Radina Soebiyanto

Towards malaria risk prediction in Afghanistan using remote sensing

BackgroundMalaria is a significant public health concern in Afghanistan. Currently, approximately 60% of the population, or nearly 14 million people, live in a malaria-endemic area. Afghanistans diverse landscape and terrain contributes to the heterogeneous malaria prevalence across the country. Understanding the role of environmental variables on malaria transmission can further the effort for malaria control programme.MethodsProvincial malaria epidemiological data (2004-2007) collected by the health posts in 23 provinces were used in conjunction with space-borne observations from NASA satellites. Specifically, the environmental variables, including precipitation, temperature and vegetation index measured by the Tropical Rainfall Measuring Mission and the Moderate Resolution Imaging Spectoradiometer, were used. Regression techniques were employed to model malaria cases as a function of environmental predictors. The resulting model was used for predicting malaria risks in Afghanistan. The entire time series except the last 6 months is used for training, and the last 6-month data is used for prediction and validation.ResultsVegetation index, in general, is the strongest predictor, reflecting the fact that irrigation is the main factor that promotes malaria transmission in Afghanistan. Surface temperature is the second strongest predictor. Precipitation is not shown as a significant predictor, as it may not directly lead to higher larval population. Autoregressiveness of the malaria epidemiological data is apparent from the analysis. The malaria time series are modelled well, with provincial average R2 of 0.845. Although the R2 for prediction has larger variation, the total 6-month cases prediction is only 8.9% higher than the actual cases.ConclusionsThe provincial monthly malaria cases can be modelled and predicted using satellite-measured environmental parameters with reasonable accuracy. The Third Strategic Approach of the WHO EMRO Malaria Control and Elimination Plan is aimed to develop a cost-effective surveillance system that includes forecasting, early warning and detection. The predictive and early warning capabilities shown in this paper support this strategy.

Use of the NASA Giovanni Data System for Geospatial Public Health Research: Example of Weather-Influenza Connection

Abstract: The NASA Giovanni data analysis system ha s been recognized as a useful tool to access and analyze many different types of remote sensing data. The variety of environmental data types has allowed the use of Giovanni for different application areas, such as agriculture, hydrology, a nd air quality research. The us e of Giovanni for researching connections between public health issues and Earth’s environment and climate, potentially exacerbated by anthropogenic influence, has been increasingly demonstrated. In this communication, the pertinence of several different data parameters to public health will be described. This communication also provides a case study of the use of remote sensing data from Giovanni in assessing the associations between seasonal influenza and meteorological parameters. In this study, logistic regression was employed with precipitation, temperature and specific humidity as predictors. Specific humidity was found to be associated ( p < 0.05) with influenza activity in both temperate and tr opical climate. In the two temperate locations

Meteorological parameters as predictors for seasonal influenza

Seasonal influenza causes 5 million severe illnesses and 500,000 deaths annually worldwide. Among the factors that have been linked to influenza transmission are meteorological parameters, especially temperature and humidity. Low temperature and humidity have been associated with influenza seasonality in the temperate regions, whereas the tropics typically observe higher influenza transmission during rainy season. In this study, we assessed the role of meteorological factors on influenza transmission using both satellite-derived and ground station data for temperate and sub-tropical regions. Auto Regressive Integrated Moving Average and Neural Network were employed to assess the meteorological indicators and for forecasting. Our findings show that measures of temperature, humidity, rainfall and solar radiation can be used as indicators to forecast influenza. We also found that rainfall can be used as a predictor for sub-tropical region, but not in all temperate regions. Overall, our models can predict the timing of influenza peak.