Sampling Plan for Serosurveillance and Seromonitoring

of FMD in India under National FMD Control

Programme ( Revised )

(Volume I : Serosurveillance)

ICAR-National

Institute of Veterinary

Epidemiology and

Disease Informatics

(NIVEDI)

Dr K P Suresh,

Principal Scientist

Dr S S Patil,

Principal Scientist

Dr Parimal Roy,

Director

ICAR-Directorate of Foot

and Mouth Disease

(DFMD)

Dr J K Mohapatra,

Sr. Scientist

Dr Saravanan S,

Sr. Scientist

Dr R K Singh,

Director (Acting)

Citation: Suresh K P. Patil S S, Parmial Roy, . Mohapatra J K. Saravanan S and Singh R K

(2019) Sampling Plan for Serosurveillance and Seromonitoring of FMD in India under National

FMD Control Programme (Volume I: Serosurveillance), ICAR-NIVEDI. Bengaluru

Year: January, 2020

Published by: Director, ICAR-NationaI Institute of Veterinary Epidemiology and Disease

Informatics (NIVEDI), PBNO-6450. Yelahanka. Bengaluru-64

Printed by

M/S Naveen Printers, #07, MIG, 1st Main. 4th Phase, 707. Yelahanka Town, Bengaluru-64

FOREWORD

Foot and Mouth Disease (FMD) is an important contagious transboundary animal

disease (TAD) in the world, caused by different serotypes and many genotypes of FMD

virus, posing constant threat to livestock husbandry and food security locally and globally.

FMD is widely prevalent in most parts of Africa, West Eurasia, Asia and the Middle East.

About 70 countries are free from FMD but always under threat of virus incursion from

endemic countries. Spread of the disease occurs through contaminated air, water, feed,

fomites, livestock movement and trade etc. The burden of FMD in developing countries is

usually underestimated. The disease threatens livelihood security and associated with

reduced productive performance of farm animals and economic losses. As suggested by

FAO, with a fast-moving infection, surveillance is essential and building up national and

regional epidemio-surveillance capacities is a priority. The direct economic impact of FMD

is attributed to drop in milk production, abortions, and death of young calves. The indirect

adverse effect includes loss of draught power, the cost of treatment, trade embargo by FMD

free countries and implementation of FMD control programme. In India, annual direct loss

is estimated at Rs. 20,000 (about 3.5bn USD) crore. Indirect loss due to trade barrier could

be much more. Controlling FMD and reducing its economic impact would improve

livelihood of farming community and will have huge impact on both FMD endemic and non-

endemic countries. As we know the FMD control strategy includes: (1) Mass awareness (2)

Mass vaccination (3) Post vaccinal Seromonitoring (4) Serological Surveillance (5) Disease

detection and reporting in real-time (5) Management of risks in virus spread and prevent

escape of virus from laboratories. Govt. of India has taken lots of initiatives to control the

disease in India. Recently the Union Cabinet has granted considerable amount of money on

priority basis to control FMD in next five years by mass vaccination and subsequent

eradication of the disease from India. Post vaccination seromonitoring is essential to assess

the efficacy of vaccine or vaccination in the fields. Sero surveillance is essential to

understand the prevalence of the disease. Considering the huge livestock resource of the

country, two-stage sampling plan is very important for systematic collection of

representative samples and thereafter testing in laboratories. The sampling plan provided in

this bulletin is the guideline for collection of samples from different parts of the country to

be tested for FMD vaccine induced antibody response under FMDCP and National FMD

Sero surveillance. I thank the Directors and Scientists of ICAR-NIVEDI and ICAR-D FMD

for taking initiatives to prepare this useful bulletin to be used in FMDCP of the country.

( T. MOHAPATRA )

Dated the 12th June, 2019

New Delhi

ACKNOWLEDGEMENTS

Authors would like to express their sincere gratitude to Secretary DARE and Director General,

ICAR, Ministry of Agriculture & Farmer`s Welfare, for his constant guidance and support in

bringing out this bulletin on sampling plan for FMD CP

It is to place on record our acknowledgement to Deputy Director General (Animal Science) for

his valuables suggestions and moral support in bringing out this bulletin on sampling plan for

FMDCP.

Our special thanks are due to Department of Animal husbandry, dairying and Fisheries,

Ministry of Agriculture & Farmer`s Welfare, Government of India, New Delhi for their

encouragement in planning to bring this bulletin.

Authors thank all the Scientists of ICAR-NIVEDI and ICAR-DFMD for their inputs in bringing

this bulletin.

We also thank our research fellows working in Spatial Epidemiology lab of ICAR-NIVEDI for

their hard work in programming and technical assistance in generating sampling plan

ICAR-National Institute of

Veterinary Epidemiology and

Disease Informatics (NIVEDI)

Dr K P Suresh, Principal Scientist

Dr S S Patil, Principal Scientist

Dr Parimal Roy, Director

ICAR-Directorate of Foot and

Mouth Disease (DFMD)

Dr J K Mohapatra, Sr. Scientist

Dr Saravanan S, Sr. Scientist

Dr R K Singh, Director (Acting)

Table of Contents

Foreword by Hon’ble Secretary (DARE) and DG(ICAR)…………………………………….i

Foreword by Hon’ble Deputy Director General (AS), ICAR…………………………………ii

Acknowledgements…………………………………………………………………………...iii

I. Introduction………………………………………………………………………….1

II. Sampling Plan for Serosurveillance of FMD

1. Andaman and Nicobar Islands…………………………………………………..7

2. Andhra Pradesh…………………………………………………………………………..10

3. Arunachal Pradesh……………………………………………………………………….16

4. Assam……………………………………………………………………………………19

5. Bihar……………………………………………………………………………………...23

6. Chhattisgarh……………………………………………………………………………...28

7. Goa……………………………………………………………………………………….32

8. Gujarat……………………………………………………………………………………34

9. Haryana…………………………………………………………………………………..39

10. Himachal Pradesh………………………………………………………………………..42

11. Jammu and Kashmir……………………………………………………………………..46

12. Jharkhand………………………………………………………………………………...50

13. Karnataka………………………………………………………………………………...55

14. Kerala…………….………………………………………………………………………59

15. Madhya Pradesh………………………………………………………………………….64

16. Maharashtra………………………………………………………………………………68

17. Manipur…………………………………………………………………………………..74

18. Meghalaya………………………………………………………………………………..76

19. Mizoram …………………………………………………………………………………79

20. Nagaland…………………………………………………………………………………82

21. Odisha……………………………………………………………………………………84

22. Puducherry……………………………………………………………………………….89

23. Punjab……………………………………………………………………………………91

24. Rajasthan…………………………………………………………………………………95

25. Sikkim……………………………………………………………………………………100

26. Tamil Nadu………………………………………………………………………………102

27. Telangana………………………………………………………………………………...105

28. Tripura……………………………………………………………………………………112

29. Uttarakhand………………………………………………………………………………115

30. Uttar Pradesh……………………………………………………………………………..121

31. West Bengal………………………………………………………………………………129

1

FMD Serosurveillance

Introduction and objectives

In general, surveillance is aimed at demonstrating the absence of disease or infection,

determining the presence or distribution of disease or infection or detecting as early as possible

exotic or emerging diseases. The type of surveillance applied depends on the outputs needed

to support decision-making (OIE, 2018).

Animal health surveillance is also a tool to monitor disease trends, to facilitate the control of

disease or infection, to provide data for use in risk analysis, for animal or public health

purposes, and to substantiate the rationale for sanitary measures. Both domestic animals and

wildlife are susceptible to certain diseases or infections. However, the presence of a disease or

infection in wildlife does not mean it is necessarily present in domestic animals in the same

country or zone or vice versa. Wildlife may be included in a surveillance system because they

can serve as reservoirs of infection and as indicators of disease risk to humans and domestic

animals (OIE, 2018).

The objective of serosurveillance of FMD in India

1. To provide the Sampling Scheme for estimation of total number of animals to be

sampled, total number of villages (epi units) to be selected, number of animals within

the village to be sampled and location of villages for surveillance of FMD in India.

National FMD Serosurveillance

In India, vaccination with inactivated vaccine is the primary mode of FMD control.

There is challenge to identify the infected animals among the vaccinated animals for

appropriate implementation of the control programme. Differentiation of these two categories

of animals is important during serological surveys to detect evidence of infection, as a follow

up to ring vaccination in FMD free countries and for import/ export serology. During active

viral replication following FMD virus infection, arrays of non structural proteins (NSPs) are

produced that elicit anti-NSP antibodies, which is not the case in animals which are vaccinated

against FMD with inactivated virus vaccine. A recombinant 3AB3 non structural protein based

2

ELISA test for differentiation of FMD virus infected from vaccinated animals (DIVA) has been

developed. This indigenously developed rDIVA-FMD kit, amenable to mass scale

comprehensive serosurveillance has been designed as per the OIE approved guidelines and is

at least four-fold cheaper than the commercial DIVA kits available on import. The diagnostic

sensitivity and specificity of this kit is 95% and 98%, respectively. Now this kit is in extensive

use in the country as a companion diagnostic in the National FMD control programme. Use of

DIVA assay is essential in identification of potential disease free zones (DFZs) with

vaccination in India.

Sampling Strategy currently used for Serosurveillance

For 3AB3-NSP based serosurveillance activity, 200 bovine serum samples (animals 6

months- 18 months of age) per annum were collected at random from various districts of India.

The sample size (n=200)/district has been arbitrarily calculated. However, the experts of the

OIE FMD Mission to India suggested that a two-stage sampling strategy with a minimum

design prevalence of 1% between the first-stage level (village) and 5% between the village

should be followed for NSP serosurveillance. The two-stage sampling strategy for NSP sero-

surveillance would be followed for FMD-Disease free Zones (DFZ) as soon as possible. The

current sampling design was developed jointly by ICAR-NIVEDI and ICAR-DFMD. For NSP

serosurveillance, the study design usually focusses on younger animals (6 months to 18

months old animals) since repeated vaccination even with good quality vaccine can generate

positive signal in NSP ELISA that may provide false positive. Risk based sampling strategies

may be more effective than simple random sampling.

Table 1: NSP antibody prevalence

Sl.

No.

State No of samples

tested

Positive %3AB3

reactors

1 Telangana 1695 17 1.00

2 Tamil Nadu 6400 1919 29.98

3 Andhra Pradesh 2600 43 1.654

4 Karnataka 5979 1375 23.00

5 Kerala 1500 79 4.94

6 Madhya Pradesh 9280 1504 16.2

7 Chhattisgarh Unknown

8 Maharashtra 2976 110 3.7

9 Gujarat 5200 2164 42.0

10 Rajasthan 3510 1003 28.6

11 West Bengal 1994 1015 51.79

3

12 Odisha 1014 463 44.86

13 Bihar 35

14 Jharkhand 555 312 56.2

15 Haryana 1700 138 8.12

16 Uttar Pradesh 4315 405 9.4

17 Himachal Pradesh 346 69 19.9

18 Jammu and Kashmir 1800 442 24.55

19 Punjab 3788 386 10.19

20 Uttarakhand 1078 75 7.0

21 Assam 730 149 20.41

22 Meghalaya 86 26 30.23

23 Nagaland Unknown

24 Arunachal Unknown

25 Tripura 900 28 3.1

26 Manipur 1800 227 12.6

27 Mizoram 760 108 14.2

28 Sikkim Unknown

29 Goa Unknown

30 Andaman & Nicobar 82 6 11.12

31 Delhi Unknown

32 Pondicherry Unknown

Note: For states/UT with unknown prevalence, the rate of NSP prevalence of adjacent states

were used as baseline for estimation of sample size under OIE recommended Two-stage

stratified random sampling. States where NSP -antibody prevalence is less than 5%, OIE

recommended prevalence of 5% was used for preparation of sampling plan.

Recommended Two -stage sampling plan for serosurveillance

All form of epidemiological investigations requires the scientific sampling plan for collection

of data on health problems includes serum samples in a specific population. Estimation of

frequency of disease is a prerequisite to establish the disease control program, hence sampling

the populations in order to estimate the disease frequency is common task for epidemiologists.

In sampling, we ensure that animals are typical of the target population that the estimate of

disease frequency is unbiased and precise (low standard error). Simple random sampling,

systematic or stratified random samplings are the most commonly used sampling methods in

which the animal is sampling unit, provides the precise estimates of disease frequency.

In a large population where animals are separated into herds (villages can be treated as herd in

some country where herd information is not available), disease has a strong tendency to cluster.

This is because the disease agent or agents (whether infectious, environmental or genetic) are

generally not evenly distributed throughout the population. With rare diseases, this clustering

is usually even more pronounced. As a result, a very low proportion of herds may be affected

4

by a particular disease-but within those affected herds, the prevalence of the disease amongst

animals may be quite high. If a survey designed to detect the presence of disease fails to take

into account the clustering of disease in the population, the results of the survey are likely to

be very unreliable. This is because the probability formulae that the surveys are based on

assume that every unit in the population has the same probability of being affected. Another

problem with large-area surveys is the logistics of sampling. Probability formulae assume

simple random sampling. Simple random sampling of individual animals from a national herd

requires the creation of a sampling frame which may need to list millions of animals (each

uniquely identified). Such a sampling frame is usually impossible to construct.

The solution to both these problems is to use a two-stage sampling strategy in which herds

form the first stage, and individual animals within selected herds, the second stage. In this way,

the sample sizes at each stage can be adjusted to reflect the different disease prevalence’s (the

proportion of herds affected in the first stage, and the proportion of animals affected in the herd

at the second stage). Two-stage sampling also means that the construction of sampling frames

is much simpler. At the first stage, only a list of all herds or villages (defined epi unit) in the

population is required, and at the second stage, only animals in each of the selected herds is to

be included in the list. However, the use of two-stage sampling presents particular problems

for sample-size calculation and analysis.

The use of two-stage sampling has evolved to meet surveillance objectives for two reasons.

First, list of frames of animals for randomized sample selection do not typically exist at a

regional or national level, but the list frames of herds can be developed and maintained more

readily.

In this scheme we employed stratified random sampling with district as stratification variable

for buffalo and cattle population in different states. The input requirement of sampling were

animal level prevalence, cluster or herd level prevalence obtained from previous study or

reports as presented in Table 1, Sensitivity and Specificity of tests were also accounted for

while generating sampling plan. Two -stage stratified random sampling plan was generated at

95% confidence using in-house developed epi-calculator

https://nivedi.res.in/Nadres_v2/Epical/stratified/random_sampling.php. Sampling plan will

provide the guidelines for selecting villages and its location like block, district and state level

information along with livestock population, number of animals to be sampled within village,

and proportion of buffaloes and cattle to be sampled in each selected village. The abstract of

sampling plan generated using two-stage stratified random sampling scheme is presented in

Table 2.

5

Table 2: Summary of sampling plan using Two-stage stratified random sampling technique

for serosurveillance of FMD in India

STATE NO OF

DISTRICTS

NO OF

BLOCKS

Total no of

Cattle +

Buffalo **

No of

Animals

to be

Sampled

Average

No of

Samples

Per

Districts

ANDAMAN & NICOBAR 3 10 40138 1615 538

ANDHRA PRADESH 13 226 10819586 10332 795

ARUNACHAL PRADESH 17 76 345600 1651 97

ASSAM 33 99 11330954 2145 65

BIHAR 38 203 23117774 2410 63

CHHATTISGARH 27 103 11158676 2640 98

GOA 2 14 87454 792 396

GUJARAT 33 150 20176887 1928 58

HARYANA 22 60 6296705 4064 185

HIMACHAL PRADESH 12 74 2474582 2310 193

JAMMU & KASHMIR 22 75 3230069 1815 83

JHARKHAND 24 140 12573365 2410 100

KARNATAKA 30 107 11453564 1920 64

KERALA 14 70 1443500 10322 737

MADHYA PRADESH 51 155 29057959 2640 52

MAHARASHTRA 35 171 19595996 10322 295

MANIPUR 9 29 260702 1785 198

MEGHALAYA 11 36 919284 990 9

MIZORAM 8 28 47810 1615 202

NAGALAND 10 52 93950 1785 179

ODISHA 30 188 10362294 1928 64

PUDHUCHERRY 3 9 74379 469 156

PUNJAB 22 67 6547407 4290 195

RAJASTHAN 33 152 27630946 2508 76

SIKKIM 4 15 149154 1066 267

TRIPURA 8 44 10037455 3420 428

TELANGANA 31 221 8458845 10332 333

TAMIL NADU 32 96 746162 990 30

UTTAR PRADESH 75 361 52036426 9296 124

UTTARAKHAND 13 95 2718441 7462 574

WEST BENGAL 22 122 19708837 1120 51

TOTAL 687 3248 302994901 108372 158

** 20th Livestock Census ( DAHD, GoI )

Animals of 6 months to 18 months of age to be sampled, samples to be collected preferably 4

to 5 months after previous vaccination

6

Guidelines for Collection and transport of serum samples

1. Serum should be collected using a sterile needle and syringe

2. At least 2 ml of serum should be submitted to the testing laboratory and for that 4-8

ml whole blood needs to be collected without anticoagulant.

3. The blood is usually collected from the jugular vein

4. After collection, the sample should be allowed to stand in a cool area, out of direct

sunlight for at least 15 minutes to allow clot formation

5. Serum should be separated before submission to the laboratory.

6. The sample should be centrifuged or, if this is not available, stand the sample in an

upright position overnight in a cool box or refrigerator

7. Place serum into a plain, sterile tube and send to the laboratory under cool condition

8. If dispatch to a laboratory is delayed, serum samples should be frozen and stored at -

20 ◦C

9. Samples should be accompanied by proper epidemiological data sheet

Download The Result

Stratified Random Sampling - Rajasthan (Code -28)

Species Selected for Stratification = Cattle + Buffalo

Number of Villages Having 500 + (Cattle + Buffalo) = 18444

Design Level Prevalence = 0.286

Cluster Level Prevalence = 0.015

Sensitivity of the test used = 0.95

Total No of Villages (Clusters) Selected = 209

Total No of Animals to be Sampled = 2508

DISTRICT_NAME BLOCK_CODE BLOCK_NAME VILLAGE_NAME Buffaloes CattleCattle+Buffalo

all

Numberof unitstosample

BuffaloProportion

CattleProportion

Ajmer 4 Ajmer Ajmer (M Cl) -Ward No.25 709 575 1284 2240 11 6 5

Ajmer 4 Ajmer Muhami (Mohami) 699 654 1353 3280 11 6 5Ajmer 242 Nasirabad Bhatiyani 1533 824 2357 4634 12 8 4Ajmer 226 Masuda Masooda 1017 1556 2573 7493 12 5 7Alwar 167 Kathumar Rampura 636 51 687 693 11 10 1Alwar 200 Lachhmangarh Soorajgarh 571 121 692 777 11 9 2Alwar 33 Bansur Deosan 803 86 889 1743 11 10 1Alwar 33 Bansur Fatehpur 679 237 916 1186 11 8 3Alwar 358 Tijara Burera 760 344 1104 1104 11 8 3Alwar 50 Behror Hameedpur 904 452 1356 1356 11 7 4Alwar 285 Rajgarh Dubbi 1227 140 1367 1487 11 10 1Alwar 6 Alwar Toolera 1799 249 2048 2495 11 10 1Alwar 167 Kathumar Tasai 2070 866 2936 4246 12 8 4Banswara 199 Kushalgarh Surwan 100 636 736 1204 11 1 10Banswara 22 Bagidora Naya Padariya 243 523 766 1281 11 3 8

Banswara 82 CHHOTISARWAN Nadiya 162 741 903 1953 11 2 9

Banswara 22 Bagidora Bhoyan 257 850 1107 1742 11 3 8Banswara 313 Sajjangarh Jalim pura 403 949 1352 3706 11 3 8Banswara 127 Garhi Parheda 488 1038 1526 1634 11 4 7Banswara 129 Ghatol Delwara Lokiya 1525 1244 2769 3053 12 7 5Baran 182 Kishanganj Kheruna 299 367 666 892 11 5 6Baran 80 Chhipabarod Baday 386 410 796 942 11 5 6Baran 80 Chhipabarod Gagchana 600 403 1003 1062 11 7 4Baran 332 Shahbad Niwari 488 653 1141 1240 11 5 6Baran 182 Kishanganj Halawani 538 695 1233 1239 11 5 6Barmer 134 Gudha Malani Nehron Ka Tala 129 426 555 2282 11 3 8Barmer 329 SEDWA Fakiron Ka Niwan 149 459 608 1661 11 3 8Barmer 41 Barmer Ramsar Ka Kua 50 625 675 4121 11 1 10Barmer 134 Gudha Malani Bhilon Ki Dhani 274 408 682 1764 11 4 7Barmer 99 Dhanau Pawariya Tala 128 622 750 2368 11 2 9Barmer 99 Dhanau Gohar Ka Tala 49 715 764 6805 11 1 10Barmer 329 SEDWA Sonri 133 647 780 1126 11 2 9Barmer 334 Sheo Kashmir 0 953 953 4292 11 0 11Barmer 329 SEDWA Meethri 339 720 1059 1821 11 4 7Barmer 254 Pachpadra Bhagvanpura 5 1117 1122 1736 11 0 11

Back to Calculation

95

Barmer 342 Siwana Relon Ki Dhani 903 269 1172 2586 11 8 3Barmer 340 Sindhari Toonkiya 854 915 1769 3899 11 5 6Barmer 254 Pachpadra Gangawas 222 1590 1812 3794 11 1 10Bharatpur 236 Nadbai Gangroli 449 275 724 769 11 7 4Bharatpur 374 Weir Umrend 639 181 820 1696 11 9 2Bharatpur 374 Weir Jahanpur 709 189 898 1091 11 9 2Bharatpur 159 Kaman Luhesar 842 231 1073 1614 11 9 2Bharatpur 159 Kaman Kalawata 524 667 1191 1359 11 5 6Bharatpur 159 Kaman Indroli 931 312 1243 1283 11 8 3Bharatpur 236 Nadbai Unch 1038 209 1247 1441 11 9 2Bharatpur 54 Bharatpur Malah 1423 627 2050 2223 11 8 3Bhilwara 140 Hurda Laxmanpura 398 295 693 889 11 6 5Bhilwara 140 Hurda Kushalpura 372 389 761 1293 11 5 6Bhilwara 193 Kotri Paroli Genta 334 540 874 1648 11 4 7Bhilwara 193 Kotri Shree Pura 594 332 926 1991 11 7 4Bhilwara 140 Hurda Barantiya 414 708 1122 3202 11 4 7Bhilwara 281 Raipur Mokhunda 682 664 1346 2194 11 6 5Bhilwara 140 Hurda Kaniya 721 1440 2161 3377 11 4 7Bhilwara 140 Hurda Dantra 1160 1216 2376 5443 12 6 6Bhilwara 32 Banera Dabla 1014 1369 2383 4322 12 5 7Bikaner 172 Khajuwala 22 Kyd 242 1150 1392 1860 11 2 9Bikaner 252 Nokha Uttmamdesar 546 1054 1600 2676 11 4 7Bikaner 345 Sridungargarh Delwa 367 1760 2127 3141 11 2 9Bikaner 345 Sridungargarh Lakhasar 587 2182 2769 7259 12 3 9

Bikaner 210 Lunkaransar Rajasar UrfKarnisar 188 2922 3110 5210 12 1 11

Bundi 141 Indragarh Chanda Kalan 279 355 634 1541 11 5 6Bundi 139 Hindoli Rampuriya 622 251 873 1594 11 8 3Bundi 139 Hindoli Kabul 626 285 911 1867 11 8 3Bundi 240 Nainwa Khori 791 669 1460 2801 11 6 5Bundi 353 TALERA Seeta Pura 998 591 1589 1597 11 7 4Chittorgarh 40 Bari Sadri Pandera 537 181 718 1118 11 8 3Chittorgarh 86 Chittaurgarh Segawa 423 341 764 1096 11 6 5Chittorgarh 124 Gangrar Singola 697 137 834 1290 11 9 2Chittorgarh 162 Kapasan Karjali 760 281 1041 1481 11 8 3Chittorgarh 49 Begun Parsoli 270 797 1067 2155 11 3 8

Chittorgarh 297 Rawatbhata DhangadmauKhurd 366 945 1311 2113 11 3 8

Chittorgarh 86 Chittaurgarh Ghosunda 757 647 1404 2329 11 6 5Chittorgarh 86 Chittaurgarh Theekariya 1202 299 1501 1702 11 9 2Churu 285 Rajgarh Chainpura Bara 654 249 903 2109 11 8 3Churu 285 Rajgarh Bhagela 667 314 981 1926 11 7 4Churu 323 Sardarshahar Dheerasar Hadan 111 889 1000 1621 11 1 10Churu 323 Sardarshahar Bhojasar Bara 108 1642 1750 2921 11 1 10Churu 354 Taranagar Rajpura 1275 1396 2671 7060 12 6 6

Dausa 241 NangalRajawthan Basna 610 200 810 1585 11 8 3

Dausa 241 NangalRajawthan Hapawas 745 215 960 1245 11 9 2

Dausa 289 Ramgarhpacwara Gangalyawas 756 372 1128 1491 11 7 4

Dausa 338 Sikrai Udaipura 1344 385 1729 2445 11 9 2Dausa 211 Mahwa Peepalkhera 2854 240 3094 3524 12 11 1Dholpur 39 Bari Rajai Khurd 558 103 661 868 11 9 2Dholpur 39 Bari Rewai 748 123 871 1197 11 9 2Dholpur 39 Bari Afzalpur 877 257 1134 1259 11 9 2Dholpur 42 Baseri Siholi 940 228 1168 1265 11 9 2Dungarpur 339 Simalwara Ambau 459 399 858 1556 11 6 5Dungarpur 17 Aspur Raiki 941 138 1079 1870 11 10 1Dungarpur 17 Aspur Katisor 1084 129 1213 1672 11 10 1Dungarpur 308 Sabla Myala 430 1006 1436 2686 11 3 8Dungarpur 311 Sagwara Tamtiya 1313 427 1740 2439 11 8 3

96

Ganganagar 351 Suratgarh 1O Spd 147 459 606 625 11 3 8Ganganagar 351 Suratgarh 2 Gdm 143 542 685 1507 11 2 9Ganganagar 128 Gharsana 3 Dd-A 248 467 715 866 11 4 7

Ganganagar 310 Sadulshahar 24 Ptp(Hathyawala) 179 585 764 1483 11 3 8

Ganganagar 351 Suratgarh 1 Srpm 269 548 817 859 11 4 7Ganganagar 128 Gharsana 29 As-B 169 657 826 2141 11 2 9

Ganganagar 310 Sadulshahar Sadulshahar (M) -Ward No.19 6 851 857 1072 11 0 11

Ganganagar 310 Sadulshahar 2 Llg(Chak Kera) 276 800 1076 1197 11 3 8Ganganagar 351 Suratgarh Bhojoosar 73 1100 1173 1882 11 1 10Ganganagar 351 Suratgarh 17 Stb 323 872 1195 1673 11 3 8Ganganagar 128 Gharsana 2 Km 344 888 1232 1608 11 3 8Ganganagar 119 Ganganagar Shyamsinghwala 507 749 1256 1460 11 4 7

Ganganagar 119 GanganagarChak GaneshgarhWithDungarsinghpura

376 982 1358 1920 11 3 8

Ganganagar 351 Suratgarh Bachhrara 121 1561 1682 2868 11 1 10Hanumangarh 269 Pilibanga 4 Nr 241 533 774 938 11 3 8Hanumangarh 251 Nohar 26 Dpn 399 407 806 1133 11 5 6Hanumangarh 269 Pilibanga 6 Sgr 339 904 1243 1703 11 3 8Hanumangarh 137 Hanumangarh 50 Ngc (Rural) 604 728 1332 1544 11 5 6Hanumangarh 137 Hanumangarh 4 Rrw 659 1078 1737 2526 11 4 7Hanumangarh 137 Hanumangarh 37 Ssw 838 1320 2158 2900 11 4 7Hanumangarh 298 Rawatsar 30 Rwd 619 1754 2373 3456 12 3 9Hanumangarh 52 Bhadra Ajeetpura 1257 1283 2540 4893 12 6 6Hanumangarh 251 Nohar 19 Ntr 1512 2055 3567 4686 12 5 7

Jaipur 267 Phulera(Hq.Sambhar) Bhartolao 333 310 643 2974 11 6 5

Jaipur 88 Chomu Harad Rampura 540 147 687 748 11 9 2Jaipur 148 Jamwa Ramgarh Manota 426 309 735 1463 11 6 5Jaipur 191 Kotputli HEER KI BAWRI 1010 85 1095 1095 11 10 1

Jaipur 186 KISHANGARHRENWAL Looniyawas 664 540 1204 1588 11 6 5

Jaipur 333 Shahpura Parman Pura 1310 45 1355 1489 11 11 0Jaipur 318 Sanganer Sanjariya 874 1305 2179 2690 11 4 7

Jaipur 186 KISHANGARHRENWAL Badhal 1339 986 2325 4384 12 7 5

Jaipur 7 Amber Shripura 1120 1255 2375 2460 12 6 6Jaipur 7 Amber Achrol 3490 1039 4529 9475 12 9 3Jaisalmer 275 Pokaran Sohanpura 19 838 857 989 11 0 11Jaisalmer 113 Fatehgarh Baiya 11 983 994 4092 11 0 11Jaisalmer 275 Pokaran Sanawara 18 2436 2454 12996 12 0 12Jalore 293 Raniwara Peethapura 765 125 890 929 11 9 2Jalore 61 Bhinmal Narta 1307 198 1505 3300 11 10 1Jalore 328 Sayla Punawas 1691 178 1869 3515 11 10 1Jalore 85 Chitalwana Bhootel 1145 819 1964 2621 11 6 5Jhalawar 176 Khanpur Kherli Meeran 230 361 591 1040 11 4 7Jhalawar 176 Khanpur Bhagwan Pura 344 291 635 812 11 6 5Jhalawar 348 Sunel Chachhalao 430 378 808 1487 11 6 5Jhalawar 16 ASNAWAR Mandawar 348 534 882 1131 11 4 7Jhalawar 274 Pirawa Sangariya 718 330 1048 1643 11 8 3Jhalawar 176 Khanpur Khair Khera 696 435 1131 1887 11 7 4Jhalawar 274 Pirawa Banor 1343 383 1726 2136 11 9 2Jhunjhunu 365 Udaipurwati Pachlangi 454 333 787 1807 11 6 5Jhunjhunu 72 Buhana Sultana Ahiran 909 133 1042 2275 11 10 1Jhunjhunu 246 Nawalgarh Dhaka Ka Bas 383 668 1051 1918 11 4 7Jhunjhunu 181 Khetri Doodhwa Nangliya 1370 169 1539 3585 11 10 1Jhunjhunu 181 Khetri Gothra 1671 816 2487 3106 12 8 4Jodhpur 207 LOHAWAT Shri Ram Nagar 76 646 722 1292 11 1 10Jodhpur 35 Baori Surpura Kalan 351 490 841 1114 11 5 6Jodhpur 253 Osian Hadman Nagar 322 729 1051 2031 11 3 8Jodhpur 27 Balesar Beega Dev Nagar 152 952 1104 2344 11 2 9

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Jodhpur 27 Balesar Ketoo Kalan 98 1171 1269 4101 11 1 10Jodhpur 209 Luni Hamir Nagar 685 901 1586 1586 11 5 6Jodhpur 273 Pipar shahar Ramrawas Kalan 897 1456 2353 4739 12 5 7Jodhpur 37 Bapini Bapini 133 2549 2682 10199 12 1 11

Jodhpur 156 Jodhpur Jodhpur (M Corp.)- Ward No.1 177 2921 3098 3523 12 1 11

Karauli 165 Karauli Teekaitpura 652 35 687 915 11 10 1Karauli 360 Todabhim Ranmalpara 732 209 941 1375 11 9 2Karauli 138 Hindaun Todoopura 1056 148 1204 1403 11 10 1Karauli 360 Todabhim Nangal Sherpur 1460 131 1591 1904 11 10 1Karauli 237 Nadoti Kaima 1550 284 1834 2258 11 9 2Kota 161 KANWAS Gurayta 381 225 606 954 11 7 4Kota 202 Ladpura Kishanpura Takiya 331 385 716 843 11 5 6Kota 287 Ramganj Mandi Alod 458 445 903 1190 11 6 5Kota 271 Pipalda Khatauli 445 719 1164 2287 11 4 7Kota 161 KANWAS Kanwas 646 1409 2055 2735 11 3 8Nagaur 245 Nawa Motipura 435 242 677 2051 11 7 4Nagaur 94 Degana Bharli 520 169 689 1707 11 8 3Nagaur 304 RIYAN BADI Mori Kalan 324 698 1022 2423 11 3 8Nagaur 245 Nawa Rewasa Dalelpura 585 558 1143 5312 11 6 5Nagaur 150 Jayal Silariya 859 336 1195 2327 11 8 3Nagaur 245 Nawa Hudeel 497 837 1334 3946 11 4 7Nagaur 259 Parbatsar Bhadwa 887 570 1457 2793 11 7 4

Nagaur 239 Nagaur Nagaur (M) - WardNo.32 240 1718 1958 2458 11 1 10

Nagaur 245 Nawa Meethri 1081 1923 3004 6181 12 4 8Pali 145 Jaitaran Jajanwas 302 498 800 2029 11 4 7Pali 98 Desuri Mada 496 466 962 1528 11 6 5Pali 343 Sojat Karmawas 523 687 1210 2765 11 5 6Pali 292 Rani Khiwara 868 629 1497 3409 11 6 5Pali 305 Rohat Lalki 1174 1214 2388 3452 12 6 6Pratapgarh 14 Arnod Veerawali 331 430 761 960 11 5 6Pratapgarh 81 Chhoti Sadri Mahuriya 363 568 931 1179 11 4 7Pratapgarh 277 Pratapgarh Phoolda 236 751 987 1346 11 3 8Pratapgarh 100 Dhariawad Dhariawad (Ct) 598 1161 1759 3017 11 4 7Pratapgarh 260 Peepalkhoont Morwaniya 487 2108 2595 5172 12 2 10Rajsamand 280 Railmagra Chhapri 351 332 683 1064 11 6 5Rajsamand 197 Kumbhalgarh Kaleenjar 517 244 761 1600 11 7 4Rajsamand 244 Nathdwara Gudla 265 500 765 1552 11 4 7Rajsamand 8 Amet Kishan Puriya 424 619 1043 1667 11 4 7Rajsamand 280 Railmagra Khatookra 988 751 1739 3215 11 6 5Sawai Madhopur 326 Sawai Madhopur Koshali 467 125 592 867 11 9 2

Sawai Madhopur 214 MalarnaDoongar Donaycha 765 36 801 1637 11 11 0

Sawai Madhopur 174 Khandar Rodawad 931 237 1168 1839 11 9 2Sawai Madhopur 30 Bamanwas Goth 1129 141 1270 2793 11 10 1Sikar 90 Danta Ramgarh Hanuman Nagar 466 219 685 1239 11 7 4Sikar 247 Neem-Ka-Thana Nyorana 490 285 775 1116 11 7 4Sikar 247 Neem-Ka-Thana Mangalpura 995 92 1087 2266 11 10 1Sikar 337 Sikar Jagmalpura 501 681 1182 1719 11 5 6Sikar 178 KHENDELA Dayra (Rural) 977 438 1415 4726 11 8 3Sikar 200 Lachhmangarh Palri 643 1129 1772 2925 11 4 7Sikar 102 DHOD Tatanwa 861 971 1832 3056 11 5 6Sirohi 270 Pindwara Semli 81 766 847 2884 11 1 10Sirohi 302 Reodar Malpura 994 228 1222 1384 11 9 2Sirohi 2 Abu Road Nichlagarh 269 1837 2106 5713 11 1 10Tonk 215 Malpura Lamya Joonardar 392 235 627 1215 11 7 4Tonk 215 Malpura Dokriya 705 125 830 1035 11 9 2Tonk 367 Uniara Shop 568 420 988 1766 11 6 5Tonk 363 Tonk Chhan 1206 329 1535 1902 11 9 2

Tonk 363 Tonk Tonk (M CI) -Ward No.3 3124 961 4085 6267 12 9 3

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Udaipur 314 Salumbar Jailawata Phala 229 285 514 2053 11 5 6Udaipur 151 Jhadol Changla 260 408 668 880 11 4 7Udaipur 132 Gogunda Royda 421 288 709 1357 11 7 4Udaipur 59 Bhindar Baroriya 504 296 800 944 11 7 4Udaipur 192 Kotra Bakhel 223 609 832 2171 11 3 8Udaipur 151 Jhadol Amaliya 215 683 898 1411 11 3 8Udaipur 228 Mavli Kham Ki Madri 293 610 903 1447 11 4 7Udaipur 228 Mavli Dholi Magri 779 408 1187 1348 11 7 4Udaipur 228 Mavli Mavli (Ct) 646 546 1192 1424 11 6 5Udaipur 228 Mavli Ghanoli 696 717 1413 2032 11 5 6Udaipur 151 Jhadol Makradeo 748 1148 1896 3042 11 4 7Udaipur 205 Lasadiya Agar 508 1406 1914 7249 11 3 8Udaipur 369 Vallabhnagar Menar 2039 1438 3477 3860 12 7 5

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