ArticleJMIR public health and surveillance2020
COVID-19 in India: Statewise Analysis and Prediction.
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.
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Who cites it
24 citing papers in PubMed.
- Impact of COVID-19 vaccinations in India: a state-wise analysis.BMC public health · 2025Article
- Psychological and physical impact of wearing personal protective equipment among health care workers during the COVID-19 pandemic.Bioinformation · 2024Article
- Mental health concerns of interns posted in COVID-19-affected regions of Maharashtra.Industrial psychiatry journal · 2023Article
- COVID-19 Pandemic: Did Strict Mobility Restrictions Save Lives and Healthcare Costs in Maharashtra, India?Healthcare (Basel, Switzerland) · 2023Article
- Correlation of Mucormycosis with Various Clinical Parameters Among COVID-19 Patients: A Cross-Sectional Study.Journal of maxillofacial and oral surgery · 2023Article
- Jump-Drop Adjusted Prediction of Cumulative Infected Cases Using the Modified SIS Model.Annals of data science · 2023Article
- Multicriteria group decision making via generalized trapezoidal intuitionistic fuzzy number-based novel similarity measure and its application to diverse COVID-19 scenarios.Artificial intelligence review · 2023Article
- 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 · 2023Review
- Picture fuzzy set-based decision-making approach using Dempster-Shafer theory of evidence and grey relation analysis and its application in COVID-19 medicine selection.Soft computing · 2023Article
- COVID-19 vaccination acceptance and adherence among pregnant and lactating high-risk group individuals of Maharashtra State, India.Journal of education and health promotion · 2023Article
- 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 · 2023Article
- COVID-19, adversities and depression among older adolescents in urban slums of India.Asian journal of psychiatry · 2022Article
- Epidemiological trend and clinical profile of COVID-19 patients: Experience from a designated COVID-19 center in Delhi.Journal of family medicine and primary care · 2022Article
- Clusters of COVID-19 Indicators in India: Characterization, Correspondence and Change Analysis.SN computer science · 2022Article
- COVID-19 Epidemic Analysis in India with Multi-Source State-Level Datasets.BioMed research international · 2022Article
- Article
- On willingness to pay for Covid-19 vaccines: a case study from India.Human vaccines & immunotherapeutics · 2021Article
- Spatio-temporal variation in fine particulate matter and effect on air quality during the COVID-19 in New Delhi, India.Urban climate · 2021Article
- COVID-19 Pandemic in India: Through the Lens of Modeling.Global health, science and practice · 2021Article
- COVID-19 in India: Who are we leaving behind?Progress in disaster science · 2021Article
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3 authors.
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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.
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