ArticleClinical epidemiology and global health
Forecasting COVID-19 epidemic in India and high incidence states using SIR and logistic growth models.
Article in Clinical epidemiology and global health. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 38 papers, 2 of them syntheses that pooled it.
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
38 citing papers in PubMed, 2 syntheses or guidelines pooled it.
- Gauging the Impact of Artificial Intelligence and Mathematical Modeling in Response to the COVID-19 Pandemic: A Systematic Review.BioMed research international · 2022Pooled it
- Application of Artificial Intelligence-Based Regression Methods in the Problem of COVID-19 Spread Prediction: A Systematic Review.International journal of environmental research and public health · 2021Pooled it
- Impact of COVID-19 vaccinations in India: a state-wise analysis.BMC public health · 2025Article
- Article
- Real-time infectious disease endurance indicator system for scientific decisions using machine learning and rapid data processing.PeerJ. Computer science · 2024Article
- Growth modeling approach with the Verhulst coexistence dynamic properties for regulation purposes.Theory in biosciences = Theorie in den Biowissenschaften · 2023Article
- COVID-19 Pandemic: Did Strict Mobility Restrictions Save Lives and Healthcare Costs in Maharashtra, India?Healthcare (Basel, Switzerland) · 2023Article
- Modelling epidemic growth models for lumpy skin disease cases in Thailand using nationwide outbreak data, 2021-2022.Infectious Disease Modelling · 2023Article
- Machine learning and automatic ARIMA/Prophet models-based forecasting of COVID-19: methodology, evaluation, and case study in SAARC countries.Stochastic environmental research and risk assessment : research journal · 2023Article
- Analysis and prediction of improved SEIR transmission dynamics model: taking the second outbreak of COVID-19 in Italy as an example.Frontiers in public health · 2023Article
- COVID-19 outbreak prediction using Seq2Seq + Attention and Word2Vec keyword time series data.PloS one · 2023Article
- Data Mining Based Techniques for Covid-19 Predictions.Procedia computer science · 2023Article
- Twitter conversations predict the daily confirmed COVID-19 cases.Applied soft computing · 2022Article
- Time-Series Analysis and Healthcare Implications of COVID-19 Pandemic in Saudi Arabia.Healthcare (Basel, Switzerland) · 2022Article
- Predictive approach of COVID-19 propagation via multiple-terms sigmoidal transition model.Infectious Disease Modelling · 2022Article
- EKF-SIRD model algorithm for predicting the coronavirus (COVID-19) spreading dynamics.Scientific reports · 2022Article
- A novel hybrid fuzzy time series model for prediction of COVID-19 infected cases and deaths in India.ISA transactions · 2022Article
- Complex Contact Network of Patients at the Beginning of an Epidemic Outbreak: An Analysis Based on 1218 COVID-19 Cases in China.International journal of environmental research and public health · 2022Article
- Numerical simulation of the force of infection and the typical times of SARS-CoV-2 disease for different location countries.Modeling earth systems and environment · 2022Article
- Hospital preparedness during epidemics using simulation: the case of COVID-19.Central European journal of operations research · 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
6 authors.
Funding
No grant is acknowledged in the PubMed record.
Abstract
backgroundEver since the Coronavirus disease (COVID-19) outbreak emerged in China, there has been several attempts to predict the epidemic across the world with varying degrees of accuracy and reliability. This paper aims to carry out a short-term projection of new cases; forecast the maximum number of active cases for India and selected high-incidence states; and evaluate the impact of three weeks lock down period using different models.
methodsWe used Logistic growth curve model for short term prediction; SIR models to forecast the maximum number of active cases and peak time; and Time Interrupted Regression model to evaluate the impact of lockdown and other interventions.
resultsThe predicted cumulative number of cases for India was 58,912 (95% CI: 57,960, 59,853) by May 08, 2020 and the observed number of cases was 59,695. The model predicts a cumulative number of 1,02,974 (95% CI: 1,01,987, 1,03,904) cases by May 22, 2020. As per SIR model, the maximum number of active cases is projected to be 57,449 on May 18, 2020. The time interrupted regression model indicates a decrease of about 149 daily new cases after the lock down period, which is statistically not significant.
conclusionThe Logistic growth curve model predicts accurately the short-term scenario for India and high incidence states. The prediction through SIR model may be used for planning and prepare the health systems. The study also suggests that there is no evidence to conclude that there is a positive impact of lockdown in terms of reduction in new cases.
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.