GETTING TO BETTER PANDEMIC AND EPIDEMIC PREPAREDNESS AND RESPONSE

The earlier we can detect and identify health threats, the faster we can respond and the more lives we can save...not to mention the impact on other aspects of societies and economies, as we clearly see through COVID-19 and other infectious disease events in our history. But responding faster requires us to do something with those things that we detect earlier first. It requires us to transform what we get through surveillance systems and the other vast amounts of information available to us in our increasingly digital world to intelligence that can then lead to appropriate actions. This should inform our collective priorities for surveillance;we need to ask ourselves how we can improve our intelligence so that the decisions that are made and the policies that are put in place are better informed, more timely and, ultimately, more effective in protecting lives and livelihoods. Seeking to address this very question, and in the midst of the COVID-19 pandemic, the World Health Organization's Hub for Pandemic and Epidemic Intelligence was created. This presentation will provide an overview of some of the intelligence work preceding its creation through the Epidemic Intelligence from Open Sources (EIOS) initiative and highlight some of its key activities and ambitions moving forward. [ FROM AUTHOR] Copyright of International Journal of Infectious Diseases is the property of Elsevier B.V. and its content may not be copied or emailed to multiple sites or posted to a listserv without the copyright holder's express written permission. However, users may print, download, or email articles for individual use. This may be abridged. No warranty is given about the accuracy of the copy. Users should refer to the original published version of the material for the full . (Copyright applies to all s.)

COVID-19 Relief Measures assimilating Open Source Intelligence

The COVID-19 pandemic brought down the entire world to a standstill. There was a sudden surge in the infection occurrences referred to as waves during which hospitals and treatment facilities experienced multiple challenges because of sudden and unexpected demands. Timely diagnosis, treatment, and medication are very important for the survival of the patients. India, being the second most populous nation in the world, required technology based innovations to overcome the Covid challenges. As an answer to this challenge, we identified multiple sources on the internet providing reliable information for relief measures and collected data and presented them in one platform. This helps in connecting the affected users from the Internet, social media platforms etc to the right facility in the fastest and most efficient way possible by aggregating and disseminating the relevant data. This one-stop website with all the imperative features directly benefits citizens/general public and help desk / emergency responders. © 2022 IEEE.

Applications of Open Source Intelligence in Crisis Analysis—A COVID-19 Case Study

The ongoing Coronavirus pandemic caused by severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2), also known as COVID-19, was first identified in the city of Wuhan in late December 2019. It was declared a pandemic during march 2020, as it caused widespread infections and deaths. COVID-19 has caused millions of deaths across the world, making it one of the deadliest pandemics that has ever occurred. The great pandemic affected billions of lives economically and emotionally. Traditionally, in-person surveys were organised in order to perform crisis analysis to identify and create statistics of people affected during such pandemic outbreaks or disasters. Being an extensive process by itself, the amount of time these surveys consume and the requirement of a large workforce to collect such statistics do not favour the government or the governing authorities in coming up with solutions and timely services that would upbring the affected set of people. Apart from taking into account the physical damage caused, there is no mechanism that gives importance to the mental state of the masses in such situations. This study aims at performing crisis analysis based on the emotions/reactions exhibited by the people on the Internet. The collection of the entire dataset is done with the help of open-source intelligence tools. A monthly sentiment analysis is performed to compare the emotions, followed by performing sentiment analysis on the dataset collected from March 2020 to March 2021 to analyse the emotions shown by people during COVID-19 and sentiment prediction with projected accuracies on the collected dataset that is done using six machine learning classification algorithms. © 2022, The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd.