Basic reproduction number (R0), an epidemiological tool for prioritizing livestock diseases' An example of Karnataka
Abstract views: 126 / PDF downloads: 81
https://doi.org/10.56093/ijans.v90i4.104179
Keywords:
Herd immunity threshold, Karnataka, Livestock diseases, Prioritization, R0, Vaccination coverageAbstract
Livestock diseases become burden to the dairy farmers and state animal husbandry departments, and causes huge economic loss. Basic reproduction number [R0], indicates the number of secondary cases in susceptible population from one diseased animal. In the present study, R0 was calculated by using 5 statistical methods for 13 livestock diseases, which was used to prioritize livestock diseases and calculated herd immunity threshold, vaccination coverage required. Time series data on livestock disease outbreaks, month, year, clinically diagnosed cases, death cases were collected from Department of Animal Husbandary and Veterinary Services. Govt. of Karnataka during the period 2000–18. The mean R0 values were >1 for bacterial (4), viral (5) and parasitic (4) diseases. The livestock diseases were prioritized for high transmission potential as haemorrhagic septicaemia [HS] (2.51) followed by Peste des petits ruminants [PPR] (2.22), black quarter [BQ] (1.89), foot-and-mouth disease [FMD] (1.71), theileriosis [TE] (1.70), enterotoxaemia [ET] (1.54), anthrax [AX] (1.48), sheep and goat pox [SGP] (1.44), rabies [RA] (1.39), babesiosis [BA] (1.38), bluetongue [BT] (1.31) and fasciolosis [FA] (1.27) based on mean R0 values for Karnataka. The herd immunity threshold was high for HS [60.2%] followed by PPR [55.0%], BQ [47.1%], FMD [41.5%] and other diseases. The vaccination coverage required showed highest levels for HS, followed by PPR, BQ, FMD, TE, ET, etc. Thus, R0 values may be used for prioritizing livestock diseases by policy makers and for planning the necessary preventive measures. The herd immunity threshold and vaccination coverage obtained for livestock diseases will help in allocating the scarce resources for vaccination effectively and to prevent livestock diseases outbreaks in Karnataka.
Downloads
References
Fine P, Eames K and Heymann D L. 2011. Herd Immunity: A rough guide. Clinical Infectious Diseases 52(7): 911–16. DOI: https://doi.org/10.1093/cid/cir007
Krishnamoorthy P, Govindaraj G, Shome B R and Rahman H. 2016. Spatio-temporal epidemiological analysis of livestock diseases-a case of Tamil Nadu state in India. International Journal of Livestock Research 6(8): 27–38. DOI: https://doi.org/10.5455/ijlr.20160804055652
Krishnamoorthy P, Kurli R, Patil S S, Roy P and Suresh K P. 2019. Trends and future prediction of livestock diseases outbreaks by periodic regression analysis. Indian Journal of Animal Sciences 89(4): 369–76.
Krishnamoorthy P, Suresh K P, Saha S, Govindaraj G, Shome B R and Roy P. 2017. Meta-analysis of prevalence of subclinical and clinical mastitis, major mastitis pathogens in dairy cattle in India. International Journal of Current Microbiology and Applied Sciences 6(3): 1214–34. DOI: https://doi.org/10.20546/ijcmas.2017.603.141
Obadia T, Haneef R and Boelle P Y. 2012. The R0 package: a toolbox to estimate reproduction numbers for epidemic outbreaks. BMC Medical Informatics and Decision Making 12: 147. DOI: https://doi.org/10.1186/1472-6947-12-147
Rothman K J. 1998. Modern Epidemiology. Lippincott Williams and Wilkins, Philadelphia, USA.
Snedecor G W and Cochran W G. 1980. Statistical Methods. 7th edn. The Iowa State University Press, Ames, Iowa, USA. pp. 196– 252.
Swaminathan M, Rana R, Ramakrishnan M A, Karthik K, Malik Y S and Dhama K. 2016. Prevalence, diagnosis, management and control of important diseases of ruminants with special reference to Indian scenario. Journal of Experimental Biology and Agricultural Sciencies 4(35): 338–67. DOI: https://doi.org/10.18006/2016.4(3S).338.367
Wallinga J and Lipstich M. 2007. How generation intervals shape the relationships between growth rates and reproduction numbers. Proceedings of the Royal Society B 274: 599–604. DOI: https://doi.org/10.1098/rspb.2006.3754
Walling J and Teunis P. 2004. Different epidemic curves for severe acute respiratory syndrome reveal similar impacts of control measures. American Journal of Epidemiology 160: 509–16. DOI: https://doi.org/10.1093/aje/kwh255
White F L and Pagano M. 2008. A likelihood-based method for real-time estimation of the serial interval and reproductive number of an epidemic. Statistics in Medicine 27(16): 2999– 3016. DOI: https://doi.org/10.1002/sim.3136
Downloads
Submitted
Published
Issue
Section
License
The copyright of the articles published in The Indian Journal of Animal Sciences is vested with the Indian Council of Agricultural Research, which reserves the right to enter into any agreement with any organization in India or abroad, for reprography, photocopying, storage and dissemination of information. The Council has no objection to using the material, provided the information is not being utilized for commercial purposes and wherever the information is being used, proper credit is given to ICAR.