Location-based Rapid Testing For Epidemic (COVID-19) Modeling

What is it about?

COVID-19 has resulted in millions of confirmed deaths globally. The global-scale response to the COVID-19 pandemic triggered drastic measures including economic shutdowns, travel bans, stay-home orders, and even complete lockdowns of entire cities, regions, and countries. Therefore, there is an urgent need for research into enhancing the understanding of the complex relationships characterizing pandemics and interventions under crisis. In particular, we need to look at communities at possibly different scales and investigate the impact of testing, preventative measures, and vaccines, when used in combination, to improve community response and resilience at different scales. Within the context of COVID-19, several specific questions arise: What is the value of testing? Should we only test sick people for virus detection? How should we handle limited testing and hospitalization capacity? How many resources should be devoted to the development of highly accurate tests (low false positives, low false negatives)? Should we only use one type of test aiming at the best cost/effectiveness trade-off or should we rather adopt a non-homogeneous testing policy? The key contribution of this paper is two folds: Firstly, we present a novel extended spatially-informed epidemic model, SIRTEM, Spatially Informed Rapid Testing for Epidemic Modeling and Response to Covid-19, that integrates a multi-modal testing strategy considering test accuracies. Our second contribution is an optimization model to provide a cost-effective testing strategy when multiple test types are available. The developed optimization model incorporates realistic spatially based constraints, such as testing capacity and hospital bed limitation as well.

Featured Image

Photo by Prasesh Shiwakoti (Lomash) on Unsplash

Photo by Prasesh Shiwakoti (Lomash) on Unsplash

Why is it important?

I had the opportunity to work with the COVID-19 dataset when the policymakers as well as the general population were not sure how COVID-19 would affect our lives in the years to come. Two significant findings of our paper are: a) When dealing with limited resources, it is essential to optimize the use of testings and hospital facilities to minimize infections and deaths, and b) It is important to take the spatial information into consideration when predicting the epidemic spread to forecast the time series effectively and efficiently.

Perspectives