Shiloh
Editor, Senior Moderator
Source: http://environmentalresearchweb.org/cws/article/research/39068
May 13, 2009
El Ni?o helps predict dengue fever outbreaks
Researchers at the Universities of Miami and Costa Rica are the first to accurately model a major dengue fever epidemic using data on climate and vegetation greenness. Doug Fuller and colleagues used sea-surface temperature anomalies related to the El Ni?o Southern Oscillation (ENSO) and satellite observations to predict the incidence of the fever with an accuracy of 64%. The model could be used as a tool to predict future outbreaks of the disease around the world.
Dengue fever and dengue haemorrhagic fever are the most important vector-borne (i.e. transmitted by other organisms) viral diseases on the planet today. Some 2.5 billion people are at risk around the world and up to 100 million cases occur each year. A staggering two thirds of the world's population resides in areas infected with the dengue vectors ? Aedes aegypti and Ae. Albopictus mosquitoes.
Dengue transmission is strongly influenced by environmental conditions, human behaviour and demography. This is because the mosquitoes responsible live in close contact with humans in urban areas, laying their eggs in containers like drums, buckets, tyres, flowers pots and vases.
The incidence of dengue fever has been increasing in recent years. The main reasons for this are globalization, population growth and uncontrolled urban development. Poor housing and sanitation, and lack of waste collection and disposal services, encourage larvae to grow and spread.
Fuller's team focused on Costa Rica, which saw over 100 000 cases of the disease from 2003 to 2007. The researchers used sea-surface temperature anomalies related to the El Ni?o Southern Oscillation (ENSO) and satellite observations of vegetation greenness from the moderate resolution imaging spectrometer (MODIS) on the Terra satellite. They then employed a simple Fourier-series equation to fit the weather and vegetation observations to the incidence of a dengue fever break out.
The model works because increased, lush, vegetation cover and higher moisture levels in the atmosphere (which occur during tropical storms) speed up the rate at which mosquitoes breed and larvae grow. Similar models have already been used to predict malaria outbreaks.
The new model can explain 83% of the variance in weekly dengue fever/dengue haemorrhagic fever cases when run back in time. And when run forwards in time, it can explain 64% of the variance. According to the team, it may be able to predict dengue disease outbreaks as early as 40 weeks in advance as well as provide crucial information on the extent of future epidemics.
"We think our model may be operationalized quite quickly as a tool to predict future outbreaks, not only in Costa Rica but also in other countries where ENSO affects the climate," Fuller told environmentalresearchweb. Indeed, the researchers have tested the model for Trinidad and it works very well there too. This study will be published later this year.
"In its current form we believe the model may be used to inform national vector control programs and policies regarding control measures, including prevention and planning of medical services for those likely to be affected during future outbreaks," write the researchers.
The model can also predict disease outbreak over a shorter time period (weeks) than other tools such as malaria early warning systems that typically rely on monthly data. This is important because epidemics have a habit of spreading rapidly once they start.
The work was reported in Environmental Research
May 13, 2009
El Ni?o helps predict dengue fever outbreaks
Researchers at the Universities of Miami and Costa Rica are the first to accurately model a major dengue fever epidemic using data on climate and vegetation greenness. Doug Fuller and colleagues used sea-surface temperature anomalies related to the El Ni?o Southern Oscillation (ENSO) and satellite observations to predict the incidence of the fever with an accuracy of 64%. The model could be used as a tool to predict future outbreaks of the disease around the world.
Dengue fever and dengue haemorrhagic fever are the most important vector-borne (i.e. transmitted by other organisms) viral diseases on the planet today. Some 2.5 billion people are at risk around the world and up to 100 million cases occur each year. A staggering two thirds of the world's population resides in areas infected with the dengue vectors ? Aedes aegypti and Ae. Albopictus mosquitoes.
Dengue transmission is strongly influenced by environmental conditions, human behaviour and demography. This is because the mosquitoes responsible live in close contact with humans in urban areas, laying their eggs in containers like drums, buckets, tyres, flowers pots and vases.
The incidence of dengue fever has been increasing in recent years. The main reasons for this are globalization, population growth and uncontrolled urban development. Poor housing and sanitation, and lack of waste collection and disposal services, encourage larvae to grow and spread.
Fuller's team focused on Costa Rica, which saw over 100 000 cases of the disease from 2003 to 2007. The researchers used sea-surface temperature anomalies related to the El Ni?o Southern Oscillation (ENSO) and satellite observations of vegetation greenness from the moderate resolution imaging spectrometer (MODIS) on the Terra satellite. They then employed a simple Fourier-series equation to fit the weather and vegetation observations to the incidence of a dengue fever break out.
The model works because increased, lush, vegetation cover and higher moisture levels in the atmosphere (which occur during tropical storms) speed up the rate at which mosquitoes breed and larvae grow. Similar models have already been used to predict malaria outbreaks.
The new model can explain 83% of the variance in weekly dengue fever/dengue haemorrhagic fever cases when run back in time. And when run forwards in time, it can explain 64% of the variance. According to the team, it may be able to predict dengue disease outbreaks as early as 40 weeks in advance as well as provide crucial information on the extent of future epidemics.
"We think our model may be operationalized quite quickly as a tool to predict future outbreaks, not only in Costa Rica but also in other countries where ENSO affects the climate," Fuller told environmentalresearchweb. Indeed, the researchers have tested the model for Trinidad and it works very well there too. This study will be published later this year.
"In its current form we believe the model may be used to inform national vector control programs and policies regarding control measures, including prevention and planning of medical services for those likely to be affected during future outbreaks," write the researchers.
The model can also predict disease outbreak over a shorter time period (weeks) than other tools such as malaria early warning systems that typically rely on monthly data. This is important because epidemics have a habit of spreading rapidly once they start.
The work was reported in Environmental Research