Forecasting the novel coronavirus COVID-19

Petropoulos, Fotios; Makridakis, Spyros

doi:10.1371/journal.pone.0231236

Cited by 537 publications

(434 citation statements)

References 8 publications

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“…Similarly, the data regarding diagnostic tests performed per country, or death rates were omitted, given they are highly influenced by the countries' economic wellbeing, health care systems, facilities and capacities and other factors. 29,30 There are lots of unforeseen uncertainties and coincidences, which could not be implemented in our model, for example there were days, when a large number of people were diagnosed with COVID-19 one day (for example in care homes in France or in Hungary) that caused a large increase in the number of the daily new cases. 14 Summarizing, the COVID-19 disease is a global health challenge, which caused the WHO to declare a "public health emergency of international concern on 30/01/2020".…”

Section: Discussionmentioning

confidence: 99%

Predicting the Epidemic Curve of the Coronavirus (SARS-CoV-2) Disease (COVID-19) Using Artificial Intelligence

Kolozsvári

Bérczes

Hajdú

et al. 2020

Preprint

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25Objectives 26 The current form of severe acute respiratory syndrome called coronavirus disease 2019 27 (COVID-19) caused by a coronavirus (SARS-CoV-2) is a major global health problem. The 28 aim of our study was to use the official epidemiological data and predict the possible outcomes 29 of the COVID-19 pandemic using artificial intelligence (AI)-based RNNs (Recurrent Neural 30 Networks), then compare and validate the predicted and observed data. 31 Materials and Methods 32We used the publicly available datasets of World Health Organization and Johns Hopkins 33 University to create the training dataset, then have used recurrent neural networks (RNNs) with 34 gated recurring units (Long Short-Term Memory -LSTM units) to create 2 Prediction Models. 35Information collected in the first t time-steps were aggregated with a fully connected (dense) 36 neural network layer and a consequent regression output layer to determine the next predicted 37 value. We used root mean squared logarithmic errors (RMSLE) to compare the predicted and 38 observed data, then recalculated the predictions again. 39 Results 40The result of our study underscores that the COVID-19 pandemic is probably a propagated 41 source epidemic, therefore repeated peaks on the epidemic curve (rise of the daily number of 42 the newly diagnosed infections) are to be anticipated. The errors between the predicted and 43 validated data and trends seems to be low. 44 Conclusions 45 3The influence of this pandemic is great worldwide, impact our everyday lifes. Especially 46 decision makers must be aware, that even if strict public health measures are executed and 47 sustained, future peaks of infections are possible. The AI-based predictions might be useful 48 tools for predictions and the models can be recalculated according to the new observed data, 49 to get more precise forecast of the pandemic. 50 51 52 53 54 55 56 57 58 59 60 61 4 62

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Section: Discussionmentioning

confidence: 99%

Predicting the Epidemic Curve of the Coronavirus (SARS-CoV-2) Disease (COVID-19) Using Artificial Intelligence

Kolozsvári

Bérczes

Hajdú

et al. 2020

Preprint

View full text Add to dashboard Cite

show abstract

“…In current study, we have used the country-wise temporal data of CCC, cases to obtain adequate forecast models, which will be helpful for the policy makers and health care service providers in combating the spread of this disease. Reproduction number, R 0 , is sensitive to initial values and is time dependent [14,15]. As discussed in section 1, the initial data in developing countries are not available with high accuracy because the number and quality of infection test centers are limited.…”

Section: Methodsmentioning

confidence: 99%

A new forecast model for fluctuating Pattern of Covid-19 spread in South Asia

Hussain

Zaheer

Haroon

et al. 2020

Preprint

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Exponential growths are observed in several natural phenomena, including cosmological expansion of the universe, nuclear fission, population growth, global viral spread, and financial markets fluctuations. Several deterministic and stochastic methods have been used for the modeling of dynamics of these phenomena. Global reliable data are essential to parameterise a defined model so as to enhance confidence in model outputs. Four-parameter logistic model comes out to be best forecast model for the total Covid-19 infected people in Pakistan and India. Five-parameter logistic model is best forecast model for Bangladesh. Simulation results revel that maximum total number of cases will be about 1.0, 0.6 and 0.25 million in India, Pakistan and Bangladesh respectively. Covid-19 infected rate will be closed to 11000, 8000 and 2000 cases per day during peak period in these countries.

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“…With the multifaceted socio and pathological dynamics of Covid-19, it is hard to predict when and how it grows and ends [65]. Different groups of scientists have come up with many different models still with wide deviations [66,67,68,69,70,71]. Selecting too many parameters would increase the risk of non-sampling error due to imprecise and inadequate data, e.g., to estimate the number of people commuting en masse [DTH3] at a given time and location with acceptable accuracy in a country like Bangladesh could be very difficult if not impossible.…”

Section: Kinetics-modeling-fittingmentioning

confidence: 99%

4P Model for Dynamic Prediction of Covid-19 with statistical and machine learning approach: Social Dynamics, Government Roles and Pathogen Behavior on Bangladesh Perspective

Hasan

Ahmmed

Chowdhury

et al. 2020

Preprint

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Introduction: Around the world, scientists are racing hard to understand how Covid-19 epidemic is spreading and growing, thus trying to find ways to prevent it before medications come to pass. Many different models have been proposed so far correlating different factors. Some of them are too localized to indicate a general trend of the pandemic while some others have established transient correlations only.Methods: Hence, in this study, a 4P model has been proposed based on four probabilities (4P) which has been found to be true for all affected countries taking Bangladesh as a case. Efficiency scores have been estimated from survey analysis not only for governing authorities on managing the situation (P(G)) but also for the compliance of the citizens ((P(P)). Since the immune responses of all the people are not uniform to a specific pathogen, the probability of a person getting infected ((P(I)) after being exposed has also been estimated. And the vital one is the probability of Test Positivity ((P(T)) which is a strong indicator of how effectively the infected people are diagnosed and isolated from the rest of the group that affects the rate of growth.Results and Conclusion: All the four parameters have been fitted in a non-linear exponential model that partly updates itself periodically with everyday facts. Along with the model, all the four probabilistic parameters are engaged to train a recurrent neural network using Long-Short Term Memory neural network and the followed trial confirmed a ruling functionality of the 4Ps.

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Forecasting the novel coronavirus COVID-19

Cited by 537 publications

References 8 publications

Predicting the Epidemic Curve of the Coronavirus (SARS-CoV-2) Disease (COVID-19) Using Artificial Intelligence

Predicting the Epidemic Curve of the Coronavirus (SARS-CoV-2) Disease (COVID-19) Using Artificial Intelligence

A new forecast model for fluctuating Pattern of Covid-19 spread in South Asia

4P Model for Dynamic Prediction of Covid-19 with statistical and machine learning approach: Social Dynamics, Government Roles and Pathogen Behavior on Bangladesh Perspective

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