ReviewFrontiers in public health
Statistical Modeling for the Prediction of Infectious Disease Dissemination With Special Reference to COVID-19 Spread.
Review in Frontiers in public health. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 26 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
26 citing papers in PubMed.
- Modelling African Swine Fever Transmission and Epidemiology: A Scoping Review of Mechanistic, Statistical, and Machine Learning Approaches.Tropical medicine and infectious disease · 2026Review
- Digital epidemiology: Utilizing big data for public health surveillance and disease outbreak prediction.Journal of public health research · 2026Review
- Feasibility of short-term hospital mask demand forecasting using a backpropagation neural network under data scarcity.Scientific reports · 2026Article
- Evaluation and Comparison of Statistical Methods for Early Detection of Respiratory Symptom Outbreaks: A Real-World Observational Study From Iran.Health science reports · 2025Article
- Web-based models to inform health policy: A scoping review.Health research policy and systems · 2025Article
- Analysis of a mathematical model for malaria using data-driven approach.Scientific reports · 2025Article
- Impact of meteorological factors on the incidence of hand, foot and mouth disease in Ningbo from 2014 to 2019: a causal convolutional neural networks.BMC public health · 2025Article
- Zoonotic diseases in China: epidemiological trends, incidence forecasting, and comparative analysis between real-world surveillance data and Global Burden of Disease 2021 estimates.Infectious diseases of poverty · 2025Article
- Global infectious disease early warning models: An updated review and lessons from the COVID-19 pandemic.Infectious Disease Modelling · 2025Review
- How Does Vaccine-Induced Immunity Compare to Infection-Acquired Immunity in the Dynamics of COVID-19?Pathogens (Basel, Switzerland) · 2025Article
- Trend determination of methicillin-resistantNorthern clinics of Istanbul · 2025Article
- Insights from respiratory virus co-infections.World journal of virology · 2024Review
- Review
- Predictive modeling for infectious diarrheal disease in pediatric populations: A systematic review.Learning health systems · 2024Article
- Review
- Article
- Emerging advances in biosecurity to underpin human, animal, plant, and ecosystem health.iScience · 2023Review
- Fitting Early Phases of the COVID-19 Outbreak: A Comparison of the Performances of Used Models.Healthcare (Basel, Switzerland) · 2023Article
- COVID-19 Patterns in Araraquara, Brazil: A Multimodal Analysis.International journal of environmental research and public health · 2023Article
- Article
Corrections and comments
- Commented on by
Authors and funding
2 authors.
Funding
No grant is acknowledged in the PubMed record.
Abstract
In this review, we have discussed the different statistical modeling and prediction techniques for various infectious diseases including the recent pandemic of COVID-19. The distribution fitting, time series modeling along with predictive monitoring approaches, and epidemiological modeling are illustrated. When the epidemiology data is sufficient to fit with the required sample size, the normal distribution in general or other theoretical distributions are fitted and the best-fitted distribution is chosen for the prediction of the spread of the disease. The infectious diseases develop over time and we have data on the single variable that is the number of infections that happened, therefore, time series models are fitted and the prediction is done based on the best-fitted model. Monitoring approaches may also be applied to time series models which could estimate the parameters more precisely. In epidemiological modeling, more biological parameters are incorporated in the models and the forecasting of the disease spread is carried out. We came up with, how to improve the existing modeling methods, the use of fuzzy variables, and detection of fraud in the available data. Ultimately, we have reviewed the results of recent statistical modeling efforts to predict the course of COVID-19 spread.
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.