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Background: Early detection and isolation of COVID-19 patients are essentialfor successful implementation of mitigation strategies and eventually curbingthe disease spread. With a limited number of daily COVID-19 tests performed inevery country, simulating the COVID-19 spread along with the potential effectof each mitigation strategy currently remains one of the most effective ways inmanaging the healthcare system and guiding policy-makers. Methods: We introduceCOVIDHunter, a flexible and accurate COVID-19 outbreak simulation model thatevaluates the current mitigation measures that are applied to a region andprovides suggestions on what strength the upcoming mitigation measure shouldbe. The key idea of COVIDHunter is to quantify the spread of COVID-19 in ageographical region by simulating the average number of new infections causedby an infected person considering the effect of external factors, such asenvironmental conditions (e.g., climate, temperature, humidity) and mitigationmeasures. Results: Using Switzerland as a case study, COVIDHunter estimatesthat if the policy-makers relax the mitigation measures by 50% for 30 days thenboth the daily capacity need for hospital beds and daily number of deathsincrease exponentially by an average of 5.1x, who may occupy ICU beds andventilators for a period of time. Unlike existing models, the COVIDHunter modelaccurately monitors and predicts the daily number of cases, hospitalizations,and deaths due to COVID-19. Our model is flexible to configure and simple tomodify for modeling different scenarios under different environmentalconditions and mitigation measures. Availability: We release the source code ofthe COVIDHunter implementation at https://github.com/CMU- SAFARI/COVIDHunterand show how to flexibly configure our model for any scenario and easily extendit for different measures and conditions than we account for.

COVIDHunter: An Accurate, Flexible, and Environment-Aware Open-Source COVID-19 Outbreak Simulation Model