Evidence map›Paper›PMID 32666005›Full record

ArticleInfectious Disease Modelling2020

Mathematical modeling and the transmission dynamics in predicting the Covid-19 - What next in combating the pandemic.

A Anirudh

Abstract read
In one paragraph

Article in Infectious Disease Modelling, 2020. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 21 papers.

0numbers the graph read from it
0cells of the map it votes in
21citing papers in PubMed
–field-weighted citation impact
1 · What the graph read from 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.

2 · The registry

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.

3 · Its place in the literature

Who cites it

21 citing papers in PubMed.

  1. Article
  2. Determining Hopf Bifurcation for Epidemic Model by Quantifier Elimination (QE).Acta informatica medica : AIM : journal of the Society for Medical Informatics of Bosnia & Herzegovina : casopis Drustva za medicinsku informatiku BiH · 2025
    Article
  3. Dynamic hierarchical state space forecasting.Statistics in medicine · 2024
    Article
  4. Article
  5. Article
  6. Application of Quantifier Elimination in Epidemiology.Acta informatica medica : AIM : journal of the Society for Medical Informatics of Bosnia & Herzegovina : casopis Drustva za medicinsku informatiku BiH · 2023
    Article
  7. Article
  8. Article
  9. Article
  10. Article
  11. Mobility indicators and COVID-19 growth ratio in Iraq: a correlation study.Journal of public health (Oxford, England) · 2021
    Article
  12. Article
  13. Article
  14. Article
  15. Article
  16. Article
  17. Article
  18. Review
  19. Article
  20. Article
4 · The record

Corrections and comments

PubMed lists nothing against this paper. Absence here is not a guarantee, only a check that was made.

5 · Who and what money

Authors and funding

1 author.

A AnirudhBirla Institute of Technology and Science Pilani, Hyderabad, Shameer Pet, Telangana, 500078, India.

Funding

No grant is acknowledged in the PubMed record.

6 · The paper itself

Abstract

Mathematical predictions in combating the epidemics are yet to reach its perfection. The rapid spread, the ways, and the procedures involved in containment of a pandemic demand the earliest understanding in finding solutions in line with the habitual, physiological, biological, and environmental aspects of life with better computerised mathematical modeling and predictions. Epidemiology models are key tools in public health management programs despite having a high level of uncertainty in each one of these models. This paper describes the outcome and the challenges of SIR, SEIR, SEIRU, SIRD, SLIAR, ARIMA, SIDARTHE, etc models used in prediction of spread, peak, and reduction of Covid-19 cases.

Indexed as

ChallengesCovid-19EpidemicMathematical modelingPrediction

Identifiers

PMID32666005
PMCPMC7335626

What OpenQuestion holds

Textmetadata
LicenceCC BY-NC-ND
Read underepoch 390

Registered trials

None linked

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.