  Background: Early detection and isolation of COVID-19 patients are essential
for successful implementation of mitigation strategies and eventually curbing
the disease spread. With a limited number of daily COVID-19 tests performed in
every country, simulating the COVID-19 spread along with the potential effect
of each mitigation strategy currently remains one of the most effective ways in
managing the healthcare system and guiding policy-makers. Methods: We introduce
COVIDHunter, a flexible and accurate COVID-19 outbreak simulation model that
evaluates the current mitigation measures that are applied to a region and
provides suggestions on what strength the upcoming mitigation measure should
be. The key idea of COVIDHunter is to quantify the spread of COVID-19 in a
geographical region by simulating the average number of new infections caused
by an infected person considering the effect of external factors, such as
environmental conditions (e.g., climate, temperature, humidity) and mitigation
measures. Results: Using Switzerland as a case study, COVIDHunter estimates
that if the policy-makers relax the mitigation measures by 50% for 30 days then
both the daily capacity need for hospital beds and daily number of deaths
increase exponentially by an average of 5.1x, who may occupy ICU beds and
ventilators for a period of time. Unlike existing models, the COVIDHunter model
accurately monitors and predicts the daily number of cases, hospitalizations,
and deaths due to COVID-19. Our model is flexible to configure and simple to
modify for modeling different scenarios under different environmental
conditions and mitigation measures. Availability: We release the source code of
the COVIDHunter implementation at https://github.com/CMU- SAFARI/COVIDHunter
and show how to flexibly configure our model for any scenario and easily extend
it for different measures and conditions than we account for.
