ArticleSN computer science2021
Multiplicative Holts Winter Model for Trend Analysis and Forecasting of COVID-19 Spread in India.
Article in SN computer science, 2021. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 7 papers.
What it found
Each row is one number read from the abstract, on the scale the paper reported it, with its interval. Left of the dashed line favours the treatment, right favours the comparator. Under each row is the sentence it came from. New to these charts? A ten-minute tutorial.
The abstract states no effect estimate the extractor could read, or names no intervention and outcome on the map, so this paper lights no cell and moves no belief. It is still indexed, cited and linked below.
The trial behind it
Trials whose registry record cites this paper, or whose number appears in the abstract. A trial that started after this paper was published is citing it as background, not reporting it.
Neither the registry nor the abstract names a trial number. If this is a trial report, that itself is worth knowing.
Who cites it
7 citing papers in PubMed, 30 citations in OpenAlex.
- Analysis of influenza-like illness trends in Saudi Arabia: a comparative study of statistical and deep learning techniques.Osong public health and research perspectives · 2025Article
- Is Brazil reaching malaria elimination? A time series analysis of malaria cases from 2011 to 2023.PLOS global public health · 2024Article
- The impact of the Covid-19 pandemic on work accidents with exposure to biological material in Brazil: an interrupted time series analysis.Revista brasileira de epidemiologia = Brazilian journal of epidemiology · 2024Article
- Trends and Patterns of Antibiotic Prescriptions in Primary Care Institutions in Southwest China, 2017-2022.Infection and drug resistance · 2023Article
- Malaria time series in the extra-Amazon region of Brazil: epidemiological scenario and a two-year prediction model.Malaria journal · 2022Article
- Predicting the Number of Reported Pulmonary Tuberculosis in Guiyang, China, Based on Time Series Analysis Techniques.Computational and mathematical methods in medicine · 2022Article
- On the accuracy of Covid-19 forecasting methods in Russia for two years.Procedia computer science · 2022Article
Corrections and comments
PubMed lists nothing against this paper. Absence here is not a guarantee, only a check that was made.
Authors and funding
5 authors at 3 institutions in 1 country.
Funding
No grant is acknowledged in the PubMed record.
Abstract
The surge of the novel COVID-19 caused a tremendous effect on the health and life of the people resulting in more than 4.4 million confirmed cases in 213 countries of the world as of May 14, 2020. In India, the number of cases is constantly increasing since the first case reported on January 30, 2020, resulting in a total of 81,997 cases including 2649 deaths as of May 14, 2020. To assist the government and healthcare sector in preventing the transmission of disease, it is necessary to predict the future confirmed cases. To predict the dynamics of COVID-19 cases, in this paper, we project the forecast of COVID-19 for five most affected states of India such as Maharashtra, Tamil Nadu, Delhi, Gujarat, and Andhra Pradesh using the real-time data. Using Holt-Winters method, a forecast of the number of confirmed cases in these states has been generated. Further, the performance of the method has been determined using RMSE, MSE, MAPE, MAE and compared with other standard algorithms. The analysis shows that the proposed Holt-Winters model generates RMSE value of 76.0, 338.4, 141.5, 425.9, 1991.5 for Andhra Pradesh, Maharashtra, Gujarat, Delhi and Tamil Nadu, which results in more accurate predictions over Holt's Linear, Auto-regression (AR), Moving Average (MA) and Autoregressive Integrated Moving Average (ARIMA) model. These estimations may further assist the government in employing strong policies and strategies for enhancing healthcare support all over India.
Indexed as
Identifiers
What OpenQuestion holds
Registered trials
Read under generation 80e0d062 · epoch 390. Bibliography from PubMed, PubMed Central and OpenAlex; grants from NIH RePORTER; trial links from ClinicalTrials.gov; estimates, votes and beliefs from the OpenQuestion graph.