Added readme and license files

LICENSE

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+MIT License
+
+Copyright (c) 2021 EcoGRAPH: Ecological Geospatial Research and Applications in Planetary Health
+
+Permission is hereby granted, free of charge, to any person obtaining a copy
+of this software and associated documentation files (the "Software"), to deal
+in the Software without restriction, including without limitation the rights
+to use, copy, modify, merge, publish, distribute, sublicense, and/or sell
+copies of the Software, and to permit persons to whom the Software is
+furnished to do so, subject to the following conditions:
+
+The above copyright notice and this permission notice shall be included in all
+copies or substantial portions of the Software.
+
+THE SOFTWARE IS PROVIDED "AS IS", WITHOUT WARRANTY OF ANY KIND, EXPRESS OR
+IMPLIED, INCLUDING BUT NOT LIMITED TO THE WARRANTIES OF MERCHANTABILITY,
+FITNESS FOR A PARTICULAR PURPOSE AND NONINFRINGEMENT. IN NO EVENT SHALL THE
+AUTHORS OR COPYRIGHT HOLDERS BE LIABLE FOR ANY CLAIM, DAMAGES OR OTHER
+LIABILITY, WHETHER IN AN ACTION OF CONTRACT, TORT OR OTHERWISE, ARISING FROM,
+OUT OF OR IN CONNECTION WITH THE SOFTWARE OR THE USE OR OTHER DEALINGS IN THE
+SOFTWARE.

README.md

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+
+Project contains a code accompaniment to the article in BMC Public Health: "Comparing Malaria Early Detection Methods in a Declining Transmission Setting in Northwestern Ethiopia" by Nekorchuk et al. (2021). 
+
+The project contains functions to perform the following two analyses which were presented in the paper:
+
+1) Our novel Trend Weighted Seasonal Thresholds (TWST) approach which was designed to identify malaria events retrospectively in the context of seasonal patterns and decreasing long-term trends in disease transmission, while allowing for variation in patterns across geographical districts as well as slight time-shifts in seasonal peaks between years.
+
+2) Event Detection Comparison: Comparing various Early Detection algorithms used in the paper: Random alarms (naive model), weekly statistics-based thresholds (e.g. WHO, Cullen), CDC EARS, and Farrington algorithms. 
+
+This project also contains demo run scripts for TWST, 'run_twst_demo.R', and event detection comparison, 'run_ed_compare_demo.R', with synthetic data for demonstration purposes ONLY. 
+
+---
+This work is part of a larger project, Epidemic Prognosis Incorporating Disease and Environmental Monitoring for Integrated Assessment (EPIDEMIA). The EPIDEMIA Forecasting System integrate surveillance and environmental data to model and create short-term forecasts for environmentally-mediated diseases. 
+
+For more information, please see the demo project based on malaria in Ethiopia (with demo data): https://github.com/EcoGRAPH/epidemiar-demo
+
+EPIDEMIA project: http://ecograph.net/epidemia/
+