scholarly journals Evaluating Short-term Forecast among Different Epidemiological Models under a Bayesian Framework

2020 ◽  
Author(s):  
Qiwei Li ◽  
Tejasv Bedi ◽  
Guanghua Xiao ◽  
Yang Xie
Keyword(s):  
Public Health ◽  
Predictive Modeling ◽  
Growth Models ◽  
Sir Model ◽  
Global Scale ◽  
Bayesian Framework ◽  
Epidemiological Models ◽  
Short Term ◽  
Predictive Capacity ◽  
Term Forecast

AbstractForecasting of COVID-19 daily confirmed cases has been one of the several challenges posed on the governments and health sectors on a global scale. To facilitate informed public health decisions, the concerned parties rely on short-term daily projections generated via predictive modeling. We calibrate stochastic variants of growth models and the standard SIR model into one Bayesian framework to evaluate their short-term forecasts. In summary, it was noted that none of the models proved to be golden standards across all the regions in their entirety, while all outperformed ARIMA in a predictive capacity as well as in terms of interpretability.

scholarly journals Growth Models for Covid-19 Death Figures of Turkey

2020 ◽  
pp. 1-11
Author(s):  
Muzaffer Balaban
Keyword(s):  
Growth Models ◽  
Exponential Model ◽  
Prediction Errors ◽  
Short Term ◽  
Von Bertalanffy Model ◽  
Peak Point ◽  
Term Forecast ◽  
Reported Data ◽  
Second Wave ◽  
Better Than

This paper presents modeling of the COVID-19 pandemic deaths to understand behavior of it, predict the peak point of the deaths and cases and produces a short-term forecast using the growth models for the reported data of Turkey. The data which is used in this study are gathered of daily announced by Minister of Health. Von Bertalanffy model has outperformed to the other models considered in this study. However, exponential model has predicted the total deaths and total cases better than the others. And, exponential model has given the best prediction errors among them regarding to the death and positive case figures for last months. Observed data have tended to increase since the last days of August. This could mean that the COVİD-19 threat has reached to a critical stage to crack down on prevention of pandemics spread. Or it could sign the beginning of a second wave of epidemics. More studies must be realized to learn more about the pandemic when the new data are available.

scholarly journals The SIR and SEIR Epidemiological Models Revisited

2020 ◽  
Author(s):  
John P. Maassen
Keyword(s):  
Public Health ◽  
Monte Carlo ◽  
Stochastic Model ◽  
Monte Carlo Simulations ◽  
Sir Model ◽  
Epidemiological Models ◽  
Seir Model ◽  
Social Distancing

We review and assess the classic SIR and SEIR epidemiological models regarding possible applications to the COVID-19 pandemic. In spite of numerous more complicated models, we show how the qualitative features of the solution to the SIR and SEIR models continue to provide valuable public health insights in some scenarios. Using estimated COVID-19 data as of this date, the SEIR model shows that if it were possible to reduce R0 from 2.5 to 1.25 through social distancing and other measures, the maximum fraction of the population that would become infected at any particular time would drop from 17% to 4%, provided that all of the model assumptions are satisfied. Finally, we compare the classic SIR model with a recent stochastic model with favorable results. Since this comparison underscores the importance of underlying connectivity assumptions, we conclude with Monte-Carlo simulations with specific connectivity that reproduce the classical SIR model with standard incidence.

scholarly journals Mathematical Modeling of COVID-19 Pandemic in the African Continent

2020 ◽  
Author(s):  
Nawel Aries ◽  
Houdayfa Ounis
Keyword(s):  
Public Health ◽  
Mathematical Modeling ◽  
Preventive Measures ◽  
Sir Model ◽  
African Continent ◽  
Mathematical Epidemiology ◽  
Short Term ◽  
Health Measures ◽  
Realistic Assessment ◽  
Official Data

AbstractThe present work aims to give a contribution to the understanding of the highly infectious pandemic caused by the COVID-19 in the African continent. The study focuses on the modelling and the forecasting of COVID-19 spread in the most affected African continent, namely: Morocco, Algeria, Tunisia, Egypt and South Africa and for the sake of comparison two of the most affected European country are also considered, namely: France and Italy. To this end, an epidemiological SEIQRDP model is presented, which is an adaptation of the classic SIR model widely used in mathematical epidemiology. In order to better coincide with the preventive measures taken by the governments to deal with the spread of COVID-19, this model considers the quarantine. For the identification of the model’s parameters, official data of the pandemic up to August 1st, 2020 are considered. The results show that the number of infections due to the use of quarantine is expected to be very low provided the isolation is effective. However, it is increasing in some countries with the early lifting of containment. Finally, the information provided by the SEIQRDP model could help to establish a realistic assessment of the short-term pandemic situation. Moreover, this will help maintain the most appropriate and necessary public health measures after the lockdown lifting.

scholarly journals COVID-19: Short-term forecast of ICU beds in times of crisis

PLoS ONE ◽  
2021 ◽  
Vol 16 (1) ◽  
pp. e0245272
Author(s):  
Marcel Goic ◽  
Mirko S. Bozanic-Leal ◽  
Magdalena Badal ◽  
Leonardo J. Basso
Keyword(s):  
Incomplete Data ◽  
Capacity Planning ◽  
Urgent Care ◽  
Epidemiological Models ◽  
Short Term ◽  
Short Term Forecast ◽  
First Order ◽  
Term Forecast ◽  
Health Authorities ◽  
Forecasting Errors

By early May 2020, the number of new COVID-19 infections started to increase rapidly in Chile, threatening the ability of health services to accommodate all incoming cases. Suddenly, ICU capacity planning became a first-order concern, and the health authorities were in urgent need of tools to estimate the demand for urgent care associated with the pandemic. In this article, we describe the approach we followed to provide such demand forecasts, and we show how the use of analytics can provide relevant support for decision making, even with incomplete data and without enough time to fully explore the numerical properties of all available forecasting methods. The solution combines autoregressive, machine learning and epidemiological models to provide a short-term forecast of ICU utilization at the regional level. These forecasts were made publicly available and were actively used to support capacity planning. Our predictions achieved average forecasting errors of 4% and 9% for one- and two-week horizons, respectively, outperforming several other competing forecasting models.

scholarly journals Evaluating short-term forecasting of COVID-19 cases among different epidemiological models under a Bayesian framework

GigaScience ◽  
2021 ◽  
Vol 10 (2) ◽  
Author(s):  
Qiwei Li ◽  
Tejasv Bedi ◽  
Christoph U Lehmann ◽  
Guanghua Xiao ◽  
Yang Xie
Keyword(s):  
Moving Average ◽  
Health Sector ◽  
Bayesian Framework ◽  
Autoregressive Moving Average ◽  
Epidemiological Models ◽  
Average Model ◽  
Short Term ◽  
Autoregressive Moving Average Model ◽  
Moving Average Model ◽  
Short Term Forecasting

Abstract Background Forecasting of COVID-19 cases daily and weekly has been one of the challenges posed to governments and the health sector globally. To facilitate informed public health decisions, the concerned parties rely on short-term daily projections generated via predictive modeling. We calibrate stochastic variants of growth models and the standard susceptible-infectious-removed model into 1 Bayesian framework to evaluate and compare their short-term forecasts. Results We implement rolling-origin cross-validation to compare the short-term forecasting performance of the stochastic epidemiological models and an autoregressive moving average model across 20 countries that had the most confirmed COVID-19 cases as of August 22, 2020. Conclusion None of the models proved to be a gold standard across all regions, while all outperformed the autoregressive moving average model in terms of the accuracy of forecast and interpretability.

scholarly journals Ocular manifestations of emerging viral diseases

Eye ◽  
2021 ◽  
Author(s):  
Ashwin Venkatesh ◽  
Ravi Patel ◽  
Simran Goyal ◽  
Timothy Rajaratnam ◽  
Anant Sharma ◽  
...  
Keyword(s):  
Public Health ◽  
Infectious Diseases ◽  
Influenza A ◽  
Emerging Infectious Diseases ◽  
Global Scale ◽  
Viral Diseases ◽  
Valley Fever ◽  
Ocular Manifestations ◽  
Influenza A H1n1 ◽  
Emerging Viral Diseases

AbstractEmerging infectious diseases (EIDs) are an increasing threat to public health on a global scale. In recent times, the most prominent outbreaks have constituted RNA viruses, spreading via droplets (COVID-19 and Influenza A H1N1), directly between humans (Ebola and Marburg), via arthropod vectors (Dengue, Zika, West Nile, Chikungunya, Crimean Congo) and zoonotically (Lassa fever, Nipah, Rift Valley fever, Hantaviruses). However, specific approved antiviral therapies and vaccine availability are scarce, and public health measures remain critical. Patients can present with a spectrum of ocular manifestations. Emerging infectious diseases should therefore be considered in the differential diagnosis of ocular inflammatory conditions in patients inhabiting or returning from endemic territories, and more general vigilance is advisable in the context of a global pandemic. Eye specialists are in a position to facilitate swift diagnosis, improve clinical outcomes, and contribute to wider public health efforts during outbreaks. This article reviews those emerging viral diseases associated with reports of ocular manifestations and summarizes details pertinent to practicing eye specialists.

scholarly journals Using Unstated Cases to Correct for COVID-19 Pandemic Outbreak and Its Impact on Easing the Intervention for Qatar

Biology ◽  
2021 ◽  
Vol 10 (6) ◽  
pp. 463
Author(s):  
Narjiss Sallahi ◽  
Heesoo Park ◽  
Fedwa El Mellouhi ◽  
Mustapha Rachdi ◽  
Idir Ouassou ◽  
...  
Keyword(s):  
Public Health ◽  
A Priori ◽  
Epidemiological Models ◽  
Sir Epidemic Model ◽  
Gulf Region ◽  
Epidemiological Modeling ◽  
Public Health Policies ◽  
Sir Epidemic ◽  
The Impact ◽  
Case Data

Epidemiological Modeling supports the evaluation of various disease management activities. The value of epidemiological models lies in their ability to study various scenarios and to provide governments with a priori knowledge of the consequence of disease incursions and the impact of preventive strategies. A prevalent method of modeling the spread of pandemics is to categorize individuals in the population as belonging to one of several distinct compartments, which represents their health status with regard to the pandemic. In this work, a modified SIR epidemic model is proposed and analyzed with respect to the identification of its parameters and initial values based on stated or recorded case data from public health sources to estimate the unreported cases and the effectiveness of public health policies such as social distancing in slowing the spread of the epidemic. The analysis aims to highlight the importance of unreported cases for correcting the underestimated basic reproduction number. In many epidemic outbreaks, the number of reported infections is likely much lower than the actual number of infections which can be calculated from the model’s parameters derived from reported case data. The analysis is applied to the COVID-19 pandemic for several countries in the Gulf region and Europe.

scholarly journals THE VALUE OF ROBUST STATISTICAL FORECASTS IN THE COVID-19 PANDEMIC

2021 ◽  
Vol 256 ◽  
pp. 19-43
Author(s):  
Jennifer L. Castle ◽  
Jurgen A. Doornik ◽  
David F. Hendry
Keyword(s):  
Structural Change ◽  
Measurement Errors ◽  
Epidemiological Models ◽  
Short Term ◽  
Data Measurement ◽  
Policy Interventions ◽  
Changing Trends ◽  
Technological Advances ◽  
The Difference ◽  
The Uk

The Covid-19 pandemic has put forecasting under the spotlight, pitting epidemiological models against extrapolative time-series devices. We have been producing real-time short-term forecasts of confirmed cases and deaths using robust statistical models since 20 March 2020. The forecasts are adaptive to abrupt structural change, a major feature of the pandemic data due to data measurement errors, definitional and testing changes, policy interventions, technological advances and rapidly changing trends. The pandemic has also led to abrupt structural change in macroeconomic outcomes. Using the same methods, we forecast aggregate UK unemployment over the pandemic. The forecasts rapidly adapt to the employment policies implemented when the UK entered the first lockdown. The difference between our statistical and theory based forecasts provides a measure of the effect of furlough policies on stabilising unemployment, establishing useful scenarios had furlough policies not been implemented.

Predictive Modeling for Public Health

2015 ◽  
Author(s):  
Eric Potash ◽  
Joe Brew ◽  
Alexander Loewi ◽  
Subhabrata Majumdar ◽  
Andrew Reece ◽  
...  
Keyword(s):  
Public Health ◽  
Predictive Modeling

scholarly journals A systemic socio-ecological recovery from Covid-19

2021 ◽  
pp. 146801812110191
Author(s):  
William Hynes
Keyword(s):  
Financial Economic ◽  
Global Scale ◽  
Economic Approach ◽  
Short Term ◽  
Current Crisis ◽  
Policy Alternatives ◽  
Economic Thinking ◽  
Emergency Measures ◽  
Ecological Recovery

New economic thinking and acting through a systemic approach could outline policy alternatives to tackle the global-scale systemic challenges of financial, economic, social and environmental emergencies, and help steer our recovery out of the current crisis. A systemic recovery requires an economic approach that balances several factors - markets and states, efficiency and resilience, growth and sustainability, national and global stability, short-term emergency measures and long-term structural change. To achieve this, we need to think beyond our policy silos, comprehend our interconnections, and build resilience into our systems.

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