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Cited by in F6Publishing
For: Martheswaran TK, Hamdi H, Al-Barty A, Zaid AA, Das B. Prediction of dengue fever outbreaks using climate variability and Markov chain Monte Carlo techniques in a stochastic susceptible-infected-removed model. Sci Rep 2022;12:5459. [PMID: 35361845 DOI: 10.1038/s41598-022-09489-y] [Cited by in Crossref: 3] [Cited by in F6Publishing: 4] [Article Influence: 3.0] [Reference Citation Analysis]
Number Citing Articles
1 Javadinejad S, Dara R, Krause S, Riyahi M, Mohammadi A, nasseri M. Analysis and forecasting drought characterisation with using Markov chain model.. [DOI: 10.21203/rs.3.rs-2055668/v1] [Reference Citation Analysis]
2 Dey SK, Rahman MM, Howlader A, Siddiqi UR, Uddin KMM, Borhan R, Rahman EU. Prediction of dengue incidents using hospitalized patients, metrological and socio-economic data in Bangladesh: A machine learning approach. PLoS ONE 2022;17:e0270933. [DOI: 10.1371/journal.pone.0270933] [Cited by in F6Publishing: 1] [Reference Citation Analysis]
3 Yaladanda N, Mopuri R, Vavilala HP, Mutheneni SR. Modelling the impact of perfect and imperfect vaccination strategy against SARS CoV-2 by assuming varied vaccine efficacy over India. Clinical Epidemiology and Global Health 2022;15:101052. [DOI: 10.1016/j.cegh.2022.101052] [Reference Citation Analysis]
4 Xu T, Cui Y. Seasonal Variation Analysis for Weekly Cases, Deaths, and Hospitalizations of COVID-19 in the United States. Advances in Experimental Medicine and Biology 2022. [DOI: 10.1007/5584_2022_750] [Reference Citation Analysis]