Comparison of various epidemic models on the COVID-19 outbreak in Indonesia (original) (raw)
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Predictive Mathematical Modelling of the Total Number of COVID-19 Cases for Indonesia
Journal of Environmental Microbiology and Toxicology, 2020
In the current article, we showcase various growth models like Von Bertalanffy, Baranyi-Roberts, Morgan-Mercer-Flodin (MMF), modified Richards, modified Gompertz, modified Logistics and Huang in the fitting and analysis of the COVID-19 epidemic trend as of 15 July 2020 in Indonesia in the form of the total number of SARS-CoV-2 infections. The MMF model was proved as the suitable model with the highest adjusted R2 value and lowest RMSE value. The Accuracy and Bias Factors values were near to unity (1.0). The parameters obtained from the MMF model consist of maximum growth rate (µm) (log) of 0.025 (95% CI from 0.020 to 0.028), curve constant (ï¤) that affects the inflection point of 0.770 (95% CI from 0.691 to 0.849), lower asymptote value (ï¢) of 0.297 (95% CI from 0.229 to 0.365) and maximal total number of cases (ymax) of 4,634,469 (95% CI from 1,967,886 to 15,417,005). The MMF forecast that the total number of cases in Indonesia on the coming 15th of August and 15th of Septembe...
2020 International Conference on ICT for Smart Society (ICISS)
Coronavirus Disease 2019 or COVID-19 is a new disease that can cause respiratory and inflammatory disorders. As a new model virus the general public has difficulty finding its match and then consider it trivial. The spread of the disease caused by COVID virus 19 is set to become a pandemic by the WHO as of March 12, 2020. Development of covid-19 pandemic data in Indonesia, has claimed 1089 lives on May 17, 2020 (source: http: //covid19.bnpb.go.id/) and is a major threat to global public health especially Indonesia. The pandemic behavior in one area can be learned by comparing behavior in other regions. We propose SEIR epidemic models (S = Suspect, E = Expose, I = Infected, and R = Recovered) to predict the behavior of covid-19 transmission in Indonesia with parameters of distribution, cure rate, mortality rate, communication rate and movement. The appropriate parameters to predict the behavior of the Covid-19 virus spreading in Indonesia, firstly, the number of cases that occurred in Indonesia are compared with other countries that were first exposed to this pandemic. Several countries in Asia, Australia, Europe and America are chosen for comparison. Comparisons are performed by examining the maximum correlation values in each country. The pattern of the number of cases that occurred in Indonesia is very similar to the UK, Malaysia and Australia. The first prediction maximum number of new cases per daily is 1,343 people occurring on May 15, 2020. The end of the pandemic is predicted on August 8-10, 2020 (circumstance 1). The second prediction maximum number of new cases per daily is 1,034 people occurring on May 30, 2020. The end of the pandemic is predicted on September 9-10, 2020 (circumstance 2). The SEIR model for predicting the number of Covid-19 cases is sufficient when there is no further development of this pandemic.
Real-time Forecasting of the COVID-19 Epidemic using the Richards Model in South Sulawesi, Indonesia
2020
This paper discussed Real-time Forecasting of the COVID-19 Epidemic using daily cumulative cases of COVID-19 in South Sulawesi Our aim is to make model for the growth of COVID-19 cases in South Sulawesi in the top 5 provinces with the largest COVID-19 cases in Indonesia and predict when this pandemic reaches the peak of spread and when it ends This paper used the Richards model, which is an extension of a simple logistic growth model with additional scaling parameters Data used in the paper as of June 24, 2020 were taken from the official website of the Indonesian government Our results are that the maximum cumulative number of COVID-19 cases has reached 10,000 to 12,000 cases The peak of the pandemic is estimated to occur from June to July 2020 while continuing to impose social restrictions The condition in South Sulawesi shows a sloping curve around October 2020, which means that there are still additional cases but not significant When entering November, the curve starts to flat ...
Modeling and Sensitivity Analysis of Coronavirus Disease (COVID-19) Outbreak Prediction
Preprint, 2020
The susceptible-infectious-recovered-deceased (SIRD) model is an essential model for outbreak prediction. This paper evaluates the performance of the SIRD model for the outbreak of COVID-19 in Kuwait, which initiated on 24 February 2020 by five patients in Kuwait. This paper investigates the sensitivity of the SIRD model for the development of COVID-19 in Kuwait based on the duration of the progressed days of data. For Kuwait, we have fitted the SIRD model to COVID-19 data for 20, 40, 60, 80, 100, and 116 days of data and assessed the sensitivity of the model with the number of days of data. The parameters of the SIRD model are obtained using an optimization algorithm (lsqcurvefit) in MATLAB. The total population of 50,000 is equally applied for all Kuwait time intervals. Results of the SIRD model indicate that after 40 days, the peak infectious day can be adequately predicted. Although error percentage from sensitivity analysis suggests that different exposed population sizes are not correctly predicted. SIRD type models are too simple to robustly capture all features of COVID-19, and more precise methods are needed to tackle the correct trends of a pandemic.
Modeling of COVID-19 Epidemic Growth Curve in Indonesia
Jurnal Matematika MANTIK, 2021
Aim of this study is to make parametric modeling of the COVID-19 epidemic growth curve so that the maximum value and time at that point can be obtained from the cumulative cases of COVID-19. The data used in this study is the cumulative number of positive confirmed cases of COVID-19 from https://covid19.go.id/. The method used in this study is fitting data with the Logistic and Gompertz models. Result of this study are (1) the Logistic and Gompertz models are very fit in modeling the COVID-19 epidemic growth curve, indicated from the value of R2 (coefficient of determination) which reaches more than 99%; (2) From the Logistics model it is obtained that the estimated amount of the maximum cumulative case at the end of the COVID-19 epidemic is 7,714 positive confirmed cases, achieved in about 82 days (May 22, 2020) from Mar 2, 2020, when the first positive COVID-19 case was announced by the government; and (3) From the Gompertz model, it is obtained that the estimated maximum cumulati...
Prediction Modelling of COVID-19 Outbreak in Indonesia using a Logistic Regression Model
2021
The COVID-19 outbreak has changed the world at large since it was announced by the World Health Organization (WHO). Many policies in various countries were then implemented to control its spread. Most aspects of human life and the environment are affected by this pandemic. This paper aims to determine the prediction model for the COVID-19 outbreak in Indonesia. The approach used for this modelling employs a logistic regression model.
Data Prediction and Analysis of Covid-19 Using Epidemic Models
International Journal for Research in Applied Science & Engineering Technology (IJRASET), 2022
Coronavirus scourge is manifested because the general fortune quandary of planetary stew by the planet Health Organization within the second multi day stretch of March 2020.This illness starts from China on December 2019 has simply caused destruction over the world, as well as Bharat. The initial case in quite an whereas was accounted on twenty third Gregorian calendar month 2020, with the cases crossing nearly 6000 on the day, paper was composed. Complete isolation of the country for twenty one days and fast disengagement of contaminated cases square measure the energetic advances took by the specialists during this work, Indian Covid dataset is taken for the Analysis and Prediction. 2 epidemic models name SIR and SEIR square measure accustomed analyse the dataset. Introductory clarification of each models square measure mentioned. Comparison of each the models were additionally carried and SEIR model is acting higher prediction than the SIR for our dataset.
Pakistan Journal of Statistics and Operation Research, 2022
COVID-19 has spread throughout the world, including in Southeast Asia. Many studies have made predictions using various models. However, very few are data-driven based. Meanwhile for the COVID-19 case, which is still ongoing, it is very suitable to use data-driven approach with phenomenological models. This paper aimed to obtain effective forecasting models and then predict when COVID-19 in Southeast Asia will peak and end using daily cumulative case data. The research applied the Richards curve and the logistic growth model, combining the two models to make prediction of the COVID-19 cases in Southeast Asia, both the countries with one pandemic wave or those with more than one pandemic wave. The best prediction results were obtained using the Richards curve with the logistic growth model parameters used as the initial values. In the best scenario, the Southeast Asia region is expected to be free from the COVID-19 pandemic at the end of 2021. These modeling results are expected to p...
On 30 July 2020, a total number of 301,530 diagnosed COVID-19 cases were reported in Iran, with 261,200 recovered and 16,569 dead. The COVID-19 pandemic started with 2 patients in Qom city in Iran on 20 February 2020. Accurate prediction of the end of the COVID-19 pandemic and the total number of populations affected is challenging. In this study, several widely used models, including Richards, Gompertz, Logistic, Ratkowsky, and SIRD models, are used to project dynamics of the COVID-19 pandemic in the future of Iran by fitting the present and the past clinical data. Iran is the only country facing a second wave of COVID-19 infections, which makes its data difficult to analyze. The present study's main contribution is to forecast the near-future of COVID-19 trends to allow non-pharmacological interventions (NPI) by public health authorities and/or government policymakers. We have divided the COVID-19 pandemic in Iran into two waves, Wave I, from February 20, 2020 to May 4, 2020, and Wave II from May 5, 2020, to the present. Two statistical methods, i.e., Pearson correlation coefficient (R) and the coefficient of determination (R2), are used to assess the accuracy of studied models. Results for Wave I Logistic, Ratkowsky, and SIRD models have correctly fitted COVID-19 data in Iran. SIRD model has fitted the first peak of infection very closely on April 6, 2020, with 34,447 cases (The actual peak day was April 7, 2020, with 30,387 active infected patients) with the reproduction number R0=3.95. Results of Wave II indicate that the SIRD model has precisely fitted with the second peak of infection, which was on June 20, 2020, with 19,088 active infected cases compared with the actual peak day on June 21, 2020, with 17,644 cases. In Wave II, the reproduction number R0=1.45 is reduced, indicating a lower transmission rate. We aimed to provide even a rough project future trends of COVID-19 in Iran for NPI decisions. Between 180,000 to 250,000 infected cases and a death toll of between 6,000 to 65,000 cases are expected in Wave II of COVID-19 in Iran. There is currently no analytical method to project more waves of COVID-19 beyond Wave II.
STATISTICAL MODELLING OF COVID-19 PANDEMIC
African Journal of Science & Nature, 2020
Coronavirus pandemic data is a family of clinical data which requires the attention of flexible model for modeling its nature. In this study, modeling and analysis based on the available data gathered from each state by the Nigeria Centre for Disease Control are carried-out on Nigerian cases of COVID-19 pandemic using some selected univariate continuous models include: Cauchy, Gumbel, Logistic, Lognormal and Weibull model to fit each data set from each case such as confirmed, discharged, deaths and total active case. Percentage of each case in the affected state are obtained and presented to ascertain the level at which each state has been affected by COVID-19. Secondary data collected from Nigeria centre for disease control site are used for the study; and the data consists 36 states including FCT Abuja. Therefore, with all indications the results shown from exploratory data analysis, goodness-of-fit criteria and statistics that Lognormal model has better fit to all the cases considered in the study than other models despite the outliers in the data sets of all cases.