Biplav Srivastava, Debtanu Dutta, Hemant MittalMarch 12, 2016

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DataView 2016 App Contest for Social Impact at COMAD 2016

Contest Highlights• First contest focused on data analysis with open data• Two round contest, first asking for insights and second asking

for apps (and insights)• The first round begins at Indian Standard Time (IST) 00:00:00 on

November 1, 2015 and ended at 23:59:59 IST on December 15, 2015. • The second round begins at Indian Standard Time (IST) 00:00:00 on

January 01, 2016 and ends at 23:59:59 IST on February 15, 2016.

• Evaluation by organization committee and feedback taken from data.gov.in and COMAD organizers

• 6 teams participated in first round; 3 carried on to second round

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Insights Sought

1.What diseases are most prevalent in a given area (e.g., state, district, city, by keyword)?

2.Which diseases have been better controlled than others in India? What states have done better than others? Are there approaches which have worked for controlling / reducing instances of diseases better than others?

3.How much money has been allocated to tackle specific diseases compared to others? Which regions do better than others in controlling diseases relative to money spent?

4.Is their a relationship between water-borne diseases and their relation to water pollution? 3

Open Data SetsHealth•H-DS-1: http://data.gov.in/catalog/number-cases-and-deaths-due-diseases , AllIndia (from 2000 to 2011) and State-wise (2010 and 2011) number of cases and deaths due to specified diseases (Acute Diarrhoeal Diseases, Malaria, Acute Respiaratory Infection, Japanese Encephalitis, Viral Hepatitis).•H-DS-2: http://data.gov.in/catalog/cases-and-deaths-due-kala-azar , Cases and Deaths due to the illness Kala-Azar in Bihar, West Bengal and Country during the years 1996 till 2000.•H-DS-3: https://data.gov.in/catalog/cases-and-deaths-due-japanese-encephalitis-and-dengue-dhf-during-tenth-plancases and deaths due to Japanese Encephalitis and Dengue / DHF during Tenth Plan.•H-DS-4: https://data.gov.in/catalog/water-quality-affected-habitations, Water Quality Affected Habitations•H-DS-5: Hospital Directory with Geo Code as on September 2015, https://data.gov.in/catalog/hospital-directory-national-health-portal Expenditure•F-DS-1: https://data.gov.in/catalog/outlays-and-expenditure-aids-control-programme-during-ninth-plan, outlays and expenditure of AIDS Control Programme during Ninth Plan.•F-DS-2: https://data.gov.in/catalog/public-sector-outlaysexpenditure-during-eleventh-five-year-plan, public sector outlays and expenditures during Eleventh Five Year Plan (2007-12) under various Heads of Development (Rs. Crore).•F-DS-3: http://data.gov.in/catalog/outlays-department-health-agreed-planning-commission-during-tenth-plan , data related to 9th Plan Allocation, 9th Plan Anticipated Expenditure, 10th Plan Allocation as Agreed by Planning Commission.•F-DS-4: https://data.gov.in/catalog/percentage-share-household-expenditure-health-and-drugs-various-states-during-eleventh-five, data related to percentage share of household expenditure on health and drugs in various states during Eleventh Five Year Plan.•F-DS-5: https://data.gov.in/catalog/state-wise-plan-outlays-and-expenditure, table provides state-wise plan outlays and expenditure during 2011-2012.•F-DS-6: https://data.gov.in/catalog/outlay-tenth-plan-tenth-plan-sum-annual-outlay-and-tenth-plan-actual-expenditure-department, data related to Outlay Tenth Plan, Tenth Plan (200207) sum of Annual Outlay and Tenth Plan (2002-07) Actual Expenditure for Department of Health and Family Welfare. Water Quality •W-DS-1: https://data.gov.in/catalog/status-water-quality-india-2012, http://data.gov.in/catalog/number-cases-and-deaths-due-diseases , status of Water Quality in India in 2012•W-DS-2: https://data.gov.in/catalog/status-water-quality-india-2008-and-2011, status of Water Quality in India - 2008 and 2011

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Overall WinnersFirst PrizeiFuse:A Visual Data Fusion Approach, by Gunjan Sehgal, Kaushal Paneri, Aditeya Pandey, and Garima Gupta, TCS ResearchApp: http://apps.web2labs.net/BDFusion/HomePage.html (Username: Comad/ Password: Comad/ Dataset : Comad) Video: https://www.youtube.com/watch?v=eniSZKFpq_o

Second Prize (Joint)Aniya Aggarwal, Mayur Saxena, Varun Parashar, Nishtha Madaan, IBM ResearchApp: http://vpronaldo.github.io/insights-demo/; Video: https://youtu.be/POsdsHPCHjA

Planning Disease Control amidst Water Pollution, K Kumar Ajella, Sravanthi Reddy Akavaram, Sravan Daggupati and Prasad Aluru, ValSoftApp: http://dataview2016.valsoftech.com

5Round 1 ResultsiFuse:A Visual Data Fusion Approach, by Gunjan Sehgal, Kaushal Paneri, Aditeya Pandey, and Garima Gupta, TCS Research Special mentions :1. DataPeRceivers, Abhishek Dubey, Aishwarya Kaneri, Ajit Dhobale, Apurva Mulay, Karthik Prabhu2. Planning Disease Control amidst Water Pollution, k Kumar Ajella, Sravanthi Reddy Akavaram, Sravan Daggupati and Prasad Aluru, ValSoft

Team’s Assessment• TCS team

• Strong focus on questions; gave summarized as well as detailed answers; used innovative in-house tools

• Strong submissions in both rounds

• IBM team• Detailed and exhaustive answers; used off-the-shelf tools• Strong second round submission

• Valsoft team• Focused on visualization but did not give specific answers; used

off-the-shelf tools• Participated in both rounds

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A Data Community’s Attempt to Answers Public Health Questions

A snapshot from the winning entries. See explanations by each team and their demos for details.

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#1. What diseases are most prevalent in a given area (e.g., state, district, city, by keyword)?

(TCS team) “We discovered that seven sister states and eastern states of Jharkhand,Chattisgarh and Odisha have recorded higher per capita cases of MALARIA when compared with other states/regions.”

(IBM team)

(Valsoft team)• Verbose

visualization8

#2. Which diseases have been better controlled than others in India? What states have done better than others? Are there approaches which have worked for controlling / reducing instances of diseases better than others?

(TCS team)•Malaria has been better controlled in India as compared to other vector borne diseases •Chattisgarh, Odisha, Gujarat,Jharkhand and Tamil Nadu have done better in controlling Malaria as they reported a higher survival ratio. •Expenditure on vector-borne diseases has helped in curbing malaria whereas this is not the case for Japanese Encephalitis.

(IBM team)

(Valsoft team)• Verbose

visualization

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#3. How much money has been allocated to tackle specific diseases compared to others? Which regions do better than others in controlling diseases relative to money spent?

• (TCS team)• In the 10th five year plan government allocated maximum money on vector- borne

diseases followed by Tuberculosis. • Gujarat, Kerala, Tamil Nadu and Rajasthan performed better than others relative

to money spent whereas A&N Islands and Mizoram were unable to do so.

• (IBM team)

• (Valsoft team)• Verbose

visualization

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#4. Is their a relationship between water-borne diseases and their relation to water pollution?• (TCS team) We found a positive correlation between per

capita acute diarrhoeal cases and (1) Avg. Conductivity , (2) Avg. NITRATE- N+ NITRITE-N

• (IBM team)• Moderate {Oxygen, pH, Conductivity} => Moderate {Viral

hepatitis, Acute Diorrhea }, Low {malaria, Japanese encephalitis}

• (Valsoft team)• Verbose visualization

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Lessons• Data Science can help make a beginning to answer public

health questions with open data• Students did not participate but this may become popular in

future; good area for research and serious innovations• Participating teams experienced challenges with data quality

and sufficiency• Made assumptions which reflects in results• Lot of room for improvement

• Public health officials, NGOs, open data community should use results and encourage more contests and hackathons in this area in future

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