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Forewarning of Livestock Diseases June 2019

ANDHRA PRADESH,JHARKHAND,KARNATAKA,KERALA,MADHYA PRADESH,MEGHALAYA,ODISHA,TAMIL NADU are predicted for likely occurrence of Anthrax in June 2019

JHARKHAND,KERALA,RAJASTHAN,TRIPURA,PUDUCHERRY are predicted for likely occurrence of Babesiosis in June 2019

ASSAM,JHARKHAND,KARNATAKA,MANIPUR,ODISHA,TRIPURA,WEST BENGAL are predicted for likely occurrence of Black quarter in June 2019

KARNATAKA,RAJASTHAN are predicted for likely occurrence of Enterotoxaemia in June 2019

ARUNACHAL PRADESH,ASSAM,JHARKHAND,KARNATAKA,MANIPUR,ODISHA,ANDAMAN & NICOBAR ISLANDS,PUDUCHERRY are predicted for likely occurrence of Fascioliasis in June 2019
HIMACHAL PRADESH,JAMMU & KASHMIR,JHARKHAND,KERALA,MANIPUR,MEGHALAYA,MIZORAM,ODISHA,RAJASTHAN,WEST BENGAL,ANDAMAN & NICOBAR ISLANDS,CHANDIGARH are predicted for likely occurrence of Foot and mouth disease in June 2019
ANDHRA PRADESH,ASSAM,JHARKHAND,KERALA,ODISHA,RAJASTHAN,TRIPURA,UTTAR PRADESH,WEST BENGAL are predicted for likely occurrence of Haemorrhagic septicaemia in June 2019 ANDHRA PRADESH,ASSAM,HARYANA,JHARKHAND,KERALA,RAJASTHAN,TRIPURA,WEST BENGAL are predicted for likely occurrence of PPR in June 2019 ANDHRA PRADESH,HIMACHAL PRADESH,JAMMU & KASHMIR,KARNATAKA,MAHARASHTRA,MEGHALAYA,ODISHA,RAJASTHAN,WEST BENGAL,ANDAMAN & NICOBAR ISLANDS are predicted for likely occurrence of S & G Pox in June 2019 ASSAM,MANIPUR,MEGHALAYA,MIZORAM,UTTARAKHAND,WEST BENGAL are predicted for likely occurrence of Swine fever in June 2019 ASSAM,HARYANA,JHARKHAND,WEST BENGAL are predicted for likely occurrence of Theileriosis in June 2019 JHARKHAND are predicted for likely occurrence of Trypanosomiasis in June 2019

OB Prediction June-19

Anthrax -32, with Accuracy of 99.69%

Babesiosis - 40, with Accuracy of 99.69%

Black quarter - 60, with Accuracy of 94.29%

Enterotoxaemia- 3, with Accuracy of 96.29%

Fasciolosis - 50, with Accuracy of 98.91%

FMD - 26, with Accuracy of 95.52%

HS - 48, with Accuracy of 93.98%

PPR - 25, with Accuracy of 92.90%

S&G Pox - 15, with Accuracy of 95.52%

Swine Fever - 27, with Accuracy of 99.07%

Theileriosis - 26, with Accuracy of 98.14%

Trypanosomiasis - 25, with Accuracy of 98.76%

Auto Messaging

AICRP Centers
Every Thursday at 11 am
Request to send the monthly disease outbreak report and provide feedback for forewarning in the format. Please ignore if already sent. ICAR-NIVEDI,Bangalore


Block Level
Veterinary Officer's in the sample format
Anthrax Predicted for the month April in your region/taluk Lingasuguru. Kindly take appropriate preventive measures-ICAR-NIVEDI, Bangalore

About NADRES


During 1987, The Indian Council of Agricultural Research (ICAR) established an All India Coordinated Research Project on Animal Disease Monitoring and Surveillance, (AICRP on ADMAS) with four regional centers located one each at Bangalore, Pune, Ludhiana and Kolkata. On 1st April 2000, the AICRP on ADMAS was upgraded to Project Directorate on Animal Disease Monitoring and Surveillance (PD_ADMAS) (during IX Plan). The Directorate got further impetus with the addition of five more collaborating units in X plan and two mission mode NATP projects viz., Animal Health Information System and Data monitoring System (AHIS_DMS) and Weather based Animal Disease Forecasting (WB_ADF) having 17 and 20 collaborating units respectively. Combining the input from AHIS_DMS and WB_ADF, an interactive, dynamic, relational online animal disease forewarning system ,NADRES (National Animal Disease Referral Expert System) was developed with overall aim to improve the early warning and response capacity to animal disease threats in the country for the benefit of farmers and policy makers in Animal Husbandry department. Fifteen AICRP Centers were continuously provided disease events information till 2015. Another sixteen AICRP centres were added in 2015 , totalling to 31 centers spread across the country. These centres supply the disease events data monthly basis and data has been entered into NADRES database in double-data-entry model.
Early warning of disease incidence or outbreaks and the capacity of prediction of risk of spread to new areas is an essential pre-requisite for the effective containment and control of epidemic animal diseases, including zoonosis. Early warning is based on the concept that dealing with a disease epidemic in its early stages is easier and more economical than having to deal with it once it is wide spread. From the public health prospective, early warning of disease outbreaks with a known zoonotic potential will enable control measures that can prevent human morbidity and mortality. National Institute of Veterinary Epidemiology & Disease Informatics developed the software application, NADRES that systematically collect, verify, analyze and respond to the information from designated AICRP-ADMAS, unofficial media reports and informal networks. NADRES builds on the added value combining the alert and response mechanisms of different organisations like state animal husbandry departments, Departments from universities, department of animal husbandry, Dairying and Fisheries (Govt. of India), AICRP on ADMAS and other agencies including NGO's, enhancing the capacity for the benefit of the farmers in the country and other stakeholders to assist in prediction, prevention and control of animal disease threats, including zoonosis, through sharing information, epidemiological analysis and joint missions to assess and control the outbreak, whenever needed. For Zoonotic disease events, alerts of animal outbreaks or incidence can provide the direct early warning so that human surveillance could be enhanced and preventive action can be taken. Sharing assessments of an outbreak will enable a joint and comprehensive analysis of the disease event and its possible consequences. Joint dissemination will furthermore allow harmonized communication by the Central and State Animal Husbandry Departments, ICAR-NIVEDI, regarding disease control strategies.
Regarding the joint response to disease emergencies, the three organizations will be able to respond to a larger number and cover a wider range of outbreaks or exceptional epidemiological events with the provision of a wider range of expertise. This will improve the national preparedness for epidemics and provide rapid, efficient and coordinated assistance in developing disease control strategies.

Specific Objectives of NADRES

* Allow state and central animal husbandry departments to better prepare themselves to prevent incursion of animal diseases/infection and enable their rapid containment.
* Increase timeliness and sensitivity of alerts.
* Improve the detection of exceptional epidemiological events at country level.
* Improve the transparency among different stakeholders.
* Improve the national surveillance and monitoring systems and strengthen the networks of veterinary laboratories working in the country.
* Improve national preparedness for animal and zoonotic epidemics and provide rapid, efficient and coordinated assistance to states experiencing them.
* Provide the technical support to states on issues at the animal/human interface of outbreak control.

Disease Outbreak database

Database on disease outbreaks were collected though the networks of AICRP on ADMAS with 31 centres across the country, provide the regular outbreak information along with date and location of outbreaks, susceptible population, deaths, attacks etc., Disease data obtained on a format is entered in to NADRES database in a double –data-entry validation mode to achieve to zero error entry. Database contains the disease events since 1990 was further improved by inclusion of additional 16 AICRP centres.

Risk factors database

Risk factors such as weather parameters from different sources includes the monthly precipitation(mm), sea level pressure (millibar),minimum temperature (°C), maximum temperature(°C), wind speed (m/s), vapour pressure (millibar), soil moisture(%) , perceptible water(mm), potential evaporation transpiration (mm), cloud cover(okta) etc., extracted from various databases such as National Centre for environmental prediction (NCEP), Indian Meteorological Department(IMD),National Innovations Climate Resilient Agriculture (NICRA) and other sources. The remote sensing variables like Normalised Difference Vegetative Index (NDVI) and Land Surface Temperature(LST) were extracted from MODIS/LANDSAT/LISS III or IV satellite images. The livestock population and densities were extracted from Livestock census 2012.

Artificial Intelligence Algorithm

Two regression models viz. Generalized linear models(GLM) and Generalized additive models (GAM) and six Machine learning methods viz. Random Forest (RF), Boosted Regression Tree (BRT), Artificial Neural Network (ANN), Multiple Adaptive Regression Spline(MARS), Flexible Discriminant Analysis (FDA) and Classification Tree Analysis (CTA). Different modelling methods return different type of `model object’, all these model objects could be used to with the predict function to make predictions for any combinations of values of independent variables. Response plots were created to explore and understand model predictions. To assesses the models for reasonable predictions and spatial patterns, different measures are used to assess how good the model fits the data. ​

Difference between NADRS and NADRES

NADRS is the acronym for National Animal Disease Reporting System, which is an online animal disease reporting system developed by Dept of Animal Husbandry Dairying and Fisheries, Ministry of Agriculture, Govt of India. In this system, 7500 block level veterinary hospitals across India are electronically networked for real time disease reporting. The disease data entered in the system are analysed to understand the livestock disease status across India.

The National Animal Disease Referral Expert System (NADRES) developed by ICAR- National Institute of Veterinary Epidemiology and Disease Informatics (ICAR-NIVEDI) uses the livestock disease data so generated by the DADF & its own 31 AICRP centers for forewarning of 13 predetermined (most reported) livestock diseases two months in advance. Briefly, the livestock disease data aggregated at the district level is integrated with 24 other climatic [land surface temperature (LST), precipitation, wind velocity, humidity etc.,] and non climatic parameters [such as district level livestock population, density, Normalized Differential Vegetation Index (NDVI), soil moisture etc], which are extracted/collected from various sources including remote sensed images, in the server located at ICAR-NIVEDI. Following the data integration, sophisticated statistical models are run in R environment using R programming language and districts are classified into six different risk categories for each of the thirteen diseases. The forewarning outputs are generally depicted both in tables and GIS maps. The so generated forewarning information is circulated to both DADF, GOI and the state animal husbandry department two months in advance.