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1.
EMBO Rep ; 21(7): e50883, 2020 07 03.
Article in English | MEDLINE | ID: covidwho-648828

ABSTRACT

During the COVID-19 pandemic, virtual conferences provide a much-needed alternative to cancelled meetings. Here are insights and lessons from organizing a virtual meeting.


Subject(s)
Congresses as Topic , Virtual Reality , Congresses as Topic/trends , Forecasting , Humans , Type VI Secretion Systems
2.
Age Ageing ; 49(5): 696-700, 2020 08 24.
Article in English | MEDLINE | ID: covidwho-759920

ABSTRACT

The coronavirus disease 2019 (COVID-19) pandemic and the response to the pandemic are combining to produce a tidal wave of need for rehabilitation. Rehabilitation will be needed for survivors of COVID-19, many of whom are older, with underlying health problems. In addition, rehabilitation will be needed for those who have become deconditioned as a result of movement restrictions, social isolation, and inability to access healthcare for pre-existing or new non-COVID-19 illnesses. Delivering rehabilitation in the same way as before the pandemic will not be practical, nor will this approach meet the likely scale of need for rehabilitation. This commentary reviews the likely rehabilitation needs of older people both with and without COVID-19 and discusses how strategies to deliver effective rehabilitation at scale can be designed and implemented in a world living with COVID-19.


Subject(s)
Aging , Chronic Disease , Coronavirus Infections , Delivery of Health Care , Health Services Accessibility/standards , Pandemics , Pneumonia, Viral , Rehabilitation , Aged , Aging/physiology , Aging/psychology , Betacoronavirus , Chronic Disease/epidemiology , Chronic Disease/rehabilitation , Coronavirus Infections/epidemiology , Coronavirus Infections/physiopathology , Coronavirus Infections/psychology , Coronavirus Infections/rehabilitation , Delivery of Health Care/methods , Delivery of Health Care/trends , Forecasting , Health Services Needs and Demand , Humans , Organizational Innovation , Physical Functional Performance , Pneumonia, Viral/epidemiology , Pneumonia, Viral/physiopathology , Pneumonia, Viral/psychology , Pneumonia, Viral/rehabilitation , Recovery of Function , Rehabilitation/methods , Rehabilitation/organization & administration , Rehabilitation/trends
6.
Rev Soc Bras Med Trop ; 53: e20200481, 2020.
Article in English | MEDLINE | ID: covidwho-740418

ABSTRACT

INTRODUCTION: Mathematical models have been used to obtain long-term forecasts of the COVID-19 epidemic. METHODS: The daily COVID-19 case count in two Brazilian states was used to show the potential limitations of long-term forecasting through the application of a mathematical model to the data. RESULTS: The predicted number of cases at the end of the epidemic and at the moment that the peak occurs, is highly dependent on the length of the time series used in the predictive model. CONCLUSIONS: Predictions obtained during the course of the COVID-19 pandemic need to be viewed with caution.


Subject(s)
Coronavirus Infections/epidemiology , Coronavirus , Pandemics , Pneumonia, Viral/epidemiology , Betacoronavirus , Forecasting , Humans , Models, Statistical
7.
J Biol Dyn ; 14(1): 730-747, 2020 12.
Article in English | MEDLINE | ID: covidwho-740143

ABSTRACT

In this study, we estimate the severity of the COVID-19 outbreak in Pakistan prior to and after lockdown restrictions were eased. We also project the epidemic curve considering realistic quarantine, social distancing and possible medication scenarios. The pre-lock down value of R 0 is estimated to be 1.07 and the post lock down value is estimated to be 1.86. Using this analysis, we project the epidemic curve. We note that if no substantial efforts are made to contain the epidemic, it will peak in mid-September, 2020, with the maximum projected active cases being close to 700, 000. In a realistic, best case scenario, we project that the epidemic peaks in early to mid-July, 2020, with the maximum active cases being around 120, 000. We note that social distancing measures and medication will help flatten the curve; however, without the reintroduction of further lock down, it would be very difficult to make R 0 < 1 .


Subject(s)
Betacoronavirus , Coronavirus Infections/epidemiology , Disease Outbreaks , Pneumonia, Viral/epidemiology , Basic Reproduction Number/statistics & numerical data , Biostatistics , Coronavirus Infections/prevention & control , Coronavirus Infections/transmission , Disease Outbreaks/prevention & control , Disease Outbreaks/statistics & numerical data , Epidemics , Forecasting/methods , Humans , Mathematical Concepts , Models, Biological , Pakistan/epidemiology , Pandemics/prevention & control , Pneumonia, Viral/prevention & control , Pneumonia, Viral/transmission , Quarantine/statistics & numerical data
8.
Nat Commun ; 11(1): 4264, 2020 08 26.
Article in English | MEDLINE | ID: covidwho-733526

ABSTRACT

The pressing need to restart socioeconomic activities locked-down to control the spread of SARS-CoV-2 in Italy must be coupled with effective methodologies to selectively relax containment measures. Here we employ a spatially explicit model, properly attentive to the role of inapparent infections, capable of: estimating the expected unfolding of the outbreak under continuous lockdown (baseline trajectory); assessing deviations from the baseline, should lockdown relaxations result in increased disease transmission; calculating the isolation effort required to prevent a resurgence of the outbreak. A 40% increase in effective transmission would yield a rebound of infections. A control effort capable of isolating daily  ~5.5% of the exposed and highly infectious individuals proves necessary to maintain the epidemic curve onto the decreasing baseline trajectory. We finally provide an ex-post assessment based on the epidemiological data that became available after the initial analysis and estimate the actual disease transmission that occurred after weakening the lockdown.


Subject(s)
Communicable Disease Control/standards , Coronavirus Infections/epidemiology , Coronavirus Infections/prevention & control , Pandemics/prevention & control , Pneumonia, Viral/epidemiology , Pneumonia, Viral/prevention & control , Basic Reproduction Number , Betacoronavirus , Communicable Disease Control/trends , Coronavirus Infections/transmission , Forecasting , Geography , Hospitalization/statistics & numerical data , Hospitalization/trends , Humans , Italy/epidemiology , Models, Theoretical , Pneumonia, Viral/transmission , Social Isolation
9.
PLoS One ; 15(8): e0238090, 2020.
Article in English | MEDLINE | ID: covidwho-733001

ABSTRACT

In the article a virus transmission model is constructed on a simplified social network. The social network consists of more than 2 million nodes, each representing an inhabitant of Slovenia. The nodes are organised and interconnected according to the real household and elderly-care center distribution, while their connections outside these clusters are semi-randomly distributed and undirected. The virus spread model is coupled to the disease progression model. The ensemble approach with the perturbed transmission and disease parameters is used to quantify the ensemble spread, a proxy for the forecast uncertainty. The presented ongoing forecasts of COVID-19 epidemic in Slovenia are compared with the collected Slovenian data. Results show that at the end of the first epidemic wave, the infection was twice more likely to transmit within households/elderly care centers than outside them. We use an ensemble of simulations (N = 1000) and data assimilation approach to estimate the COVID-19 forecast uncertainty and to inversely obtain posterior distributions of model parameters. We found that in the uncontrolled epidemic, the intrinsic uncertainty mostly originates from the uncertainty of the virus biology, i.e. its reproduction number. In the controlled epidemic with low ratio of infected population, the randomness of the social network becomes the major source of forecast uncertainty, particularly for the short-range forecasts. Virus transmission models with accurate social network models are thus essential for improving epidemics forecasting.


Subject(s)
Computer Simulation , Coronavirus Infections/transmission , Pneumonia, Viral/transmission , Social Networking , Basic Reproduction Number , Betacoronavirus , Coronavirus Infections/epidemiology , Disease Progression , Family Characteristics , Forecasting , Humans , Models, Theoretical , Pandemics , Pneumonia, Viral/epidemiology , Slovenia/epidemiology , Uncertainty
10.
Zhonghua Yu Fang Yi Xue Za Zhi ; 54(6): 602-607, 2020 Jun 06.
Article in Chinese | MEDLINE | ID: covidwho-731281

ABSTRACT

During the epidemics of COVID-19 in domestic China and recently continuing rapid spread worldwide, a bunch of studies fitted the epidemics by transmission dynamics model to nowcast and forecast the trend of epidemics of COVID-19. However, due to little known of the new virus in early stage and much uncertainty in the comprehensive strategies of prevention and control for epidemics, majority of models, not surprisingly, predict in less accuracy, although the dynamics model has its great value in better understanding of transmission. This comment discusses the principle assumptions and limitations of the dynamics model in forecasting the epidemic trend, as well as its great potential role in evaluating the efforts of prevention and control strategies.


Subject(s)
Epidemics , Models, Biological , China/epidemiology , Coronavirus Infections/epidemiology , Epidemics/prevention & control , Forecasting , Humans , Pandemics , Pneumonia, Viral/epidemiology , Reproducibility of Results
11.
Cell Physiol Biochem ; 54(4): 767-790, 2020 Aug 25.
Article in English | MEDLINE | ID: covidwho-729851

ABSTRACT

The pandemic of the severe acute respiratory syndrome coronavirus (SARS-CoV)-2 at the end of 2019 marked the third outbreak of a highly pathogenic coronavirus affecting the human population in the past twenty years. Cross-species zoonotic transmission of SARS-CoV-2 has caused severe pathogenicity and led to more than 655,000 fatalities worldwide until July 28, 2020. Outbursts of this virus underlined the importance of controlling infectious pathogens across international frontiers. Unfortunately, there is currently no clinically approved antiviral drug or vaccine against SARS-CoV-2, although several broad-spectrum antiviral drugs targeting multiple RNA viruses have shown a positive response and improved recovery in patients. In this review, we compile our current knowledge of the emergence, transmission, and pathogenesis of SARS-CoV-2 and explore several features of SARS-CoV-2. We emphasize the current therapeutic approaches used to treat infected patients. We also highlight the results of in vitro and in vivo data from several studies, which have broadened our knowledge of potential drug candidates for the successful treatment of patients infected with and discuss possible virus and host-based treatment options against SARS-CoV-2.


Subject(s)
Betacoronavirus , Coronavirus Infections , Pandemics , Pneumonia, Viral , Animals , Antiviral Agents/pharmacology , Antiviral Agents/therapeutic use , Betacoronavirus/drug effects , Betacoronavirus/genetics , Betacoronavirus/physiology , Coronaviridae/pathogenicity , Coronaviridae Infections/epidemiology , Coronaviridae Infections/virology , Coronavirus Infections/drug therapy , Coronavirus Infections/epidemiology , Coronavirus Infections/prevention & control , Coronavirus Infections/therapy , Coronavirus Infections/transmission , Cytokine Release Syndrome/etiology , Cytokine Release Syndrome/prevention & control , Cytokines/antagonists & inhibitors , Drug Delivery Systems , Endocytosis/drug effects , Forecasting , Genome, Viral , Global Health , Humans , Immunity, Herd , Immunization, Passive , Pandemics/prevention & control , Peptide Hydrolases/pharmacology , Peptide Hydrolases/therapeutic use , Pneumonia, Viral/drug therapy , Pneumonia, Viral/epidemiology , Pneumonia, Viral/prevention & control , Pneumonia, Viral/transmission , RNA, Viral/genetics , Receptors, Virus/antagonists & inhibitors , Receptors, Virus/metabolism , Spike Glycoprotein, Coronavirus/antagonists & inhibitors , Spike Glycoprotein, Coronavirus/metabolism , Viral Vaccines , Virus Internalization/drug effects , Virus Replication/drug effects , Zoonoses
12.
Sci Rep ; 10(1): 14042, 2020 08 20.
Article in English | MEDLINE | ID: covidwho-725830

ABSTRACT

The severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) has resulted in thousands of deaths in the world. Information about prediction model of prognosis of SARS-CoV-2 infection is scarce. We used machine learning for processing laboratory findings of 110 patients with SARS-CoV-2 pneumonia (including 51 non-survivors and 59 discharged patients). The maximum relevance minimum redundancy (mRMR) algorithm and the least absolute shrinkage and selection operator logistic regression model were used for selection of laboratory features. Seven laboratory features selected in the model were: prothrombin activity, urea, white blood cell, interleukin-2 receptor, indirect bilirubin, myoglobin, and fibrinogen degradation products. The signature constructed using the seven features had 98% [93%, 100%] sensitivity and 91% [84%, 99%] specificity in predicting outcome of SARS-CoV-2 pneumonia. Thus it is feasible to establish an accurate prediction model of outcome of SARS-CoV-2 pneumonia based on laboratory findings.


Subject(s)
Betacoronavirus/genetics , Coronavirus Infections/blood , Models, Statistical , Pneumonia, Viral/blood , Aged , Bilirubin/blood , Coronavirus Infections/therapy , Coronavirus Infections/virology , Data Accuracy , Feasibility Studies , Female , Fibrin Fibrinogen Degradation Products/analysis , Forecasting/methods , Humans , Leukocytes , Machine Learning , Male , Myoglobin/blood , Pandemics , Pneumonia, Viral/therapy , Pneumonia, Viral/virology , Prognosis , Prothrombin/analysis , Receptors, Interleukin-2/blood , Retrospective Studies , Sensitivity and Specificity , Treatment Outcome , Urea/blood
13.
Fam Med Community Health ; 8(3)2020 08.
Article in English | MEDLINE | ID: covidwho-724797

ABSTRACT

A narrative review was conducted to examine the current state of the utilisation of telemedicine amid the current COVID-19 pandemic and to evaluate the benefits of continuing telemedicine usage in the future. A literature review was performed for articles related to telemedicine. Databases including PubMed, Google Scholar, Cochrane Library and Ovid MEDLINE were searched. Three reviewers independently performed article selection based on relevance to our topic. We included all articles between 1990 and 2020 related to telemedicine using the following keywords: 'telemedicine', 'telehealth', 'policy', 'COVID-19', 'regulation', 'rural', 'physical examination', 'future'. A total of 60 articles were identified, and through careful selection we narrowed the final number of articles to 42 based on relevance to our topic. Telemedicine has been rapidly evolving over the past several decades. Issues with regulation and reimbursement have prevented its full immersion into the healthcare system. During the current pandemic, Centers for Medicare and Medicaid services have expanded access to telemedicine services. The advantages of telemedicine moving forward include its cost-effectiveness, ability to extend access to specialty services and its potential to help mitigate the looming physician shortage. Disadvantages include lack of available technological resources in certain parts of the country, issues with security of patient data, and challenges in performing the traditional patient examination. It is critically important that changes are made to fully immerse telemedicine services into the healthcare landscape in order to be prepared for future pandemics as well as to reap the benefits of this service in the future.


Subject(s)
Coronavirus Infections/epidemiology , Pneumonia, Viral/epidemiology , Telemedicine , Betacoronavirus , Cost-Benefit Analysis , Forecasting , Health Services Accessibility , Humans , Pandemics , Physicians/supply & distribution , United States
14.
J Med Internet Res ; 22(7): e20912, 2020 07 30.
Article in English | MEDLINE | ID: covidwho-724770

ABSTRACT

BACKGROUND: Intervention measures have been implemented around the world to mitigate the spread of the coronavirus disease (COVID-19) pandemic. Understanding the dynamics of the disease spread and the effectiveness of the interventions is essential in predicting its future evolution. OBJECTIVE: The aim of this study is to simulate the effect of different social distancing interventions and investigate whether their timing and stringency can lead to multiple waves (subepidemics), which can provide a better fit to the wavy behavior observed in the infected population curve in the majority of countries. METHODS: We have designed and run agent-based simulations and a multiple wave model to fit the infected population data for many countries. We have also developed a novel Pandemic Response Index to provide a quantitative and objective way of ranking countries according to their COVID-19 response performance. RESULTS: We have analyzed data from 18 countries based on the multiple wave (subepidemics) hypothesis and present the relevant parameters. Multiple waves have been identified and were found to describe the data better. The effectiveness of intervention measures can be inferred by the peak intensities of the waves. Countries imposing fast and stringent interventions exhibit multiple waves with declining peak intensities. This result strongly corroborated with agent-based simulations outcomes. We also provided an estimate of how much lower the number of infections could have been if early and strict intervention measures had been taken to stop the spread at the first wave, as actually happened for a handful of countries. A novel index, the Pandemic Response Index, was constructed, and based on the model's results, an index value was assigned to each country, quantifying in an objective manner the country's response to the pandemic. CONCLUSIONS: Our results support the hypothesis that the COVID-19 pandemic can be successfully modeled as a series of epidemic waves (subepidemics) and that it is possible to infer to what extent the imposition of early intervention measures can slow the spread of the disease.


Subject(s)
Communicable Disease Control , Computer Simulation , Coronavirus Infections/epidemiology , Pneumonia, Viral/epidemiology , Public Health Informatics/methods , Algorithms , Betacoronavirus , Forecasting , Global Health , Humans , Pandemics , Population Dynamics , Quarantine
15.
J Med Internet Res ; 22(8): e21413, 2020 08 18.
Article in English | MEDLINE | ID: covidwho-723432

ABSTRACT

BACKGROUND: In Brazil, a substantial number of coronavirus disease (COVID-19) cases and deaths have been reported. It has become the second most affected country worldwide, as of June 9, 2020. Official Brazilian government sources present contradictory data on the impact of the disease; thus, it is possible that the actual number of infected individuals and deaths in Brazil is far larger than those officially reported. It is very likely that the actual spread of the disease has been underestimated. OBJECTIVE: This study investigates the underreporting of cases and deaths related to COVID-19 in the most affected cities in Brazil, based on public data available from official Brazilian government internet portals, to identify the actual impact of the pandemic. METHODS: We used data from historical deaths due to respiratory problems and other natural causes from two public portals: DATASUS (Department of Informatics of the Unified Healthcare System) (2010-2018) and the Brazilian Transparency Portal of Civil Registry (2019-2020). These data were used to build time-series models (modular regressions) to predict the expected mortality patterns for 2020. The forecasts were used to estimate the possible number of deaths that were incorrectly registered during the pandemic and posted on government internet portals in the most affected cities in the country. RESULTS: Our model found a significant difference between the real and expected values. The number of deaths due to severe acute respiratory syndrome (SARS) was considerably higher in all cities, with increases between 493% and 5820%. This sudden increase may be associated with errors in reporting. An average underreporting of 40.68% (range 25.9%-62.7%) is estimated for COVID-19-related deaths. CONCLUSIONS: The significant rates of underreporting of deaths analyzed in our study demonstrate that officially released numbers are much lower than actual numbers, making it impossible for the authorities to implement a more effective pandemic response. Based on analyses carried out using different fatality rates, it can be inferred that Brazil's epidemic is worsening, and the actual number of infectees could already be between 1 to 5.4 million.


Subject(s)
Coronavirus Infections/mortality , Coronavirus Infections/transmission , Federal Government , Internet , Pneumonia, Viral/mortality , Pneumonia, Viral/transmission , Brazil/epidemiology , Forecasting , Humans , Pandemics/statistics & numerical data , Reproducibility of Results , Uncertainty
16.
Eur Urol ; 78(3): 301-303, 2020 09.
Article in English | MEDLINE | ID: covidwho-723117

ABSTRACT

The speed and reach of the COVID-19 pandemic have forced rapid changes in how we conduct medical practice and research. The rapid evolution in how scientific meetings are conducted may have long-term benefits. A new reality in which technology and sociality are merged may offer a more engaging and adaptable scientific congress experience with more flexible and dynamic use of content modulated to the needs of each attendee.


Subject(s)
Communicable Disease Control , Congresses as Topic , Coronavirus Infections , Pandemics , Pneumonia, Viral , Telecommunications , Betacoronavirus , Congresses as Topic/organization & administration , Congresses as Topic/trends , Coronavirus Infections/epidemiology , Coronavirus Infections/prevention & control , Disease Transmission, Infectious/prevention & control , Forecasting , Humans , Inventions , Pandemics/prevention & control , Pneumonia, Viral/epidemiology , Pneumonia, Viral/prevention & control , Telecommunications/organization & administration , Telecommunications/trends
17.
MEDICC Rev ; 22(3): 32-39, 2020 Jul.
Article in English | MEDLINE | ID: covidwho-722917

ABSTRACT

INTRODUCTION On March 11, 2020, WHO declared COVID-19 a pandemic and called on governments to impose drastic measures to fi ght it. It is vitally important for government health authorities and leaders to have reliable estimates of infected cases and deaths in order to apply the necessary measures with the resources at their disposal. OBJECTIVE Test the validity of the logistic regression and Gompertz curve to forecast peaks of confi rmed cases and deaths in Cuba, as well as total number of cases. METHODS An inferential, predictive study was conducted using lo-gistic and Gompertz growth curves, adjusted with the least squares method and informatics tools for analysis and prediction of growth in COVID-19 cases and deaths. Italy and Spain-countries that have passed the initial peak of infection rates-were studied, and it was inferred from the results of these countries that their models were ap-plicable to Cuba. This hypothesis was tested by applying goodness-of-fi t and signifi cance tests on its parameters.RESULTS Both models showed good fi t, low mean square errors, and all parameters were highly signifi cant. CONCLUSIONS The validity of models was confi rmed based on logis-tic regression and the Gompertz curve to forecast the dates of peak infections and deaths, as well as total number of cases in Cuba. KEYWORDS COVID-19, SARS-CoV-2, logistic models, pandemic, mortality, Cuba.


Subject(s)
Coronavirus Infections/epidemiology , Forecasting/methods , Logistic Models , Pneumonia, Viral/epidemiology , Betacoronavirus , Cuba/epidemiology , Humans , Italy/epidemiology , Pandemics , Spain/epidemiology
18.
Med Arch ; 74(3): 164-167, 2020 Jun.
Article in English | MEDLINE | ID: covidwho-721611

ABSTRACT

Probably in the history of medicine, doctors were not as united as they are today, in that fight against COVID-19, when the pandemic spread incredibly fast - from East to West, from North to South. The COVID-19 pandemic is likely to have unprecedented and unforeseeable consequences, from those on a worldwide/global level to those at the local level - at the level of local communities and families, and individuals (and not just humans, but all other living beings), of which the future will testify in various ways. The consequences will be political, economic, social, but probably to the greatest degree, the consequences of a health nature - systemic and individual. The death toll is high, despite the therapy being applied. We do not currently have a specific and effective therapy against COVID-19. In addition, we do not have a single clinical study that would support prophylactic therapy that could affect COVID-19. All of the therapeutic options now available to us are based on the experience we have gained in treating SARS and MERS. When the vaccine is discovered, at that moment we will be able to say that we have an appropriate and effective method in fighting against COVID-19. Some historians of medicine believe that voluntary vaccination against COVID-19 would be, not only less politically risky but also more effective in protecting the population from coronavirus. It remains to be seen what the new wave of the COVID-19 pandemic, announced by WHO experts these days, and which is expected in the fall of 2020, will bring us.


Subject(s)
Coronavirus Infections , Global Health/trends , Life Change Events , Mass Vaccination/organization & administration , Pandemics , Pneumonia, Viral , Public Health/trends , Betacoronavirus , Coronavirus Infections/economics , Coronavirus Infections/epidemiology , Coronavirus Infections/prevention & control , Coronavirus Infections/psychology , Forecasting , Humans , Pandemics/economics , Pandemics/prevention & control , Pneumonia, Viral/economics , Pneumonia, Viral/epidemiology , Pneumonia, Viral/prevention & control , Pneumonia, Viral/psychology , Politics , Socioeconomic Factors
19.
Int J Clin Pharmacol Ther ; 58(9): 467-474, 2020 Sep.
Article in English | MEDLINE | ID: covidwho-721608

ABSTRACT

AIMS OF THE STUDY: To obtain predictions for the course of the COVID-19 pandemic in Germany using the modified Bateman SIZ model and input variables based on the status quo in July 2020. To predict the effect of a change in tα on the course of the pandemic. To evaluate the robustness and sensitivity of the model in response to a change in the input parameters. MATERIALS AND METHODS: Start parameters for the modified Bateman SIZ model were obtained from observational data published by the Robert-Koch-Institute in Berlin for the period June 1 to July 13, 2020. The robustness and sensitivity of the model were determined by changing the input parameter for the doubling-time (tα) by ± 5% and ± 10%. RESULTS: The predictions show that small changes, ± 5%, in the doubling-time, tα for the rate of increase in the number of new infections, can have a major effect, both positive and negative, on the course of the pandemic. The model predicted that the number of persons infected with the virus would reach 1 million within 8 years. A 5% longer tα would reduce the number of infected persons by ~ 75%. In contrast, a 5% shorter doubling-time would increase the number of infections over 8 years to ~ 9 million when the number of infectious persons would exceed 100,000 at the end of 2022. The pandemic is predicted to have disappeared by the end of 2024. DISCUSSION: Predictions for the course of the COVID-19 pandemic in Germany based on the status quo up to July 13, 2020 have been obtained using the modified Bateman SIZ model. There are several important assumptions necessary to apply the model and thus the results must be interpreted with caution. The model, previously used to predict the course of the COVID-19 pandemic in the city of Heidelberg (pop. 166,000) gives comparable predictive data for the whole of Germany (pop. 83 million) and thus appears to be both sensitive and robust. CONCLUSION: Since a shorter doubling-time for the number of infectious persons by only 5% would result in a major clinical emergency, interventional measures such as vaccination are urgently needed. Taking into consideration that a SARS-CoV-2 vaccine is not yet available and the efficacy of the Corona-Warn-App has yet to be shown, a relaxation in the lockdown conditions in Germany in 2020 appears premature.


Subject(s)
Coronavirus Infections/epidemiology , Models, Theoretical , Pneumonia, Viral/epidemiology , Betacoronavirus , Forecasting , Germany/epidemiology , Humans , Pandemics
20.
Braz J Microbiol ; 51(3): 1109-1115, 2020 Sep.
Article in English | MEDLINE | ID: covidwho-718579

ABSTRACT

COVID-19 has killed more than 500,000 people worldwide and more than 60,000 in Brazil. Since there are no specific drugs or vaccines, the available tools against COVID-19 are preventive, such as the use of personal protective equipment, social distancing, lockdowns, and mass testing. Such measures are hindered in Brazil due to a restrict budget, low educational level of the population, and misleading attitudes from the federal authorities. Predictions for COVID-19 are of pivotal importance to subsidize and mobilize health authorities' efforts in applying the necessary preventive strategies. The Weibull distribution was used to model the forecast prediction of COVID-19, in four scenarios, based on the curve of daily new deaths as a function of time. The date in which the number of daily new deaths will fall below the rate of 3 deaths per million - the average level in which some countries start to relax the stay-at-home measures - was estimated. If the daily new deaths curve was bending today (i.e., about 1250 deaths per day), the predicted date would be on July 5. Forecast predictions allowed the estimation of overall death toll at the end of the outbreak. Our results suggest that each additional day that lasts to bend the daily new deaths curve may correspond to additional 1685 deaths at the end of COVID-19 outbreak in Brazil (R2 = 0.9890). Predictions of the outbreak can be used to guide Brazilian health authorities in the decision-making to properly fight COVID-19 pandemic.


Subject(s)
Coronavirus Infections/epidemiology , Forecasting/methods , Pneumonia, Viral/epidemiology , Algorithms , Brazil/epidemiology , Coronavirus Infections/mortality , Coronavirus Infections/prevention & control , Detergents/supply & distribution , Education/statistics & numerical data , Humans , Least-Squares Analysis , Nonlinear Dynamics , Pandemics/prevention & control , Pneumonia, Viral/mortality , Pneumonia, Viral/prevention & control , Politics , Population Density , Poverty , Socioeconomic Factors , Statistics as Topic , Time Factors , Water Supply/standards
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