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Cited by in F6Publishing
For: Niu B, Liang R, Zhou G, Zhang Q, Su Q, Qu X, Chen Q. Prediction for Global Peste des Petits Ruminants Outbreaks Based on a Combination of Random Forest Algorithms and Meteorological Data. Front Vet Sci 2020;7:570829. [PMID: 33490125 DOI: 10.3389/fvets.2020.570829] [Cited by in Crossref: 2] [Cited by in F6Publishing: 4] [Article Influence: 2.0] [Reference Citation Analysis]
Number Citing Articles
1 Zhang S, Li N, Xu M, Huang ZYX, Gu Z, Yin S. Urbanization and Habitat Characteristics Associated with the Occurrence of Peste des Petits Ruminants in Africa. Sustainability 2022;14:8978. [DOI: 10.3390/su14158978] [Reference Citation Analysis]
2 Afshari Safavi E. Assessing machine learning techniques in forecasting lumpy skin disease occurrence based on meteorological and geospatial features. Trop Anim Health Prod 2022;54:55. [PMID: 35029707 DOI: 10.1007/s11250-022-03073-2] [Reference Citation Analysis]
3 Fathelrahman EM, Reeves A, Mohamed MS, Ali YME, El Awad AI, Bensalah OK, Abdalla AA. Epidemiology and Cost of Peste des Petits Ruminants (PPR) Eradication in Small Ruminants in the United Arab Emirates-Disease Spread and Control Strategies Simulations. Animals (Basel) 2021;11:2649. [PMID: 34573618 DOI: 10.3390/ani11092649] [Reference Citation Analysis]
4 Wang J, Chen J, Zhang S, Ding Y, Wang M, Zhang H, Liang R, Chen Q, Niu B. Risk assessment and integrated surveillance of foot-and-mouth disease outbreaks in Russia based on Monte Carlo simulation. BMC Vet Res 2021;17:268. [PMID: 34376207 DOI: 10.1186/s12917-021-02967-x] [Reference Citation Analysis]