ArticleMedical journal, Armed Forces India2020
Predictive models of COVID-19 in India: A rapid review.
Article in Medical journal, Armed Forces India, 2020. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 19 papers.
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Who cites it
19 citing papers in PubMed.
- Deep learning in public health: Comparative predictive models for COVID-19 case forecasting.PloS one · 2024Article
- Designing, Implementing and Optimising a Capacity‑Building Model for Infectious Disease Modelling in India.Annals of global health · 2024Article
- A systematic review of excess all-cause mortality estimation studies in India during COVID-19 pandemic.Medical journal, Armed Forces India · 2023Review
- Vaccine rate forecast for COVID-19 in Africa using hybrid forecasting models.African health sciences · 2023Article
- Association of greenness with COVID-19 deaths in India: An ecological study at district level.Environmental research · 2023Article
- Novel indicator for the spread of new coronavirus disease 2019 and its association with human mobility in Japan.Scientific reports · 2023Article
- Dynamic transmission modeling of COVID-19 to support decision-making in Brazil: A scoping review in the pre-vaccine era.PLOS global public health · 2023Article
- Modelling the first wave of COVID-19 in India.PLoS computational biology · 2022Article
- Exo-SIR: an epidemiological model to analyze the impact of exogenous spread of infection.International journal of data science and analytics · 2022Article
- An Effective Strategy and Mathematical Model to Predict the Sustainable Evolution of the Impact of the Pandemic Lockdown.Healthcare (Basel, Switzerland) · 2022Article
- A novel hybrid SEIQR model incorporating the effect of quarantine and lockdown regulations for COVID-19.Scientific reports · 2021Article
- Fusion of intelligent learning for COVID-19: A state-of-the-art review and analysis on real medical data.Neurocomputing · 2021Article
- Predictive models for COVID-19 cases, deaths and recoveries in Algeria.Results in physics · 2021Article
- A review of mathematical model-based scenario analysis and interventions for COVID-19.Computer methods and programs in biomedicine · 2021Review
- COVID-19 prediction models: a systematic literature review.Osong public health and research perspectives · 2021Article
- Systematic review of predictive mathematical models of COVID-19 epidemic.Medical journal, Armed Forces India · 2021Article
- A systematic review on AI/ML approaches against COVID-19 outbreak.Complex & intelligent systems · 2021Article
- Sen's Innovative Method for Trend Analysis of Epidemic: A Case Study of Covid-19 Pandemic in India.Transactions of the Indian National Academy of Engineering : an international journal of engineering and technology · 2021Article
- ARIMA modelling & forecasting of COVID-19 in top five affected countries.Diabetes & metabolic syndromeArticle
Corrections and comments
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Authors and funding
5 authors.
Funding
No grant is acknowledged in the PubMed record.
Abstract
backgroundThe mathematical modelling of coronavirus disease-19 (COVID-19) pandemic has been attempted by a wide range of researchers from the very beginning of cases in India. Initial analysis of available models revealed large variations in scope, assumptions, predictions, course, effect of interventions, effect on health-care services, and so on. Thus, a rapid review was conducted for narrative synthesis and to assess correlation between predicted and actual values of cases in India.
methodsA comprehensive, two-step search strategy was adopted, wherein the databases such as Medline, google scholar, MedRxiv, and BioRxiv were searched. Later, hand searching for the articles and contacting known modelers for unpublished models was resorted. The data from the included studies were extracted by the two investigators independently and checked by third researcher.
resultsBased on the literature search, 30 articles were included in this review. As narrative synthesis, data from the studies were summarized in terms of assumptions, model used, predictions, main recommendations, and findings. The Pearson's correlation coefficient (r) between predicted and actual values (n = 20) was 0.7 (p = 0.002) with R2 = 0.49. For Susceptible, Infected, Recovered (SIR) and its variant models (n = 16) 'r' was 0.65 (p = 0.02). The correlation for long-term predictions could not be assessed due to paucity of information.
conclusionReview has shown the importance of assumptions and strong correlation between short-term projections but uncertainties for long-term predictions. Thus, short-term predictions may be revised as more and more data become available. The assumptions too need to expand and firm up as the pandemic evolves.
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