EGU2020-20045, updated on 12 Jun 2020
https://doi.org/10.5194/egusphere-egu2020-20045
EGU General Assembly 2020
© Author(s) 2020. This work is distributed under
the Creative Commons Attribution 4.0 License.

Hydro-meteorological Impact on Malaria Diseases at Regional Scale in India

Reshama Kumari1, Krushna Chandra Gouda1, Ujjwal Singh2, Petr Maca2, Kantha Rao Bimla1, Himesh s.1, Nikhila Suma1, Mahendra Vishnu Benke1, Srinivas Rao3, and Murty Usn4
Reshama Kumari et al.
  • 1CSIR Fourth Paradigm Institute, Bangalore, India, CMMACS, India (reshmakumari08@gmail.com)
  • 2Faculty of Environmental Sciences, Czech University of Life Sciences Prague, Kamycka 129, Praha-Suchdol, 165 00 Czech Republic
  • 3CSIR Indian Institute of Chemical Technology, Hyderabad
  • 4National Institute of Pharmaceutical Education Research Guwahati

Several studies have revealed that rainfall and temperature are highly correlated with malaria spread. There are several studies relating the combined effect of hydrological and meteorological information for the malaria diseases1–4 . In this study, attempts are being made for assessing the combined effect of hydro-meteorological variables on malaria disease at the regional scale. It reveals that evaporation is one of the essential climatic variables in this context, which is jointly derived by hydrological and meteorological variables. To our best knowledge, there are very few studies which have been performed to analyse the relations between malaria and the ratio of precipitation (P) and actual evaporation (AET). This study analyses the impact of the ratio of P and actual AET on malaria diseases. The work has performed at regional scale using annual data of malaria disease over the Tirap district of Arunachal Pradesh in India. Annual P data from Indian Meteorological (IMD) and GRUN5  global surface runoff during the period of 1995 to 2012 are used for this analysis. The AET was estimated as difference e between P and runoff time series. The AET and P relationship with Plasmodium vivax (PV), Plasmodium falciparum (PF) is analysed. The sum of PV and PF is BSB indicator, it shows the total number of people affected by malaria. The study has revealed that fraction P/AET is negatively correlated with PV, PB and BSB. In comparison to hydrological and meteorological variables like P, surface runoff, AET and AET/P which are mostly positively correlated with BSB, PV and PF. This preliminary result will be further explored in order to find a connection on improving the forecast of malaria diseases using hydrometeorological inputs for better health management In the studied district.

 

References

  1. Martens W J, Niessen L W, Rotmans J, Jetten T H & McMichael A J. Potential impact of global climate change on malaria risk. Environ. Health Perspect. 103, 458–464 (1995).
  2. Blanford, J. I. et al. Implications of temperature variation for malaria parasite development across Africa. Sci. Rep. 3, 1–11 (2013).
  3. Kim, Y. et al. Malaria predictions based on seasonal climate forecasts in South Africa: A time series distributed lag nonlinear model. Sci. Rep. 9, 1–10 (2019).
  4. Kibret, S., Ryder, D., Wilson, G. G. & Kumar, L. Modeling reservoir management for malaria control in Ethiopia. Sci. Rep. 9, 1–11 (2019).
  5. Gudmundsson, L. & Seneviratne, S. I. Observation-based gridded runoff estimates for Europe (E-RUN version 1.1). Earth Syst. Sci. Data 8, 279–295 (2016).

How to cite: Kumari, R., Gouda, K. C., Singh, U., Maca, P., Bimla, K. R., s., H., Suma, N., Benke, M. V., Rao, S., and Usn, M.: Hydro-meteorological Impact on Malaria Diseases at Regional Scale in India, EGU General Assembly 2020, Online, 4–8 May 2020, EGU2020-20045, https://doi.org/10.5194/egusphere-egu2020-20045, 2020

Comments on the presentation

AC: Author Comment | CC: Community Comment | Report abuse

Presentation version 1 – uploaded on 04 May 2020
  • AC2: Comment on EGU2020-20045, S Himesh, 05 May 2020

    Overall an interesting study. This study needs to be repeated for different climatic and geographic regions to arrive at more generic and robust conlusions   

    • AC4: Reply to AC2, Reshama Kumari, 05 May 2020

      Thank you, Sir, for your valuable suggestion. We will consider this in our extended work for the upcoming paper.

  • AC3: Comment on EGU2020-20045, ujjwal singh, 05 May 2020

    Thank you for comment.

     

    Thank you for your comment.

    The initial work was only tested in the Tirap district of A.P.

    Now this work has been tested all over India, using different ML and deep learning techniques with respect to the sensitive parameter. The current result shows, this study sensitive parameter is prominent to the future prediction of malaria.