consultancy mission assistance in the geospatial analysis ... · ms bana bodri at bps - statistics...

Report 9 February 2006

Dirk Pfeiffer Professor of Veterinary Epidemiology Department of Veterinary Clinical Sciences Royal Veterinary College Hawkshead Lane North Mymms, Hatfield, Herts AL9 7TA Tel: +44 (1707) 666 205 Fax: +44 (1707) 666 346 E-mail: [email protected]

Consultancy Mission

Assistance in the Geospatial Analysis of HPAI Outbreaks in Indonesia This report summarizes the conclusions from a consultancy mission conducted between November 23 and December 9, 2005 involving a field mission to Jakarta, Indonesia, from November 28 to December 2, 2005.

Tasks addressed by the consultancy

Introduce the use and techniques of risk analysis and mapping to the national veterinary services Following a meeting involving the Director of Animal Health, Dr Sjamsul Bahri, discussions were held with staff from Directorate General of Livestock Services (DGLS) in the Ministry of Agriculture, Jakarta, Indonesia, in relation to available data sources. The use of databases in combination with GIS for visualisation of disease occurrence data as well as environmental and population data was demonstrated to DGLS staff working in disease surveillance.

A further meeting was held with Dr Yulvian Sani from Central Research and Development Institute for Veterinary Science (BALITVET), Bogor, Indonesia. Surveillance of AI and possible data sources were discussed.

Ms Bana Bodri at BPS - Statistics Indonesia, Jakarta, Indonesia, was visited to explore access to further data sources.

Collate all the geo-referenced HPAI outbreak data (including human health data) and other relevant livestock production and agro-ecological information, as made available by the veterinary services and other services, for the incorporation into an appropriate geographical information system AI data

An Excel spreadsheet containing AI outbreak data since August 2003 was provided by DGLS. The spreadsheet file contained district (regency) level data on number of poultry affected for aggregated periods of Aug-Dec 2003, Jan-Dec 2004, and then monthly until Oct 2005. The data for the remaining 3 months in 2005 was then obtained from DGLS outbreak

PFEIFFER - GEOSPATIAL ANALYSIS OF HPAI OUTBREAKS IN INDONESIA

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reports published on the OIE website (Follow-up report No.11: http://www.oie.int/eng/info/hebdo/AIS_36.HTM#Sec0 ). The spreadsheet data was converted from a non-normal (see Appendix I) into a normalised data table format for importation into a Microsoft Access database table (see Table 1). The advantages of this format are that it minimises data entry requirements and allows maximum flexibility with respect to analyses that are to be undertaken with this data. The normalised data structure should also be used even if Microsoft Excel continues to be used for data entry and tabular analysis. If Microsoft Access was used for data entry and management, it would be possible to link other non-changing information to the disease data such as the poultry population data. If a normalised data format is used it is important that the event data (in this case number of affected poultry) is accompanied by unique identifiers, such as province and district names (use both in case there are district names which are repeated in other provinces), year and month of event. It would be desirable to replace use of province and district names by a standard code for identification of the district. This will simplify importation of data into a GIS as well as linking of different data tables. There may be existing national codes, or alternatively the codes used by FAO with the district boundary files could be used (see Appendix II; note that not the complete numeric sequence would be required). During data preparation it was noticed that for some districts outbreaks had been reported, but the number of affected chickens was not known. In addition, linking the DGLS data to the ArcView shape attribute file using the Query function in MS Access had to be based on province and district (regency) names. Since names differed between the two files, the province / district names used in the DGLS file were adjusted manually to match those in the ArcView shape attribute file. Information available for Indonesia in relation to changes of administrative units on the website http://www.statoids.com was used for this purpose. It was also noted that the administrative boundary files provided by FAO did not represent of all current provinces and districts (regencies). To be able to map the data, new provinces and districts were changed to those which were represented in FAO’s shape files.

Table 1: Structure of a normalised data table (note that only data for outbreaks needs to be entered)

Province District Year Month No. affected poultry Jawa Barat Sukabumi 2005 1 2,430

Jawa Barat Indramayu 2005 1 72

Jawa Barat Kota Cirebon 2005 1 12,000

Jawa Barat Subang 2005 1 6,800

Jawa Tengah Karanganyar 2005 1 1,250

Jawa Tengah Tegal 2005 1 70,000

D.I Yogyakarta Sleman 2005 1 900

D.I Yogyakarta Bantul 2005 1 500

D.I Yogyakarta Kota Yogyakarta 2005 1 703

Jawa Barat Purwakarta 2005 2 1,200

Jawa Barat Indramayu 2005 2 5,000

Jawa Tengah Tegal 2005 2 3,500

D.I Yogyakarta Gunung Kidul 2005 2 415

D.I Yogyakarta Kulonprogo 2005 2 0

D.I Yogyakarta Sleman 2005 2 54

D.I Yogyakarta Bantul 2005 2 0

D.I Yogyakarta Kota Yogyakarta 2005 2 415

Lampung Metro 2005 2 137

Sulawesi Selatan Maros 2005 2 4,100

Sulawesi Selatan Pinrang 2005 2 8,367

Jambi Kota Jambi 2005 2 160

Jawa Barat Bandung 2005 3 3

Jawa Barat Indramayu 2005 3 4,790


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In addition to the surveillance data, DGLS provided summaries of AI data collected by Disease Investigation Centres responsible for several provinces. This also included results from serological investigations of vaccinated poultry populations. The data was not further explored for the purpose of this current analysis, since it was not presented in a standardised format, and represented a range of different investigations.

The diagnostic accuracy of the HPAI data was not investigated. All data were assumed as having been confirmed to be caused by H5N1 HPAI.

A range of other geo-referenced data sources were obtained through FAO and BPS-Statistics Indonesia. Note that the data was available at different spatial resolutions and levels of aggregation.

NDVI (averages for period 1998-2004 covering each year, May –Oct, Nov – Apr; source: FAO GIEWS – SPOT-4 Satellite images South-East Asia, resolution: 1km)

Land cover (source: FAO GeoNetwork - ORNL Land cover of South Asia; resolution: 30 arc seconds, date: 2002)

Elevation (source: FAO GeoNetwork – Elevation in South Asia; resolution: 1km, date: 1996)

Census data about livestock population numbers aggregated at province level for each year between 1995 and 2005 (source: DGLS; note that this was separated into native chickens, layers, broilers and ducks, in addition to other livestock species)

Various socio-economic data aggregated at province level (source: Statistical Yearbook of Indonesia 2004; published by BPS Statistik Indonesia; note that the yearbook was provided in PDF format, and data of interest was extracted manually by copying tables from the PDF file and editing them so that they became suitable for importation into an Access data table).

If possible and according to the data collected, carry out an epidemiological analysis, in order to establish how the disease behaves in time and space and defining the variables explaining the patterns observed Descriptive temporal pattern

The number of districts reporting outbreaks were 65 in Aug-Dec 2003, 111 in 2004 and 74 in the period 2005 out of a total of 154 districts. Figure 1 shows that the number of months with outbreak reports in 2005 varied between 1 in November and December and 24 in September. The Ramadhan festival was from October 4 until November 2, 2005. An association with the shape of the temporal pattern of outbreaks is not obvious. The reduction in outbreaks in Nov/Dec is difficult to interpret, and may be unrelated to the religious festival.


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Figure 1: Bar chart of number of districts reporting outbreaks by month in 2005

Descriptive spatial analysis

An overview map of the province boundaries for Indonesia is presented in Appendix IV. The map presented in Figure 2 shows that outbreaks were first reported in 2003 affecting a large proportion of districts during 2003-4. The part of Sumatra adjoining Java was first affected in 2003, but it had the largest number of districts first affected in 2005, and there are districts which have not reported any outbreaks. Kalimantan has had some districts first outbreaks in each of the three years, and Sulawesi first reported outbreaks in 2005. The Eastern provinces of Indonesia have not reported any outbreaks.

Year of first outbrea200320042005

Province Figure 2: Choropleth map presenting year of first reported outbreak by district

The situation in 2005 shows that outbreaks still occur in Java, and repeated outbreak reports come particularly from Sumatra and Sulawesi (see Figure 3).

No outbreaks 20052005 Outbreaks

012

3- 10Province

Figure 3: Choropleth map showing the number of months during which outbreaks were reported in 2005

The choropleth maps presented in Figure 4 and Figure 5 present the data which were used as outcome variables in the multivariate analysis. A comparison of the two figures show how the focus of the epidemic has shifted to Sumatra in 2005.

0

5

10

15

20

25

Jan Feb Mar Apr May Jun Jul Aug Sep Oct Nov Dec

Month of year

No

of d

istr

ic


5

ProvinceProvinceProp. districts with outbreaks

0.00 - 0.080.09 - 0.290.30 - 0.500.51 - 0.670.68 - 1.00

Figure 4: Proportion of districts experiencing outbreaks within each province between 2003-5

ProvinceProvinceProp. districts with outbreaks

0.00 - 0.080.09 - 0.290.30 - 0.500.51 - 0.670.68 - 1.00

Figure 5: Proportion of districts experiencing outbreaks within each province in 2005

Figure 6 shows the temporal variation the spatial pattern of district level outbreak occurrence in 2005. It indicates that during the early part of the year outbreaks occurred mainly in Java, during the remainder of the year focussed on Sumatra and Sulawesi. Sumatra did experience the majority of outbreaks.


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Jan 05

Feb 05

Mar 05

Apr 05

May 05

Jun 05

Jul 05 Aug 05

Sep 05 Oct 05

Nov 05

Dec 05

Figure 6: Time series of mapped districts with outbreaks in 2005

Figure 7 shows that Java has had outbreaks recurring throughout the whole period 2003-5, whereas Sumatra has only become a focus in 2005.

ProvinceProvinceYears with infection

none040503, 0404, 0503, 04, 05

Figure 7: Choropleth map of years during which outbreaks were reported in a province


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Multivariable analysis of the factors associated with HPAI outbreaks

Data were analysed using aggregation at province level as the unit of analysis. A dataset consisting of 30 observations (ie provinces) and 29 variables was constructed. Two outcome variables were selected: 1. the number of districts (regencies) within a province experiencing outbreaks in 2005 relative to the total number of district in the province and 2. the number of district within a province experiencing outbreaks in 2003-5 relative to the total number of district in each province. Appendix IV presents summary statistics for the variables stratified by the outcome variable ‘Presence/absence of outbreaks in 2005’. For the analysis, all continuous scale variables were categorised into three terciles to prevent estimation problems resulting from non-normally distributed data. Logistic regression for group data was used with forward stepwise selection of variables based on the likelihood ratio test (p <=0.05 for inclusion, p > 0.10 for removal) to define the most important variables. PROC LOGISTIC in the statistical software SAS for Windows Version 9.1 was used for the analyses. Overdispersion in the aggregated data was taken account of by multiplying the covariance matrix by the dispersion parameter (calculated by dividing the deviance by its degrees of freedom – heterogeneity factor). The risk factors included in the final models are presented in Table 2 and Table 3, for each of the two outcome variables. Receiver operating characteristic (ROC) curves are presented in Figure 8 to describe the goodness-of-fit of the models.

Both models indicate that the density of ducks and sector 1+2 industry chicken density are important risk factors. The predictive accuracy of the models as depicted by the ROC curves is limited, with the model for 2005 outbreaks performing slightly better. Given the small sample size of 30 provinces and the high level of aggregation, these results should be interpreted cautiously. They will provide an indication for hypotheses which could be tested using more detailed, possibly district level data, particularly in relation to the role of duck density.

Table 2: Final logistic regression model for number of districts with outbreaks in 2005 relative to the total number of districts per province (N=336; residual chisq = 40.1, 23df, p = 0.01; max rescaled R2=0.26; heterogeneity factor 1.87)

Risk factor Heads per sqkm Odds ratio Lower 95% CI Upper 95% CI <= 58 Reference

58 – 405 13.1 1.9 89.9 Industry chicken density (sector 1 + 2)

> 405 1.1 0.25 4.8 <=62 Reference

62 – 204 9.9 0.57 174.3 Native chicken density (Sector 3 + 4)

> 204 63.4 2.6 >999 <= 1.84 Reference

1.84 – 4.1 3.2 1.2 8.7 Ducks per wetland paddy area > 4.1 2.1 0.7 6.0

Table 3: Final logistic regression model for number of districts with outbreaks across all years relative to the total number of

districts per province (N=336; residual chisq = 40.0, 25df, p = 0.03; max rescaled R2=0.26; heterogeneity factor 1.78)

Risk factor Heads per sqkm Odds ratio Lower 95% CL Upper 95% CL <= 58 Reference

58 – 405 4.96 1.49 16.5 Industry chicken density (sector 1 + 2)

> 405 3.6 1.03 12.5 <= 3.5 Reference

3.5 – 24 5.4 1.44 20.1 Duck density > 24 9.9 2.12 46.45


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a) Districts with outbreaks in 2005

b) Districts with outbreak during period 2003-5

Figure 8: ROC curves describing goodness-of-fit of final logistic regression models with the outcome variable districts with

outbreaks in 2005 (a) and all outbreaks during 2003-5 (b)

The relationship between province level native (sectors 3+4) and industry (sectors 1+2) chicken density together with the infection status in 2005 is shown in Figure 9a. The graph shows that all provinces which were not affected in 2005 had densities below 100-200 heads per sqkm, and only few infected provinces were in this category for industry chicken density. Figure 9b shows that native chicken density separated better between provinces with outbreaks in 2005 and those without than number of ducks relative wetland paddy area.

a)

10000.001000.00100.0010.001.00

0.001

Native chicken density (headsper sqkm)

10000.00

1000.00

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Indu

stry

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(hea

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Outbreaks in2005

b)

10000.001000.00100.0010.001.00

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Native chicken density (headsper sqkm)

1000.00

100.00

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1.00E-4Duc

ks p

er w

etla

nd p

addy

are

a(h

eads

/ sq

km) yes

no

Outbreaks in2005

Figure 9: Scatterplot of province data of log transformed values for native chicken density versus industry chicken density (a) and duck density per wetland paddy area (b) (stratified by outbreak occurrence in 2005)

Examining outbreaks throughout the whole period from 2003-5, duck density is a better indicator of likelihood of infection than is industry chicken density (see Figure 10). The same pattern can be deduced from the dot plots in Figure 11 and the scatterplot in Figure 12.


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10000.001000.00100.0010.001.00

0.001

Industry chicken density (headsper sqkm)

140.00

120.00

100.00

80.00

60.00

40.00

20.00

0.00Duc

k de

nsity

(hea

ds p

er s

qkm

)

yesno

Outbreaks

Figure 10: Scatterplot of province data of log transformed values for industry chicken density versus duck density (stratified by

outbreak occurrence between 2003-5)

01

23 N

o years with H

PAI outbreaks

140120100806040200

Duck density per sqkm

01

23 N

o years with H

PAI outbreaks

10,0001,0001001010.001 Industry chicken density (heads per sqkm)

Figure 11: Dot plots of duck density (a) and log-transformed industry chicken density (b) stratified by number of years with HPAI

outbreaks

10000.001000.00100.0010.001.00

0.001

Industry chicken density (headsper sqkm)

140.00

120.00

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40.00

20.00

0.00Duc

k de

nsity

(hea

ds p

er s

qkm

)

3210

No years withHPAI

outbreaks

Figure 12: Scatterplot of log transformed industry chicken density versus duck density stratified by number of years with outbreaks

Figure 13 and Figure 14 show the spatial distribution of the two variables duck and industry chicken density. Duck density is highest on Java and in Aceh province. The density of industry chicken production is highest on Java.


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ProvinceProvinceDuck density per sqkm

0.5 - 3.53.6 - 7.98.0 - 27.827.9 - 79.579.6 - 138.0

Figure 13: Choropleth map of duck density (2004) at the province level

ProvinceProvinceIndustry poultry density per sqkm

1 - 4445 - 7071 - 264265 - 763764 - 5339

Figure 14: Choropleth map of industry chicken production (2004) at the province level

Conclusions

HPAI outbreaks occurred during most months of 2005. But there was a significant drop in November and December. Infection continued to spread to new districts mainly in Sumatra, but also through Sulawesi throughout the year. No seasonality appeared to be present.

The results of the multivariable analysis suggest that provinces with higher densities of ducks and industry chicken production (sector 1+2) are more likely to experience outbreaks. The outbreak risk in 2005 also seems to increase with the density of native chickens (sector 3+4). This may suggest that both duck and backyard chicken production had higher relevance during the 2005 than 2003-4 period. Due to the highly aggregated nature of the data and small dataset (30 provinces) these results should be interpreted cautiously, but it may indicate that ducks fulfil a significant role in the epidemiology of HPAI infection in Indonesia. Other factors which were not significant in this analysis should not be disregarded, due to the limited statistical power and highly aggregated data in this study.

Upon request, assist the epidemiology unit in any other field of epidemiological analysis No such request was made during the period of the visit.

Perform other related duties as required None were required or requested.

Recommendations

1. The data provided by DGLS based on the disease reporting from the provinces (not from disease investigation centres) was presented in a tabular format which is not suitable for routine analyses using descriptive techniques (spatial or temporal). Since such analyses are crucial for informing the disease control strategy, and should therefore be conducted on at least a monthly basis, DGLS should be encouraged to adopt a data entry approach based on a normalised data format. It is also important to adopt consistent coding for provinces and districts (regencies), ideally using a numeric system. Appropriate codes should be obtained from other Indonesian government agencies.


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2. The data based on the disease investigation centre records needs to be standardised in a format that is consistent across centres, and allows aggregated analyses across centres. This is currently not possible without major data manipulation. It is also important to adopt the same administrative unit codes as for information generated by the provinces, discussed above. The DICs have, for example, information about the percentage of poultry with vaccine serological titres which will be useful for informing disease control strategy development.

3. Data should be obtained from Statistics Indonesia in relation to various socio-economic parameters for use in epidemiological analyses, probably to be conducted once a year. The data should be presented at the district or regency level.

4. DGLS staff should be trained in data management and descriptive spatial analysis.

5. More detailed epidemiological investigations should be conducted to examine the role of ducks in the epidemiology of HPAI in Indonesia.

Acknowledgements

I would like to thank Dr Benni Sormin, Assistant FAO Representative, and staff from the FAO office in Jakarta, as well as Dr Peter Roeder from FAO AGAH, Rome for their help during my visit to Jakarta. Ms Francesca Pozzi from FAO AGAH, Rome kindly provided most of the non-disease geo-referenced data. Staff from DGLS, Jakarta, Indonesia, provided the disease and livestock census information.


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Appendix I

Current data format used for AI surveillance and reporting Rekap : 25 November 2005, Sumber : Dinas PeternakanNo. Propinsi Kabupaten/Kota Populasi Jenis Mulai Total

saat tertular Tertular Agust-Des '03 Jan-Des '04 Jan-05 Feb-05 Mar-05 Apr-05 May-05 Jun-05 Jul-05 Aug-05 Sep-05 Oct-05 Nov-051. Banten 1 Tangerang 8,671,500 RT Aug-03 388,000 0 388,000

2 Pandeglang 3,401,000 BR Oct-04 5,334 5,334Subtotal = 2 12,072,500 388,000 5,334 0 0 0 0 0 0 0 0 0 0 393,334

2. DKI Jakarta 3 Jakarta Utara PY Oct-03 23500 0 50 23,5504 Jakarta Selatan BR Feb-04 0 19 195 Jakarta Pusat MR Dec-04 5 5

Subtotal = 3 23,500 5 0 0 0 0 0 50 0 0 0 19 23,5743. Jawa Barat 6 Sukabumi 6,172,917 RT,PY, BR Sep-03 30,000 0 2,430 700 33,130

7 Purwakarta 2,735,452 RT,BR,Bangkok,IT,BR Dec-03 200,000 115,457 1,200 316,6578 Bandung 8,109,718 RT, RD, BR Nov-03 4,000 85,200 3 230 89,4339 Bogor 11,316,473 RT,RD,Bangkok,IT,BR Oct-03 1,000,000 133,923 1,133,923

10 Bekasi 2,001,208 RT, BR Oct-03 3,000 0 15 3,01511 Kuningan 1,232,983 RT Oct-03 6,000 49,000 55,00012 Indramayu 3,397,502 RT,RD,BR,IT,PY Jan-04 0 42,621 72 5,000 4,790 52,48313 Majalengka 4,973,262 BR Jan-04 0 101,327 101,32714 Tasikmalaya BR,PY Mar-04 0 783 78315 Cirebon RT Aug-04 400 40016 Kota Cirebon PY Jan-05 0 12,000 12,00017 Subang PY Jan-05 0 6,800 6,80018 Kt. Bekasi PY Sep-05 0 3,000 3,000

Subtotal = 13 39,939,515 1,243,000 528,711 21,302 6,200 4,793 0 0 0 700 0 3,245 0 1,807,9514. Jawa Tengah 19 Purbalingga 1,941,425 RT Aug-03 10,400 0 10,400

20 Pekalongan 1,452,535 RT Aug-03 5,000 20,000 25,00021 Wonosobo 1,402,377 RT,BR,RD Sep-03 37,200 0 37,20022 Banyumas 2,612,630 RT,PY Oct-03 100,000 0 1,002 101,00223 Karanganyar 2,329,181 RT,RD,BR,PY,IT Oct-03 10,000 125,000 1,250 1,877 138,12724 Temanggung 4,685,712 RT,BR,PY Oct-03 26,000 0 26,00025 Kendal 6,451,864 RT,RD,BR,PY,IT,ET,ANG Oct-03 85,000 11,190 tad 96,19026 Pati 1,412,425 RT,RD,BR,PY,IT Oct-03 50,000 0 50,000

` 27 Cilacap 2,983,532 BR,PY Oct-03 13,100 0 13,10028 Semarang 3,740,970 RT,RD,PY,BR Sep-03 27,000 317,907 344,90729 Grobogan 2,099,657 RT,RD,BR Nov-03 7,150 450 7,60030 Magelang 1,256,486 Ras Oct-03 21,000 0 tad 21,00031 Sragen 2,365,303 PY,RT,IT,BR,RD Nov-03 371,500 328,550 700,05032 Sukoharjo 1,953,021 RT,RD,PY Oct-03 300 12,657 6,000 18,95733 Klaten 2,728,505 RT,RD,BR,PY,IT Nov-03 5,000 562,600 567,60034 Tegal 2,460,884 BR,PY,IT Oct-03 400 20,000 70,000 3,500 2,200 96,10035 Demak 1,203,872 RD,RT,A Arab,PY Nov-03 112,810 3,000 115,81036 Kudus 1,076,559 BR Dec-03 5,000 0 5,00037 Wonogiri 1,978,040 RD,Bangkok,Arab,BR,PY Jan-04 2,660 3,000 5,66038 Boyolali 1,827,124 BR,PY Jan-04 136,000 146,650 40,000 12,200 334,85039 Purworejo 61,533 RD,BR Jan-04 23,088 tad 23,08840 Surakarta Arab,BR,PY,IT,Perkutut Feb-04 26,152 26,15241 Brebes RD,Arab Mar-04 28,000 28,00042 Pemalang PY,BR Feb-04 5,000 5,00043 Banjarnegara PY Nov-04 1,650 1,65044 Jepara RT,RD,PY,IT,BR Dec-04 0 34,600 34,60045 Kota Semarang BR Feb-04 20,462 20,46246 Kebumen BR Okt-05

SubtotalJateng =28 48,023,635 886,860 1,678,966 71,250 3,500 157,527 40,000 12,200 0 1,002 0 2,200 0 2,853,505

Jml kematian


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Appendix II

Table showing the numeric codes for provinces and districts

Province name Province code District name District codeTimor Timur 101027 Lautem 101027001 Sumatera Utara 101026 Kodya Sibolga 101026007 Sumatera Utara 101026 Dairi 101026001 Sumatera Utara 101026 Deli Serdang 101026002 Sumatera Utara 101026 Karo 101026003 Sumatera Utara 101026 Kodya Binjai 101026004 Sumatera Utara 101026 Kodya P.Siantar 101026006


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Appendix III


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Appendix IV

Summary statistics for potential risk factor variables stratified by outbreak occurrence in 2005

Outbreaks in 2005

no yes Variables Count Maximum Median Minimum Count Maximum Median Minimum

Average household size 10 4.80 4.25 3.70 20 4.50 4.15 3.30

Average paddy yield (quintal per ha) 10 44.00 33.00 26.00 20 55.00 43.00 25.00

Average paddy yield from wetland (quintal per ha) 10 45.00 36.00 29.00 20 58.00 45.00 31.00

Average sweet potato yield (quintal per ha) 10 101.00 87.00 70.00 20 126.00 98.00 .00

Percentage population below poverty line 10 38.69 22.04 8.94 20 28.47 14.01 3.18

Percentage population below poverty line in urban area 10 25.43 12.64 4.37 20 32.66 11.62 3.18

Percentage population below poverty line in rural area 10 49.28 22.25 11.76 20 32.66 18.51 .00

Regional gross domestic product per capita at 1993 market prices (rupiah) 10 9474895.00 1314697.50 868602.00 20 8002894.00 1795061.00 1185733.00

Beef cattle density (heads per sqkm) 10 10.81 2.04 .16 20 101.95 4.31 .00

Dairy cattle density (heads per sqkm) 10 .01 .00 .00 20 6.53 .00 .00

Cattle density (heads per sqkm) 10 10.81 2.04 .16 20 101.96 5.34 .31

Buffalo density (heads per sqkm) 10 2.83 .09 .00 20 7.99 1.41 .08

Goat density (heads per sqkm) 10 9.55 1.86 .12 20 85.17 8.30 .37

Sheep density (heads per sqkm) 10 1.17 .05 .00 20 55.43 .88 .00

Sheep and goat density (heads per sqkm) 10 10.72 1.89 .12 20 140.59 9.85 .37

Horse density (heads per sqkm) 10 1.99 .05 .00 20 3.87 .13 .00

Broiler density (heads per sqkm) 10 96.43 30.52 1.07 20 4921.26 358.55 14.26

Layer density (heads per sqkm) 10 15.82 1.59 .19 20 575.62 39.46 .00

Industry chicken density (heads per sqkm) 10 112.25 44.21 1.44 20 5339.48 427.01 15.54

Native chicken density (heads per sqkm) 10 194.13 32.82 4.55 20 1313.03 212.74 16.02

Chicken density (heads per sqkm) 10 252.90 85.20 7.55 20 6652.51 621.89 133.09


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Outbreaks in 2005

no yes Variables Count Maximum Median Minimum Count Maximum Median Minimum

Proportion native chickens amongst all chickens 10 .90 .60 .21 20 .95 .33 .10

Duck density (heads per sqkm) 10 6.41 2.03 .54 20 138.02 25.76 1.80

Ratio ducks per chickens 10 .12 .07 .02 20 .83 .14 .04

Chickens per person 10 5.32 2.54 1.05 20 21.24 5.45 .02

Ducks per person 10 .11 .09 .03 20 1.42 .16 .01

Native chicken density per number of rural poor (heads per person) 10 3.45 .88 .23 20 13.49 1.47 .72

Native chicken density per all poor (heads per person) 10 2.87 .73 .22 20 8.64 .97 .03

Percentage agricultural wetland 10 7.31 4.60 2.13 20 72.53 16.24 .08

Duck density per wetland paddy area (heads per sqkm) 10 15.81 1.73 .68 20 431.92 3.40 1.49

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