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Weather patterns can be used to forecast rotavirus outbreaks

Date:
May 31, 2012
Source:
Tufts University
Summary:
By correlating weather factors like temperature, rain and snowfall, a professor of civil and environmental engineering is able to predict the timing and intensity of rotavirus, a disease that causes extreme diarrhea, dehydration and thousands of death annually, particularly among children. Her research focused on one of the hardest-hit regions of the world, South Asia.

Monitoring weather factors like temperature, rain, and snowfall is one way to predict the timing and intensity of rotavirus, a disease that causes extreme diarrhea, dehydration and thousands of death annually, particularly among children.

In a paper published May 31 in the journal PLoS One, a research team led by Elena Naumova, Ph.D., professor of civil and environmental engineering at Tufts School of Engineering, correlated temperature and precipitation with rotavirus outbreaks in one of the hardest-hit regions of the world, South Asia.

In 2004, rotavirus resulted in 527,000 deaths worldwide in children younger than five years, the study noted. The majority of deaths are clustered in poor areas of developing countries in Africa and Asia. Being able to predict infection increases opportunities for health professionals to take effective preventive measures such as vaccination that could substantially reduce deaths.

Naumova's research focuses on developing methodology for analysis of large databases to enhance disease surveillance. In this study the team examined seasonal differences in the environment by creating mathematical models based on factors such as temperature, humidity and precipitation in the region over 22 years.

"We found that rotavirus is sensitive to seasonal patterns that are defined as a combined effect of temperature and precipitation," said Naumova, who is senior author of the study. This work builds on Naumova's previous research developing mathematical models to predict the timing, severity, and impact of diseases. "Our goal is to develop an integrated model which will allow monitoring the virus and also forecasting outbreaks."

Naumova and the research team analyzed monthly rates of rotavirus from 39 published epidemiological studies that were conducted on outbreaks between 1988 and 2010.

In this new model, the researchers considered meteorological characteristics based on data from the National Climatic Data Center and the Global Historical Climatology Network to classify four geographic regions in South Asia -- moist tropical climates; arid and semiarid; humid, mid-latitude areas, and cold temperature areas.

The incidence of rotavirus throughout Bangladesh, Bhutan, India, Nepal, Pakistan, and Sri Lanka was higher during the coldest, driest months of the year -- from December to March--the study indicated. Increases in temperature and precipitation in other parts of the year resulted in lower levels of the virus. Patterns were consistent across the geographical regions, though the fluctuations varied in intensity.

Additionally, the researchers found an association between rotavirus and vegetation density. Using remote sensing data derived from satellite images produced by moderate resolution imaging spectroradiometer (MODIS), a sensor on NASA's Terra satellite, the researchers were able to monitor and measure the amount of fresh vegetation across the region. A second source of remote sensing data was provided by the Global Inventory Monitoring and Mapping Studies (GIMMS).

The images were processed using mathematical techniques for turning satellite data into vegetation growth measurements. Researchers were able to measure changes in vegetation from 2000 to 2007. An analysis of data associated decreases of vegetation with an increase in the virus.

Naumova is the director of the Tufts Initiative for the Forecasting and Modeling of Infectious Diseases (InForMID), which works to improve biomedical research by developing computational tools in order to assist life science researchers, public health professionals, and policy makers.

Collaborators on the study included Jyotsna Jagai of the USA Environmental Protection Agency, Rajiv Sarkar, Deepthi Kattula, and Gagandeep Kang of the Christian Medical College in Vellore India; Denise Castronovo of Mapping Sustainability LLC; Jesse McEntee of Cardiff University in Cardiff Wales; and Honorine Ward of Tufts University School of Medicine.

This study was funded by the National Institute of Environmental Health Sciences, the National Institutes of Health, the Ruth L. Kirchstein NIH National Research Trainee Fellowship and the FIC Global Infectious Disease Research Training grant. The study received additional support from the Indo-U.S. Collaboration on Environmental and Occupational Health.


Story Source:

The above story is based on materials provided by Tufts University. Note: Materials may be edited for content and length.


Journal Reference:

  1. Jyotsna S. Jagai, Rajiv Sarkar, Denise Castronovo, Deepthi Kattula, Jesse McEntee, Honorine Ward, Gagandeep Kang, Elena N. Naumova. Seasonality of Rotavirus in South Asia: A Meta-Analysis Approach Assessing Associations with Temperature, Precipitation, and Vegetation Index. PLoS ONE, 2012; 7 (5): e38168 DOI: 10.1371/journal.pone.0038168

Cite This Page:

Tufts University. "Weather patterns can be used to forecast rotavirus outbreaks." ScienceDaily. ScienceDaily, 31 May 2012. <www.sciencedaily.com/releases/2012/05/120531200609.htm>.
Tufts University. (2012, May 31). Weather patterns can be used to forecast rotavirus outbreaks. ScienceDaily. Retrieved September 2, 2014 from www.sciencedaily.com/releases/2012/05/120531200609.htm
Tufts University. "Weather patterns can be used to forecast rotavirus outbreaks." ScienceDaily. www.sciencedaily.com/releases/2012/05/120531200609.htm (accessed September 2, 2014).

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