Prediction of high-risk areas for visceral leishmaniasis using socioeconomic indicators and remote sensing data

The official method of collecting county-level GDP values in the Chinese Mainland relies mainly on administrative reporting data and suffers from high costs of time, money, and human labor. To date, a series of studies have been conducted to generate fine-grained maps of socioeconomic indicators from the easily accessed remote sensing data and achieved satisfactory results. This paper proposes a transfer learning framework that regards nightlight intensities as a proxy of economic activity degrees to estimate county-level GDP around the Chinese Mainland. In the framework, paired daytime satellite images and nightlight intensity levels were applied to train a VGG-16 architecture, and the output features at a specific layer, after dimensional reduction and statistics calculation, were fed into a simple regressor to estimate county-level GDP. We trained the model with data of 2017 and utilized it to predict county-level GDP of 2018, achieving an R-squared of 0.71. Furthermore, the results of gradient visualization confirmed the validity of the proposed framework qualitatively. To the best of our knowledge, this is the first time that county-level GDP values around the Chinese Mainland have been estimated from both daytime and nighttime remote sensing data relying on attention-augmented CNN. We believe that our work will shed light on both the evolution of fine-grained socioeconomic surveys and the application of remote sensing data in economic research.

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Temporal Monitoring and Predicting of the Abundance of Malaria Vectors Using Time Series Analysis of Remote Sensing Data through Google Earth Engine

10.21203/rs.3.rs-1103260/v1 ◽

2021 ◽

Author(s):

Fahimeh Youssefi ◽

Mohmmad Javad Valadan Zoej ◽

Ahmad Ali Hanafi-Bojd ◽

Alireza Borhani Darian ◽

Mehdi Khaki ◽

...

Keyword(s):

Remote Sensing ◽

Time Series ◽

High Risk ◽

Environmental Factors ◽

Remote Sensing Data ◽

Google Earth ◽

Landsat 8 ◽

Sensing Data ◽

Temporal Prediction ◽

Daily Precipitation Data

Abstract Background: In many studies in the field of malaria, environmental factors have been acquired in single-time, multi-time or a short time series using remote sensing and meteorological data. Selecting the best periods of the year to monitor the habitats of Anopheles larvae can be effective in better and faster control of malaria outbreak.Methods: In this article, high-risk times for three regions in Iran, including Qaleh-Ganj, Sarbaz and Bashagard counties with history of malaria prevalence had been estimated. For this purpose, a series of environmental factors affecting the growth and survival of Anopheles had been used over a seven-year period through the GEE. Environmental factors used in this study include NDVI and LST extracted from Landsat-8 satellite images, daily precipitation data from PERSIANN-CDR, soil moisture data from NASA-USDA Enhanced SMAP, ET data from MODIS sensor, and vegetation health indices included TCI and VCI extracted from MODIS sensors. All these parameters were extracted on a monthly average for seven years and, their results were fused at the decision level using majority voting method to estimate high-risk time in a year.Results: The results of this study indicated that there were two high-risk times for all three study areas in a year to increase the abundance of Anopheles mosquitoes. The first peak occurred from late winter to late spring and the second peak from late summer to mid-autumn. If there is a malaria patient in the area, after the end of the Anopheles larvae growth period, the disease will spread throughout the region. Further evaluation of the results against the entomological data available in previous studies showed that the high-risk times predicted in this study were consistent with the increase in the abundance of Anopheles mosquitoes in the study areas. Conclusions: The proposed method is very useful for temporal prediction of the increase of the abundance of Anopheles mosquitoes and also the use of optimal data with the aim of monitoring the exact location of Anopheles habitats. This study extracted high-risk time based on the analysis of the time series of remote sensing data.

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