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

Excretion of SARS-CoV-2 through faecal specimens

Coronavirus disease 2019 (COVID-19) has become a pandemic with increasing numbers of cases worldwide. SARS-CoV-2, the causative virus of COVID-19, is mainly transmitted through respiratory droplets or through direct and indirect contact with an infected person. The possibility of potential faecal-oral transmission was investigated in this study. We collected 258 faecal specimens from nine provinces in China and detected the nucleic acid of SARS-CoV-2 using real-time RT–PCR. Vero cells were used to isolate the virus from SARS-CoV-2 nucleic acid positive samples, after which sequencing of Spike gene in eight samples was performed. In all, 93 of 258 (36%) stool samples were positive for SARS-CoV-2 RNA. The positive rates of critical, severe, moderate, and mild patients were 54.4%, 56.1%, 30.8%, and 33.3%, respectively. The content of nucleic acid increased within 2 weeks after the onset of the disease. From the perspective of clinical typing, the nucleic acid can be detected in the faeces of critical patients within two weeks and until four to five weeks in the faeces of severe and mild patients. SARS-CoV-2 was isolated from stool specimens of two severe patients. Four non-synonymous mutations in Spike gene were newly detected in three stool samples. A small number of patients had strong faecal detoxification ability. The live virus in faeces could be an important source of contamination, which may lead to infection and further spread in areas with poor sanitary conditions. The findings of this study have public health significance and they should be considered when formulating disease control strategies.

Comparison of patients hospitalized with COVID-19, H7N9 and H1N1

Background

There is an urgent need to better understand the novel coronavirus, severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2), for that the coronavirus disease 2019 (COVID-19) continues to cause considerable morbidity and mortality worldwide. This paper was to differentiate COVID-19 from other respiratory infectious diseases such as avian-origin influenza A (H7N9) and influenza A (H1N1) virus infections.

Methods

We included patients who had been hospitalized with laboratory-confirmed infection by SARS-CoV-2 (n = 83), H7N9 (n = 36), H1N1 (n = 44) viruses. Clinical presentation, chest CT features, and progression of patients were compared. We used the Logistic regression model to explore the possible risk factors.

Results

Both COVID-19 and H7N9 patients had a longer duration of hospitalization than H1N1 patients (P < 0.01), a higher complication rate, and more severe cases than H1N1 patients. H7N9 patients had higher hospitalization-fatality ratio than COVID-19 patients (P = 0.01). H7N9 patients had similar patterns of lymphopenia, neutrophilia, elevated alanine aminotransferase, C-reactive protein, lactate dehydrogenase, and those seen in H1N1 patients, which were all significantly different from patients with COVID-19 (P < 0.01). Either H7N9 or H1N1 patients had more obvious symptoms, like fever, fatigue, yellow sputum, and myalgia than COVID-19 patients (P < 0.01). The mean duration of viral shedding was 9.5 days for SARS-CoV-2 vs 9.9 days for H7N9 (P = 0.78). For severe cases, the meantime from illness onset to severity was 8.0 days for COVID-19 vs 5.2 days for H7N9 (P < 0.01), the comorbidity of chronic heart disease was more common in the COVID-19 patients than H7N9 (P = 0.02). Multivariate analysis showed that chronic heart disease was a possible risk factor (OR > 1) for COVID-19, compared with H1N1 and H7N9.

Conclusions

The proportion of severe cases were higher for H7N9 and SARS-CoV-2 infections, compared with H1N1. The meantime from illness onset to severity was shorter for H7N9. Chronic heart disease was a possible risk factor for COVID-19.The comparison may provide the rationale for strategies of isolation and treatment of infected patients in the future.

Ten Epidemiological Parameters of COVID-19: Use of Rapid Literature Review to Inform Predictive Models During the Pandemic

Objective: To describe the methods used in a rapid review of the literature and to present the main epidemiological parameters that describe the transmission of SARS-Cov-2 and the illness caused by this virus, coronavirus disease 2019 (COVID-19). Methods: This is a methodological protocol that enabled a rapid review of COVID-19 epidemiological parameters. Findings: The protocol consisted of the following steps: definition of scope; eligibility criteria; information sources; search strategies; selection of studies; and data extraction. Four reviewers and three supervisors conducted this review in 40 days. Of the 1,266 studies found, 65 were included, mostly observational and descriptive in content, indicating relative homogeneity as to the quality of the evidence. The variation in the basic reproduction number, between 0.48 and 14.8; and the median of the hospitalization period, between 7.5 and 20.5 days stand out as key findings. Conclusion: We identified and synthesized 10 epidemiological parameters that may support predictive models and other rapid reviews to inform modeling of this and other future public health emergencies.

Analyzing the impact of the media campaign and rapid testing for COVID-19 as an optimal control problem in East Java, Indonesia

Without any vaccine or medical intervention to cure the infected individual from COVID-19, the non-pharmaceutical intervention become the most reasonable intervention against the spread of COVID-19. In this paper, we proposed a deterministic model governed by a system of nonlinear differential equations which consider the intervention of media campaign to increase human awareness, and rapid testing to track the undetected cases in the field. Analysis of the autonomous model shows the existence of transcritical bifurcation at a basic reproduction number equal to one. We estimate our parameter using the incidence data from East Java, Indonesia. Using these parameters, we analyze the sensitivity of the parameters in determining the size of the basic reproduction number. An optimal control problem which transforms media campaign and rapid testing as a time-dependent control was conducted also in this article. Cost-effectiveness analysis using the Infection averted ratio (IAR) and the Average cost-effectiveness ratio (ACER) conducted to analyze the best strategies to eradicate COVID-19 spread. We observe that the combination of the media campaign and rapid testing as time-dependent interventions reduces the number of an infected individual significantly and also minimizes the economic burden due to these strategies in East Java.

A modified SEIR model applied to the data of COVID-19 spread in Saudi Arabia

The Susceptible-Exposed-Infectious-Recovered (SEIR) model is an established and appropriate approach in many countries to ascertain the spread of the coronavirus disease 2019 (COVID-19) epidemic. We wished to create a new COVID-19 model to be suitable for patients in any country. In this work, a modified SEIR model was constructed. We used the real data of COVID-19 spread in Saudi Arabia for statistical analyses and complex analyses. The reproduction number and detailed review of stability demonstrated the complexities of our proposed SEIR model. The solution and equilibrium condition were explored based on Jacobian's linearization approach to the proposed SEIR model. The state of equilibrium was demonstrated, and a stability study was conducted in the disease-free environment. The reproduction number was measured sensitively against its internal parameters. Using the Lyapunov principle of equilibrium, the overall consistency of balance of our model was demonstrated. Findings using the SEIR model and observed outcomes due to COVID-19 spread in Saudi Arabia were compared. The modified SEIR model could enable successful analyses of the spread of epidemics such as COVID-19. An "ideal protocol" comprised essential steps to help Saudi Arabia decelerate COVID-19 spread. The most important aspects are to stay at home as much as possible and for infected people to remain in an isolated zone or secure area.

Quantifying the shift in social contact patterns in response to non-pharmaceutical interventions

Social contact mixing plays a critical role in influencing the transmission routes of infectious diseases. Moreover, quantifying social contact mixing patterns and their variations in a rapidly evolving pandemic intervened by changing public health measures is key for retroactive evaluation and proactive assessment of the effectiveness of different age- and setting-specific interventions. Contact mixing patterns have been used to inform COVID-19 pandemic public health decision-making; but a rigorously justified methodology to identify setting-specific contact mixing patterns and their variations in a rapidly developing pandemic, which can be informed by readily available data, is in great demand and has not yet been established. Here we fill in this critical gap by developing and utilizing a novel methodology, integrating social contact patterns derived from empirical data with a disease transmission model, that enables the usage of age-stratified incidence data to infer age-specific susceptibility, daily contact mixing patterns in workplace, household, school and community settings; and transmission acquired in these settings under different physical distancing measures. We demonstrated the utility of this methodology by performing an analysis of the COVID-19 epidemic in Ontario, Canada. We quantified the age- and setting (household, workplace, community, and school)-specific mixing patterns and their evolution during the escalation of public health interventions in Ontario, Canada. We estimated a reduction in the average individual contact rate from 12.27 to 6.58 contacts per day, with an increase in household contacts, following the implementation of control measures. We also estimated increasing trends by age in both the susceptibility to infection by SARS-CoV-2 and the proportion of symptomatic individuals diagnosed. Inferring the age- and setting-specific social contact mixing and key age-stratified epidemiological parameters, in the presence of evolving control measures, is critical to inform decision- and policy-making for the current COVID-19 pandemic.

Optimal control and sensitivity analysis for transmission dynamics of Coronavirus

Analysis of mathematical models designed for COVID-19 results in several important outputs that may help stakeholders to answer disease control policy questions. A mathematical model for COVID-19 is developed and equilibrium points are shown to be locally and globally stable. Sensitivity analysis of the basic reproductive number (R₀) showed that the rate of transmission from asymptomatically infected cases to susceptible cases is the most sensitive parameter. Numerical simulation indicated that a 10% reduction of R₀ by reducing the most sensitive parameter results in a 24% reduction of the size of exposed cases. Optimal control analysis revealed that the optimal practice of combining all three (public health education, personal protective measure, and treating COVID-19 patients) intervention strategies or combination of any two of them leads to the required mitigation of transmission of the pandemic.

European and US lockdowns and second waves during the COVID-19 pandemic

This paper investigates the lockdowns to contain the spread of the SARS-CoV-2 coronavirus in France, Germany, Italy, Spain, the UK and the US and also recent developments since these lockdowns have been relaxed. The analysis employs a two-stage SEIR model with different reproductive numbers pre- and post-lockdown. These parameters are estimated from data on the daily number of confirmed cases in a process that automatically detects the time at which the lockdown became effective. The model is evaluated by considering its predictive accuracy on current data and is then extended to a three-stage version to explore relaxations. The results show the extent to which each country was successful in reducing the reproductive number and demonstrate how the approach is able to model recent increases in the number of cases in all six countries, including the second peak in the US. The results also indicate that the current levels of relaxation in all five European countries could lead to significant second waves that last longer than the corresponding first waves. While there is uncertainty about the implications of these findings at this stage, they do suggest that a lot of vigilance is needed.

New Caputo-Fabrizio fractional order SEIASqEqHR model for COVID-19 epidemic transmission with genetic algorithm based control strategy

Fractional derivative has a memory and non-localization features that make it very useful in modelling epidemics’ transition. The kernel of Caputo-Fabrizio fractional derivative has many features such as non-singularity, non-locality and an exponential form. Therefore, it is preferred for modeling disease spreading systems. In this work, we suggest to formulate COVID-19 epidemic transmission via SEIASqEqHR paradigm using the Caputo-Fabrizio fractional derivation method. In the suggested fractional order COVID-19 SEIASqEqHR paradigm, the impact of changing quarantining and contact rates are examined. The stability of the proposed fractional order COVID-19 SEIASqEqHR paradigm is studied and a parametric rule for the fundamental reproduction number formula is given. The existence and uniqueness of stable solution of the proposed fractional order COVID-19 SEIASqEqHR paradigm are proved. Since the genetic algorithm is a common powerful optimization method, we propose an optimum control strategy based on the genetic algorithm. By this strategy, the peak values of the infected population classes are to be minimized. The results show that the proposed fractional model is epidemiologically well-posed and is a proper elect.

Optimal control strategies for the transmission risk of COVID-19

In this paper, we apply optimal control theory to a novel coronavirus (COVID-19) transmission model given by a system of non-linear ordinary differential equations. Optimal control strategies are obtained by minimizing the number of exposed and infected population considering the cost of implementation. The existence of optimal controls and characterization is established using Pontryagin's Maximum Principle. An expression for the basic reproduction number is derived in terms of control variables. Then the sensitivity of basic reproduction number with respect to model parameters is also analysed. Numerical simulation results demonstrated good agreement with our analytical results. Finally, the findings of this study shows that comprehensive impacts of prevention, intensive medical care and surface disinfection strategies outperform in reducing the disease epidemic with optimum implementation cost.

Estimation of the Outbreak Severity and Evaluation of Epidemic Prevention Ability of COVID-19 by Province in China

Objectives. To compare the epidemic prevention ability of COVID-19 of each province in China and to evaluate the existing prevention and control capacity of each province.Methods. We established a quasi-Poisson linear mixed-effects model using the case data in cities outside Wuhan in Hubei Province, China. We adapted this model to estimate the number of potential cases in Wuhan and obtained epidemiological parameters. We estimated the initial number of cases in each province by using passenger flowrate data and constructed the extended susceptible-exposed-infectious-recovered model to predict the future disease transmission trends.Results. The estimated potential cases in Wuhan were about 3 times the reported cases. The basic reproductive number was 3.30 during the initial outbreak. Provinces with more estimated imported cases than reported cases were those in the surrounding provinces of Hubei, including Henan and Shaanxi. The regions where the number of reported cases was closer to the predicted value were most the developed areas, including Beijing and Shanghai.Conclusions. The number of confirmed cases in Wuhan was underestimated in the initial period of the outbreak. Provincial surveillance and emergency response capabilities vary across the country.

Evaluation of the effect of different policies in the containment of epidemic spreads for the COVID-19 case

The paper presents a new mathematical model for the SARS-CoV-2 virus propagation, designed to include all the possible actions to prevent the spread and to help in the healing of infected people. After a discussion on the equilibrium and stability properties of the model, the effects of each different control actions on the evolution of the epidemic spread are analysed, through numerical evaluations for a more intuitive and immediate presentation, showing the consequences on the classes of the population.

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A new mathematical model for the spread of the SARS-CoV-2 is proposed and analysed.

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Available control actions are considered including social and political decisions.

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The effects on populations of containment policies and medical efforts are described.

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The model can support choices for large scale strategies of diseases contrast.

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Control actions can be defined according to economic, healthy and social issues.

Mathematical modelling and optimal cost-effective control of COVID-19 transmission dynamics

The novel coronavirus disease (COVID-19) caused by a new strain of severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) remains the current global health challenge. In this paper, an epidemic model based on system of ordinary differential equations is formulated by taking into account the transmission routes from symptomatic, asymptomatic and hospitalized individuals. The model is fitted to the corresponding cumulative number of hospitalized individuals (active cases) reported by the Nigeria Centre for Disease Control (NCDC), and parameterized using the least squares method. The basic reproduction number which measures the potential spread of COVID-19 in the population is computed using the next generation operator method. Further, Lyapunov function is constructed to investigate the stability of the model around a disease-free equilibrium point. It is shown that the model has a globally asymptotically stable disease-free equilibrium if the basic reproduction number of the novel coronavirus transmission is less than one. Sensitivities of the model to changes in parameters are explored, and safe regions at certain threshold values of the parameters are derived. It is revealed further that the basic reproduction number can be brought to a value less than one in Nigeria, if the current effective transmission rate of the disease can be reduced by 50%. Otherwise, the number of active cases may get up to 2.5% of the total estimated population. In addition, two time-dependent control variables, namely preventive and management measures, are considered to mitigate the damaging effects of the disease using Pontryagin's maximum principle. The most cost-effective control measure is determined through cost-effectiveness analysis. Numerical simulations of the overall system are implemented in MatLab ® for demonstration of the theoretical results.

Global strategies and effectiveness for COVID-19 prevention through contact tracing, screening, quarantine, and isolation: a systematic review

BACKGROUND

COVID-19 is an emerging disease caused by highly contagious virus called SARS-CoV-2. It caused an extensive health and economic burden around the globe. There is no proven effective treatment yet, except certain preventive mechanisms. Some studies assessing the effects of different preventive strategies have been published. However, there is no conclusive evidence. Therefore, this study aimed to review evidences related to COVID-19 prevention strategies achieved through contact tracing, screening, quarantine, and isolation to determine best practices.

METHODS

We conducted a systematic review in accordance with the PRISMA and Cochrane guidelines by searching articles from major medical databases such as PubMed/Medline, Global Health Database, Embase, CINAHL, Google Scholar, and clinical trial registries. Non-randomized and modeling articles published to date in areas of COVID prevention with contact tracing, screening, quarantine, and isolation were included. Two experts screened the articles and assessed risk of bias with ROBINS-I tool and certainty of evidence with GRADE approach. The findings were presented narratively and in tabular form.

RESULTS

We included 22 (9 observational and 13 modeling) studies. The studies consistently reported the benefit of quarantine, contact tracing, screening, and isolation in different settings. Model estimates indicated that quarantine of exposed people averted 44 to 81% of incident cases and 31 to 63% of deaths. Quarantine along with others can also halve the reproductive number and reduce the incidence, thus, shortening the epidemic period effectively. Early initiation of quarantine, operating large-scale screenings, strong contact tracing systems, and isolation of cases can effectively reduce the epidemic. However, adhering only to screening and isolation with lower coverage can miss more than 75% of asymptomatic cases; hence, it is not effective.

CONCLUSION

Quarantine, contact tracing, screening, and isolation are effective measures of COVID-19 prevention, particularly when integrated together. In order to be more effective, quarantine should be implemented early and should cover a larger community.

How successful Bangladesh is in controlling the coronavirus pandemic?

BACKGROUND

The reported number of COVID-19 patients increases on average along with the increased laboratory tests in Bangladesh implying a possibility of the spread of deadly coronavirus being out of control. Contrary to that, the government claims that it controls the spread of coronavirus through undertaking stringent policy measures. This different scenario leads this study on whether these measures have any positive impact on controlling the pandemic.

RESULTS

The results show that simulated number of patients (without policy measures) surpassed the actual number of patients (with policy measures) from the first week of July 2020 which may provide a signal about the positive impact of policy measures taken by the government.

CONCLUSION

This study concludes that policy measures taken by the government are useful to some extent in controlling the coronavirus pandemic. As this pandemic lingers, people may lose their patience to stay at home. Consequently, some of the policies need further correction and change.

Impact of Hospital Bed Shortages on the Containment of COVID-19 in Wuhan

The global outbreak of COVID-19 has caused worrying concern amongst the public and health authorities. The first and foremost problem that many countries face during the outbreak is a shortage of medical resources. In order to investigate the impact of a shortage of hospital beds on the COVID-19 outbreak, we formulated a piecewise smooth model for describing the limitation of hospital beds. We parameterized the model while using data on the cumulative numbers of confirmed cases, recovered cases, and deaths in Wuhan city from 10 January to 12 April 2020. The results showed that, even with strong prevention and control measures in Wuhan, slowing down the supply rate, reducing the maximum capacity, and delaying the supply time of hospital beds all aggravated the outbreak severity by magnifying the cumulative numbers of confirmed cases and deaths, lengthening the end time of the pandemic, enlarging the value of the effective reproduction number during the outbreak, and postponing the time when the threshold value was reduced to 1. Our results demonstrated that establishment of the Huoshenshan, Leishenshan, and Fangcang shelter hospitals avoided 22,786 people from being infected and saved 6524 lives. Furthermore, the intervention of supplying hospital beds avoided infections in 362,360 people and saved the lives of 274,591 persons. This confirmed that the quick establishment of the Huoshenshan, Leishenshan Hospitals, and Fangcang shelter hospitals, and the designation of other hospitals for COVID-19 patients played important roles in containing the outbreak in Wuhan.

Law of mass action and saturation in SIR model with application to Coronavirus modelling

When using SIR and related models, it is common to assume that the infection rate is proportional to the product of susceptible and infected individuals. While this assumption works at the onset of the outbreak, the infection force saturates as the outbreak progresses, even in the absence of any interventions. We use a simple agent-based model to illustrate this saturation effect. Its continuum limit leads a modified SIR model with exponential saturation. The derivation is based on first principles incorporating the spread radius and population density. We use the data for coronavirus outbreak for the period from March to June, to show that using SIR model with saturation is sufficient to capture the disease dynamics for many jurstictions, including the overall world-wide disease curve progression. Our model suggests the R 0 value of above 8 at the onset of infection, but with infection quickly "flattening out", leading to a long-term sustained sub-exponential spread.

Trends of 2019-nCoV in South Asian countries and prediction of the epidemic peaks

A deterministic compartmental model of the corona virus diseases has been introduced to investigate the current outbreak and epidemic peaks of the 2019-nCoV in South Asian countries. We have done details analysis of the above mentioning model and demonstrated its application using publicly reported data. Based on the reported data, we have determined the new infective rate, β = 0.0017, β = 0.00069, β = 0.0011 and β = 0.00047 for India, Pakistan, Bangladesh and Afghanistan, respectively and these values are not found for other countries due to insufficient data. According to present model, the epidemic under the most restrictive measures was observed of peak on around June 29 in Bangladesh with a peak size of 4100 infectious individuals which was closed to real size of 4014. In the same way, the model results also showed that the epidemic peaks were found on around September 16 in India, June 13 in Pakistan and on June 5 in Afghanistan with the peak sizes of 97,500, 6950 and 935, respectively those were closed to the real values. In addition, we have derived a model-implied basic reproduction number for each day of currently infected cases so that the mitigation and defeat strategies can be imposed to control the size of the epidemic.

Reproductive number of coronavirus: A systematic review and meta-analysis based on global level evidence

BACKGROUND

The coronavirus (SARS-COV-2) is now a global concern because of its higher transmission capacity and associated adverse consequences including death. The reproductive number of coronavirus provides an estimate of the possible extent of the transmission. This study aims to provide a summary reproductive number of coronavirus based on available global level evidence.

METHODS

A total of three databases were searched on September 15, 2020: PubMed, Web of Science, and Science Direct. The searches were conducted using a pre-specified search strategy to record studies reported the reproductive number of coronavirus from its inception in December 2019. It includes keywords of coronavirus and its reproductive number, which were combined using the Boolean operators (AND, OR). Based on the included studies, we estimated a summary reproductive number by using the meta-analysis. We used narrative synthesis to explain the results of the studies where the reproductive number was reported, however, were not possible to include in the meta-analysis because of the lack of data (mostly due to confidence interval was not reported).

RESULTS

Total of 42 studies included in this review whereas 29 of them were included in the meta-analysis. The estimated summary reproductive number was 2.87 (95% CI, 2.39-3.44). We found evidence of very high heterogeneity (99.5%) of the reproductive number reported in the included studies. Our sub-group analysis was found the significant variations of reproductive number across the country for which it was estimated, method and model that were used to estimate the reproductive number, number of case that was considered to estimate the reproductive number, and the type of reproductive number that was estimated. The highest reproductive number was reported for the Diamond Princess Cruise Ship in Japan (14.8). In the country-level, the higher reproductive number was reported for France (R, 6.32, 95% CI, 5.72-6.99) following Germany (R, 6.07, 95% CI, 5.51-6.69) and Spain (R, 3.56, 95% CI, 1.62-7.82). The higher reproductive number was reported if it was estimated by using the Markov Chain Monte Carlo method (MCMC) method and the Epidemic curve model. We also reported significant heterogeneity of the type of reproductive number- a high-value reported if it was the time-dependent reproductive number.

CONCLUSION

The estimated summary reproductive number indicates an exponential increase of coronavirus infection in the coming days. Comprehensive policies and programs are important to reduce new infections as well as the associated adverse consequences including death.

A stochastic time-delayed model for the effectiveness of Moroccan COVID-19 deconfinement strategy

Coronavirus disease 2019 (COVID-19) poses a great threat to public health and the economy worldwide. Currently, COVID-19 evolves in many countries to a second stage, characterized by the need for the liberation of the economy and relaxation of the human psychological effects. To this end, numerous countries decided to implement adequate deconfinement strategies. After the first prolongation of the established confinement, Morocco moves to the deconfinement stage on May 20, 2020. The relevant question concerns the impact on the COVID-19 propagation by considering an additional degree of realism related to stochastic noises due to the effectiveness level of the adapted measures. In this paper, we propose a delayed stochastic mathematical model to predict the epidemiological trend of COVID-19 in Morocco after the deconfinement. To ensure the well-posedness of the model, we prove the existence and uniqueness of a positive solution. Based on the large number theorem for martingales, we discuss the extinction of the disease under an appropriate threshold parameter. Moreover, numerical simulations are performed in order to test the efficiency of the deconfinement strategies chosen by the Moroccan authorities to help the policy makers and public health administration to make suitable decisions in the near future.

Simulation-free estimation of an individual-based SEIR model for evaluating nonpharmaceutical interventions with an application to COVID-19 in the District of Columbia

The ongoing COVID-19 pandemic has overwhelmingly demonstrated the need to accurately evaluate the effects of implementing new or altering existing nonpharmaceutical interventions. Since these interventions applied at the societal level cannot be evaluated through traditional experimental means, public health officials and other decision makers must rely on statistical and mathematical epidemiological models. Nonpharmaceutical interventions are typically focused on contacts between members of a population, and yet most epidemiological models rely on homogeneous mixing which has repeatedly been shown to be an unrealistic representation of contact patterns. An alternative approach is individual based models (IBMs), but these are often time intensive and computationally expensive to implement, requiring a high degree of expertise and computational resources. More often, decision makers need to know the effects of potential public policy decisions in a very short time window using limited resources. This paper presents a computation algorithm for an IBM designed to evaluate nonpharmaceutical interventions. By utilizing recursive relationships, our method can quickly compute the expected epidemiological outcomes even for large populations based on any arbitrary contact network. We utilize our methods to evaluate the effects of various mitigation measures in the District of Columbia, USA, at various times and to various degrees. Rcode for our method is provided in the supplementry material, thereby allowing others to utilize our approach for other regions.

The time-varying transmission dynamics of COVID-19 and synchronous public health interventions in China

Objectives

We aimed to estimate the time-varying transmission dynamics of COVID-19 in China, Wuhan City, and Guangdong province, and compare to that of severe acute respiratory syndrome (SARS).

Methods

Data on COVID-19 cases in China up to 20 March 2020 was collected from epidemiological investigations or official websites. Data on SARS cases in Guangdong Province, Beijing, and Hong Kong during 2002–3 was also obtained. We estimated the doubling time, basic reproduction number (R₀), and time-varying reproduction number (R_t) of COVID-19 and SARS.

Results

As of 20 March 2020, 80,739 locally acquired COVID-19 cases were identified in mainland China, with most cases reported between 20 January and 29 February 2020. The R₀ value of COVID-19 in China and Wuhan was 5.0 and 4.8, respectively, which was greater than the R₀ value of SARS in Guangdong (R₀ = 2.3), Hong Kong (R₀ = 2.3), and Beijing (R₀ = 2.6). At the start of the COVID-19 epidemic, the R_t value in China peaked at 8.4 and then declined quickly to below 1.0 in one month. With SARS, the R_t curve saw fluctuations with more than one peak, the highest peak was lower than that for COVID-19.

Conclusions

COVID-19 has much higher transmissibility than SARS, however, a series of prevention and control interventions to suppress the outbreak were effective. Sustained efforts are needed to prevent the rebound of the epidemic in the context of the global pandemic.

Modelling the role of optimal social distancing on disease prevalence of COVID-19 epidemic

COVID-19 first spread from Wuhan, China in December 2019 but it has already created one of the greatest pandemic situations ever witnessed. According to the current reports, a situation has arisen when people need to understand the importance of social distancing and take enough precautionary measures more seriously. Maintaining social distancing and proper hygiene, staying at isolation or adopting the self-quarantine strategy are some common habits which people should adopt to avoid from being infected. And the growing information regarding COVID-19, its symptoms and prevention strategies help the people to take proper precautions. In this present study, we have considered a SAIRS epidemiological model on COVID-19 transmission where people in the susceptible environment move into asymptotically exposed class after coming contact with asymptotically exposed, symptomatically infected and even hospitalised people. The numerical study indicates that if more people from asymptotically exposed class move into quarantine class to prevent further virus transmission, then the infected population decreases significantly. The disease outbreak can be controlled only if a large proportion of individuals become immune, either by natural immunity or by a proper vaccine. But for COVID-19, we have to wait until a proper vaccine is developed and hence natural immunity and taking proper precautionary measures is very important to avoid from being infected. In the latter part, a corresponding optimal control problem has been set up by implementing control strategies to reduce the cost and count of overall infected individuals. Numerical figures show that the control strategy, which denotes the social distancing to reduce disease transmission, works with a higher intensity almost after one month of implementation and then decreases in the last few days. Further, the control strategy denoting the awareness of susceptible population regarding precautionary measures first increases up to one month after implementation and then slowly decreases with time. Therefore, implementing control policies may help to reduce the disease transmission at this current pandemic situation as these controls reduce the overall infected population and increase the recovered population.

Beyond the peak: A deterministic compartment model for exploring the Covid-19 evolution in Italy

Novel Covid-19 has had a huge impact on the world's population since December 2019. The very rapid spreading of the virus worldwide, with its heavy toll of death and overload of the healthcare systems, induced the scientific community to focus on understanding, monitoring and foreseeing the epidemic evolution, weighing up the impact of different containment measures. An immense literature was produced in few months. Many papers were focused on predicting the peak features through a variety of different models. In the present paper, combining the surveillance data-set with data on mobility and testing, we develop a deterministic compartment model aimed at performing a retrospective analysis to understand the main modifications occurred to the characteristic parameters that regulate the epidemic spreading. We find that, besides self-protective behaviors, a reduction of susceptibility should have occurred in order to explain the fast descent of the epidemic after the peak. A sensitivity analysis of the basic reproduction number, in response to variations of the epidemiological parameters that can be influenced by policy-makers, shows the primary importance of a rigid isolation procedure for the diagnosed cases, combined with an intensive effort in performing extended testing campaigns. Future scenarios depend on the ability to protect the population from the injection of new cases from abroad, and to pursue in applying rigid self-protective measures.

Deep learning-based model for detecting 2019 novel coronavirus pneumonia on high-resolution computed tomography

Computed tomography (CT) is the preferred imaging method for diagnosing 2019 novel coronavirus (COVID19) pneumonia. We aimed to construct a system based on deep learning for detecting COVID-19 pneumonia on high resolution CT. For model development and validation, 46,096 anonymous images from 106 admitted patients, including 51 patients of laboratory confirmed COVID-19 pneumonia and 55 control patients of other diseases in Renmin Hospital of Wuhan University were retrospectively collected. Twenty-seven prospective consecutive patients in Renmin Hospital of Wuhan University were collected to evaluate the efficiency of radiologists against 2019-CoV pneumonia with that of the model. An external test was conducted in Qianjiang Central Hospital to estimate the system's robustness. The model achieved a per-patient accuracy of 95.24% and a per-image accuracy of 98.85% in internal retrospective dataset. For 27 internal prospective patients, the system achieved a comparable performance to that of expert radiologist. In external dataset, it achieved an accuracy of 96%. With the assistance of the model, the reading time of radiologists was greatly decreased by 65%. The deep learning model showed a comparable performance with expert radiologist, and greatly improved the efficiency of radiologists in clinical practice.

Deep learning-based model for detecting 2019 novel coronavirus pneumonia on high-resolution computed tomography

Computed tomography (CT) is the preferred imaging method for diagnosing 2019 novel coronavirus (COVID19) pneumonia. We aimed to construct a system based on deep learning for detecting COVID-19 pneumonia on high resolution CT. For model development and validation, 46,096 anonymous images from 106 admitted patients, including 51 patients of laboratory confirmed COVID-19 pneumonia and 55 control patients of other diseases in Renmin Hospital of Wuhan University were retrospectively collected. Twenty-seven prospective consecutive patients in Renmin Hospital of Wuhan University were collected to evaluate the efficiency of radiologists against 2019-CoV pneumonia with that of the model. An external test was conducted in Qianjiang Central Hospital to estimate the system's robustness. The model achieved a per-patient accuracy of 95.24% and a per-image accuracy of 98.85% in internal retrospective dataset. For 27 internal prospective patients, the system achieved a comparable performance to that of expert radiologist. In external dataset, it achieved an accuracy of 96%. With the assistance of the model, the reading time of radiologists was greatly decreased by 65%. The deep learning model showed a comparable performance with expert radiologist, and greatly improved the efficiency of radiologists in clinical practice.

Thermodynamics of the Interaction between the Spike Protein of Severe Acute Respiratory Syndrome Coronavirus-2 and the Receptor of Human Angiotensin-Converting Enzyme 2. Effects of Possible Ligands

Since the end of 2019, the coronavirus SARS-CoV-2 has caused more than 1000000 deaths all over the world and still lacks a medical treatment despite the attention of the whole scientific community. Human angiotensin-converting enzyme 2 (ACE2) was recently recognized as the transmembrane protein that serves as the point of entry of SARS-CoV-2 into cells, thus constituting the first biomolecular event leading to COVID-19 disease. Here, by means of a state-of-the-art computational approach, we propose a rational evaluation of the molecular mechanisms behind the formation of the protein complex. Moreover, the free energy of binding between ACE2 and the active receptor binding domain of the SARS-CoV-2 spike protein is evaluated quantitatively, providing for the first time the thermodynamics of virus-receptor recognition. Furthermore, the action of different ACE2 ligands is also examined in particular in their capacity to disrupt SARS-CoV-2 recognition, also providing via a free energy profile the quantification of the ligand-induced decreased affinity. These results improve our knowledge on molecular grounds of the SARS-CoV-2 infection and allow us to suggest rationales that could be useful for the subsequent wise molecular design for the treatment of COVID-19 cases.

A numerical simulation of fractional order mathematical modeling of COVID-19 disease in case of Wuhan China

The novel Covid-19 was identified in Wuhan China in December, 2019 and has created medical emergency world wise and distorted many life in the couple of month, it is being burned challenging situation for the medical scientist and virologists. Fractional order derivative based modeling is quite important to understand the real world problems and to analyse realistic situation of the proposed model. In the present investigation a fractional model based on Caputo-Fabrizio fractional derivative has been developed for the transmission of CORONA VIRUS (COVID-19) in Wuhan China. The existence and uniqueness solutions of the fractional order derivative has been investigated with the help of fixed point theory. Adamas- Bashforth numerical scheme has been used in the numerical simulation of the Caputo-Fabrizio fractional order derivative. The analysis of susceptible population, exposed population, infected population, recovered population and concentration of the virus of COVID-19 in the surrounding environment with respect to time for different values of fractional order derivative has been shown by means of graph. The comparative analysis has also been performed from classical model and fractional model along with the certified experimental data.

An SEIARD epidemic model for COVID-19 in Mexico: Mathematical analysis and state-level forecast

We propose an SEIARD mathematical model to investigate the current outbreak of coronavirus disease (COVID-19) in Mexico. Our model incorporates the asymptomatic infected individuals, who represent the majority of the infected population (with symptoms or not) and could play an important role in spreading the virus without any knowledge. We calculate the basic reproduction number (R 0) via the next-generation matrix method and estimate the per day infection, death and recovery rates. The local stability of the disease-free equilibrium is established in terms of R 0. A sensibility analysis is performed to determine the relative importance of the model parameters to the disease transmission. We calibrate the parameters of the SEIARD model to the reported number of infected cases, fatalities and recovered cases for several states in Mexico by minimizing the sum of squared errors and attempt to forecast the evolution of the outbreak until November 2020.

Mathematical modeling for infectious viral disease: The COVID-19 perspective

In this study, we examined various forms of mathematical models that are relevant for the containment, risk analysis, and features of COVID-19. Greater emphasis was laid on the extension of the Susceptible-Infectious-Recovered (SIR) models for policy relevance in the time of COVID-19. These mathematical models play a significant role in the understanding of COVID-19 transmission mechanisms, structures, and features. Considering that the disease has spread sporadically around the world, causing large scale socioeconomic disruption unwitnessed in contemporary ages since World War II, researchers, stakeholders, government, and the society at large are actively engaged in finding ways to reduce the rate of infection until a cure or vaccination procedure is established. We advanced argument for the various forms of the mathematical model of epidemics and highlighted their relevance in the containment of COVID-19 at the present time. Mathematical models address the need for understanding the transmission dynamics and other significant factors of the disease that would aid policymakers to make accurate decisions and reduce the rate of transmission of the disease.

Modeling and forecasting the spread and death rate of coronavirus (COVID-19) in the world using time series models

Coronaviruses are a huge family of viruses that affect neurological, gastrointestinal, hepatic and respiratory systems. The numbers of confirmed cases are increased daily in different countries, especially in Unites State America, Spain, Italy, Germany, China, Iran, South Korea and others. The spread of the COVID-19 has many dangers and needs strict special plans and policies. Therefore, to consider the plans and policies, the predicting and forecasting the future confirmed cases are critical. The time series models are useful to model data that are gathered and indexed by time. Symmetry of error's distribution is an essential condition in classical time series. But there exist cases in the real practical world that assumption of symmetric distribution of the error terms is not satisfactory. In our methodology, the distribution of the error has been considered to be two-piece scale mixtures of normal (TP-SMN). The proposed time series models works well than ordinary Gaussian and symmetry models (especially for COVID-19 datasets), and were fitted initially to the historical COVID-19 datasets. Then, the time series that has the best fit to each of the dataset is selected. Finally, the selected models are applied to predict the number of confirmed cases and the death rate of COVID-19 in the world.

An SEIARD epidemic model for COVID-19 in Mexico: Mathematical analysis and state-level forecast

We propose an SEIARD mathematical model to investigate the current outbreak of coronavirus disease (COVID-19) in Mexico. Our model incorporates the asymptomatic infected individuals, who represent the majority of the infected population (with symptoms or not) and could play an important role in spreading the virus without any knowledge. We calculate the basic reproduction number (R ₀) via the next-generation matrix method and estimate the per day infection, death and recovery rates. The local stability of the disease-free equilibrium is established in terms of R ₀. A sensibility analysis is performed to determine the relative importance of the model parameters to the disease transmission. We calibrate the parameters of the SEIARD model to the reported number of infected cases, fatalities and recovered cases for several states in Mexico by minimizing the sum of squared errors and attempt to forecast the evolution of the outbreak until November 2020.

Occurrence of backward bifurcation and prediction of disease transmission with imperfect lockdown: A case study on COVID-19

The outbreak of COVID-19 caused by SARS-CoV-2 is spreading rapidly around the world, which is causing a major public health concerns. The outbreaks started in India on March 2, 2020. As of April 30, 2020, 34,864 confirmed cases and 1154 deaths are reported in India and more than 30,90,445 confirmed cases and 2,17,769 deaths are reported worldwide. Mathematical models may help to explore the transmission dynamics, prediction and control of COVID-19 in the absence of an appropriate medication or vaccine. In this study, we consider a mathematical model on COVID-19 transmission with the imperfect lockdown effect. The basic reproduction number, R 0, is calculated using the next generation matrix method. The system has a disease-free equilibrium (DFE) which is locally asymptotically stable whenever R 0 < 1. Moreover, the model exhibits the backward bifurcation phenomenon, where the stable DFE coexists with a stable endemic equilibrium when R 0 < 1. The epidemiological implications of this phenomenon is that the classical epidemiological requirement of making R 0 less than unity is only a necessary, but not sufficient for effectively controlling the spread of COVID-19 outbreak. It is observed that the system undergoes backward bifurcation which is a new observation for COVID-19 disease transmission model. We also noticed that under the perfect lockdown scenario, there is no possibility of having backward bifurcation. Using Lyapunov function theory and LaSalle Invariance Principle, the DFE is shown globally asymptotically stable for perfect lockdown model. We have calibrated our proposed model parameters to fit daily data from India, Mexico, South Africa and Argentina. We have provided a short-term prediction for India, Mexico, South Africa and Argentina of future cases of COVID-19. We calculate the basic reproduction number from the estimated parameters. We further assess the impact of lockdown during the outbreak. Furthermore, we find that effective lockdown is very necessary to reduce the burden of diseases.

A mathematical model for COVID-19 transmission dynamics with a case study of India

The ongoing COVID-19 has precipitated a major global crisis, with 968,117 total confirmed cases, 612,782 total recovered cases and 24,915 deaths in India as of July 15, 2020. In absence of any effective therapeutics or drugs and with an unknown epidemiological life cycle, predictive mathematical models can aid in understanding of both coronavirus disease control and management. In this study, we propose a compartmental mathematical model to predict and control the transmission dynamics of COVID-19 pandemic in India with epidemic data up to April 30, 2020. We compute the basic reproduction number R 0, which will be used further to study the model simulations and predictions. We perform local and global stability analysis for the infection free equilibrium point E 0 as well as an endemic equilibrium point E* with respect to the basic reproduction number R 0. Moreover, we showed the criteria of disease persistence for R 0 > 1. We conduct a sensitivity analysis in our coronavirus model to determine the relative importance of model parameters to disease transmission. We compute the sensitivity indices of the reproduction number R 0 (which quantifies initial disease transmission) to the estimated parameter values. For the estimated model parameters, we obtainedR 0 = 1.6632 , which shows the substantial outbreak of COVID-19 in India. Our model simulation demonstrates that the disease transmission rate βs is more effective to mitigate the basic reproduction number R 0. Based on estimated data, our model predict that about 60 days the peak will be higher for COVID-19 in India and after that the curve will plateau but the coronavirus diseases will persist for a long time.

Perception Toward Quarantine for COVID-19 Among Adult Residents of Selected Towns in Southwest Ethiopia

BACKGROUND

COVID-19 is a global pandemic caused by a transmissible respiratory virus, SARS-Cov-2. Because it is a novel pathogen, limited information is available to characterize the spectrum of clinical illness, transmission efficiency, and the duration of viral shedding for patients with COVID-19. Quarantine is an important component of a multilayered strategy to prevent sustained spread of COVID-19 everywhere. The aim of this study was to assess public perceptions toward quarantine for COVID-19 and associated factors among adult residents of selected towns in Southwest Ethiopia, 2020.

METHODS

The community-based cross-sectional study was conducted from 1 to 15 June 2020 among adult residents of selected towns in south west Ethiopia. Systematic random sampling was used to select 844 participants. A structured and interviewer-administered questionnaire was used to collect data. Descriptive statistics were used to describe different variables. Multiple linear regression was carried out to determine the predictors of outcome variables. In multiple linear regression, variables with p-value <0.05 were taken as statistically significant association with community perception toward quarantine.

RESULTS

The total of 816 study participants were interviewed from which 450 (55.1%) were male. The median age of respondents was 30 years. Four hundred and twenty-seven (52.3%) of the respondents have a positive perception toward quarantine. Factors associated with perception toward quarantine were having training on COVID-19 (β=2.76, p=0.005), educational status of secondary (β=2.73, p=0.001), educational status to above secondary (β=2.24, p=0.011), occupational status of merchant (β=1.73, p=0.020), and knowledge of COVID-19 (β=0.23, p=0.001).

CONCLUSION

Only 52.3% of the respondents have a positive perception toward quarantine. Having training on COVID-19, educational status of secondary and above, being a merchant, knowledge of COVID-19 and knowledge of quarantine were significantly associated with a positive perception toward quarantine. Concerned bodies should work on enhancing the awareness of the community through information education and communication/behavior changing communication materials.

An Updated Understanding of the Current Emerging Respiratory Infection: COVID-19

According to the World Health Organization (WHO), the COVID-19 pandemic has been declared as a priority disease. Some patients with COVID-19 had symptoms of multiple organ failure and death. The published articles on COVID-19 infection were reviewed. The origin of SARS-CoV-2 is still not completely established. Person-to-person transmission via droplets, probable aerosols, or close contact is considered as the main mode of transmission. With increased mortality due to SARS-CoV-2, valuable clinical indicators or treatments should be further identified and summarized. CT scanning plays an important role in the diagnosis and evaluation of COVID-19 in asymptomatic patients or those with initially negative RT-PCR results. No specific antiviral therapy is recommended, except the main supportive treatments, and effective measures should be taken into consideration to protect important organs and prevent the development of acute respiratory distress syndrome (ARDS) in patients with severe infection.

Epidemiological Investigation of the First 135 COVID-19 Cases in Brunei: Implications for Surveillance, Control, and Travel Restrictions

Studies on the early introduction of SARS-CoV-2 in a naive population have important epidemic control implications. We report findings from the epidemiological investigation of the initial 135 COVID-19 cases in Brunei and describe the impact of control measures and travel restrictions. Epidemiological and clinical information was obtained for all confirmed COVID-19 cases, whose symptom onset was from March 9 to April 5, 2020. The basic reproduction number (R0), incubation period, and serial interval (SI) were calculated. Time-varying R was estimated to assess the effectiveness of control measures. Of the 135 cases detected, 53 (39.3%) were imported. The median age was 36 (range = 0.5–72) years. Forty-one (30.4%) and 13 (9.6%) were presymptomatic and asymptomatic cases, respectively. The median incubation period was 5 days (interquartile range [IQR] = 5, range = 1–11), and the mean SI was 5.4 days (SD = 4.5; 95% CI: 4.3, 6.5). The reproduction number was between 3.9 and 6.0, and the doubling time was 1.3 days. The time-varying reproduction number (Rt) was below one (Rt = 0.91; 95% credible interval: 0.62, 1.32) by the 13th day of the epidemic. Epidemic control was achieved through a combination of public health measures, with emphasis on a test–isolate–trace approach supplemented by travel restrictions and moderate physical distancing measures but no actual lockdown. Regular and ongoing testing of high-risk groups to supplement the existing surveillance program and a phased easing of physical distancing measures has helped maintain suppression of the COVID-19 outbreak in Brunei, as evidenced by the identification of only six additional cases from April 5 to August 5, 2020.

A mathematical model of COVID-19 transmission in a tertiary hospital and assessment of the effects of different intervention strategies

Novel coronavirus (named SARS-CoV-2) can spread widely in confined settings including hospitals, cruise ships, prisons, and places of worship. In particular, a healthcare-associated outbreak could become the epicenter of coronavirus disease (COVID-19). This study aimed to evaluate the effects of different intervention strategies on the hospital outbreak within a tertiary hospital. A mathematical model was developed for the COVID-19 transmission within a 2500-bed tertiary hospital of South Korea. The SEIR (susceptible-exposed-infectious-recovered) model with a compartment of doctor, nurse, patient, and caregiver was constructed. The effects of different intervention strategies such as front door screening, quarantine unit for newly admitted patients, early testing of suspected infected people, and personal protective equipment for both medical staff and visitors were evaluated. The model suggested that the early testing (within eight hours) of infected cases and monitoring the quarantine ward for newly hospitalized patients are effective measures for decreasing the incidence of COVID-19 within a hospital (81.3% and 70% decrease of number of incident cases, respectively, during 60 days). Front door screening for detecting suspected cases had only 42% effectiveness. Screening for prohibiting the admission of COVID-19 patients was more effective than the measures for patients before emergency room or outpatient clinic. This model suggests that under the assumed conditions, some effective measures have a great influence on the incidence of COVID-19 within a hospital. The implementation of the preventive measures could reduce the size of a hospital outbreak.

Population migration, confirmed COVID-19 cases, pandemic prevention, and control: evidence and experiences from China

Aim

The virulence of the novel coronavirus disease (COVID-19) has facilitated its rapid transition towards becoming a pandemic. Hence, this study aims to investigate the association between population migration and the number of confirmed COVID-19 cases in China while investigating its measures for pandemic prevention and control.

Subject and methods

A susceptible–exposed–infected–recovered–dormancy (SEIRD) model for the spread of COVID-19 in China was created to theoretically simulate the relationship between the populations migrating from Wuhan and the number of confirmed cases. Data from Baidu’s real-time dynamic pandemic monitoring system were elicited to empirically examine the theoretical inferences.

Results

Populations migrating from Wuhan to other cities increased the initial number of latently infected cases in these cities, raising the number of confirmed cases. Hence, implementing social distancing between the susceptible and infected populations could effectively lower the number of infected cases. Using data from Baidu’s real-time dynamic pandemic monitoring system, the empirical results revealed that an increase of 1000 persons migrating from Wuhan raised the number of confirmed cases by 4.82 persons.

Conclusion

This study confirmed the positive association between population migration and the number of confirmed COVID-19 cases. Based on the theoretical and empirical analysis, China’s pandemic prevention and control measures are discussed.

COVID-19 Spread in Saudi Arabia: Modeling, Simulation and Analysis

The novel coronavirus Severe Acute Respiratory Syndrome (SARS)-Coronavirus-2 (CoV-2) has resulted in an ongoing pandemic and has affected over 200 countries around the world. Mathematical epidemic models can be used to predict the course of an epidemic and develop methods for controlling it. As social contact is a key factor in disease spreading, modeling epidemics on contact networks has been increasingly used. In this work, we propose a simulation model for the spread of Coronavirus Disease 2019 (COVID-19) in Saudi Arabia using a network-based epidemic model. We generated a contact network that captures realistic social behaviors and dynamics of individuals in Saudi Arabia. The proposed model was used to evaluate the effectiveness of the control measures employed by the Saudi government, to predict the future dynamics of the disease in Saudi Arabia according to different scenarios, and to investigate multiple vaccination strategies. Our results suggest that Saudi Arabia would have faced a nationwide peak of the outbreak on 21 April 2020 with a total of approximately 26 million infections had it not imposed strict control measures. The results also indicate that social distancing plays a crucial role in determining the future local dynamics of the epidemic. Our results also show that the closure of schools and mosques had the maximum impact on delaying the epidemic peak and slowing down the infection rate. If a vaccine does not become available and no social distancing is practiced from 10 June 2020, our predictions suggest that the epidemic will end in Saudi Arabia at the beginning of November with over 13 million infected individuals, and it may take only 15 days to end the epidemic after 70% of the population receive a vaccine.

Impact of non-pharmaceutical interventions for reducing transmission of COVID-19: a systematic review and meta-analysis protocol

INTRODUCTION

Implementing non-pharmaceutical interventions (NPIs) protect the public from COVID-19. However, the impact of NPIs has been inconsistent and remains unclear. This study, therefore, aims to measure the impact of major NPIs (social distancing, social isolation and quarantine) on reducing COVID-19 transmission.

METHODS AND ANALYSIS

We will conduct a systematic review and meta-analysis research of both randomised and non-randomised controlled trials. We will undertake a systematic search of: MEDLINE, Embase, Allied & Complementary Medicine, COVID-19 Research, WHO database on COVID-19, ClinicalTrails.Gov for clinical trials on COVID-19, Cochrane Resources on Coronavirus (COVID-19), Oxford COVID-19 Evidence Service and Google Scholar for published and unpublished literatures on COVID-19 including preprint engines such as medRxiv, bioRxiv, Litcovid and SSRN for unpublished studies on COVID-19 and will be reported in accordance with Preferred Reporting Items for Systematic Reviews and Meta-Analyses. Outcomes of interest for impact analysis will include the reduction of COVID-19 transmission, avoiding crowds and restricting movement, isolating ill and psychological impacts. The Preferred Reporting Items for Systematic Review and Meta-Analysis Protocols checklist has been used for this protocol. For quality of included studies, we will use the Cochrane Collaboration's tool for assessing risk of bias for randomised controlled trials and the Newcastle-Ottawa Scale for observational studies. The Grading of Recommendations Assessment, Development and Evaluation approach will grade the certainty of the evidence for all outcome measures across studies. Random-effects model for meta-analysis will measure the effect size of NPIs or the strengths of relationships. For quantitative data, risk ratio or OR, absolute risk difference (for dichotomous outcome data), or mean difference or standardised mean difference (for continuous data) and their 95% CIs will be calculated. Where statistical pooling is not possible, a narrative synthesis will be conducted for the included studies. To assess the heterogeneity of effects, I2 together with the observed effects will be evaluated to provide the true effects in the analysis.

ETHICS AND DISSEMINATION

Formal ethical approval from an institutional review board or research ethics committee is not required as primary data will not be collected. The final results of this study will be published in an open-access peer-reviewed journal, and abstract will be presented at suitable national/international conferences or workshops. We will also share important information with public health authorities as well as with the WHO. In addition, we may post the submitted manuscript under review to medRxiv, or other relevant preprint servers.

TRIAL REGISTRATION NUMBER

CRD42020207338.

Mathematical modeling and analysis of COVID-19 pandemic in Nigeria

A mathematical model is designed and used to study the transmission dynamics and control of COVID-19 in Nigeria. The model, which was rigorously analysed and parametrized using COVID-19 data published by the Nigeria Centre for Disease Control (NCDC), was used to assess the community-wide impact of various control and mitigation strategies in some jurisdictions within Nigeria (notably the states of Kano and Lagos, and the Federal Capital Territory, Abuja). Numerical simulations of the model showed that COVID-19 can be effectively controlled in Nigeria using moderate levels of social-distancing strategy in the jurisdictions and in the entire nation. Although the use of face masks in public can significantly reduce COVID-19 in Nigeria, its use, as a sole intervention strategy, may fail to lead to a substantial reduction in disease burden. Such substantial reduction is feasible in the jurisdictions (and the entire Nigerian nation) if the public face mask use strategy is complemented with a social-distancing strategy. The community lockdown measures implemented in Nigeria on March 30, 2020 need to be maintained for at least three to four months to lead to the effective containment of COVID-19 outbreaks in the country. Relaxing, or fully lifting, the lockdown measures sooner, in an effort to re-open the economy or the country, may trigger a deadly second wave of the pandemic.

Modeling the COVID-19 Pandemic Using an SEIHR Model With Human Migration

The 2019 novel coronavirus disease (COVID-19) outbreak has become a worldwide problem. Due to globalization and the proliferation of international travel, many countries are now facing local epidemics. The existence of asymptomatic and pre-symptomatic transmissions makes it more difficult to control disease transmission by isolating infectious individuals. To accurately describe and represent the spread of COVID-19, we suggest a susceptible-exposed-infected-hospitalized-removed (SEIHR) model with human migrations, where the "exposed" (asymptomatic) individuals are contagious. From this model, we derive the basic reproduction number of the disease and its relationship with the model parameters. We find that, for highly contagious diseases like COVID-19, when the adjacent region's epidemic is not severe, a large migration rate can reduce the speed of local epidemic spreading at the price of infecting the neighboring regions. In addition, since "infected" (symptomatic) patients are isolated almost immediately, the transmission rate of the epidemic is more sensitive to that of the "exposed" (asymptomatic) individuals. Furthermore, we investigate the impact of various interventions, e.g. isolation and border control, on the speed of disease propagation and the resultant demand on medical facilities, and find that a strict intervention measure can be more effective than closing the borders. Finally, we use some real historical data of COVID-19 caseloads from different regions, including Hong Kong, to validate the modified SEIHR model, and make an accurate prediction for the third wave of the outbreak in Hong Kong.

SARS-CoV-2 in wastewater: State of the knowledge and research needs

The ongoing global pandemic of coronavirus disease 2019 (COVID-19) caused by severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) has been a Public Health Emergency of International Concern, which was officially declared by the World Health Organization. SARS-CoV-2 is a member of the family Coronaviridae that consists of a group of enveloped viruses with single-stranded RNA genome, which cause diseases ranging from common colds to acute respiratory distress syndrome. Although the major transmission routes of SARS-CoV-2 are inhalation of aerosol/droplet and person-to-person contact, currently available evidence indicates that the viral RNA is present in wastewater, suggesting the need to better understand wastewater as potential sources of epidemiological data and human health risks. Here, we review the current knowledge related to the potential of wastewater surveillance to understand the epidemiology of COVID-19, methodologies for the detection and quantification of SARS-CoV-2 in wastewater, and information relevant for human health risk assessment of SARS-CoV-2. There has been growing evidence of gastrointestinal symptoms caused by SARS-CoV-2 infections and the presence of viral RNA not only in feces of infected individuals but also in wastewater. One of the major challenges in SARS-CoV-2 detection/quantification in wastewater samples is the lack of an optimized and standardized protocol. Currently available data are also limited for conducting a quantitative microbial risk assessment (QMRA) for SARS-CoV-2 exposure pathways. However, modeling-based approaches have a potential role to play in reducing the impact of the ongoing COVID-19 outbreak. Furthermore, QMRA parameters obtained from previous studies on relevant respiratory viruses help to inform risk assessments of SARS-CoV-2. Our understanding on the potential role of wastewater in SARS-CoV-2 transmission is largely limited by knowledge gaps in its occurrence, persistence, and removal in wastewater. There is an urgent need for further research to establish methodologies for wastewater surveillance and understand the implications of the presence of SARS-CoV-2 in wastewater.

Outbreak of COVID-19 and SARS in mainland China: a comparative study based on national surveillance data

OBJECTIVE

To compare the epidemiological characteristics and transmission dynamics in relation to interventions against the COVID-19 and severe acute respiratory syndrome (SARS) outbreak in mainland China.

DESIGN

Comparative study based on a unique data set of COVID-19 and SARS.

SETTING

Outbreak in mainland China.

PARTICIPANTS

The final database included 82 858 confirmed cases of COVID-19 and 5327 cases of SARS.

METHODS

We brought together all existing data sources and integrated them into a comprehensive data set. Individual information on age, sex, occupation, residence location, date of illness onset, date of diagnosis and clinical outcome was extracted. Control measures deployed in mainland China were collected. We compared the epidemiological and spatial characteristics of COVID-19 and SARS. We estimated the effective reproduction number to explore differences in transmission dynamics and intervention effects.

RESULTS

Compared with SARS, COVID-19 affected more extensive areas (1668 vs 230 counties) within a shorter time (101 vs 193 days) and had higher attack rate (61.8 vs 4.0 per million persons). The COVID-19 outbreak had only one epidemic peak and one epicentre (Hubei Province), while the SARS outbreak resulted in two peaks and two epicentres (Guangdong Province and Beijing). SARS-CoV-2 was more likely to infect older people (median age of 52 years), while SARS-CoV tended to infect young adults (median age of 34 years). The case fatality rate (CFR) of either disease increased with age, but the CFR of COVID-19 was significantly lower than that of SARS (5.6% vs 6.4%). The trajectory of effective reproduction number dynamically changed in relation to interventions, which fell below 1 within 2 months for COVID-19 and within 5.5 months for SARS.

CONCLUSIONS

China has taken more prompt and effective responses to combat COVID-19 by learning lessons from SARS, providing us with some epidemiological clues to control the ongoing COVID-19 pandemic worldwide.

COVID-19 in Shanghai: IPC Policy Exploration in Support of Work Resumption Through System Dynamics Modeling

Purpose

It is unclear how and to what extent various infection prevention and control (IPC) policies affect the spread of an epidemic during work resumption. In order to assess the impact of IPC policies, this research addresses the results of a policy simulation in Shanghai, China, which estimates the transmission dynamics of COVID-19 under various IPC policies and offers evidence-based outcomes of work resumption policies for the world.

Materials and Methods

This simulation research is based on a system dynamics (SD) model that integrates IPC work resumption policies implemented in Shanghai into the classical susceptible-exposed-infected-removed (SEIR) epidemiological model. Input data were obtained from official websites, the Baidu migration index and published literature. The SD model was validated by comparing results with real-world data.

Results

The simulations show that a non-quarantined and non-staged approach to work resumption (Policy 1) would bring a small secondary outbreak of COVID-19. The quarantined but non-staged approach (Policy 2) and the non-quarantined but staged approach (Policy 3) would not bring a secondary outbreak of COVID-19. However, they both would generate more newly confirmed cases than the staged and quarantined approach (Policy 4). Moreover, the 14-day quarantine policy alone appears to be more effective in reducing transmission risk than the staged work resumption policy alone. The combined staged and quarantined IPC policy led to the fewest confirmed cases caused by work resumption in Shanghai, and the spread of COVID-19 stopped (ie, the number of newly confirmed cases reduced to zero) at the earliest date.

Conclusion

Conservative IPC policies can prevent a second outbreak of COVID-19 during work resumption. The dynamic systems model designed in this study can serve as a tool to test various IPC work resumption policies, facilitating decision-making in responses to combating the COVID-19 pandemic.

Impact of media reports on the early spread of COVID-19 epidemic

Media reports can modify people's knowledge of emerging infectious diseases, and thus changing the public attitudes and behaviors. However, how the media reports affect the development of COVID-19 epidemic is a key public health issue. Here the Pearson correlation and cross-correlation analyses are conducted to find the statistically significant correlations between the number of new hospital notifications for COVID-19 and the number of daily news items for twelve major websites in China from January 11th to February 6th 2020. To examine the implication for transmission dynamics of these correlations, we proposed a novel model, which embeds the function of individual behaviour change (media impact) into the intensity of infection. The nonlinear least squares estimation is used to identify the best-fit parameter values in the model from the observed data. To determine impact of key parameters with media impact and control measures for the later outcome of the outbreak, we also carried out the uncertainty and sensitivity analyses. These findings confirm the importance of the responses of individuals to the media reports, and the crucial role of experts and governments in promoting the public under self-quarantine. Therefore, for mitigating epidemic COVID-19, the media publicity should be focused on how to guide people's behavioral changes by experts, and the management departments and designated hospitals of the COVID-19 should take effective quarantined measures, which are critical for the control of the disease.