# Remote Sensing for International Peace and Security: Its Role and Implications
## 3 Results
### Remote Sensing for Disease Control and Prevention  
With accelerating global warming and climate change combined with human-animal conflict, there is a rise in communicable and non-communicable diseases. Infectious diseases such as HIV/AIDS, Ebola, SARS, Zika, and COVID-19 posed a challenge to the global peace order [99]. The spread of a disease, whether it is within a developed or a developing country, can have severe security repercussions. A widespread outbreak can shut down a country, causing both fear and an economic downturn. Consequently, improvements in methods by which the government can identify and stem the outbreak of disease early on are necessary. Remote sensing has been suggested as such a method,using different characteristics of diseases to identify either the outbreak or possibilities of risk. It can be used for monitoring, surveying, or conducting a risk analysis of vector-borne diseases [100]. Disease mapping once believed to be a final frontier for remote sensing [101] has come a long way with better spatio-temporal availability of remote sensing platforms and socially sensed datasets [102; 103].  
Remote sensing has been used during studies done on malaria [102], Lyme disease [100], Rift Valley fever, and the COVID-19 pandemic [101; 104; 105; 106; 107; 108; 109], among others [101; 104; 107; 108; 109]. However, it should be noted that these studies have only been done post facto, as a way to verify whether different remote sensing indicators matched the actual occurrence on the ground. Subsequently, this means that these methods have never been tested ex-ante, either as an outbreak was occurring or to prevent an outbreak from happening. An example of such a study concerned the spread of Lyme disease in the Northeastern United States [110]. Remote sensing was used in several ways to examine the \"map relative tick abundance on residential properties by using Landsat TM-derived indices of vegetation greenness and wetness\" [110]. Essentially, this study aims to identify risk factors for Lyme disease exposure in different populated areas. The study extracted information about contiguous forest patches using Landsat TM data. Contiguous forest patches are a favorite location of white-tailed deer, and its illegal transfer from the region. Furthermore, it was examined whether these were areas for human host exposure. Using these factors, as well as examining the suitable \"tasseled cap greened and wetness,\" which measures the greenness and wetness of an area, residential areas harboring abundant tick populations were identified. The researchers could then determine the areas that were most at risk of exposure to the ticks and, therefore, Lyme disease [110].  
Another example is the case of cholera in Bangladesh. Using remote sensing, the researchers looked for sediment loads within the Bay of Bengal, which contain nutrients that could support the plankton that could contain _Vibrio cholerae_[110]. The researchers also examined the height, temperature of the sea surface, and the amount of chlorophyll within the water to determine the possibility of there being _Vibrio cholerae_ in the Bay (Ibid.). Since this study was done after the event, the different factors showed a positive correlation in the appearance of cholera in the water. By completing this research, it is hoped that remote sensing could be used to examine the existence of cholera within the water before an outbreak happens or within its beginning stages. Both the above studies mentioned above were done after a disease outbreak. Currently, the Japanese Aerospace Exploration Agency (JAXA) operates the Public-health Monitor and Analysis Platform (JMAP) for surveillance and prediction of malaria and cholera in African countries [111]. However, the reliability of remote sensing-based predictions is still unclear for this type of investigation. Furthermore, the hope is that this type of analysis can be expanded to include other water-borne and soil-borne diseases through model analysis to identify warning signs of a disease outbreak.