Prediction of the COVID-19 Pandemic for the Top 15 Affected Countries: Advanced Autoregressive Integrated Moving Average (ARIMA) Model
Bibliographic record
Abstract
BACKGROUND: The coronavirus disease (COVID-19) pandemic has affected more than 200 countries and has infected more than 2,800,000 people as of April 24, 2020. It was first identified in Wuhan City in China in December 2019. OBJECTIVE: The aim of this study is to identify the top 15 countries with spatial mapping of the confirmed cases. A comparison was done between the identified top 15 countries for confirmed cases, deaths, and recoveries, and an advanced autoregressive integrated moving average (ARIMA) model was used for predicting the COVID-19 disease spread trajectories for the next 2 months. METHODS: The comparison of recent cumulative and predicted cases was done for the top 15 countries with confirmed cases, deaths, and recoveries from COVID-19. The spatial map is useful to identify the intensity of COVID-19 infections in the top 15 countries and the continents. The recent reported data for confirmed cases, deaths, and recoveries for the last 3 months was represented and compared between the top 15 infected countries. The advanced ARIMA model was used for predicting future data based on time series data. The ARIMA model provides a weight to past values and error values to correct the model prediction, so it is better than other basic regression and exponential methods. The comparison of recent cumulative and predicted cases was done for the top 15 countries with confirmed cases, deaths, and recoveries from COVID-19. RESULTS: The top 15 countries with a high number of confirmed cases were stratified to include the data in a mathematical model. The identified top 15 countries with cumulative cases, deaths, and recoveries from COVID-19 were compared. The United States, the United Kingdom, Turkey, China, and Russia saw a relatively fast spread of the disease. There was a fast recovery ratio in China, Switzerland, Germany, Iran, and Brazil, and a slow recovery ratio in the United States, the United Kingdom, the Netherlands, Russia, and Italy. There was a high death rate ratio in Italy and the United Kingdom and a lower death rate ratio in Russia, Turkey, China, and the United States. The ARIMA model was used to predict estimated confirmed cases, deaths, and recoveries for the top 15 countries from April 24 to July 7, 2020. Its value is represented with 95%, 80%, and 70% confidence interval values. The validation of the ARIMA model was done using the Akaike information criterion value; its values were about 20, 14, and 16 for cumulative confirmed cases, deaths, and recoveries of COVID-19, respectively, which represents acceptable results. CONCLUSIONS: The observed predicted values showed that the confirmed cases, deaths, and recoveries will double in all the observed countries except China, Switzerland, and Germany. It was also observed that the death and recovery rates were rose faster when compared to confirmed cases over the next 2 months. The associated mortality rate will be much higher in the United States, Spain, and Italy followed by France, Germany, and the United Kingdom. The forecast analysis of the COVID-19 dynamics showed a different angle for the whole world, and it looks scarier than imagined, but recovery numbers start looking promising by July 7, 2020.
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How this classification was reachedexpand
Full frame machine prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.001 | 0.003 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 0.000 |
Machine scores (provisional)
The two teacher heads of the student model, read on this work. A score orders the frame for review; it never asserts a category, and the validation status ships verbatim with every row.
Baseline scores from an immature model (maturity gate not passed, 7 training rounds). Scores rank; they never assert a category.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.
How this classification was reached, model by model and score by score, is at the end of the page under "How this classification was reached".