Remote Sensing for Recognition of Vibrio Cholerae

Muralidharan Velappan¹*, Deecaraman Munusamy²

¹Research Scholar, Department of Biotechnology, AMET University, Kanathur, Chennai-603112, India.

²Dean/Emeritus Professor, Department of Industrial Biotechnology,

Dr. M.G.R Educational and Research Institute, Maduravoyal, Chennai-600095, India.

*Corresponding Author E-mail: muralidharanmicro@gmail.com

ABSTRACT:

The wide spread of Cholerae is caused by the bacterium Vibrio cholera, which is sufficiently found in unpurified river also associated with drinking water which grows depends on certain environmental factors responsible for the reproduction of bacterium. These environmental parameters of the water sample are routinely measured by the research ship to acquire the updated data is expensive and time consuming. Interruption of regional scale can also be problematic. The Vibrio cholera are highly remote sensitive to deduce its presence which is not seen nakedly .This study is reported with the satellite data which is used to monitor the spreading time of the cholera. They also correlated the remote sensing data which includes temperature and height of sea surface of Bay of Bengal directly with the cholera case data of Bangladesh from 1992-1995.It predicts that sea surface temperature was an annual cycle comparable to cholera case data. Similarly, sea surface height can be as indicator of attack of plankton loaded with internal water, to be correlated with cholera occurrence. The massive study on V.cholerae during the past 25 years, confirms that its growth and spreading is depend on aquatic environment and zooplanktons, i.e., copepods respectively. Such results are when combined with satellite data, concludes to prove the widespread of cholera is a climatic factor.

KEYWORDS: Vibrio Cholera, Plankton, Zooplankton, temperature and environment parameters.

1. INTRODUCTION:

V.Cholera is a severe epidemic in causing cholera which is sensitive in intestinal parts frequently in Bangladesh and other developing country, approximately showing annual periodicity to be influenced by the climatic changes. The widespread of cholera started in 1961 and at present it affects six more continents extensively [1]. This study reports to develop a data on cholera prediction model to monitor ocean parameters based on remote sensing (RS) data with early warning conditions of cholera occurrence. This model is designed basically concerning on the source of public domain data to correlate with the RS ocean parameters and cholera case data of Bangladesh.

When such a model is developed for Bangladesh it can also be used globally to minimize the widespread of cholera and its early prevention by verifying the sequence of ocean linking parameters associated with cholera case data along with the field data on seawater parameters including plankton biomass [2]. Water samples collected from Bay of Bengal does not give sufficient data due to difficult interruption by the collection of water and plankton specimens by shipboard is expensive and time consuming. Phytoplanktons are food source for the zooplanktons, so the relation between oceanic phytoplanktons and zooplanktons are remote sensed to estimate the phytoplankton concentrations [3]. One of the marine reservoirs is V.Cholerae (plankton) which attaches to copepods (zooplanktons) specifically. The field data for Bangladesh was predicted from archived RS data and cholera case data including sea surface temperature (SST) and sea surface height (SSH). SST and SSH are relate to phytoplankton concentration and zooplankton contact respectively [4]. The RS data for the coastal region of Bangladesh were obtained from the existing archives of Ganges and Brahmaputra to analyze the correlation between RS measurement and widespread of Cholera.

2. DATA:

2.1 CASE DATA:

Cholera case data for 1980-1995 were obtained weekly from the Hospital surveillance program of International Centre for Diarrhea Disease Research [5]. They provided this data each week from all patients (in & out) to the hospital. This data consist the details of number of persons tested, positive for cholera and the percent positive for cholera [6-8]. The widespread of cholera in Bangladesh are found to be in spring and fall. This case data correlated with SST data for 1989-1995.The results of ”percent cholera” was retrieved later in this study[9-10].

2.2 RS DATA:

RS data is based on the SST and SSH measurements. Ocean surface temperature quantity is measured by thermal infrared wavelengths by the emitted radiance. National Oceanographic and Atmospheric Administration computes multichannel SST with Advanced Very High Resolution Radiometer (AVHRR) sensor by combining two ambient temperature channels. With this weekly available data it is re-sampled to 18-km global coverage by NASA in Jet Propulsion Laboratory Physical Oceanography Distributed Active Archive Center. Similarly, SSH were measured by deriving the difference between corrected altimeter and 3-year mean value of ocean surface. Jet Propulsion Laboratory predicts SSH data by TOPEXy Poseidon radar altimeter, which was launched in September 1992, which has no existed public domain ocean altimeter data before. One complete global cycle of coverage by this satellite takes 10 days, which has one-degree spatial resolution to examine the data. In recent decades there are many satellites available to provide radar altimetry for the detection of global estimation of oceanic chlorophyll concentration, such satellites namely as the European Space Agency’s ERS-1 (launched July, 1991) and ERS-2 (launched April, 1995) and the U.S. launched Sea Star (OrbView-2) satellite in August,1997, which carries the Sea-Viewing Wide Field-of-View Sensor (Sea WiFS).

The RS core data for the study was interrupted with the cholera case data, sea surface temperatures and sea surface height from the late 1992 to the end of 1995. Although additional cholera case data are not currently available, these RS data are still being archived and now also includes SeaWiFS-derived chlorophyll concentration data from September, 1997 through the present years.

3. DATA INTERRUPTION:

The interrupted data in this study comprises of SST, SSH and cholera case data for Bangladesh. Data from images were obtained by downloading from internet resource and imported in image processing program (IMAGINE, ERDAS, Atlanta) to interrelate SSH and SST pixel values for each time. Similarly, relative patterns are detected for other locations also. Whereas, SST and SSH data patterns are obtained from 1989-1995 and September 1992-1995 respectively. Statistical analyses were done (E. Russek-Cohen, personal communication), with results showing statistically significant correlations. Recent Sea-WiFS derives the chlorophyll concentration data but not related to cholera case data, so it was overlaid with SST and SSH data.

4. RESULT AND DISCUSSION:

The widespread of cholera associated with SST data shows an annual bimodal cycle statistically (1992-1995).This data was expected to find with plankton blooms depending on ocean temperature because it serves as a reservoir for V. cholerae, but it includes others parameters (light availability, run-off, etc) as variables relating with SST and V. cholera directly. The time of SST increases with cholera case by delaying which accounts to be responsible for increased temperature of phytoplankton and zooplankton blooms with increase in cholera rate. Interestingly, in 1993, an association was not observed, but shows SSTs within normal range while spring SSHs shows lowest observation for the entire period between1992-1995, where cholera outbreak occurred. The relation between SSH and cholera outbreak data in 1993 suggests, that the tidal disturbances of plankton influence to vibrio- human contact as water consumption by river system without treatment used for daily residential needs in Bangladesh, which is situated slightly above sea level influencing tidal waves. In October, it shows normal SSH with an outbreak of cholera shortly.

Another pattern of cholera outbreak was observed in 1995, which is in the fall and early summer season showing unusual high SSH and decreased cholera cases had been recorded with unexplained SST or SSH data. V. cholerae has an absolute requirement for Na, Salinity has been shown to be related to cholera toxin production. Chlorophyll concentration data are detected by using SeaWiFS placed along with the SST and SSH data which is worth to be unclear with those parameters when related in connection to cholera case data. Chlorophyll concentrations are not estimated during cloudy conditions. Overall, chlorophyll concentration values showed large variations in 1998 which may be related to the unusually severe monsoon season during July through September, 1998. The field data measurements of plankton biomass cannot be verified. So, for further studies NIH funded in 1996 to obtain needed data for future analysis. For instant, to verify the interaction between ocean parameters like plankton concentration and cholera widespread along with the Phytoplankton concentration can be obtained from chlorophyll concentration by ocean color imagery systems like SeaWiFS. The NASDA (Japanese Space Agency) Advanced Earth Observing Satellite (ADEOS), which carried the Ocean Color and Temperature Scanner (OCTS), was launched in August, 1996, but its power failure in June, 1997 prevented acquisition of additional chlorophyll concentration data.

5. CONCLUSION:

RS data has been used to provide chlorophyll concentration and field data measures the plankton biomass and other taxas present in the ocean. When these data are coupled and related outbreak of cholera with SST and SSH brings out to predict the plankton distribution and spreading of cholera. Currently, a predictive model under development for Bay of Bengal and latter global model is possible for other regions of cholera outbreak.

REFERENCES:

1. Colwell, R. R. (1996) Science 274, Pp:2025–2031.

2. Patz, J. A., Epstein, P. R., Burke, T. A. & Balbus, J. M. (1996) J. Am. Med.Assoc. 275, Pp: 217–223.

3. Pollitzer, R. (1959) Cholera (World Health Organization, Geneva), Pp: 11–50.

4. Kiorboe, T. & Neilson, T. J. (1994) Limnol. Oceanogr. 39, Pp: 493–507.

5. Chaturvedi, N., Aishwarya, N. &Pandey, P. C. (1998) Ind. J. Mar. Sci. 27, Pp:286–291.

6. Huq, A. & Colwell, R. R. (1995) J. Mar. Biotechnol. 3,Pp: 60–63.

7. McClain, E., Pichel, W. & Walton, C. (1985) J. Geophys. Res. 90, Pp:11587–11601.

8. Strong, A. & McClain, E. (1984) Bull. Am. Meteorol. Soc. 85, Pp: 138–142.

9. Muller-Karger, F., Walsh, J. J., Evans, R. H. &Meyers, M. B. (1991) J. Geophys.Res. 96, Pp:12645–12665.

10. M. Jayaprakashvel, K. Abishamala, A. JaffarHussainAnd M.C. Vanitha “Isolation and Characterization of Indole Acetic Acid (IAA) Produced by a Halo Tolerant Marine Bacterium Isolated from Coastal Sand Dune Plants”Biosciences Biotechnology Research Asia (BBRA).

Received on 11.07.2017 Modified on 08.10.2017

Research J. Pharm. and Tech 2017; 10(10):3521-3523.

DOI: 10.5958/0974-360X.2017.00633.3