Share icon

Myanmar is one of the five countries with documented cases of emergence of artemisinin resistance. This project aims to develop a robust satellite data driven early warning system (Myanmar Malaria Early Warning System, MMEWS) that enables spatially-explicit monitoring and forecasting of potential surges in malaria burden in Myanmar. This approach quantifies malaria outbreak potential using an IPCC-defined risk-assessment framework which includes hazard, exposure, and vulnerability components. Data fusion from moderate (Landsat) and coarse (MODIS) resolution optical sensors supports the 8-day dynamic spatially explicit (resolved to village level) assessment of malaria burden potential. This project is developed by a team of experts in satellite remote sensing, geospatial modeling, and malariology and offers support for medical intervention and other decision-making activities in the Yangon office of the Institute for Global Health and regional partners and stakeholders. (December 2020 Update).

Key highlights:

Geographic scope: Republic of the Union of Myanmar

Earth observations: Multi-temporal Landsat imagery (Surface Water Fraction, Settlement mapping); MODIS land surface temperature (MOD/MYD11A2) and precipitable water (MOD/MYD07) datasets (downscaled to 240 & 30m resolution); Landsat-based existing publicly available datasets (Global Forest Loss, Global Impervious Surface, Global Fractional Tree & Bare Ground Cover); NASA-holdings of Very High Resolution imagery (to support baseline development)

Users: Duke Global Health Institute (DGHI), Myanmar

Malaria in Mynamar
Figure 1. The analysis of five years of historical runs has shown that Malaria Burden Potential (MBP) variable has a strong dynamic range seasonally and interannually. It allowed the team to establish that MBP values show a notable positive 5-year trend (shown in blue arrows with the slope of the trend quantified) across Myanmar (as well as within finer administrative units – not shown here). Notably, the greatest increase is observed within the areas of highest malaria burden (Shan, Kayin, and Chin States). Regions of the lowest malaria burden (Mandalay, Naypyitaw, and Yangon) show no discernable increase in malaria risk over the past five years. Considering that only environmental variables are dynamic in the current version of the model, this trend most likely indicates the increase in habitat suitability for the vector species. The model does not account for changes in access to care and social vulnerability associated with COVID-19 pandemic and the military coup.