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1.
JMIR Public Health Surveill ; 6(3): e18965, 2020 09 18.
Article in English | MEDLINE | ID: covidwho-862937

ABSTRACT

BACKGROUND: Throughout March 2020, leaders in countries across the world were making crucial decisions about how and when to implement public health interventions to combat the coronavirus disease (COVID-19). They urgently needed tools to help them to explore what will work best in their specific circumstances of epidemic size and spread, and feasible intervention scenarios. OBJECTIVE: We sought to rapidly develop a flexible, freely available simulation model for use by modelers and researchers to allow investigation of how various public health interventions implemented at various time points might change the shape of the COVID-19 epidemic curve. METHODS: "COVOID" (COVID-19 Open-Source Infection Dynamics) is a stochastic individual contact model (ICM), which extends the ICMs provided by the open-source EpiModel package for the R statistical computing environment. To demonstrate its use and inform urgent decisions on March 30, 2020, we modeled similar intervention scenarios to those reported by other investigators using various model types, as well as novel scenarios. The scenarios involved isolation of cases, moderate social distancing, and stricter population "lockdowns" enacted over varying time periods in a hypothetical population of 100,000 people. On April 30, 2020, we simulated the epidemic curve for the three contiguous local areas (population 287,344) in eastern Sydney, Australia that recorded 5.3% of Australian cases of COVID-19 through to April 30, 2020, under five different intervention scenarios and compared the modeled predictions with the observed epidemic curve for these areas. RESULTS: COVOID allocates each member of a population to one of seven compartments. The number of times individuals in the various compartments interact with each other and their probability of transmitting infection at each interaction can be varied to simulate the effects of interventions. Using COVOID on March 30, 2020, we were able to replicate the epidemic response patterns to specific social distancing intervention scenarios reported by others. The simulated curve for three local areas of Sydney from March 1 to April 30, 2020, was similar to the observed epidemic curve in terms of peak numbers of cases, total numbers of cases, and duration under a scenario representing the public health measures that were actually enacted, including case isolation and ramp-up of testing and social distancing measures. CONCLUSIONS: COVOID allows rapid modeling of many potential intervention scenarios, can be tailored to diverse settings, and requires only standard computing infrastructure. It replicates the epidemic curves produced by other models that require highly detailed population-level data, and its predicted epidemic curve, using parameters simulating the public health measures that were enacted, was similar in form to that actually observed in Sydney, Australia. Our team and collaborators are currently developing an extended open-source COVOID package comprising of a suite of tools to explore intervention scenarios using several categories of models.


Subject(s)
Contact Tracing , Coronavirus Infections/prevention & control , Models, Biological , Pandemics/prevention & control , Pneumonia, Viral/prevention & control , Public Health , Social Isolation , Australia , Betacoronavirus , Coronavirus , Coronavirus Infections/epidemiology , Coronavirus Infections/transmission , Coronavirus Infections/virology , Epidemics , Humans , Pneumonia, Viral/epidemiology , Pneumonia, Viral/transmission , Pneumonia, Viral/virology , Quarantine
2.
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
3.
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
4.
Lancet ; 395(10231): 1225-1228, 2020 04 11.
Article in English | MEDLINE | ID: covidwho-830460

ABSTRACT

The spread of severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) has already taken on pandemic proportions, affecting over 100 countries in a matter of weeks. A global response to prepare health systems worldwide is imperative. Although containment measures in China have reduced new cases by more than 90%, this reduction is not the case elsewhere, and Italy has been particularly affected. There is now grave concern regarding the Italian national health system's capacity to effectively respond to the needs of patients who are infected and require intensive care for SARS-CoV-2 pneumonia. The percentage of patients in intensive care reported daily in Italy between March 1 and March 11, 2020, has consistently been between 9% and 11% of patients who are actively infected. The number of patients infected since Feb 21 in Italy closely follows an exponential trend. If this trend continues for 1 more week, there will be 30 000 infected patients. Intensive care units will then be at maximum capacity; up to 4000 hospital beds will be needed by mid-April, 2020. Our analysis might help political leaders and health authorities to allocate enough resources, including personnel, beds, and intensive care facilities, to manage the situation in the next few days and weeks. If the Italian outbreak follows a similar trend as in Hubei province, China, the number of newly infected patients could start to decrease within 3-4 days, departing from the exponential trend. However, this cannot currently be predicted because of differences between social distancing measures and the capacity to quickly build dedicated facilities in China.


Subject(s)
Betacoronavirus , Coronavirus Infections/epidemiology , Pneumonia, Viral/epidemiology , Adult , Aged , Aged, 80 and over , Coronavirus Infections/therapy , Female , Global Health , Health Policy/trends , Hospital Bed Capacity/statistics & numerical data , Humans , Intensive Care Units/statistics & numerical data , Intensive Care Units/supply & distribution , Italy/epidemiology , Male , Middle Aged , Models, Biological , Pandemics , Pneumonia, Viral/therapy , Respiration, Artificial/statistics & numerical data
5.
Crit Care ; 24(1): 594, 2020 10 06.
Article in English | MEDLINE | ID: covidwho-818126

ABSTRACT

BACKGROUND: Animal models of COVID-19 have been rapidly reported after the start of the pandemic. We aimed to assess whether the newly created models reproduce the full spectrum of human COVID-19. METHODS: We searched the MEDLINE, as well as BioRxiv and MedRxiv preprint servers for original research published in English from January 1 to May 20, 2020. We used the search terms (COVID-19) OR (SARS-CoV-2) AND (animal models), (hamsters), (nonhuman primates), (macaques), (rodent), (mice), (rats), (ferrets), (rabbits), (cats), and (dogs). Inclusion criteria were the establishment of animal models of COVID-19 as an endpoint. Other inclusion criteria were assessment of prophylaxis, therapies, or vaccines, using animal models of COVID-19. RESULT: Thirteen peer-reviewed studies and 14 preprints met the inclusion criteria. The animals used were nonhuman primates (n = 13), mice (n = 7), ferrets (n = 4), hamsters (n = 4), and cats (n = 1). All animals supported high viral replication in the upper and lower respiratory tract associated with mild clinical manifestations, lung pathology, and full recovery. Older animals displayed relatively more severe illness than the younger ones. No animal models developed hypoxemic respiratory failure, multiple organ dysfunction, culminating in death. All species elicited a specific IgG antibodies response to the spike proteins, which were protective against a second exposure. Transient systemic inflammation was observed occasionally in nonhuman primates, hamsters, and mice. Notably, none of the animals unveiled a cytokine storm or coagulopathy. CONCLUSIONS: Most of the animal models of COVID-19 recapitulated mild pattern of human COVID-19 with full recovery phenotype. No severe illness associated with mortality was observed, suggesting a wide gap between COVID-19 in humans and animal models.


Subject(s)
Coronavirus Infections , Disease Models, Animal , Models, Biological , Pandemics , Pneumonia, Viral , Animals , Humans
6.
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
7.
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
8.
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
9.
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
10.
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
11.
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
12.
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
13.
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
14.
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
15.
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
16.
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
17.
Philos Trans A Math Phys Eng Sci ; 378(2183): 20200188, 2020 Oct 30.
Article in English | MEDLINE | ID: covidwho-801118

ABSTRACT

We suggest that the unprecedented and unintended decrease of emissions of air pollutants during the COVID-19 lock-down in 2020 could lead to declining seasonal ozone concentrations and positive impacts on crop yields. An initial assessment of the potential effects of COVID-19 emission reductions was made using a set of six scenarios that variously assumed annual European and global emission reductions of 30% and 50% for the energy, industry, road transport and international shipping sectors, and 80% for the aviation sector. The greatest ozone reductions during the growing season reached up to 12 ppb over crop growing regions in Asia and up to 6 ppb in North America and Europe for the 50% global reduction scenario. In Europe, ozone responses are more sensitive to emission declines in other continents, international shipping and aviation than to emissions changes within Europe. We demonstrate that for wheat the overall magnitude of ozone precursor emission changes could lead to yield improvements between 2% and 8%. The expected magnitude of ozone precursor emission reductions during the Northern Hemisphere growing season in 2020 presents an opportunity to test and improve crop models and experimentally based exposure response relationships of ozone impacts on crops, under real-world conditions. This article is part of a discussion meeting issue 'Air quality, past present and future'.


Subject(s)
Air Pollution/analysis , Betacoronavirus , Coronavirus Infections/epidemiology , Crops, Agricultural/drug effects , Crops, Agricultural/growth & development , Ozone/analysis , Pandemics , Pneumonia, Viral/epidemiology , Air Pollutants/analysis , Air Pollutants/toxicity , Air Pollution/prevention & control , Air Pollution/statistics & numerical data , Environmental Monitoring , Europe , Humans , Models, Biological , Nitrogen Dioxide/analysis , Nitrogen Dioxide/toxicity , Ozone/toxicity , Risk Assessment , Seasons
18.
J Med Internet Res ; 22(9): e20924, 2020 09 22.
Article in English | MEDLINE | ID: covidwho-789082

ABSTRACT

BACKGROUND: SARS-CoV-2, the novel coronavirus that causes COVID-19, is a global pandemic with higher mortality and morbidity than any other virus in the last 100 years. Without public health surveillance, policy makers cannot know where and how the disease is accelerating, decelerating, and shifting. Unfortunately, existing models of COVID-19 contagion rely on parameters such as the basic reproduction number and use static statistical methods that do not capture all the relevant dynamics needed for surveillance. Existing surveillance methods use data that are subject to significant measurement error and other contaminants. OBJECTIVE: The aim of this study is to provide a proof of concept of the creation of surveillance metrics that correct for measurement error and data contamination to determine when it is safe to ease pandemic restrictions. We applied state-of-the-art statistical modeling to existing internet data to derive the best available estimates of the state-level dynamics of COVID-19 infection in the United States. METHODS: Dynamic panel data (DPD) models were estimated with the Arellano-Bond estimator using the generalized method of moments. This statistical technique enables control of various deficiencies in a data set. The validity of the model and statistical technique was tested. RESULTS: A Wald chi-square test of the explanatory power of the statistical approach indicated that it is valid (χ210=1489.84, P<.001), and a Sargan chi-square test indicated that the model identification is valid (χ2946=935.52, P=.59). The 7-day persistence rate for the week of June 27 to July 3 was 0.5188 (P<.001), meaning that every 10,000 new cases in the prior week were associated with 5188 cases 7 days later. For the week of July 4 to 10, the 7-day persistence rate increased by 0.2691 (P=.003), indicating that every 10,000 new cases in the prior week were associated with 7879 new cases 7 days later. Applied to the reported number of cases, these results indicate an increase of almost 100 additional new cases per day per state for the week of July 4-10. This signifies an increase in the reproduction parameter in the contagion models and corroborates the hypothesis that economic reopening without applying best public health practices is associated with a resurgence of the pandemic. CONCLUSIONS: DPD models successfully correct for measurement error and data contamination and are useful to derive surveillance metrics. The opening of America involves two certainties: the country will be COVID-19-free only when there is an effective vaccine, and the "social" end of the pandemic will occur before the "medical" end. Therefore, improved surveillance metrics are needed to inform leaders of how to open sections of the United States more safely. DPD models can inform this reopening in combination with the extraction of COVID-19 data from existing websites.


Subject(s)
Coronavirus Infections/epidemiology , Coronavirus Infections/transmission , Health Policy , Models, Biological , Models, Statistical , Pneumonia, Viral/epidemiology , Pneumonia, Viral/transmission , Public Health Surveillance/methods , Betacoronavirus , Coronavirus Infections/prevention & control , Humans , Pandemics/prevention & control , Pandemics/statistics & numerical data , Pneumonia, Viral/prevention & control , Reproducibility of Results , United States/epidemiology
19.
Comput Math Methods Med ; 2020: 6862516, 2020.
Article in English | MEDLINE | ID: covidwho-788246

ABSTRACT

Coronavirus disease 2019 (COVID-19) is a disease caused by severe acute respiratory syndrome coronavirus 2 (SARS CoV-2). It was declared on March 11, 2020, by the World Health Organization as pandemic disease. The disease has neither approved medicine nor vaccine and has made governments and scholars search for drastic measures in combating the pandemic. Regrettably, the spread of the virus and mortality due to COVID-19 has continued to increase daily. Hence, it is imperative to control the spread of the disease particularly using nonpharmacological strategies such as quarantine, isolation, and public health education. This work studied the effect of these different control strategies as time-dependent interventions using mathematical modeling and optimal control approach to ascertain their contributions in the dynamic transmission of COVID-19. The model was proven to have an invariant region and was well-posed. The basic reproduction number and effective reproduction numbers were computed with and without interventions, respectively, and were used to carry out the sensitivity analysis that identified the critical parameters contributing to the spread of COVID-19. The optimal control analysis was carried out using the Pontryagin's maximum principle to figure out the optimal strategy necessary to curtail the disease. The findings of the optimal control analysis and numerical simulations revealed that time-dependent interventions reduced the number of exposed and infected individuals compared to time-independent interventions. These interventions were time-bound and best implemented within the first 100 days of the outbreak. Again, the combined implementation of only two of these interventions produced a good result in reducing infection in the population. While, the combined implementation of all three interventions performed better, even though zero infection was not achieved in the population. This implied that multiple interventions need to be deployed early in order to reduce the virus to the barest minimum.


Subject(s)
Betacoronavirus , Coronavirus Infections/prevention & control , Coronavirus Infections/transmission , Models, Biological , Pandemics/prevention & control , Pneumonia, Viral/prevention & control , Pneumonia, Viral/transmission , Basic Reproduction Number , Computer Simulation , Coronavirus Infections/epidemiology , Health Education , Humans , Mathematical Concepts , Pneumonia, Viral/epidemiology , Public Health , Quarantine
20.
Appl Environ Microbiol ; 86(18)2020 09 01.
Article in English | MEDLINE | ID: covidwho-788000

ABSTRACT

Temperature and relative humidity are major factors determining virus inactivation in the environment. This article reviews inactivation data regarding coronaviruses on surfaces and in liquids from published studies and develops secondary models to predict coronaviruses inactivation as a function of temperature and relative humidity. A total of 102 D values (i.e., the time to obtain a log10 reduction of virus infectivity), including values for severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2), were collected from 26 published studies. The values obtained from the different coronaviruses and studies were found to be generally consistent. Five different models were fitted to the global data set of D values. The most appropriate model considered temperature and relative humidity. A spreadsheet predicting the inactivation of coronaviruses and the associated uncertainty is presented and can be used to predict virus inactivation for untested temperatures, time points, or any coronavirus strains belonging to Alphacoronavirus and Betacoronavirus genera.IMPORTANCE The prediction of the persistence of SARS-CoV-2 on fomites is essential in investigating the importance of contact transmission. This study collects available information on inactivation kinetics of coronaviruses in both solid and liquid fomites and creates a mathematical model for the impact of temperature and relative humidity on virus persistence. The predictions of the model can support more robust decision-making and could be useful in various public health contexts. A calculator for the natural clearance of SARS-CoV-2 depending on temperature and relative humidity could be a valuable operational tool for public authorities.


Subject(s)
Betacoronavirus/physiology , Coronavirus Infections/virology , Models, Biological , Pneumonia, Viral/virology , Virus Inactivation , Fomites/virology , Humans , Humidity , Pandemics , Public Health , Suspensions , Temperature
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