Abdollahzadeh, H; Rahmanian, V; Sharifi, N; Zahedi, R; Bajestani, MJN; GholianAval, M; Esmaeilzadeh, N; Hadjzadeh, M and Ahmadian, M (2021). Risks assessment of Adherence to non-pharmaceutical measures towards COVID-19 among residents of Mashhad in the North-East of Iran during the awful wave of the epidemic. J. Family Med. Prim. Care. 10: 3211-3218.
Akbarein, H; Bahonar, AR; Bokaie, S; Mosavar, N; Rahimi-Foroushani, A; Sharifi, H; Makenali, AS; Rokni, ND; Marhamati-Khameneh, B and Broumanfar, S (2014). Determinants of bovine tuberculosis in dairy farms covered by the tuberculin screening test: A herd level case control study. IJE., 10: 15-24.
Becketti, S (2013). Introduction to time series using Stata. Vol. 4905, College Station, TX: Stata Press. PP: 176-182.
Brockwell, PJ (2014). Time series analysis. Wiley StatsRef: Statistics Reference Online.
Carrique-Mas, JJ; Medley, GF and Green, LE (2008). Risks for bovine tuberculosis in British cattle farms restocked after the foot and mouth disease epidemic of 2001. Prev. Vet. Med., 84: 85-93.
Ciaravino, G; García-Saenz, A; Cabras, S; Allepuz, A; Casal, J; García-Bocanegra, I; De Koeijer, A; Gubbins, S; Sáez, JL; Cano-Terriza, D and Napp, S (2018). Assessing the variability in transmission of bovine tuberculosis within Spanish cattle herds. Epidemics. 23: 110-120.
Claridge, J; Diggle, P; McCann, CM; Mulcahy, G; Flynn, R; McNair, J; Strain, S; Welsh, M; Baylis, M and Williams, DJ (2012). Fasciola hepatica is associated with the failure to detect bovine tuberculosis in dairy cattle. Nat. Commun., 3: 1-8.
Cosivi, O; Grange, JM; Daborn, CJ; Raviglione, MC; Fujikura, T; Cousins, D; Robinson, RA; Huchzermeyer, HF; de Kantor, I and Meslin, FX (1998). Zoonotic tuberculosis due to Mycobacterium bovis in developing countries. Emerg. Infect. Dis., 4: 59.
Cunha, GBD; Luitgards-Moura, JF; Naves, ELM; Andrade, AO; Pereira, AA and Milagre, ST (2010). Use of an artificial neural network to predict the incidence of malaria in the city of Canta, state of Roraima. Rev. Soc. Bras. Med. Trop., 43: 567-570.
Esmaeilzadeh, N; Bahonar, A; Rahimi Foroushani, A; Nasehi, M and Shakeri, MT (2019). Which type of univariate forecasting methods is appropriate for prediction of tuberculosis cases in Razavi Khorasan Province? A need for surveillance and biosurveillance systems. JMM., 7: e96229.
Esmaeilzadeh, N; Shakeri, M; Esmaeilzadeh, M and Rahmanian, V (2020). ARIMA models forecasting the SARS-COV-2 in the Islamic Republic of Iran.
Asian Pac. J. Trop. Dis., 13: 521.
Gharbi, M; Quenel, P; Gustave, J; Cassadou, S; La Ruche, G; Girdary, L and Marrama, L (2011). Time series analysis of dengue incidence in Guadeloupe, French West Indies: forecasting models using climate variables as predictors. BMC Infect. Dis., 11: 1-13.
Gordejo, FR and Vermeersch, JP (2006). Towards eradication of bovine tuberculosis in the European Union. Vet. Microbiol., 112: 101-109.
Guo, LC; Wu, W; Guo, JQ; Wang, P and Zhou, BS (2008). Appling grey swing model to predict the incidence trend of hemorrhagic fever with renal syndrome in Shenyang. J. China Med. Univ., 37: 839-842.
Inlamea, OF; Soares, P; Ikuta, CY; Heinemann, MB; Achá, SJ; Machado, A; Ferreira Neto, JS; Correia-Neves, M; and Rito, T (2020). Evolutionary analysis of Mycobacterium bovis genotypes across Africa suggests co-evolution with livestock and humans. PLoS Negl. Trop. Dis., 14: e0008081.
Li, Q; Guo, NN; Han, ZY; Zhang, YB; Qi, SX; Xu, YG; Wei, YM; Han, X; and Liu, YY (2012). Application of an autoregressive integrated moving average model for predicting the incidence of hemorrhagic fever with renal syndrome. Am. J. Trop. Med. Hyg., 87: 364-370.
Liu, Q; Liu, X; Jiang, B and Yang, W (2011). Forecasting incidence of hemorrhagic fever with renal syndrome in China using ARIMA model. BMC Infect. Dis., 11: 1-7.
LoBue, PA; Enarson, DA and Thoen, CO (2010). Tuberculosis in humans and animals: an overview [serialised article. Tuberculosis: a re-emerging disease in animals and humans. Number 1 in the series]. Int. J. Tuberc. Lung. Dis., 14: 1075-1078.
Lu, HM; Zeng, D and Chen, H (2009). Prospective infectious disease outbreak detection using Markov switching models. IEEE Trans. Knowl. Data. Eng., 22: 565-577.
Meng, XJ; Lindsay, DS and Sriranganathan, N (2009). Wild boars as sources for infectious diseases in livestock and humans. Philos. Trans. R. Soc. Lond. B, Biol. Sci., 364: 2697-2707.
Menzies, FD and Neill, SD (2000). Cattle-to-cattle transmission of bovine tuberculosis. Vet. J., 160: 92-106.
Michel, AL; Müller, B and Van Helden, PD (2010). Mycobacterium bovis at the animal-human interface: A problem, or not? Vet. Microbiol., 140: 371-381.
Moosazadeh, M; Khanjani, N; Nasehi, M and Bahrampour, A (2015). Predicting the incidence of smear positive tuberculosis cases in Iran using time series analysis. Iran. J. Public Health, 44: 1526-1534.
Moosazadeh, M; Nasehi, M; Bahrampour, A; Khanjani, N; Sharafi, S and Ahmadi, S (2014). Forecasting tuberculosis incidence in Iran using box-Jenkins models. Iran. Red. Crescent Med. J., 16: e11779.
Munyeme, M; Munang’andu, HM; Nambota, A; Muma, JB; Phiri, AM and Nalubamba, KS (2012). The nexus between bovine tuberculosis and fasciolosis infections in cattle of the Kafue Basin Ecosystem in Zambia: implications on abattoir surveillance. Vet. Med. Int., 2012.
Olsson, GE; Hjertqvist, M; Lundkvist, Å and Hörnfeldt, B (2009). Predicting high risk for human hantavirus infections, Sweden. Emerg. Infect. Dis., 15: 104-106.
Smith, NH; Gordon, SV; de la Rua-Domenech, R; Clifton-Hadley, RS and Hewinson, RG (2006). Bottlenecks and broomsticks: the molecular evolution of Mycobacterium bovis. Nat. Rev. Microbiol., 4: 670-681.
Tadayon, K; Mosavari, N and Feizabadi, MM (2013). An epidemiological perspective on bovine tuberculosis spotlighting facts and dilemmas in Iran, a historically zebu-dominant farming country. Iran J. Microbiol., 5: 1-13.
Torgerson, P and Torgerson, D (2009). Benefits of stemming bovine TB need to be demonstrated. Nature. 457: 657.
Wang, YJ; Zhao, TQ; Wang, P; Li, SQ; Huang, Z; Yang, GQ; Li XY;
and Liu, B (2006). Applying linear regression
statistical method to predict the epidemic of hemorrhagic fever with renal syndrome.
Chin. J. Vec. Biol. Contr., 17: 333-334.
Wongkoon, S; Jaroensutasinee, M and Jaroensutasinee, K (2012). Development of temporal modeling for prediction of dengue infection in Northeastern Thailand. Asian Pac. J. Trop. Med., 5: 249-252.
Wu, W; Guan, P; Guo, JQ and Zhou, BS (2008). Comparison of GM (1, 1) gray model and ARIMA model in forecasting the incidence of hemorrhagic fever with renal syndrome. J. China Med. Univ., 37: 52-55.
Wu, Z; Wu, W and Wang, P (2006). Prediction for incidence of hemorrhagic fever with renal syndrome with back propagation artificial neural network model.
Chin. J. Vec. Biol. Contr., 17: 223.
Zheng, YL; Zhang, LP; Zhang, XL; Wang, K and Zheng, YJ (2015). Forecast model analysis for the morbidity of tuberculosis in Xinjiang, China. PloS One. 10: e0116832.
)