{"doi":"10.1007/s12652-020-02641-4","title":"Forecasting of the SARS-CoV-2 epidemic in India using SIR model, flatten curve and herd immunity","abstract":null,"journal":"Journal of Ambient Intelligence and Humanized Computing","year":2020,"id":624043,"datarank":1.3644249415553584,"base_score":3.1780538303479458,"endowment":3.1780538303479458,"self_citation_contribution":0.47670807455219194,"citation_network_contribution":0.8877168670031664,"self_endowment_contribution":0.47670807455219194,"citer_contribution":0.8877168670031664,"corpus_percentile":null,"corpus_rank":null,"citation_count":23,"citer_count":20,"citers_with_citation_signal":16,"citers_with_endowment":16,"datacite_reuse_total":0,"is_dataset":false,"is_dataset_confidence":null,"is_data_producer":false,"deposit_databanks":null,"is_oa":false,"file_count":0,"downloads":0,"has_version_chain":false,"published_date":null,"fair_score":null,"fair_percentile":null,"algorithm_id":"datarank_citation_only_1hop_v6","ranking_scope":"data_only","authors":[{"id":1107796,"name":"Sandeep Kumar Mathivanan","orcid":"0000-0001-8572-1197","position":1,"is_corresponding":false},{"id":1613055,"name":"Prabhu Jayagopal","orcid":null,"position":2,"is_corresponding":false},{"id":1613056,"name":"Prasanna Mani","orcid":null,"position":3,"is_corresponding":false},{"id":1108153,"name":"Sukumar Rajendran","orcid":null,"position":4,"is_corresponding":false},{"id":1613057,"name":"UmaShankar Subramaniam","orcid":null,"position":5,"is_corresponding":false},{"id":1613058,"name":"Aroul Canessane Ramalingam","orcid":null,"position":6,"is_corresponding":false},{"id":1613059,"name":"Vijay Anand Rajasekaran","orcid":null,"position":7,"is_corresponding":false},{"id":1613060,"name":"Alagiri Indirajithu","orcid":null,"position":8,"is_corresponding":false},{"id":1613061,"name":"Manivannan Sorakaya Somanathan","orcid":null,"position":9,"is_corresponding":false},{"id":1613054,"name":"Maheshwari Venkatasen","orcid":null,"position":0,"is_corresponding":false}],"reference_count":0,"raw_metadata":{"has_enrichment":true,"resolved":true,"title":"Forecasting of the SARS-CoV-2 epidemic in India using SIR model, flatten curve and herd immunity","abstract":"In this paper, we are presenting an epidemiological model for exploring the transmission of outbreaks caused by viral infections. Mathematics and statistics are still at the cutting edge of technology where scientific experts, health facilities, and government deal with infection and disease transmission issues. The model has implicitly applied to COVID-19, a transmittable disease by the SARS-CoV-2 virus. The SIR model (Susceptible-Infection-Recovered) used as a context for examining the nature of the pandemic. Though, some of the mathematical model assumptions have been improved evaluation of the contamination-free from excessive predictions. The objective of this study is to provide a simple but effective explanatory model for the prediction of the future development of infection and for checking the effectiveness of containment and lock-down. We proposed a SIR model with a flattening curve and herd immunity based on a susceptible population that grows over time and difference in mortality and birth rates. It illustrates how a disease behaves over time, taking variables such as the number of sensitive individuals in the community and the number of those who are immune. It accurately model the disease and their lessons on the importance of immunization and herd immunity. The outcomes obtained from the simulation of the COVID-19 outbreak in India make it possible to formulate projections and forecasts for the future epidemic progress circumstance in India.","is_dataset_classified":null,"base_score":3.1780538303479458,"endowment":3.1780538303479458,"datacite_reuse_total":0,"file_count":0,"downloads":0,"views":0,"has_version_chain":false,"is_dataset":false,"is_oa":false,"pmid":"33224306","pmcid":"PMC7666824","openalex_id":"https://openalex.org/W3098832915","authors":[],"funders":[],"total_grants":0,"fwci":0.6643,"citation_percentile":0.76810381,"influential_citations":0,"citation_trend":[{"year":2021,"count":10},{"year":2022,"count":5},{"year":2023,"count":6},{"year":2024,"count":2}],"oa_status":"bronze","license":"http://www.springer.com/tdm","oa_locations":[{"url":"https://link.springer.com/content/pdf/10.1007/s12652-020-02641-4.pdf","host_type":"journal"},{"url":"https://link.springer.com/content/pdf/10.1007/s12652-020-02641-4.pdf","host_type":"publisher"},{"url":"http://link.springer.com/content/pdf/10.1007/s12652-020-02641-4.pdf","host_type":"publisher"},{"url":"http://link.springer.com/article/10.1007/s12652-020-02641-4/fulltext.html","host_type":"publisher"},{"url":"https://doi.org/10.1007/s12652-020-02641-4","host_type":"journal"},{"url":"https://pubmed.ncbi.nlm.nih.gov/33224306","host_type":"repository"},{"url":"https://www.ncbi.nlm.nih.gov/pmc/articles/7666824","host_type":"repository"},{"url":"https://europepmc.org/articles/PMC7666824","host_type":"Europe_PMC"},{"url":"https://europepmc.org/articles/PMC7666824?pdf=render","host_type":"Europe_PMC"}],"fields_of_study":["COVID-19 epidemiological studies","COVID-19 Pandemic Impacts","SARS-CoV-2 and COVID-19 Research"],"mesh_terms":[],"keywords":["Herd immunity","Context (archaeology)","Epidemic model","Outbreak","Pandemic","Transmission (telecommunications)","Population","Susceptible individual","Disease","Mathematical modelling of infectious disease","Demography","Infectious disease (medical specialty)","Virology","Coronavirus disease 2019 (COVID-19)","Medicine","Environmental health","Computer science","Geography","Sociology","Outbreaks","Sir Model","Covid-19","Flatten-Curve"],"sdg_mappings":[{"sdg_number":0,"sdg_label":"Good health and well-being"}],"linked_datasets":[],"clinical_trials":[],"software_tools":[],"database_accessions":[{"name":"doi"}],"source":"live","citation_network_status":"fetched"},"created_at":"2026-08-04T02:26:41.516621Z","pmid":null,"pmcid":null,"fwci":null,"citation_percentile":null,"influential_citations":0,"oa_status":null,"license":null,"views":0,"total_file_size_bytes":0,"version_count":0,"fair_f":null,"fair_a":null,"fair_i":null,"fair_r":null,"fair_zscore":null,"fair_rationale":null,"fair_model":null,"fair_agent_version":null,"fair_fulltext_source":null,"fair_has_llm":null,"fair_computed_at":null,"clinical_trials":[],"software_tools":[],"db_accessions":[],"linked_datasets":[],"topics":[]}