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Fatma Desouky Mohamed Abdallah
Faculty Veterinary Medicine - Department Aniaml Wealth Development
Scientific degrees
BSc In NULL, [2005]
MSc In NULL, [2009]
((Master of Veterinary Medical Sciences (Biostatistics)
الحالة الوظيفية الحالية :
قائم بالعمل
Academic Jobs
معيد : 25/3/2006
Doctor Assisant : 24/1/2010
Doctor : 26/8/2014
Professor Assisant : 27/8/2019
Publications
1 -
Statistical Modelling of Categorical Outcome with More than Two Nominal Categories (2018).
2 -
Parametric Survival Models for Predicting of Pregnancy in Friesian Cattle (2018).
3 -
Using Nonlinear Models to Describe Body Weight Growth Curves in Three Different Lines of Japanese Quail (2017).
4 -
Role of Time Series Analysis in Forecasting Egg Production Depending on ARIMA Model (2019).
5 -
Statistical Assessment of Some Factors Affecting Calving Interval by Using Ordinal Logistic Regression in Holstein Cows (2017).
6 -
Using Discriminant Analysis and Artificial Neural Network Models for Classification and Prediction of Fertility Status of Friesian Cattle (2017).
7 -
Statistical Approaches for Treatments over Time (Repeated Observations) in Nile Tilapia (2019).
8 -
Evaluation of Immunostimulant Activity of Spirulina platensis (Arthrospira platensis) and Sage (Salvia officinalis) in Nile Tilapia (Oreochromis niloticus) (2018).
9 -
Evaluation of Mortality in Selected Body Weight Lines of Japanese Quail Using Survival Analysis (2015).
10 -
Application of Different Biostatistical Methods in Biological Data Analysis (2019).
11 -
Application of Different Biostatistical Methods in Biological Data Analysis (2019).
12 -
Evaluation of Immunostimulant Activity of Spirulina platensis (Arthrospira platensis) and Sage (Salvia officinalis) in Nile Tilapia (Oreochromis niloticus) (2018).
Research Area
AJVS. Vol. 52(1): 81-86. January, 2017 DOI: 10.5455/ajvs.248866 Using Nonlinear Models to Describe Body Weight Growth Curves in Three Different Lines of Japanese Quail Fatma D.M. Abdallah Department of Animal Wealth Development, Faculty of Veterinary Medicine, Zagazig University, 44511Egypt. Abstract Key words: Nonlinear model, growth curve, Japanese quail. The aim of this study was to choose the best predictive model for the accurate description of data of 240 quail's body weight. Non-linear function models were used for estimating growth curves in three lines of Japanese quail, high, low and control. The non-linear models which used were Gompertz, Brody and Logistic model. The models were fitted to weight-age data, from hatch (0), 2, 4 to 6 weeks of age of 240 quails. The weight-age data were modeled by using non-linear regression procedure of Statgraphics statistical package program. Model parameters were estimated using the Levenberg–Marquardt algorithm and comparisons among models were done based on mean square error, adjusted coefficient of determination, Akaike information criterion (AIC) and number of iterations. The results showed that the body development of the three lines could be illustrated well by three models. The Logistic and Gompertz growth curve model were fitted to data especially in high group where adjusted coefficient of determination is 97.7246 and 97.5972 respectively. The smallest value of Akaike information criterion (AIC) was 1138.80 high line in Logistic model. The Gompertz model is better in case of low line and the Logistic model is better in case of high and control line. The logistic model gives the best fit of data followed by the Gompertz. The Brody equation was found to be unsuitable in control group.
AJVS. Vol. 60 (1): 49-54 Jan. 2019 DOI: 10.5455/ajvs.16828 Statistical Approaches for Treatments over Time (Repeated Observations) in Nile Tilapia Fatma D.M. Abdallah Department of Animal Wealth Development, Faculty of Veterinary Medicine, Zagazig University, 44511Egypt. ABSTRACT Key words: Repeated measure ANOVA, sphericity assumptions, lysozymes, P value and Bonferroni procedure. This study aims to model and analyze repeated observations in some biological sciences and veterinary practices (Nile tilapia fish). Repeated measure ANOVA is used to assess the effect of some treatments over a specific time. Data obtained from an experimental study examining the effect of some immunostimulants (Spirulina platensis (SP) and Salvia officinalis (SO)) on lysozymes activity in Nile tilapia fish which measured repeatedly at two times. The analysis is applied by using the software SPSS version 20 on the data collected at two time points 14th day and 28th day so the correct test was two way repeated measure design. The assumptions of the test were achieved where Huynh-Feldt and Greenhouse-Geisser measures were not significant (P-value > 0.05). The P value for time, treatments and the interaction of them was 0.000, so they have a highly significant effect on the (outcome variable) lysozymes activity of Nile tilapia. Bonferroni correction is used as a post hoc test and its result showed a difference between the groups for each parameter. These results of univariate analysis agreed with the multivariate analysis results.
Applied Mathematics 2019, 9(1): 1-5 DOI: 10.5923/j.am.20190901.01 Role of Time Series Analysis in Forecasting Egg Production Depending on ARIMA Model Fatma D. M. Abdallah Department of Animal Wealth Development, Faculty of Veterinary Medicine, Zagazig University, Egypt Abstract The goal of this study is to show the role of time series models in predicting process and to demonstrate the suitable type of it according to the data under study. Autoregressive integrated moving averages (ARIMA) model is used as a common and a more applicable model. Univariate ARIMA model is used here to forecast egg production in some layers depending on daily data from the period of May to October 2018. Different criteria of the ARIMA model can be used to choose the suitable one such as the coefficient of determination (R2), mean absolute error (MAE), root mean square error (RMSE) and mean absolute relative percentage error (MARPE). Depending on these measures the autoregressive integrated moving average model with ordering (2,2,1) is considered the best model for forecasting process. The model fit statistics such as RMSE (331.520) which was low and the lowest BIC value (11.745) indicating that the model fit the data well. The high value of R2 (0.95) and MAPE (4.542) indicated a perfect forecasting model. Also, ARIMA model with ordering (1,2,2) is good in prediction process.
Evaluation of Immunostimulant Activity of Spirulina platensis (Arthrospira platensis) and Sage (Salvia officinalis) in Nile Tilapia (Oreochromis niloticus) Suhair A. Abdellatief1*, Afaf N. Abdel Rahman2 and Fatma D.M. Abdallah3 1Pharmacology Department, Faculty of Veterinary Medicine, Zagazig University, 44511 Zagazig, Sharkia, Egypt 2 Fish Diseases and Management Department, Faculty of Veterinary Medicine, Zagazig University, 44511 Zagazig, Sharkia, Egypt 3Animal Wealth Development Department, Faculty of Veterinary Medicine, Zagazig University, 44511 Zagazig, Sharkia, Egypt Article History: Received: 11/1/2018 Received in revised form: 5/2/2018 Accepted: 15/2/2018 Abstract Immunostimulants have been found to be attractive and promising agents for disease management in aquaculture. The current study aimed to assess the impacts of feeding with Spirulina platensis (SP) and Salvia officinalis (SO) for 28 days on the immune status of Nile tilapia (Oreochromis niloticus). A total of 180 Nile tilapia (30.6 ± 0.12 g) were divided into 3 groups (triplicates of 20 fish/group). Control group was fed on a basal diet only, while the other two groups were supplemented with SP (10 gm/kg diet) and SO (7.5 gm/kg diet) respectively. Both of SP and SO showed in vitro antimicrobial activity against Pseudomonas aeruginosa. The immune response of Nile tilapia supplemented with SP and SO revealed significant increases in lysozyme, nitric oxide activities and IgM titer with enhancement of IL-1β and TNF-α genes expression before and after infection with P. aeruginosa. Fish challenged with P. aeruginosa showed signs of infection with high mortality rate and low relative percentage survival which elevated to be 83% in SP group and 75% in SO group. It could be concluded that dietary supplementation with SP and SO improved immune response and protected Nile tilapia against infection. Moreover, SP showed better results than SO before and after infection.
Global Veterinaria 14 (5): 768-772, 2015 ISSN 1992-6197 © IDOSI Publications, 2015 DOI: 10.5829/idosi.gv.2015.14.05.94231 Corresponding Author: EL-Shimaa M. Roushdy, Department of Animal Wealth Development, Faculty of Veterinary Medicine, Zagazig University, Egypt. E-mail: shimaa_production@yahoo.com. 768 Evaluation of Mortality in Selected Body Weight Lines of Japanese Quail Using Survival Analysis D.M. Fatma and M.R. EL-Shimaa Department of Animal Wealth Development, Faculty of Veterinary Medicine, Zagazig University, Egypt Abstract: Quail raising has become an important poultry business in the world. The objective of this study was to carry out survival analysis to evaluate and quantify risk factors involved in mortality of Japanese quail. Survival analysis and Cox proportional hazards model used to describe mortality in Japanese quail with hatch weight and post hatch body weight as a risk factors. Weight is a variable change with time, so it considered time dependent covariate. Survival function distribution and Kaplain Meier curve showed that mortality was higher in the line selected for high compared to low and control line. Log- rank test used for testing equality of survival distributions for the different levels of selected lines (Log-Rank value = 9.898) resulted P value = 0.007 which is a highly significant. Mortality declined when posthatch body weight increased. Body weight at hatch did not significantly affect mortality. However, posthatch body weight (BW) affects mortality significantly with P- value (0.011) and hazard ratio (1.009). Any factors affect growth after hatching may likely have a direct impact on mortality. Deficiency of water, feed, warmth and potential negative social interaction are factors that could affect mortality immediately after hatch. Through survival analysis, the present study has revealed that factors affecting post hatch Body weight negatively are likely risk factors for mortality in growing birds.
International Journal of Statistics and Applications 2017, 7(3): 192-195 DOI: 10.5923/j.statistics.20170703.05 Statistical Assessment of Some Factors Affecting Calving Interval by Using Ordinal Logistic Regression in Holstein Cows Fatma D. M. Abdallah1, Eman A. Abo Elfadl2,* 1Department of Animal Wealth Development, Faculty of Veterinary Medicine, Zagazig University, Egypt 2Department of Animal Husbandry and Development of Animal Wealth, Faculty of Veterinary Medicine, Mansoura University, Egypt Abstract Background & objectives: Calving interval considered an important trait throughout Holstein dairy cow's life. There are many risk factors which have a great impact on it. In the line with this consideration, the purpose of this study is to apply ordinal logistic regression model to estimate the effect of these risk factors on calving interval. Methods: Ordinal logistic regression analysis was used to estimate the odds ratio (OR) and probability of Holstein dairy cows conception for 3400 lactation records from Dina farms company, Egypt. The data was collected over a period extended from 1998 to 2010. The logit link function was used to predict the probability of occurrence of pregnancy using SPSS version 20.0, USA. Results: The odds ratio showed that the likelihood of pregnancy in cows with different parities was 0.931, 0.787, 0.634 and 1.000 for lactation order 2, 3, 4 and 5 respectively. Odds ratio of pregnancy of cows calving in winter were higher than those calving in summer, it was 1.234 and 1.000 respectively. Odds of different periods of days open were 0.586, 0.771, 0.638 and 1.000 respectively. Odds of different periods of dry period were 0.378, 0.525, 0.545 and 1.000 respectively. Conclusions: Findings showed fitting of this model to the data, it also showed the ability of ordinal logistic regression to provide measure which facilitates understanding of the important risk factors affecting calving interval.
International Journal of Statistics and Applications 2018, 8(3): 129-132 DOI: 10.5923/j.statistics.20180803.03 Parametric Survival Models for Predicting of Pregnancy in Friesian Cattle Fatma D. M. Abdallah1, Eman A. Abo Elfadl2,* 1Department of Animal Wealth Development, Faculty of Veterinary Medicine, Zagazig University, Egypt 2Department of Animal Husbandry and Development of Animal Wealth, Faculty of Veterinary Medicine, Mansoura University, Egypt Abstract Background & objectives: This work was performed to apply different parametric survival models instead of nonparametric ones for predicting pregnancy in Friesian dairy cattle by using days open as a time variable. The models used were exponential, normal, log-normal, Weibull, logistic, log logistic and smallest extreme value. Data for present study were obtained from animal records belongs to different Dakhalia farms (n = 1842) Covering the period between 2009 and 2011. Variables included in this study were days open as a dependent variable and the independent variables were: age at calving, dry period, calving interval, season, and lactation order. The survival time data were modeled by using life data regression procedure of Statgraphics statistical package program. Model parameters were estimated and comparisons among models were done based on Akaike Information Criterion (AIC). The Weibull model was the best option for evaluating time till occurrence of pregnancy where it had the smallest (AIC) value. It also showed a good fit with the studied data. This Weibull survival model can be used to predict the length of time from calving to conception in Friesian. But the Logistic was not appropriate to describe the dataset
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