (WHO) subsequently named the novel coronavirus pneumonia Coronavirus Disease 2019 (COVID-19) and named the virus Severe Acute Respiratory Syndrome Coronavirus-2 (SARS-CoV-2). Soon, transmission from person to person was confirmed and the virus spread to many other countries. To date, many cases have been reported in the pediatric age group, most of which were from China. The management and treatment strategies have also been improved, which we believe would be helpful to pediatric series in other countries as well. However, the characteristics of neonatal and childhood infection still have not been evaluated in detail. This review summarizes the current understanding of SARS-CoV-2 infection in neonates and children from January 24 to May 1, as an experience from China.

Modelling the effects of media coverage and quarantine on the COVID-19 infections in the UK

A new COVID-19 epidemic model with media coverage and quarantine is constructed. The model allows for the susceptibles to the unconscious and conscious susceptible compartment. First, mathematical analyses establish that the global dynamics of the spread of the COVID-19 infectious disease are completely determined by the basic reproduction number R₀. If R₀ ≤ 1, then the disease free equilibrium is globally asymptotically stable. If R₀ > 1, the endemic equilibrium is globally asymptotically stable. Second, the unknown parameters of model are estimated by the MCMC algorithm on the basis of the total confirmed new cases from February 1, 2020 to March 23, 2020 in the UK. We also estimate that the basic reproduction number is R₀ = 4.2816(95%CI: (3.8882, 4.6750)). Without the most restrictive measures, we forecast that the COVID-19 epidemic will peak on June 2 (95%CI: (May 23, June 13)) (Figure 3a) and the number of infected individuals is more than 70% of UK population. In order to determine the key parameters of the model, sensitivity analysis are also explored. Finally, our results show reducing contact is effective against the spread of the disease. We suggest that the stringent containment strategies should be adopted in the UK.

Government Intervention, Risk Perception, and the Adoption of Protective Action Recommendations: Evidence from the COVID-19 Prevention and Control Experience of China

This study examines the relationships between government interventions, risk perception, and the public’s adoption of protective action recommendations (PARs) during the COVID-19 coronavirus disease emergency in mainland China. We conducted quota sampling based on the proportion of the population in each province and gender ratios in the Sixth Census and obtained a sample size of 3837. Government intervention was divided into government communication, government prevention and control, and government rescue. We used multiple regression and a bootstrap mediation effect test to study the mechanism of these three forms of government intervention on the public’s adoption of PARs. The results show that government prevention and control and government rescue significantly increased the likelihood of the public adopting PARs. Risk perception was significantly associated with the public’s adoption of PARs. The effects of government interventions and risk perception on the public’s adoption of PARs was not found to vary by region. Risk perception is identified as an important mediating factor between government intervention and the public’s adoption of PARs. These results indicate that increasing the public’s risk perception is an effective strategy for governments seeking to encourage the public to adopt PARs during the COVID-19 pandemic.

Spread and dynamics of the COVID-19 epidemic in Italy: Effects of emergency containment measures

The spread of coronavirus disease 2019 (COVID-19) in Italy prompted drastic measures for transmission containment. We examine the effects of these interventions, based on modeling of the unfolding epidemic. We test modeling options of the spatially explicit type, suggested by the wave of infections spreading from the initial foci to the rest of Italy. We estimate parameters of a metacommunity Susceptible-Exposed-Infected-Recovered (SEIR)-like transmission model that includes a network of 107 provinces connected by mobility at high resolution, and the critical contribution of presymptomatic and asymptomatic transmission. We estimate a generalized reproduction number ([Formula: see text] = 3.60 [3.49 to 3.84]), the spectral radius of a suitable next-generation matrix that measures the potential spread in the absence of containment interventions. The model includes the implementation of progressive restrictions after the first case confirmed in Italy (February 21, 2020) and runs until March 25, 2020. We account for uncertainty in epidemiological reporting, and time dependence of human mobility matrices and awareness-dependent exposure probabilities. We draw scenarios of different containment measures and their impact. Results suggest that the sequence of restrictions posed to mobility and human-to-human interactions have reduced transmission by 45% (42 to 49%). Averted hospitalizations are measured by running scenarios obtained by selectively relaxing the imposed restrictions and total about 200,000 individuals (as of March 25, 2020). Although a number of assumptions need to be reexamined, like age structure in social mixing patterns and in the distribution of mobility, hospitalization, and fatality, we conclude that verifiable evidence exists to support the planning of emergency measures.

The spread of COVID-19 in six western metropolitan regions: a false myth on the excess of mortality in Lombardy and the defense of the city of Milan

We analyzed the spread of the COVID-19 epidemic in 6 metropolitan regions with similar demographic characteristics, daytime commuting population and business activities: the New York metropolitan area, the Île-de-France region, the Greater London county, Bruxelles-Capital, the Community of Madrid and the Lombardy region. The highest mortality rates 30-days after the onset of the epidemic were recorded in New York (81.2 x 100,000) and Madrid (77.1 x 100,000). Lombardy mortality rate is below average (41.4 per 100,000), and it is the only situation in which the capital of the region (Milan) has not been heavily impacted by the epidemic wave. Our study analyzed the role played by containment measures and the positive contribution offered by the hospital care system. (www.actabiomedica.it).

Evaluation of the Secondary Transmission Pattern and Epidemic Prediction of COVID-19 in the Four Metropolitan Areas of China

Understanding the transmission dynamics of COVID-19 is crucial for evaluating its spread pattern, especially in metropolitan areas of China, as its spread could lead to secondary outbreaks. In addition, the experiences gained and lessons learned from China have the potential to provide evidence to support other metropolitan areas and large cities outside China with their emerging cases. We used data reported from January 24, 2020, to February 23, 2020, to fit a model of infection, estimate the likely number of infections in four high-risk metropolitan areas based on the number of cases reported, and increase the understanding of the COVID-19 spread pattern. Considering the effect of the official quarantine regulations and travel restrictions for China, which began January 23~24, 2020, we used the daily travel intensity index from the Baidu Maps app to roughly simulate the level of restrictions and estimate the proportion of the quarantined population. A group of SEIR model statistical parameters were estimated using Markov chain Monte Carlo (MCMC) methods and fitting on the basis of reported data. As a result, we estimated that the basic reproductive number, R₀, was 2.91 in Beijing, 2.78 in Shanghai, 2.02 in Guangzhou, and 1.75 in Shenzhen based on the data from January 24, 2020, to February 23, 2020. In addition, we inferred the prediction results and compared the results of different levels of parameters. For example, in Beijing, the predicted peak number of cases was 467 with a peak time of March 01, 2020; however, if the city were to implement different levels (strict, moderate, or weak) of travel restrictions or regulation measures, the estimation results showed that the transmission dynamics would change and that the peak number of cases would differ by between 54% and 209%. We concluded that public health interventions would reduce the risk of the spread of COVID-19 and that more rigorous control and prevention measures would effectively contain its further spread, and awareness of prevention should be enhanced when businesses and social activities return to normal before the end of the epidemic. Further, the experiences gained and lessons learned from China offer the potential to provide evidence supporting other metropolitan areas and big cities with their emerging cases outside China.

Tracking and forecasting milepost moments of the epidemic in the early-outbreak: framework and applications to the COVID-19

Background: The outbreak of the 2019 novel coronavirus (COVID-19) has attracted global attention. In the early stage of the outbreak, the most important question concerns some meaningful milepost moments, including the time when the number of daily confirmed cases decreases, the time when the number of daily confirmed cases becomes smaller than that of the daily removed (recovered and death), and the time when the number of daily confirmed cases and patients treated in hospital, which can be called "active cases", becomes zero. Unfortunately, it is extremely difficult to make right and precise prediction due to the limited amount of available data at the early stage of the outbreak. To address it, in this paper, we propose a flexible framework incorporating the effectiveness of the government control to forecast the whole process of a new unknown infectious disease in its early-outbreak. Methods: We first establish the iconic indicators to characterize the extent of epidemic spread. Then we develop the tracking and forecasting procedure with mild and reasonable assumptions. Finally we apply it to analyze and evaluate the COVID-19 outbreak using the public available data for mainland China beyond Hubei Province from the China Centers for Disease Control (CDC) during the period of Jan 29th, 2020, to Feb 29th, 2020, which shows the effectiveness of the proposed procedure. Results: Forecasting results indicate that the number of newly confirmed cases will become zero in the mid-early March, and the number of patients treated in the hospital will become zero between mid-March and mid-April in mainland China beyond Hubei Province. Conclusions: The framework proposed in this paper can help people get a general understanding of the epidemic trends in countries where COVID-19 are raging as well as any other outbreaks of new and unknown infectious diseases in the future.

Tracking and forecasting milepost moments of the epidemic in the early-outbreak: framework and applications to the COVID-19

Background: The outbreak of the 2019 novel coronavirus (COVID-19) has attracted global attention. In the early stage of the outbreak, the most important question concerns some meaningful milepost moments, including the time when the number of daily confirmed cases decreases, the time when the number of daily confirmed cases becomes smaller than that of the daily removed (recovered and death), and the time when the number of daily confirmed cases and patients treated in hospital becomes zero. Unfortunately, it is extremely difficult to make right and precise prediction due to the limited amount of available data at the early stage of the outbreak. To address it, in this paper, we propose a flexible framework incorporating the effectiveness of the government control to forecast the whole process of a new unknown infectious disease in its early-outbreak. Methods: We first establish the iconic indicators to characterize the extent of epidemic spread. Then we develop the tracking and forecasting procedure with mild and reasonable assumption. Finally we apply it to analyze and evaluate the COVID-19 using the public available data for mainland China beyond Hubei Province from the China Centers for Disease Control (CDC) during the period of Jan 29th, 2020, to Feb 29th, 2020, which shows the effectiveness of the proposed procedure. Results: Forecasting results indicate that the number of newly confirmed cases will become zero in the mid-early March, and the number of patients treated in the hospital will become zero between mid-March and mid-April in mainland China beyond Hubei Province. Conclusions: The framework proposed in this paper can help people get a general understanding of the epidemic trends in counties where COVID-19 are raging as well as any other outbreaks of new and unknown infectious diseases in the future.

Extended SIR Prediction of the Epidemics Trend of COVID-19 in Italy and Compared With Hunan, China

Background: Coronavirus Disease 2019 (COVID-19) is currently a global public health threat. Outside of China, Italy is one of the countries suffering the most with the COVID-19 epidemic. It is important to predict the epidemic trend of the COVID-19 epidemic in Italy to help develop public health strategies. Methods: We used time-series data of COVID-19 from Jan 22 2020 to Apr 02 2020. An infectious disease dynamic extended susceptible-infected-removed (eSIR) model, which covers the effects of different intervention measures in dissimilar periods, was applied to estimate the epidemic trend in Italy. The basic reproductive number was estimated using Markov Chain Monte Carlo methods and presented using the resulting posterior mean and 95% credible interval (CI). Hunan, with a similar total population number to Italy, was used as a comparative item. Results: In the eSIR model, we estimated that the mean of basic reproductive number for COVID-19 was 4.34 (95% CI, 3.04-6.00) in Italy and 3.16 (95% CI, 1.73-5.25) in Hunan. There would be a total of 182 051 infected cases (95%CI:116 114-274 378) under the current country blockade and the endpoint would be Aug 05 in Italy. Conclusion: Italy's current strict measures can efficaciously prevent the further spread of COVID-19 and should be maintained. Necessary strict public health measures should be implemented as soon as possible in other European countries with a high number of COVID-19 cases. The most effective strategy needs to be confirmed in further studies.

Modeling the trend of coronavirus disease 2019 and restoration of operational capability of metropolitan medical service in China: a machine learning and mathematical model-based analysis

Background

To contain the outbreak of coronavirus disease 2019 (COVID-19) in China, many unprecedented intervention measures are adopted by the government. However, these measures may interfere in the normal medical service. We sought to model the trend of COVID-19 and estimate the restoration of operational capability of metropolitan medical service in China.

Methods

Real-time data of COVID-19 and population mobility data were extracted from open sources. SEIR (Susceptible, Exposed, Infectious, Recovered) and neural network models (NNs) were built to model disease trends in Wuhan, Beijing, Shanghai and Guangzhou. Combined with public transportation data, Autoregressive Integrated Moving Average (ARIMA) model was used to estimate the accumulated demands for nonlocal hospitalization during the epidemic period in Beijing, Shanghai and Guangzhou.

Results

The number of infected people and deaths would increase by 45% and 567% respectively, given that the government only has implemented traffic control in Wuhan without additional medical professionals. The epidemic of Wuhan (measured by cumulative confirmed cases) was predicted to reach turning point at the end of March and end in later April, 2020. The outbreak in Beijing, Shanghai and Guangzhou was predicted to end at the end of March and the medical service could be fully back to normal in middle of April. During the epidemic, the number of nonlocal inpatient hospitalizations decreased by 69.86%, 57.41% and 66.85% in Beijing, Shanghai and Guangzhou respectively. After the end of epidemic, medical centers located in these metropolises may face 58,799 (95% CI 48926–67,232) additional hospitalization needs in the first month.

Conclusion

The COVID-19 epidemic in China has been effectively contained and medical service across the country is expected to return to normal in April. However, the huge unmet medical needs for other diseases could result in massive migration of patients and their families, bringing tremendous challenges for medical service in major metropolis and disease control for the potential asymptomatic virus carrier.

The SARS-CoV-2 outbreak: What we know

There is a current worldwide outbreak of the novel coronavirus Covid-19 (coronavirus disease 2019; the pathogen called SARS-CoV-2; previously 2019-nCoV), which originated from Wuhan in China and has now spread to 6 continents including 66 countries, as of 24:00 on March 2, 2020. Governments are under increased pressure to stop the outbreak from spiraling into a global health emergency. At this stage, preparedness, transparency, and sharing of information are crucial to risk assessments and beginning outbreak control activities. This information should include reports from outbreak site and from laboratories supporting the investigation. This paper aggregates and consolidates the epidemiology, clinical manifestations, diagnosis, treatments and preventions of this new type of coronavirus.

Estimate of the Basic Reproduction Number for COVID-19: A Systematic Review and Meta-analysis

OBJECTIVES

The outbreak of coronavirus disease 2019 (COVID-19) is one of the main public health challenges currently facing the world. Because of its high transmissibility, COVID-19 has already caused extensive morbidity and mortality in many countries throughout the world. An accurate estimation of the basic reproduction number (R0) of COVID-19 would be beneficial for prevention programs. In light of discrepancies in original research on this issue, this systematic review and meta-analysis aimed to estimate the pooled R0 for COVID-19 in the current outbreak.

METHODS

International databases (including Google Scholar, Science Direct, PubMed, and Scopus) were searched to identify studies conducted regarding the R0 of COVID-19. Articles were searched using the following keywords: "COVID-19" and "basic reproduction number" or "R0." The heterogeneity among studies was assessed using the I2 index, the Cochran Q test, and T2. A random-effects model was used to estimate R0 in this study.

RESULTS

The mean reported R0 in the identified articles was 3.38±1.40, with a range of 1.90 to 6.49. According to the results of the random-effects model, the pooled R0 for COVID-19 was estimated as 3.32 (95% confidence interval, 2.81 to 3.82). According to the results of the meta-regression analysis, the type of model used to estimate R0 did not have a significant effect on heterogeneity among studies (p=0.81).

CONCLUSIONS

Considering the estimated R0 for COVID-19, reducing the number of contacts within the population is a necessary step to control the epidemic. The estimated overall R0 was higher than the World Health Organization estimate.

The hallmarks of COVID-19 disease

Severe acute respiratory syndrome coronavirus-2 (SARS-CoV-2) is a novel coronavirus that has caused a worldwide pandemic of the human respiratory illness COVID-19, resulting in a severe threat to public health and safety. Analysis of the genetic tree suggests that SARS-CoV-2 belongs to the same Betacoronavirus group as severe acute respiratory syndrome coronavirus (SARS-CoV) and Middle East respiratory syndrome coronavirus (MERS-CoV). Although the route for viral transmission remains a mystery, SARS-CoV-2 may have originated in an animal reservoir, likely that of bat. The clinical features of COVID-19, such as fever, cough, shortness of breath, and fatigue, are similar to those of many acute respiratory infections. There is currently no specific treatment for COVID-19, but antiviral therapy combined with supportive care is the main strategy. Here, we summarize recent progress in understanding the epidemiological, virological, and clinical characteristics of COVID-19 and discuss potential targets with existing drugs for the treatment of this emerging zoonotic disease.

The epidemiology, diagnosis and treatment of COVID-19

In December 2019, the outbreak of the novel coronavirus disease (COVID-19) in China spread worldwide, becoming an emergency of major international concern. SARS-CoV-2 infection causes clusters of severe respiratory illness similar to severe acute respiratory syndrome coronavirus. Human-to-human transmission via droplets, contaminated hands or surfaces has been described, with incubation times of 2-14 days. Early diagnosis, quarantine, and supportive treatments are essential to cure patients. This paper reviews the literature on all available information about the epidemiology, diagnosis, isolation and treatments of COVID-19. Treatments, including antiviral agents, chloroquine and hydroxychloroquine, corticosteroids, antibodies, convalescent plasma transfusion and vaccines, are discussed in this article. In addition, registered trials investigating treatment options for COVID-19 infection are listed.

TRANSMISSION DYNAMICS AND CONTROL STRATEGIES OF COVID-19 IN WUHAN, CHINA

In December 2019, a novel coronavirus, SARS-COV-2, was identified among patients in Wuhan, China. Two strict control measures, i.e., putting Wuhan on lockdown and taking strict quarantine rule, were carried out to contain the spread of COVID-19. Based on the different control measures, we divided the transmission process of COVID-19 into three stages. An SEIHR model was established to describe the transmission dynamics and was applied to fit the published data on the confirmed cases of Wuhan city from December 31, 2019 to March 25, 2020 to deduce the time when the first patient with COVID-19 appeared. The basic reproduction number was estimated in the first stage to demonstrate the number of secondary infectious cases generated by an average infectious case in the absence of policy intervention. The effective reproduction numbers in second and third stages were estimated to evaluate the effects of the two strict control measures. In addition, sensitivity analysis of the reproduction number according to model parameters was executed to demonstrate the effect of the control measures for containing the spread of COVID-19. Finally, the numerical calculation method was applied to investigate the influence of the different control measures on the spread of COVID-19. The results indicated that following the strict quarantine rule was very effective, and reducing the effective contact rates and improving the diagnosis rate were crucial in reducing the effective reproduction number, and taking control measures as soon as possible can effectively contain a larger outbreak of COVID-19. But a bigger challenge for us to contain the spread of COVID-19 was the transmission from the asymptomatic carriers, which required to raising the public awareness of self-protection and keeping a good physical protection.

Prediction of numbers of the accumulative confirmed patients (NACP) and the plateau phase of 2019-nCoV in China

In the present study, I propose a novel fitting method to describe the outbreak of 2019-nCoV in China. The fitted data were selected carefully from the non-Hubei part and Hubei Province of China respectively. For the non-Hubei part, the time period of data collection corresponds from the beginning of the policy of isolation to present day. But for Hubei Province, the subjects of Wuhan City and Hubei Province were included from the time of admission to the Huoshenshan Hospital to present day in order to ensure that all or the majority of the confirmed and suspected patients were collected for diagnosis and treatment. The employed basic functions for fitting are the hyperbolic tangent functions \documentclass[12pt]{minimal} \usepackage{amsmath} \usepackage{wasysym} \usepackage{amsfonts} \usepackage{amssymb} \usepackage{amsbsy} \usepackage{mathrsfs} \usepackage{upgreek} \setlength{\oddsidemargin}{-69pt} \begin{document}$$\tanh (.)$$\end{document}tanh(.) since in these cases the 2019-nCoV is just an epidemic. Subsequently, the 2019-nCoV will initially expand rapidly and tend to disappear. Therefore, the numbers of the accumulative confirmed patients in different cities, provinces and geographical regions will initially increase rapidly and subsequently stabilize to a plateau phase. The selection of the basic functions for fitting is crucial. In the present study, I found that the hyperbolic tangent functions \documentclass[12pt]{minimal} \usepackage{amsmath} \usepackage{wasysym} \usepackage{amsfonts} \usepackage{amssymb} \usepackage{amsbsy} \usepackage{mathrsfs} \usepackage{upgreek} \setlength{\oddsidemargin}{-69pt} \begin{document}$$\tanh (.)$$\end{document}tanh(.) could satisfy the aforementioned properties. By this novel method, I can obtain two significant results. They base on the conditions that the rigorous isolation policy is executed continually. Initially, I can predict the numbers very accurately of the cumulative confirmed patients in different cities, provinces and parts in China, notably, in Wuhan City with the smallest relative error estimated to \documentclass[12pt]{minimal} \usepackage{amsmath} \usepackage{wasysym} \usepackage{amsfonts} \usepackage{amssymb} \usepackage{amsbsy} \usepackage{mathrsfs} \usepackage{upgreek} \setlength{\oddsidemargin}{-69pt} \begin{document}$$0.021\%$$\end{document}0.021%, in Hubei Province with the smallest relative error estimated to \documentclass[12pt]{minimal} \usepackage{amsmath} \usepackage{wasysym} \usepackage{amsfonts} \usepackage{amssymb} \usepackage{amsbsy} \usepackage{mathrsfs} \usepackage{upgreek} \setlength{\oddsidemargin}{-69pt} \begin{document}$$0.012\%$$\end{document}0.012% and in the non-Hubei part of China with the smallest relative error of \documentclass[12pt]{minimal} \usepackage{amsmath} \usepackage{wasysym} \usepackage{amsfonts} \usepackage{amssymb} \usepackage{amsbsy} \usepackage{mathrsfs} \usepackage{upgreek} \setlength{\oddsidemargin}{-69pt} \begin{document}$$-$$\end{document}- 0.195% in the short-term period of infection. In addition, perhaps I can predict the times when the plateau phases will occur respectively in different regions in the long-term period of infection. Generally for the non-Hubei part of China, the plateau phase of the outbreak of the 2019-nCoV will be expected this March or at the end of this February. In the non-Hubei region of China it is expected that the epidemic will cease on the 30th of March 2020 and following this date no new confirmed patient will be expected. The predictions of the time of Inflection Points and maximum NACP for some important regions may be also obtained. A specific plan for the prevention measures of the 2019-nCoV outbreak must be implemented. This will involve the present returning to work and resuming production in China. Based on the presented results, I suggest that the rigorous isolation policy by the government should be executed regularly during daily life and work duties. Moreover, as many as possible the confirmed and suspected cases should be collected to diagnose or treat.

Statistical and network analysis of 1212 COVID-19 patients in Henan, China

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Almost all currently infected COVID-19 patients in Henan province were analyzed.

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COVID-19 patients in Henan province show gender and age preferences, migrant workers or college students are at high risk.

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The incubation period was statistically estimated.

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The state transition diagram can reveal the time-phased nature of the COVID-19 epidemic.

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Network analysis reveals the aggregate outbreak phenomena of COVID-19.

Background

COVID-19 is spreading quickly all over the world. Publicly released data for 1212 COVID-19 patients in Henan of China were analyzed in this paper.

Methods

Various statistical and network analysis methods were employed.

Results

We found that COVID-19 patients show gender (55% vs 45%) and age (81% aged between 21 and 60) preferences; possible causes were explored. The estimated average, mode and median incubation periods are 7.4, 4 and 7 days. Incubation periods of 92% of patients were no more than 14 days. The epidemic in Henan has undergone three stages and has shown high correlations with the numbers of patients recently returned from Wuhan. Network analysis revealed that 208 cases were clustering infected, and various People's Hospitals are the main force in treating COVID-19.

Conclusions

The incubation period was statistically estimated, and the proposed state transition diagram can explore the epidemic stages of emerging infectious disease. We suggest that although the quarantine measures are gradually working, strong measures still might be needed for a period of time, since ∼7.45% of patients may have very long incubation periods. Migrant workers or college students are at high risk. State transition diagrams can help us to recognize the time-phased nature of the epidemic. Our investigations have implications for the prevention and control of COVID-19 in other regions of the world.

Evaluation and prediction of the COVID-19 variations at different input population and quarantine strategies, a case study in Guangdong province, China

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The input population and output population are considered.

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108 scenarios are listed from the input population and quarantine strategies.

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The second outbreak of disease is obtained.

In this study, an epidemic model was developed to simulate and predict the disease variations of Guangdong province which was focused on the period from Jan 27 to Feb 20, 2020. To explore the impacts of the input population and quarantine strategies on the disease variations at different scenarios, four time points were assumed as Feb 6, Feb 16, Feb 24 and Mar 5 2020. The major results suggest that our model can well capture the disease variations with high accuracy. The simulated peak value of the confirmed cases is 1002 at Feb 10, 2020 which is mostly close to the reported number of 1007 at Feb 9, 2020. The disease will become extinction with peak value of 1397 at May 11, 2020. Moreover, the increased numbers of the input population can mainly shorten the disease extinction days and the increased percentages of the exposed individuals of the input population increase the number of cumulative confirmed cases at a small percentage. Increasing the input population and decreasing the quarantine strategy together around the time point of the peak value of the confirmed cases, may lead to the second outbreak.

2019 Novel coronavirus (COVID-19) overview

Novel coronaviruses (CoVs) are zoonotic pathogens, but the first human-to-human transmission has been reported. CoVs have the best known genome of all RNA viruses, and mutations in the genome have now been found. A pneumonia of unknown cause detected in Wuhan, China, was first reported to the WHO Country Office in China on 31 December 2019. This study aims to report early findings related to COVID-19 and provide methods to prevent and treat it.

Studying on the impact of media coverage on the spread of COVID-19 in Hubei Province, China

Awareness of prevention is enhanced to reduce the rate of infection by media coverage, which plays an important role in preventing and controlling infectious diseases. Based on epidemic situation of the Corona Virus Disease 2019 (COVID-19) in Hubei, an SIHRS epidemic model with media coverage was proposed. Firstly, by the basic reproduction number R0, the globally asymptotically stable of the disease-free equilibrium and the endemic equilibrium were proved. Then, based on the reported epidemic data of Hubei Province from January 26 to February 13, numerical simulations are used to verify the analysis results, and the impact of peak time and the scale of disease transmission were mainly considered with different information implementation rate and the contact rate. It was shown that with the decrease of information implementation rate, the peak of confirmed cases would be delayed to reach, and would increase significantly. Therefore, in order to do a better prevention measures after resumption of work, it is very necessary to maintain the amount of information and implementation rate of media coverage.

The Distance Teaching Practice of Combined Mode of Massive Open Online Course Micro-Video for Interns in Emergency Department During the COVID-19 Epidemic Period

Objective: To observe and analyze the application effect of the combined mode of Massive Open Online Course (MOOC) micro-video during the COVID-19 epidemic period in the distance teaching practice of interns in the emergency department. Materials and Methods: The subjects of this study were 60 trainee nurses who conducted emergency nursing practice in Tongji Hospital Affiliated to Tongji Medical College of Huazhong University of Science and Technology from January 1 to February 29, 2020. At the time of the COVID-19 outbreak in Wuhan, they were divided into two groups: (1) the experimental group (combined mode of MOOC micro-video) and (2) the control group (traditional theory teaching combined with clinical practice teaching). The differences of theoretical and practical examination scores and teaching satisfaction between the two groups were compared. Results: There was no significant difference in theoretical, practical, and total examination scores between the two groups, but in terms of teaching satisfaction, the overall satisfaction, the degree of easy understanding, the evaluation of teachers and learning results in the experimental group were higher than those in the control group, with statistical difference (p < 0.05). Conclusion: Compared with the traditional teaching methods, the effect of combined mode of MOOC micro-video in emergency nursing practice is the same as that of traditional teaching methods, but the satisfaction is higher, so it is more suitable to be used in nursing practice during the COVID-19 epidemic period, so as to effectively reduce the cross-infection between doctors, nurses, and teaching staff.