Covid-19 Spread Simulation for Nepal: A call for collaboration
Adapted from the original version by Risav.
We are a group of national and international experts and scientists in the area of public health research, quantum statistical modeling, artificial intelligence, big data analysis and semantification. Our current goal is to enhance our powerful predictive models and simulation for the spread of COVID-19 in the Nepali population given different scenarios and high level decisions, including the best and worst cases.
Models and simulations based on our current approaches have many proven benefits including helping governments and relevant authorities make informed decisions. With large amounts of real world data and latest advancements in information technologies, we can discover previously unseen patterns and conclusions that cannot be attained through classical statistics and methods. We have a rare but highly prescient and state-of-the-art tool to potentially save many Nepali lives and livelihoods.
The core algorithm and the central model for this simulation was developed by Manish J. Thapa, a quantum physicist at ETH Zürich, one of the most historically significant research institutions in the world. His own research and some other leading research in this field from his peers and contemporaries, especially regarding responding to cascading disaster spreadings and frameworks for intelligent network adaptation, have been used to construct this simulation, which brings these findings together via an intelligent many-model approach. This simulation also includes the standard SEIR model in epidemiology. However, we have focused on building upon the existing established knowledge and taking it a few steps further instead of wholly depending on one single model. We believe this research, when combined with further real-time data from various sources will be a substantially helpful tool in Nepal and even at a global scale. However, we certainly need to focus first on Nepal given our lack of national scale technical collaboration.
Teams That Formed This Team
The aforementioned ‘we’ represents a growing collaboration. Manish and I have reached out to multiple organizations and individuals after he first shared his research.
NAAMII, an applied maths and AI based research non-profit, is the central body coordinating this entire effort. It has been instrumental in bringing together its network of experts in Nepal, US, UK, and Switzerland and has effectively created the platform Nepal has lacked for a national and international scale collaboration in this area.
Previously introduced nep.work group in EU/US along with our partner company in Nepal, Meraki Corp, is providing its advanced and secure data platform and coordinating with various current and potential data providers.
Our group takes privacy concerns and data anonymization very seriously. It has protocols and technologies in place for assuring the data providers and Nepali citizens about a transparent and careful handling of their data.
Recently, we were joined by Dr. Kiran Raj Pandey, who is a prominent figure in this field and has also been in touch with government officials regarding his ongoing SEIR modelling efforts for Kathmandu.
Leapfrog, which is one of the supporters and close partners of NAAMII, and their sister company Machnet, which provides guidance and support to nep.work group especially in its fintech efforts, are providing their support in bringing in further invaluable resources and organizations to this collaboration.
Fusemachines, also a close supporter and partner of NAAMII from its early days, has been leading CovidNepal.org which is another independent and important effort for this cause, that will be of great help especially in aggregating hospital related data. Please contribute reliable data to it and/or promote it to hospitals and organizations in possession of relevant data.
Please Join Us
We would like to call upon you and all organizations, businesses, and individuals who are in a position to provide us reliable data regarding Nepal’s health infrastructure, road traffic flow and human mobility. It is of critical importance that we as a nation track human movements and hospital infrastructure to be fully informed and prepared for what’s coming ahead.
Here are some topics and examples regarding how data aggregation, especially from telecommunication service providers, can help:
Crowd Densities Analysis
- Finding out lots of phone GPS and cell phone tower pings near a large vegetable market every evening would be a sure sign that we need to decentralize grocery supply.
- Finding out that the crowd densities change more during the day than at night will further substantiate our findings based on data from a leading Nepali ISP that not enough people have been staying at home during the day
- Finding out how many cell phones previously pinging Kathmandu’s cell towers are now pinging towers elsewhere will give us an approximation of how many people have left Kathmandu valley or how they are moving from and to different places in the last few weeks.
- GPS location reports, if possible to aggregate, will be even more precise and may provide us real-time data for the simulation.
There are more possibilities than these regarding improving the models behind our simulation. Please let us know if you have further ideas or if you have already made efforts similar to ours.