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1.
Air Med J ; 39(5): 404-409, 2020.
Article in English | MEDLINE | ID: covidwho-866388

ABSTRACT

Objective: There is a coronavirus disease 2019 (COVID-19) pandemic. We aimed to describe the characteristics of patients transported by the Royal Flying Doctor Service (RFDS) for confirmed or suspected COVID-19 and to investigate the surge capacity of and operational implications for the RFDS in dealing with COVID-19. Methods: This was a prospective cohort study. To determine the characteristics of patients transported for confirmed or suspected COVID-19, we included patient data from February 2, 2020, to May 6, 2020. To investigate the surge capacity and operational implications for the RFDS in dealing with COVID-19, we built and validated an interactive operations area-level discrete event simulation decision support model underpinned by RFDS air medical activity data from 2015 to 2019 (4 years). This model was subsequently used in a factorial in silico experiment to systematically investigate both the supply of RFDS air medical services and the increased rates of demand for these services for diseases of the respiratory system. Results: The RFDS conducted 291 patient episodes of care for confirmed or suspected COVID-19. This included 288 separate patients, including 136 men and 119 women (sex missing = 33), with a median age of 62.0 years (interquartile range, 43.5-74.9 years). The simulation decision support model we developed is capable of providing dynamic and real-time support for RFDS decision makers in understanding the system's performance under uncertain COVID-19 demand. With increased COVID-19-related demand, the ability of the RFDS to cope will be driven by the number of aircraft available. The simulation model provided each aviation section with estimated numbers of aircraft required to meet a range of anticipated demands. Conclusion: Despite the lack of certainty in the actual level of COVID-19-related demand for RFDS services, modeling demonstrates that the robustness of meeting such demand increases with the number of operational and medically staffed aircraft.


Subject(s)
Air Ambulances/statistics & numerical data , Computer Simulation , Coronavirus Infections/epidemiology , Patient Transfer/statistics & numerical data , Pneumonia, Viral/epidemiology , Surge Capacity , Adult , Aged , Australia/epidemiology , Betacoronavirus , Cohort Studies , Female , Humans , Male , Middle Aged , Pandemics , Prospective Studies
2.
Comput Math Methods Med ; 2020: 9017157, 2020.
Article in English | MEDLINE | ID: covidwho-842433

ABSTRACT

This paper deals with the mathematical modeling and numerical simulations related to the coronavirus dynamics. A description is developed based on the framework of the susceptible-exposed-infectious-removed model. Initially, a model verification is carried out calibrating system parameters with data from China, Italy, Iran, and Brazil. Results show the model capability to predict infectious evolution. Afterward, numerical simulations are performed in order to analyze different scenarios of COVID-19 in Brazil. Results show the importance of the governmental and individual actions to control the number and the period of the critical situations related to the pandemic.


Subject(s)
Computer Simulation , Coronavirus Infections/epidemiology , Pneumonia, Viral/epidemiology , Algorithms , Betacoronavirus , Brazil/epidemiology , China/epidemiology , Communicable Diseases/epidemiology , Humans , Iran/epidemiology , Italy/epidemiology , Models, Theoretical , Pandemics , Public Health Informatics , Reproducibility of Results
3.
J Biol Dyn ; 14(1): 748-766, 2020 12.
Article in English | MEDLINE | ID: covidwho-842271

ABSTRACT

The outbreak of COVID-19 was first experienced in Wuhan City, China, during December 2019 before it rapidly spread over globally. This paper has proposed a mathematical model for studying its transmission dynamics in the presence of face mask wearing and hospitalization services of human population in Tanzania. Disease-free and endemic equilibria were determined and subsequently their local and global stabilities were carried out. The trace-determinant approach was used in the local stability of disease-free equilibrium point while Lyapunov function technique was used to determine the global stability of both disease-free and endemic equilibrium points. Basic reproduction number, R 0 , was determined in which its numerical results revealed that, in the presence of face masks wearing and medication services or hospitalization as preventive measure for its transmission, R 0 = 0.698 while in their absence R 0 = 3.8 . This supports its analytical solution that the disease-free equilibrium point E 0 is asymptotically stable whenever R 0 < 1 , while endemic equilibrium point E ∗ is globally asymptotically stable for R 0 > 1 . Therefore, this paper proves the necessity of face masks wearing and hospitalization services to COVID-19 patients to contain the disease spread to the population.


Subject(s)
Betacoronavirus , Coronavirus Infections/transmission , Models, Biological , Pandemics , Pneumonia, Viral/transmission , Basic Reproduction Number , Computer Simulation , Coronavirus Infections/epidemiology , Coronavirus Infections/prevention & control , Disease Susceptibility , Endemic Diseases/prevention & control , Endemic Diseases/statistics & numerical data , Humans , Masks/statistics & numerical data , Mathematical Concepts , Pandemics/prevention & control , Pneumonia, Viral/epidemiology , Pneumonia, Viral/prevention & control , Tanzania/epidemiology
4.
PLoS One ; 15(8): e0237300, 2020.
Article in English | MEDLINE | ID: covidwho-842269

ABSTRACT

The outbreak of COVID-19 across the world has posed unprecedented and global challenges on multiple fronts. Most of the vaccine and drug development has focused on the spike proteins and viral RNA-polymerases and main protease for viral replication. Using the bioinformatics and structural modelling approach, we modelled the structure of the envelope (E)-protein of novel SARS-CoV-2. The E-protein of this virus shares sequence similarity with that of SARS- CoV-1, and is highly conserved in the N-terminus regions. Incidentally, compared to spike proteins, E proteins demonstrate lower disparity and mutability among the isolated sequences. Using homology modelling, we found that the most favorable structure could function as a gated ion channel conducting H+ ions. Combining pocket estimation and docking with water, we determined that GLU 8 and ASN 15 in the N-terminal region were in close proximity to form H-bonds which was further validated by insertion of the E protein in an ERGIC-mimic membrane. Additionally, two distinct "core" structures were visible, the hydrophobic core and the central core, which may regulate the opening/closing of the channel. We propose this as a mechanism of viral ion channeling activity which plays a critical role in viral infection and pathogenesis. In addition, it provides a structural basis and additional avenues for vaccine development and generating therapeutic interventions against the virus.


Subject(s)
Betacoronavirus/chemistry , Coronavirus Infections/prevention & control , Pandemics/prevention & control , Pneumonia, Viral/prevention & control , Viral Envelope Proteins/chemistry , Viral Envelope Proteins/genetics , Betacoronavirus/isolation & purification , Computer Simulation , Coronavirus Infections/virology , Humans , Hydrogen , Hydrogen Bonding , Magnetic Resonance Spectroscopy , Models, Molecular , Pneumonia, Viral/virology , Point Mutation , Protein Conformation , Structural Homology, Protein , Vaccines, Attenuated , Vaccines, Inactivated , Viral Envelope Proteins/immunology , Viral Vaccines , Water/chemistry
5.
Respir Care ; 65(7): 920-931, 2020 07.
Article in English | MEDLINE | ID: covidwho-840991

ABSTRACT

BACKGROUND: The overwhelming demand for mechanical ventilators due to COVID-19 has stimulated interest in using one ventilator for multiple patients (ie, multiplex ventilation). Despite a plethora of information on the internet, there is little supporting evidence and no human studies. The risk of multiplex ventilation is that ventilation and PEEP effects are largely uncontrollable and depend on the difference between patients' resistance and compliance. It is not clear whether volume control ventilation or pressure control ventilation is safer or more effective. We designed a simulation-based study to allow complete control over the relevant variables to determine the effects of various degrees of resistance-compliance imbalance on tidal volume (VT), end-expiratory lung volume (EELV), and imputed pH. METHODS: Two separate breathing simulators were ventilated with a ventilator using pressure control and volume control ventilation modes. Evidence-based lung models simulated a range of differences in resistance and compliance (6 pairs of simulated patients). Differences in VT, EELV, and imputed pH were recorded. RESULTS: Depending on differences in resistance and compliance, differences in VT ranged from 1% (with equal resistance and compliance) to 79%. Differences in EELV ranged from 2% to 109%, whereas differences in pH ranged from 0% to 5%. Failure due to excessive VT (ie, > 8 mL/kg) did not occur, but failure due to excessive EELV difference (ie, > 10%) was evident in 50% of patient pairs. There was no difference in failure rate between volume control and pressure control ventilation modes. CONCLUSIONS: These experiments confirmed the potential for markedly different ventilation and oxygenation for patients with uneven respiratory system impedances during multiplex ventilation. Three critical problems must be solved to minimize risk: (1) partitioning of inspiratory flow from the ventilator individually between the 2 patients, (2) measurement of VT delivered to each patient, and (3) provision for individual PEEP. We provide suggestions for solving these problems.


Subject(s)
Airway Resistance/physiology , Coronavirus Infections , Lung Compliance/physiology , Materials Testing/methods , Pandemics , Pneumonia, Viral , Respiration, Artificial , Betacoronavirus , Computer Simulation , Coronavirus Infections/epidemiology , Coronavirus Infections/therapy , Critical Care/methods , Equipment Design , Humans , Models, Biological , Pneumonia, Viral/epidemiology , Pneumonia, Viral/therapy , Respiration, Artificial/instrumentation , Respiration, Artificial/methods , Ventilators, Mechanical/standards , Ventilators, Mechanical/supply & distribution
6.
PLoS One ; 15(10): e0240004, 2020.
Article in English | MEDLINE | ID: covidwho-810225

ABSTRACT

The SARS-CoV-2 virus has caused a pandemic and is public health emergency of international concern. As of now, no registered therapies are available for treatment of coronavirus infection. The viral infection depends on the attachment of spike (S) glycoprotein to human cell receptor angiotensin-converting enzyme 2 (ACE2). We have designed a protein inhibitor (ΔABP-D25Y) targeting S protein using computational approach. The inhibitor consists of two α helical peptides homologues to protease domain (PD) of ACE2. Docking studies and molecular dynamic simulation revealed that the inhibitor binds exclusively at the ACE2 binding site of S protein. The computed binding affinity of the inhibitor is higher than the ACE2 and thus will likely out compete ACE2 for binding to S protein. Hence, the proposed inhibitor ΔABP-D25Y could be a potential blocker of S protein and receptor binding domain (RBD) attachment.


Subject(s)
Antiviral Agents/chemistry , Betacoronavirus/drug effects , Drug Design , Spike Glycoprotein, Coronavirus/antagonists & inhibitors , Structural Homology, Protein , Binding Sites , Computer Simulation , Coronavirus Infections , Humans , Models, Molecular , Molecular Docking Simulation , Molecular Dynamics Simulation , Pandemics , Peptidyl-Dipeptidase A/chemistry , Pneumonia, Viral , Protein Domains
8.
J R Soc Interface ; 17(170): 20200518, 2020 09.
Article in English | MEDLINE | ID: covidwho-808609

ABSTRACT

We have analysed the COVID-19 epidemic data of more than 174 countries (excluding China) in the period between 22 January and 28 March 2020. We found that some countries (such as the USA, the UK and Canada) follow an exponential epidemic growth, while others (like Italy and several other European countries) show a power law like growth. Regardless of the best fitting law, many countries can be shown to follow a common trajectory that is similar to Italy (the epicentre at the time of analysis), but with varying degrees of delay. We found that countries with 'younger' epidemics, i.e. countries where the epidemic started more recently, tend to exhibit more exponential like behaviour, while countries that were closer behind Italy tend to follow a power law growth. We hypothesize that there is a universal growth pattern of this infection that starts off as exponential and subsequently becomes more power law like. Although it cannot be excluded that this growth pattern is a consequence of social distancing measures, an alternative explanation is that it is an intrinsic epidemic growth law, dictated by a spatially distributed community structure, where the growth in individual highly mixed communities is exponential but the longer term, local geographical spread (in the absence of global mixing) results in a power law. This is supported by computer simulations of a metapopulation model that gives rise to predictions about the growth dynamics that are consistent with correlations found in the epidemiological data. Therefore, seeing a deviation from straight exponential growth may be a natural progression of the epidemic in each country. On the practical side, this indicates that (i) even in the absence of strict social distancing interventions, exponential growth is not an accurate predictor of longer term infection spread, and (ii) a deviation from exponential spread and a reduction of estimated doubling times do not necessarily indicate successful interventions, which are instead indicated by a transition to a reduced power or by a deviation from power law behaviour.


Subject(s)
Computer Simulation , Coronavirus Infections/epidemiology , Pneumonia, Viral/epidemiology , Public Health Informatics , Betacoronavirus , Communicable Disease Control , Data Collection , Geography , Global Health , Humans , Kinetics , Pandemics
9.
J Biol Dyn ; 14(1): 748-766, 2020 12.
Article in English | MEDLINE | ID: covidwho-808386

ABSTRACT

The outbreak of COVID-19 was first experienced in Wuhan City, China, during December 2019 before it rapidly spread over globally. This paper has proposed a mathematical model for studying its transmission dynamics in the presence of face mask wearing and hospitalization services of human population in Tanzania. Disease-free and endemic equilibria were determined and subsequently their local and global stabilities were carried out. The trace-determinant approach was used in the local stability of disease-free equilibrium point while Lyapunov function technique was used to determine the global stability of both disease-free and endemic equilibrium points. Basic reproduction number, R 0 , was determined in which its numerical results revealed that, in the presence of face masks wearing and medication services or hospitalization as preventive measure for its transmission, R 0 = 0.698 while in their absence R 0 = 3.8 . This supports its analytical solution that the disease-free equilibrium point E 0 is asymptotically stable whenever R 0 < 1 , while endemic equilibrium point E ∗ is globally asymptotically stable for R 0 > 1 . Therefore, this paper proves the necessity of face masks wearing and hospitalization services to COVID-19 patients to contain the disease spread to the population.


Subject(s)
Betacoronavirus , Coronavirus Infections/transmission , Models, Biological , Pandemics , Pneumonia, Viral/transmission , Basic Reproduction Number , Computer Simulation , Coronavirus Infections/epidemiology , Coronavirus Infections/prevention & control , Disease Susceptibility , Endemic Diseases/prevention & control , Endemic Diseases/statistics & numerical data , Humans , Masks/statistics & numerical data , Mathematical Concepts , Pandemics/prevention & control , Pneumonia, Viral/epidemiology , Pneumonia, Viral/prevention & control , Tanzania/epidemiology
10.
Math Biosci Eng ; 17(4): 3062-3087, 2020 04 13.
Article in English | MEDLINE | ID: covidwho-806651

ABSTRACT

In this paper we introduce a method of global exponential attractor in the reaction-diffusion epidemic model in spatial heterogeneous environment to study the spread trend and long-term dynamic behavior of the COVID-19 epidemic. First, we prove the existence of the global exponential attractor of general dissipative evolution systems. Then, by using the existence theorem, the global asymptotic stability and the persistence of epidemic are discussed. Finally, combine with the official data of the COVID-19 and the national control strategy, some numerical simulations on the stability and global exponential attractiveness of the COVID-19 epidemic are given. Simulations show that the spread trend of the epidemic is in line with our theoretical results, and the preventive measures taken by the Chinese government are effective.


Subject(s)
Betacoronavirus , Coronavirus Infections/epidemiology , Epidemics , Models, Biological , Pneumonia, Viral/epidemiology , China/epidemiology , Computer Simulation , Coronavirus Infections/prevention & control , Coronavirus Infections/transmission , Epidemics/prevention & control , Epidemics/statistics & numerical data , Humans , Mathematical Concepts , Pandemics/prevention & control , Pandemics/statistics & numerical data , Pneumonia, Viral/prevention & control , Pneumonia, Viral/transmission , Quarantine/statistics & numerical data
11.
Math Biosci Eng ; 17(4): 2936-2949, 2020 03 30.
Article in English | MEDLINE | ID: covidwho-806454

ABSTRACT

The coronavirus disease 2019 (COVID-2019), a newly emerging disease in China, posed a public health emergency of China. Wuhan is the most serious affected city. Some measures have been taken to control the transmission of COVID-19. From Jan. 23rd, 2020, gradually increasing medical resources (such as health workforce, protective clothing, essential medicines) were sent to Wuhan from other provinces, and the government has established the hospitals to quarantine and treat infected individuals. Under the condition of sufficient medical resources in Wuhan, late-stage of epidemic showed a downward trend. Assessing the effectiveness of medical resources is of great significance for the future response to similar disease. Based on the transmission mechanisms of COVID-19 and epidemic characteristics of Wuhan, by using time-dependent rates for some parameters, we establish a dynamical model to reflect the changes of medical resources on transmission of COVID-19 in Wuhan. Our model is applied to simulate the reported data on cumulative and new confirmed cases in Wuhan from Jan. 23rd to Mar. 6th, 2020. We estimate the basic reproduction number R0 = 2.71, which determines whether the disease will eventually die out or not under the absence of effective control measures. Moreover, we calculate the effective daily reproduction ratio Re(t), which is used to measure the 'daily reproduction number'. We obtain that Re(t) drops less than 1 since Feb. 8th. Our results show that delayed opening the 'Fire God Hill' hospital will greatly increase the magnitude of the outbreak. This shows that the government's timely establishment of hospitals and effective quarantine via quick detection prevent a larger outbreak.


Subject(s)
Betacoronavirus , Coronavirus Infections/transmission , Models, Biological , Pandemics , Pneumonia, Viral/transmission , Basic Reproduction Number/statistics & numerical data , China/epidemiology , Computer Simulation , Coronavirus Infections/epidemiology , Coronavirus Infections/prevention & control , Hospital Design and Construction , Hospitals , Humans , Mathematical Concepts , Pandemics/prevention & control , Pandemics/statistics & numerical data , Pneumonia, Viral/epidemiology , Pneumonia, Viral/prevention & control , Quarantine/statistics & numerical data , Time Factors
12.
Math Biosci Eng ; 17(4): 3052-3061, 2020 04 08.
Article in English | MEDLINE | ID: covidwho-806451

ABSTRACT

The novel coronavirus disease 2019 (COVID-19) infection broke out in December 2019 in Wuhan, and rapidly overspread 31 provinces in mainland China on 31 January 2020. In the face of the increasing number of daily confirmed infected cases, it has become a common concern and worthy of pondering when the infection will appear the turning points, what is the final size and when the infection would be ultimately controlled. Based on the current control measures, we proposed a dynamical transmission model with contact trace and quarantine and predicted the peak time and final size for daily confirmed infected cases by employing Markov Chain Monte Carlo algorithm. We estimate the basic reproductive number of COVID-19 is 5.78 (95%CI: 5.71-5.89). Under the current intervention before 31 January, the number of daily confirmed infected cases is expected to peak on around 11 February 2020 with the size of 4066 (95%CI: 3898-4472). The infection of COVID-19 might be controlled approximately after 18 May 2020. Reducing contact and increasing trace about the risk population are likely to be the present effective measures.


Subject(s)
Betacoronavirus , Coronavirus Infections/epidemiology , Models, Biological , Pandemics/statistics & numerical data , Pneumonia, Viral/epidemiology , Algorithms , Basic Reproduction Number/statistics & numerical data , China/epidemiology , Computer Simulation , Contact Tracing/statistics & numerical data , Coronavirus Infections/prevention & control , Coronavirus Infections/transmission , Epidemics/prevention & control , Epidemics/statistics & numerical data , Geographic Mapping , Humans , Markov Chains , Mathematical Concepts , Monte Carlo Method , Pandemics/prevention & control , Pneumonia, Viral/prevention & control , Pneumonia, Viral/transmission , Quarantine/statistics & numerical data
13.
Math Biosci Eng ; 17(4): 2842-2852, 2020 03 25.
Article in English | MEDLINE | ID: covidwho-806316

ABSTRACT

Since the first case of coronavirus disease (COVID-19) in Wuhan Hubei, China, was reported in December 2019, COVID-19 has spread rapidly across the country and overseas. The first case in Anhui, a province of China, was reported on January 10, 2020. In the field of infectious diseases, modeling, evaluating and predicting the rate of disease transmission is very important for epidemic prevention and control. Different intervention measures have been implemented starting from different time nodes in the country and Anhui, the epidemic may be divided into three stages for January 10 to February 11, 2020, namely. We adopted interrupted time series method and develop an SEI/QR model to analyse the data. Our results displayed that the lockdown of Wuhan implemented on January 23, 2020 reduced the contact rate of epidemic transmission in Anhui province by 48.37%, and centralized quarantine management policy for close contacts in Anhui reduced the contact rate by an additional 36.97%. At the same time, the estimated basic reproduction number gradually decreased from the initial 2.9764 to 0.8667 and then to 0.5725. We conclude that the Wuhan lockdown and the centralized quarantine management policy in Anhui played a crucial role in the timely and effective mitigation of the epidemic in Anhui. One merit of this work is the adoption of morbidity data which may reflect the epidemic more accurately and promptly. Our estimated parameters are largely in line with the World Health Organization estimates and previous studies.


Subject(s)
Betacoronavirus , Coronavirus Infections/epidemiology , Models, Biological , Pandemics , Pneumonia, Viral/epidemiology , Basic Reproduction Number/statistics & numerical data , China/epidemiology , Computer Simulation , Coronavirus Infections/prevention & control , Coronavirus Infections/transmission , Humans , Interrupted Time Series Analysis/statistics & numerical data , Markov Chains , Mathematical Concepts , Monte Carlo Method , Morbidity/trends , Pandemics/prevention & control , Pandemics/statistics & numerical data , Pneumonia, Viral/prevention & control , Pneumonia, Viral/transmission , Quarantine/statistics & numerical data
14.
Math Biosci Eng ; 17(4): 3040-3051, 2020 04 08.
Article in English | MEDLINE | ID: covidwho-805375

ABSTRACT

We model the COVID-19 coronavirus epidemic in China. We use early reported case data to predict the cumulative number of reported cases to a final size. The key features of our model are the timing of implementation of major public policies restricting social movement, the identification and isolation of unreported cases, and the impact of asymptomatic infectious cases.


Subject(s)
Betacoronavirus , Coronavirus Infections/epidemiology , Models, Biological , Pandemics , Pneumonia, Viral/epidemiology , Asymptomatic Infections/epidemiology , Basic Reproduction Number/statistics & numerical data , China/epidemiology , Computer Simulation , Coronavirus Infections/prevention & control , Coronavirus Infections/transmission , Epidemics/prevention & control , Epidemics/statistics & numerical data , Humans , Mathematical Concepts , Pandemics/prevention & control , Pandemics/statistics & numerical data , Pneumonia, Viral/prevention & control , Pneumonia, Viral/transmission , Quarantine/statistics & numerical data , Time Factors
15.
Math Biosci Eng ; 17(4): 2853-2861, 2020 03 26.
Article in English | MEDLINE | ID: covidwho-805316

ABSTRACT

In this work, we use a within-host viral dynamic model to describe the SARS-CoV-2 kinetics in the host. Chest radiograph score data are used to estimate the parameters of that model. Our result shows that the basic reproductive number of SARS-CoV-2 in host growth is around 3.79. Using the same method we also estimate the basic reproductive number of MERS virus is 8.16 which is higher than SARS-CoV-2. The PRCC method is used to analyze the sensitivities of model parameters. Moreover, the drug effects on virus growth and immunity effect of patients are also implemented to analyze the model.


Subject(s)
Betacoronavirus , Coronavirus Infections/virology , Host Microbial Interactions , Pandemics , Pneumonia, Viral/virology , Basic Reproduction Number/statistics & numerical data , Computer Simulation , Coronavirus Infections/epidemiology , Coronavirus Infections/transmission , Host Microbial Interactions/immunology , Humans , Kinetics , Mathematical Concepts , Middle East Respiratory Syndrome Coronavirus , Models, Biological , Pneumonia, Viral/epidemiology , Pneumonia, Viral/transmission
16.
Math Biosci Eng ; 17(4): 2792-2804, 2020 03 16.
Article in English | MEDLINE | ID: covidwho-804946

ABSTRACT

The novel Coronavirus (COVID-19) is spreading and has caused a large-scale infection in China since December 2019. This has led to a significant impact on the lives and economy in China and other countries. Here we develop a discrete-time stochastic epidemic model with binomial distributions to study the transmission of the disease. Model parameters are estimated on the basis of fitting to newly reported data from January 11 to February 13, 2020 in China. The estimates of the contact rate and the effective reproductive number support the efficiency of the control measures that have been implemented so far. Simulations show the newly confirmed cases will continue to decline and the total confirmed cases will reach the peak around the end of February of 2020 under the current control measures. The impact of the timing of returning to work is also evaluated on the disease transmission given different strength of protection and control measures.


Subject(s)
Betacoronavirus , Coronavirus Infections/epidemiology , Models, Biological , Pandemics , Pneumonia, Viral/epidemiology , Basic Reproduction Number/statistics & numerical data , China/epidemiology , Computer Simulation , Coronavirus Infections/prevention & control , Coronavirus Infections/transmission , Humans , Mathematical Concepts , Pandemics/prevention & control , Pandemics/statistics & numerical data , Pneumonia, Viral/prevention & control , Pneumonia, Viral/transmission , Stochastic Processes
17.
Math Biosci Eng ; 17(4): 2781-2791, 2020 03 16.
Article in English | MEDLINE | ID: covidwho-804493

ABSTRACT

The outbreak of coronavirus disease 2019 (COVID-19) in Wuhan, which is caused by the severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2), is still severe. In order to optimize the epidemic response strategy, it is urgent to evaluate the implemented prevention and control interventions (PCIs). Based on the reported data of Chongqing and Guizhou Provinces, the phased dynamic models of COVID-19 were constructed, the average intensity of the existing PCIs (from January 25 to March 2) was estimated in these two provinces. The results indicate that both provinces have carried out better control of the infected, but there are still differences in the intensity of control for people who need close observation. Especially in Chongqing, the estimated strength is significantly smaller than that in Guizhou. Furthermore, qualitative evaluations on the epidemic of COVID-19 under different PCIs scenarios suggest that containment strategy is still necessary to ensure the safety of resumption of work and school, and quarantining the city of Wuhan is an important and effective containment strategy to reduce the epidemic in other provinces.


Subject(s)
Betacoronavirus , Coronavirus Infections/epidemiology , Coronavirus Infections/prevention & control , Pandemics/prevention & control , Pneumonia, Viral/epidemiology , Pneumonia, Viral/prevention & control , Algorithms , Basic Reproduction Number/statistics & numerical data , China/epidemiology , Computer Simulation , Coronavirus Infections/transmission , Humans , Mathematical Concepts , Models, Biological , Pandemics/statistics & numerical data , Pneumonia, Viral/transmission , Quarantine/methods , Quarantine/statistics & numerical data
18.
Math Biosci Eng ; 17(4): 3147-3159, 2020 04 17.
Article in English | MEDLINE | ID: covidwho-804370

ABSTRACT

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.


Subject(s)
Betacoronavirus , Communications Media , Coronavirus Infections/epidemiology , Pandemics , Pneumonia, Viral/epidemiology , Basic Reproduction Number/statistics & numerical data , China/epidemiology , Computer Simulation , Coronavirus Infections/prevention & control , Coronavirus Infections/transmission , Epidemics/prevention & control , Epidemics/statistics & numerical data , Humans , Mathematical Concepts , Models, Biological , Pandemics/prevention & control , Pandemics/statistics & numerical data , Pneumonia, Viral/prevention & control , Pneumonia, Viral/transmission , Quarantine/statistics & numerical data , Social Media
19.
BMC Med Inform Decis Mak ; 20(1): 247, 2020 09 29.
Article in English | MEDLINE | ID: covidwho-802031

ABSTRACT

BACKGROUND: The recent Coronavirus Disease 2019 (COVID-19) pandemic has placed severe stress on healthcare systems worldwide, which is amplified by the critical shortage of COVID-19 tests. METHODS: In this study, we propose to generate a more accurate diagnosis model of COVID-19 based on patient symptoms and routine test results by applying machine learning to reanalyzing COVID-19 data from 151 published studies. We aim to investigate correlations between clinical variables, cluster COVID-19 patients into subtypes, and generate a computational classification model for discriminating between COVID-19 patients and influenza patients based on clinical variables alone. RESULTS: We discovered several novel associations between clinical variables, including correlations between being male and having higher levels of serum lymphocytes and neutrophils. We found that COVID-19 patients could be clustered into subtypes based on serum levels of immune cells, gender, and reported symptoms. Finally, we trained an XGBoost model to achieve a sensitivity of 92.5% and a specificity of 97.9% in discriminating COVID-19 patients from influenza patients. CONCLUSIONS: We demonstrated that computational methods trained on large clinical datasets could yield ever more accurate COVID-19 diagnostic models to mitigate the impact of lack of testing. We also presented previously unknown COVID-19 clinical variable correlations and clinical subgroups.


Subject(s)
Clinical Laboratory Techniques/methods , Coronavirus Infections/diagnosis , Influenza, Human/diagnosis , Machine Learning , Pneumonia, Viral/diagnosis , Betacoronavirus , Computer Simulation , Coronavirus Infections/classification , Datasets as Topic , Diagnosis, Differential , Female , Humans , Influenza A virus , Male , Pandemics/classification , Pneumonia, Viral/classification , Sensitivity and Specificity
20.
Nat Commun ; 11(1): 4883, 2020 09 28.
Article in English | MEDLINE | ID: covidwho-801570

ABSTRACT

Early stages of the novel coronavirus disease (COVID-19) are associated with silent hypoxia and poor oxygenation despite relatively minor parenchymal involvement. Although speculated that such paradoxical findings may be explained by impaired hypoxic pulmonary vasoconstriction in infected lung regions, no studies have determined whether such extreme degrees of perfusion redistribution are physiologically plausible, and increasing attention is directed towards thrombotic microembolism as the underlying cause of hypoxemia. Herein, a mathematical model demonstrates that the large amount of pulmonary venous admixture observed in patients with early COVID-19 can be reasonably explained by a combination of pulmonary embolism, ventilation-perfusion mismatching in the noninjured lung, and normal perfusion of the relatively small fraction of injured lung. Although underlying perfusion heterogeneity exacerbates existing shunt and ventilation-perfusion mismatch in the model, the reported hypoxemia severity in early COVID-19 patients is not replicated without either extensive perfusion defects, severe ventilation-perfusion mismatch, or hyperperfusion of nonoxygenated regions.


Subject(s)
Betacoronavirus , Coronavirus Infections/complications , Coronavirus Infections/physiopathology , Hypoxia/etiology , Hypoxia/physiopathology , Lung Diseases/etiology , Lung Diseases/physiopathology , Lung/blood supply , Lung/physiopathology , Models, Biological , Pneumonia, Viral/complications , Pneumonia, Viral/physiopathology , Pulmonary Circulation/physiology , Computer Simulation , Coronavirus Infections/epidemiology , Humans , Hypoxia/therapy , Lung Diseases/therapy , Mathematical Concepts , Models, Cardiovascular , Oxygen Inhalation Therapy , Pandemics , Pneumonia, Viral/epidemiology , Time Factors , Vasoconstriction/physiology , Vasodilation/physiology , Ventilation-Perfusion Ratio/physiology
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