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
Peripheral level
Health Centre, Additional Block Animal Health Centre,
State Animal Health Centre, Policlinics, Regional
(Primary Reporter)
Laboratory, Veterinary Pathological Labs. Govt. Farms
District Level
(Intermediary Compiler)
State Level
Office of the Deputy Director, ARD &
Parisad Officer
EPIDEMIOLOGICAL UNIT of the
state, Kolkata
(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:OIE
FMD
NPRE
RDDL
DI
DATA
BANK
REF. LABS.IN IAH&VB,
KOLKATA
Diagnosis of Diseases
Prompt Report & Sample
Prompt Report & Sample
EPIDFMO
LOGICAL
UNIT
DIST.
LABS
Promt Report &
Sample
ABAHC
REPORT
ADS of
GOVT.OF
INDIA
DAH&VS
DD ARD&
PO
Consolidated
Block Level Report
on
Diseases & Control
measure
BLDO
BLDO
BAHC
ADMAS
of ICAR
OTHER
CONCERN
AGENCIES
Consolidated
Report of
District
Diagnosis of Diseases
REGIONAL
DISEASE
DIAGNOSIS
LABS
ANALYSIS
DATA
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
1- 5
6-10 OBs
11-15 OBs
OBs
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
Mon
th
Dise
ase
Location
Bloc
k
G.P.
No.
of
O/B
Species
affecte
d
(name
of the
species
)
No. of Animal /Bird
Village
Affected
Exot
ic
Cross
breed
No. of
Animal
/ Bird
at risk
Whet
her
Lab.
confi
rmati
on or
other
wise
Death
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_________
No. of
vaccina
tion
done
against
each
O/B
An approach for development of prediction model of gastrointestinal 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.
Jowai
Rainfall
( O C)
( O C)
( O C)
(Max)
(Min)
(mm)
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
Kyrdemk
Apr. – Jun
175
27.12
20.81
116
ulai
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
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
Kyrdemk
Apr. – Jun
142.85
27.12
20.81
116
ulai
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
Apr-Jun
133.33
20.61
12.63
117.4
Shillong
Jul-Sept.
314.28
21.5
15.68
284.6
Oct – Dec.
233.33
18.34
8.85
54.8
Jowai
Pig
Temp.
Cattle
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
r = 0.528319
r = 0.531665
r = 0.713168
R2 = 0.279121
R2 = 0.282668
R2 = 0.508609
b = 0.023432 ± 0.0088
b
Significant at 10% level
Significant at 5% level
sp.
=
0.028716
±
0.0107
b = 1.546692 ± 0.6900
Significant at 1% level
Fig 1. Relationship between rainfall and occurrence of Strongyle
infection in Meghalaya during 2001 – 02
y = 1.5467x - 131.2
R2 = 0.5086
Rainfall
800
600
400
200
0
0
100
200
300
400
500
Occurance of infection
Fig 2. Relationship between maximum temperature and occurrence of
Strongyle Infection
Strongyle infection
500
y = 11.912x - 56.711
R2 = 0.2791
400
300
200
100
0
0
10
20
30
Temperature (Max)
40
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