Evidence map›Paper›PMID 32763888›Full record

ArticleJMIR public health and surveillance2020

COVID-19 in India: Statewise Analysis and Prediction.

Palash Ghosh, Rik Ghosh, Bibhas Chakraborty

Abstract read
In one paragraph

Article in JMIR public health and surveillance, 2020. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 24 papers.

0numbers the graph read from it
0cells of the map it votes in
24citing 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

24 citing papers in PubMed.

  1. Article
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  8. Tackling COVID-19 Using Antiviral Nanocoating's-Recent Progress and Future Challenges.Particle & particle systems characterization : measurement and description of particle properties and behavior in powders and other disperse systems · 2023
    Review
  9. Article
  10. Article
  11. Data analytics and knowledge management approach for COVID-19 prediction and control.International journal of information technology : an official journal of Bharati Vidyapeeth's Institute of Computer Applications and Management · 2023
    Article
  12. Article
  13. Article
  14. Article
  15. Article
  16. Article
  17. Article
  18. Article
  19. COVID-19 Pandemic in India: Through the Lens of Modeling.Global health, science and practice · 2021
    Article
  20. COVID-19 in India: Who are we leaving behind?Progress in disaster science · 2021
    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

3 authors.

Palash GhoshDepartment of Mathematics, Indian Institute of Technology, Guwahati, India.ORCID 0000-0001-9605-1213
Rik GhoshDepartment of Mathematics, Indian Institute of Technology, Guwahati, India.ORCID 0000-0001-5841-7317
Bibhas ChakrabortyCentre for Quantitative Medicine & Programme in Health Services and Systems Research, Duke-National University of Singapore Medical School, Singapore, Singapore.ORCID 0000-0002-7366-0478

Funding

No grant is acknowledged in the PubMed record.

6 · The paper itself

Abstract

backgroundThe highly infectious coronavirus disease (COVID-19) was first detected in Wuhan, China in December 2019 and subsequently spread to 212 countries and territories around the world, infecting millions of people. In India, a large country of about 1.3 billion people, the disease was first detected on January 30, 2020, in a student returning from Wuhan. The total number of confirmed infections in India as of May 3, 2020, is more than 37,000 and is currently growing fast.

objectiveMost of the prior research and media coverage focused on the number of infections in the entire country. However, given the size and diversity of India, it is important to look at the spread of the disease in each state separately, wherein the situations are quite different. In this paper, we aim to analyze data on the number of infected people in each Indian state (restricted to only those states with enough data for prediction) and predict the number of infections for that state in the next 30 days. We hope that such statewise predictions would help the state governments better channelize their limited health care resources.

methodsSince predictions from any one model can potentially be misleading, we considered three growth models, namely, the logistic, the exponential, and the susceptible-infectious-susceptible models, and finally developed a data-driven ensemble of predictions from the logistic and the exponential models using functions of the model-free maximum daily infection rate (DIR) over the last 2 weeks (a measure of recent trend) as weights. The DIR is used to measure the success of the nationwide lockdown. We jointly interpreted the results from all models along with the recent DIR values for each state and categorized the states as severe, moderate, or controlled.

resultsWe found that 7 states, namely, Maharashtra, Delhi, Gujarat, Madhya Pradesh, Andhra Pradesh, Uttar Pradesh, and West Bengal are in the severe category. Among the remaining states, Tamil Nadu, Rajasthan, Punjab, and Bihar are in the moderate category, whereas Kerala, Haryana, Jammu and Kashmir, Karnataka, and Telangana are in the controlled category. We also tabulated actual predicted numbers from various models for each state. All the R

conclusionsStates with nondecreasing DIR values need to immediately ramp up the preventive measures to combat the COVID-19 pandemic. On the other hand, the states with decreasing DIR can maintain the same status to see the DIR slowly become zero or negative for a consecutive 14 days to be able to declare the end of the pandemic.

Indexed as

Coronavirus InfectionsCOVID-19HumansIndiaModels, StatisticalPandemicsPneumonia, ViralSpatial Analysis30-day predictionCOVID-19daily infection ratedisease modelingexponential modellogistic modelSIS model

Identifiers

PMID32763888
PMCPMC7431238

What OpenQuestion holds

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LicenceCC BY
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Registered trials

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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.