Perception of seasonal prediction in health sectorrcc.imdpune.gov.in/Training/SASCOF12/SASCOF_Daytwo... · 2 National Institute of Malaria Research Potential impacts of Climate on

1 National Institute of Malaria Research www.mrcindia.org

National Institute of Malaria Research (ICMR) Sector 8, Dwarka, Delhi-110077

www.mrcindia.org


www.mrcindia.org

Perception of seasonal prediction in health sector

Ramesh C Dhiman DST-ICMR Centre of Excellence on Climate Change and Vector

Borne Diseases National Institute of malaria Research (ICMR)

DELHI-110077, India [email protected]

.n


Potential impacts of Climate on Health

• Problems due to Climatic conditions ( Heat wave mortality, disasters, eye diseases, cardiovascular Diseases, neurological disorders).

• Infectious Diseases

Vector borne diseases

Water borne diseases

• Malnutrition (due to adverse impact on agriculture)

• Respiratory diseases


Major Vector Borne Diseases in India


Disease Case Death

Malaria 1090724 331

Filariasis 600 million (at risk) ‐

Kala‐azar 6249 0

Dengue 129166 245

Chikungunya 64057 ‐

Japanese Encephalitis 1676 283

Major Vector Borne Diseases in India (2016)

Source : NVBDCP



www.mrcindia.org


www.mrcindia.org

Possible effects of climatic parameters affecting life of mosquito

Temperature

Radiation

Rainfall

Relative Humidity

Wind Velocity

Resting habitats

Outdoor – Vegetation etc.

Breeding habitat

Development of immature stages

Flushing off Breeding

Affects development of immature stages

Affects toxicity of insecticide

Affects immature stages in water bodies

Affects flight speed of adult mosquitoes

Creation of Breeding Grounds

Affects survival of mosquitoes

Required for development of mosquitoes and pathogen

Indoor


Thresholds of major VBDs


10 11 12 13

Minimum temp. for parasite development

Maximum temp.

for mosquito

survival

Optimum

temp.

25 26 27 40 o +

Relative Humidity 40 60 70 80+

Relationship of Temp. & RH with Malaria Parasite and Mosquito Development

Minimum T required for transmission

P vivax: 14.5-160 C P falciparum:16-180 C

(Adapted from :Bruce chwatt ,1980 and Martens et al 1995


Seasonal forecasts available

• All India Weather Forecast: Daily forecast bulletin (Sub Division wise).

• All India Weather Warning: For next 5 days (Sub Division wise).

• Weekly Weather Report-: Forecast & warning for the next week.

• Extended Range Outlook: Current weather status and outlook for next two weeks (Sub Division).

• Heat Wave: Current temperature and warning for next five days.

• City Weather- Temp, RH for current 24 hours and next 7 days for all cities.

• Rainfall: Customized Rainfall Information System (CRIS): Rainfall information at district level.


Rainfall, Malaria and Dengue in Ahmedabad

MALARIA DENGUE

Correlation with Temperature 0.1061 0.0055

Correlation with rainfall 0.1012 0.0837

Correlation with rainfall - 1 month

lag 0.2541 0.3394


lag 0.3539 0.5177


lag 0.2806 0.3228

Correlation with RH 0.2183 0.3121

Correlation coefficient between malaria, dengue and climatic variables.

19

29

39

49

59

69

79

89

99

0100200300400500600700800900

10001100120013001400150016001700180019002000

1997 1998 1999 20002001 20022003200420052006200720082009 2010 2011 2012 2013 2014 2015 2016

Malaria cases Vs Temperature in Ahmedabad district

Cases Temperature

19

24

29

34

39

44

49

54

59

0

50

100

150

200

250

300

350

400

450

500

550

600

2004 2005 2006 2007 2008 2009 2010 2011 2012 2013 2014 2015

Dengue cases Vs Temperature in Ahmedabad district

Cases Temperature

10

National Institute of Malaria Research www.mrcindia.org

Temp., Rainfall and Japanese Encephalitis

0

2

4

6

8

10

0

50

100

150

200

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

Japanese Encephalitis in Gorakhpur ( avg 2012 to 2016)

Rainfall Temp RH cases

Climate Variable Lag months

Lag 0 Lag 1 Lag 2 Lag 3

Temperature 0.204 0.445 0.589* 0.681*

Relative Humidity 0.542* 0.554* 0.418 -0.0103

Rainfall 0.441 0.668* 0.784* 0.728*

11


Relationship between climatic parameters and KFD

0

200

400

600

800

1000

1200

0

10

20

30

40

50

60

70

80


Ra

infa

ll (

mm

)

Tem

per

atu

re°C

& C

ase

s

Kyasanur Forest Disease in Shivamogga

Rainfall Avg T Max T Min T Cases

Lag Period Shivamogga

Temperature Rainfall

lag 0 0.684 -0.491

lag 1 0.691 -0.314

lag 2 0.550 -0.003

lag 3 0.466

lag 4 0.900

lag 5 0.879

12


Relationship between climatic parameters and Scrub Typhus

0

5

10

15

20

25

30

35

0

50

100

150

200

250

300


Vellore

Cases RH(%) Rainfall(mm) Temperature(°C)

(B)

0

5

10

15

20

25

30

35

jan feb mar apr may jun jul aug sep oct nov dec0

100

200

300

400

500

600Chennai

Cases RH(%) Rainfall Temperature

(A)

Temperature Relative Humidity Rainfall

lag 0 Lag 1 Lag 2 Lag 3 lag 4 Lag 5 Lag 0 Lag 1 Lag 2 Lag 3 Lag 0 Lag 1 Lag 2 Lag 3

Vellore -0.39 -0.09 0.25 0.70 0.87 0.60 0.45 0.18 -0.05 -0.44 -0.01 0.29 0.77 0.38

Chennai -0.72 -0.39 0.01 0.26 0.40 0.51 0.71 0.78 0.45 0.12 0.67 0.87 0.30 -0.06

13


State No. of Districts Data available (No. of Districts)

1 New Delhi 11 1

2 Maharashtra 36 14

3 Puducherry 4 1 4 A&N Islands 3 1 5 Kerala 14 3 6 Lakshadweep 1 2

7 Tamil Nadu 32 12

8 Goa 2 2

9 Karnataka 30 19

10 Andhra 13 5 11 Telangana 31 3 12 Chhatisgarh 27 3 13 Tripura 8 1

14 West Bengal 22 4

15 Manipur 9 1 16 Jharkhand 24 2

17 Gujarat 33 9

18 Meghalaya 11 2 19 Bihar 38 2

20 M.P. 51 11

21 Assam 33 4

22 U.P. 75 7

23 Haryana 22 1 24 Uttarakhand 13 2 25 Himachal Pradesh 12 2 26 Punjab 22 5

27 Orissa 30 6

28 Rajasthan 33 9

Total 640 134

Comments on the usefulness of presently available products (northern districts)

14


Non-availability/data gaps (RMC- Guwahati)

S. No. state Data requested (No. of districts)

Data available

Rainfall Temperature & RH

1. Arunachal Pradesh 20 13 2

2. Assam 29 21 4 (Partial)

3. Meghalaya 10 3 1 (Partial)

4. Tripura 7 1 1 (Partial)

5. Mizoram 8 3 1 (Partial)

6. Manipur 15 1 1 (Partial)

7. Nagaland 11 4 0

15


Amount of rainfall required for P. falciparum outbreaks

Lag period between rainfall and P. falciparum outbreaks

Rainfall and malaria : the amount and lag period

16


Prediction for the Epidemics of malaria in Kutch( India)

Accumulated rainfall from May to Aug. (blue dots) could predict malaria cases in Sept. to December ( green dots) using VSEIRS model.

(Laneri et al PLoS Com. Biol. 2010)

17


NDVI is a better predictor than rainfall one month prior (September; dashed line) to the epidemic season (Oct‐Nov‐Dec) for Barmer, Bikaner and Kutch. For BMP, rainfall from Banaskantha is a better predictor. For Kheda, neither NDVI nor rainfall is a good predictor for the epidemics.

Predictability power of NDVI v/s rainfall

(Baeza et al 2011)

18


The most sensitive periods of malaria to the environment are the end of March‐ early April (weeks 14‐ 17) using TCI, second half of June (weeks 23‐ 26), using both TCI and VCI and mid October (weeks 42‐ 44) using TCI.

Temperature condition Index for early warning of malaria outbreak in Tripura

TCI < 40 in March/April was critical in 2006 (Nizamuddin et al 2013)

19


Impact of EL NINO on malaria in India

The predicted SST anomaly values for Nov, Dec 2015, Jan and Feb 2016 are 2.3, 2.8, 2.4 and 1.8 suggesting strong El Nino conditions in 2016. It means that the states of Odisha, Jharkhand, Chhattisgarh, Goa, Uttarakhand, and parts of Andhra Pradesh, Madhya Pradesh, Maharashtra, Uttar Pradesh and Meghalaya are likely to experience increased malaria from the climatic conditions point of view.

20


Requirement of additional/tailor made information

Daily data:

• Air-borne diseases, Heat stress, Eye diseases

• Analysis of sporogonic cycles in Vector-Borne Diseases

• Fortnightly data for forecasts of VBDs.

• Data gaps need to be addressed.

• Cost of data should be nominal for at least govt instituions.

21


Research needs

Role of wind velocity on mosquitoes activities.

Role of radiation on mosquito development.

Correlation between temperature, rainfall and disease on weekly cases .

Determination of thresholds for alerts.

22



www.mrcindia.org


www.mrcindia.org

Thanks for your kind attention

23


Forecasts issued by IMD

Monsoon Forecast

Agriculture Forecast

Cyclone Forecast

Marine Forecast

Aviation Forecast

Tourism Forecast

Fog Forecast

24


Comments on the usefulness of presently available products (northern districts)

Sn. No. Index Station Name Availability Year

Station Name Availability From To Missing Periods

1 6203 LUDHIANA P.A.V. MONTHLY T‐2 1999 2011 2010[Jan‐Dec] 2011[Mar‐Dec]

Total records for the above index

2 42062 DHARAMSHALA T‐2 1997 2013 1998[Oct‐Dec] 1999[Jan‐Dec] 2000[Jan‐Dec] 2001[Jan‐Dec]

2002[Jan‐Dec] 2004[Jan] 2005[Apr] 2012[Jan‐Dec] 2013[Oct

‐Dec]


3 42071 AMRITSAR T‐2 1997 2011 2011[Mar‐Dec]


4 42083 SIMLA T‐2 2007 2007 2007[Mar‐Dec]


5 42099 LUDHIANA T‐2 2010 2011 2011[Mar‐Dec]


6 42101 PATIALA T‐2 1997 2010 1999[Jan‐Dec] 2000[Jan‐Dec] 2001[Jan‐Dec] 2002[Jan‐Dec]

2003[Jan‐Dec] 2004[Jan‐Dec] 2005[Jan‐Dec] 2006[Jan‐Dec]


7 42103 AMBALA T‐2 1997 2011 2001[Jul‐Dec] 2005[Jan‐Dec] 2010[Nov] 2011[Mar‐Dec]


8 42111 DEHRA DUN T‐2 1997 2015 2014[Jan‐Dec]


Documents

Perception of seasonal prediction in health sectorrcc.imdpune.gov.in/Training/SASCOF12/SASCOF_Daytwo... · 2 National Institute of Malaria Research Potential impacts of Climate on