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.
Statistical Model
A multivariate logistic regression model was used to predict the probability disease risk in relation to weather parameters, remote sensing variables and livestock population or densities. The goal of logistic regression is to find the best fitting (yet biological reasonable) model to describe the relationship between dichotomous characteristic of interest (disease outbreak) and a set of predictors (weather parameters, RS variables and demographics). Logistic regression generates the co-efficients (and its standard error and significance level) of a formula to predict a logit transformation of the probability of presence of the characteristics of interest.