Covidhunter

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. We introduce COVIDHunter, a flexible and accurate COVID-19 outbreak simulation model that evaluates the current mitigation measures that are applied to a region, predicts COVID-19 statistics (the daily number of cases, hospitalizations, and deaths), 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), different variants of concern, vaccination rate, and mitigation measures. Using Switzerland as a case study, COVIDHunter estimates that we are experiencing a deadly new wave that will peak on 26 January 2022, which is very similar in numbers to the wave we had in February 2020. The policy-makers have only one choice that is to increase the strength of the currently applied mitigation measures for 30 days. 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. 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

Business · Disease · Econometrics · Outbreak · Pandemic · Computer Science · COVID-19 epidemiological studies · Environmental Science · Mathematics · Medicine · SARS-CoV-2 and COVID-19 Research · Viral Infections and Outbreaks Research · Virology

• The Incubation Period of Coronavirus Disease 2019 (Covid-19) From Publicly Reported Confirmed Cases

  Stephen A Lauer, Kyra H Grantz et al.•Annals of Internal Medicine•2020

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• Estimating the effects of non-pharmaceutical interventions on Covid-19 in Europe

  Open Access•Seymour Flaxman, Seth Flaxman et al.•Nature•2020

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• Impact of meteorological factors on the Covid-19 transmission

  Open Access•Jiangtao Liu, Ji Zhou et al.•The Science of The Total…•2020

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• Modelling transmission and control of the Covid-19 pandemic in Australia

  Open Access•Sheryl L Chang, Nathan Harding et al.•Nature Communications•2020

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• Epidemiology and transmission of Covid-19 in 391 cases and 1286 of their close contacts in Shenzhen, China

  Open Access•Qifang Bi, Yongsheng Wu et al.•The Lancet Infectious Diseases•2020

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• Seasonality of Respiratory Viral Infections

  Miyu Moriyama, Walter J Hugentobler et al.•Annual Review of Virology•2020

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• Inferring the effectiveness of government interventions against Covid-19

  Open Access•Jan Brauner, Sören Mindermann et al.•Science•2021

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• Association between ambient temperature and Covid-19 infection in 122 cities from China

  Open Access•Jingui Xie, Yongjian Zhu•The Science of The Total…•2020

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• The proximal origin of Sars-CoV-2

  Open Access•Kristian G Andersen, Andrew Rambaut et al.•Nature Medicine•2020

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• Early Transmission Dynamics in Wuhan, China, of Novel Coronavirus–Infected Pneumonia

  Open Access•Qun Li, Xuhua Guan et al.•New England Journal of Medicine•2020

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• Despite vaccination, China needs non-pharmaceutical interventions to prevent widespread outbreaks of Covid-19 in 2021

  Open Access•J Yang, Valentina Marziano et al.•Nature Human Behaviour•2021

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