Approach paper for linking of outbreaks and epidemiological data of animal diseases in West Bengal for development of disease based prediction model, animal health database, disease monitoring and surveillance and epidemiological mapping



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Approach paper for linking of outbreaks and epidemiological data of animal diseases in West Bengal for development of disease based

prediction model, animal health database, disease monitoring and surveillance and epidemiological mapping.
Drafted by

Dr Tara Sankar Pan, Deputy Director ARD, Epidemiological Unit, IAH&VB, Kolkata.

Dr. Sunit Mukhopadhyay, Professor and Head, West Bengal University of Animal

Science and Fisheries, Kolkata

Dr. Subhasish Bandyopadhyay, Senior scientist, Eastern Regional Station of IVRI,

Kolkata

In West Bengal ARD department has a good net work for animal disease repoting system from grass root Gram Panchayet level to State level. All outbreaks are recorded and complied in the Epidemiological Unit monthly basis on basis of clinical and laboratory diagnosis.



Sources of animal disease outbreaks data :

The data are collected for Epidemiological study under Disease Surveillance Scheme from the existing infrastructure of the Animal Resources & Animal Health Directorate of the state. For the shake of convenience, the infrastructure has been divided into three tires viz., a) Primary reporter i.e. at peripheral level detection, b) Intermediary compiler i.e. at the district level, c) Immediate consumers i.e. state level (Epidemiological Unit).



Govt. Sectors

Animal Development Aid Centre, Block Animal Health Centre, Additional Block Animal Health Centre, State Animal Health Centre, Policlinics, Regional Laboratory, Veterinary Pathological Labs. Govt. Farms


Peripheral level

(Primary Reporter)



Office of the Deputy Director, ARD & Parisad Officer


District Level

(Intermediary Compiler)



EPIDEMIOLOGICAL UNIT of the state, Kolkata

State Level

(Immediate Consumers)

NOTIFIABLE DISEASES OF THE STATE:

Out of the large number of notifiable disease prevalent in the country, only 22 (twenty two) are being routinely reported by the state to the Animal Husbandry Deptt., Ministry of Agriculture, Govt. of India. These diseases are listed below and out of which a few are described elaborately with maps which were prevailed in this state during the years



Disease reported to the govt. of India monthly :

  1. Rinderpest (2) Foot & Mouth Disease (3) Contagious Bovine Pleuropneumonia (4) Blue Tongue (5) Swine Fever (6) Sheep & Goat Pox (7) Ranikhet Disease (8) Duck Plague (9) Black Quarter (10) Anthrax (11) Haemorrhagic Septicaemia (12) Fowl Cholera (13) Marek’s Disease (14) Infectious Bursal Disease (15) Salmonellosis (Poultry) (16) Rabies (17) Theileriasis (18) Anaplasmosis (19) Trypanosomiasis (20) Contagious Pastular Dermatitis (21) PPR in Goat & Sheep (22) Avian Influenza


Diseases are diagnosed on the basis of available diagnostic facility exiting in the Animal Resources Development department of the state indifferent level as mentioned bellow:-

FMD


DI

RDDL

DATA BANK

NPRE

OIE


ADS of GOVT.OF INDIA

ANALYSIS

DATA

ADMAS of ICAR

REF. LABS.IN IAH&VB,
KOLKATA










DAH&VS

REPORT






OTHER CONCERN

AGENCIES








DD ARD& PO


REGIONAL DISEASE DIAGNOSIS LABS

DIST. LABS






















BLDO


BLDO



BAHC


ABAHC

SAHC

Vety. Pathologist.

LDSs, PBs of GPs



Mapping is done on the basis of total number of outbreaks recorded during a particular year. One Example is given bellow:-

Epidemiological Map on Distribution B.Q. Outbreaks 2008-09







0 - OBs00


Format recording of outbreaks through existing animal health information system.
ANIMAL DISEASE SURVEILLANCE

WEST BENGAL
EPIDEMIOLOGICAL DATA

Name of the Institution _______________________________________


Name of the Unit / District_________________________________Month__________________/20

Month

Disease

Location

No. of O/B

Species affected (name of the species)

No. of Animal /Bird

No. of Animal/ Bird at risk

Whether Lab. confirmation or otherwise

No. of vaccination done against each O/B







Block

G.P.

Village







Affected

Death































Exotic

Cross breed

Indg.

Exotic

Cross breed

Indg.


















































































































































































































































































































































































































































Comments:

Additional information


Signature____________________

Designation__________________


Memo No._____________________/ Dated_______________/

Copy forwarded for information and necessary action to:-

1) In charge, Epidemiological Unit, Instt. of Animal Health & Veterinary Biologicals, 37, Belgachia Road, Kolkata – 700 037
Signature____________________

Designation_________
An approach for development of prediction model of gastro-intestinal parasitic infestation in organised Govt. farm of Meghalaya
The most common gastrointestinal parasite prevalent throughout the year in Meghalaya, India is Strongyle infection. This is because of the high rainfall and humidity prevalent in the North Eastern region. The aim of gastro-intestinal parasite control programme is to ensure that parasite populations do not exceed levels compatible with economic production. Monitoring of parasite infestation (e.g. faecal egg counts or pasture sampling) throghout the year and forecasting on the basis of meteorological data and computer simulation provide an alternate approach to control parasitic infection in a given geographical area (Brunsdon, RV, 1980)

As the prevalence of this infection is mainly dependent on rainfall and humidity, study was initiated to identify the relationship between rainfall and strongyle infection. For this study, a total of 303 cattle and 253 pig faecal (stool) samples were collected from Govt. livestock farms located at Kyrdemkulai, Upper Shillong and Jowai in Meghalaya during the year 2001 and 2004. Meteorological data were collected from Govt. Meteorological department of Shillong. Samples were collected in the early morning and were processed and examined using standard parasitological procedures. The egg per gram of faeces (epg) were counted using stoll egg counting method.

The incidence of strongyle infection in different areas of Meghalaya and the Meteorological parameters are presented in Table 1. Rainfall and egg per gram of faeces (epg) of strongyle infection has shown a linear and positive relationship. Rainfall contributed maximum effect on parasitic infection as compared to maximum and minimum temperature (Table 2). Rainfall contributed more than 50 per cent for the occurrence of the parasitic infection in cattle and pig but maximum and minimum environmental temperature contributed above 25 percent for the occurrence of strongyle infection in animals (Table 2). Regression analysis between strongyle infection and rainfall showed that 1 percent increase in rainfall predict 0.03 percent increase in strongyle infection .

The predicted strongyle infection was calculated using the equation depicted in the regression analysis which showed a higher strongyle infection than the observed infection ( Fig 1 to 6). This might be due to anthelmintic (drug) treatment and other control measures taken by the Govt. farm for preventing the parasitic infection. This might also be due to the fact that 50 percent of strongyle infection is dependent on rainfall. Onyiah (1985) also predicted the environmental temperature and development of parasites on pasture using Stochastic Development Fraction Model (SDFM).



Finally the multiple regression of disease infected with all the above-mentioned parameters were analysed. The coefficient of multiple determination ( R2) explained more when we include temperature (max, min) and rainfall together as compared to single multiple regression of individual factor like rainfall, maximum temperature and minimum temperature. Interestingly, except rainfall, all other factors are statistically insignificant both at 5 per cent and 10 per cent probability level, whereas, the coefficient of rainfall is significant at 1 per cent probability level. From the above discussion it may be concluded that the occurrence of the strongyle infection can mainly be predicted through rainfall instead of temperature


Table 1. Egg per gram of faeces (epg) of strongyle parasite and meteorological

parameters in Govt. farms of pig and cattle in Meghalaya during 2001 – 02


Areas

Animals

Months




Meteorological data

Strongyle sp.

Temp. ( O C)

(Max)

Temp.

( O C)

(Min)

Rainfall

( O C)

(mm)

Jowai

Pig

Apr-Jun

240

26.72

18.9

341.4

Jul-Sept.

426.66

26.67

20.09

693.4

Oct – Dec.

330

21.59

14.2

91.5

Jan - Mar

140

17.8

10.36

28

Kyrdemkulai

Apr. – Jun

175

27.12

20.81

116

Jul-Sept.

370

29.55

23.63

417.7

Oct – Dec.

260

23.65

17.89

63.3

Jan - Mar

140

20.42

12.89

73.7

Jowai

Cattle


Apr-Jun

142.85

26.72

18.9

341.4

Jul-Sept.

287.5

26.67

20.09

693.4

Oct – Dec.

133.33

21.59

14.2

91.5

Jan - Mar

100

17.8

10.36

28

Kyrdemkulai

Apr. – Jun

142.85

27.12

20.81

116

Jul-Sept.

350

29.55

23.63

417.7

Oct – Dec.

150

23.65

17.89

63.3

Jan - Mar

160

20.42

12.89

73.7

Upper Shillong

Apr-Jun

133.33

20.61

12.63

117.4

Jul-Sept.

314.28

21.5

15.68

284.6

Oct – Dec.

233.33

18.34

8.85

54.8

Jan - Mar

133.33

13.96

3.4

16.63


Table 2. Statistical analysis of parasitic infection and meteorological parameters


Parasite

Temperature (max)

Temperature (Min)

Rainfall

Strongyle sp.

r = 0.528319

R2 = 0.279121

b = 0.023432 ± 0.0088 Significant at 10% level

r = 0.531665

R2 = 0.282668

b = 0.028716 ± 0.0107 Significant at 5% level

r = 0.713168

R2 = 0.508609

b = 1.546692 ± 0.6900 Significant at 1% level

Fig 1. Relationship between rainfall and occurrence of Strongyle infection in Meghalaya during 2001 – 02



Fig 2. Relationship between maximum temperature and occurrence of Strongyle Infection



Fig 3. Relationship between minimum Temperature and occurrence of Strongyle infection





Fig 4. Predicted Strongyle infection depending on maximum temperature





Fig 5. Predicted and observed Strongyle infection in relation to minimum temperature





Fig 6. Predicted and observed Strongyle infection in relation to rainfall




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