# COVIDHunter

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

A COVID-19 outbreak simulation model that evaluates the current mitigation measures (i.e., non-pharmaceutical intervention) that are applied to a region and provides insight into what strength the upcoming mitigation measure should be and for how long it should be applied, while considering the potential effect of environmental conditions. We explain our approach in detail and provide a comprehensive treatment of all datasets, models, and evaluation results with different model configurations here and here. COVIDHunter accurately forecasts for a given day:

- The reproduction number, R.
- The number of infected persons.
- The number of hospitalized persons.
- The number of deaths.
- The number of individuals at each stage of the COVID-19 infection (healthy, infected, contagious, and immune).
- The strength and the duration of each mitigation measure.

#### Epidemiological Situation in Switzerland Using COVIDHunter

We use Switzerland as a use-case for all the experiments. However, our model is not limited to any specific region as the parameters of COVIDHunter are completely configurable.
Using COVIDHunter on **19 April 2021**, we make two key observations:

- The spread of COVID-19 in Switzerland is
**still active**as the reproduction number (R) is still greater than 1.0. The R number value will remain greater than 1 until the last week of May 2021. - COVIDHunter forecasts the effect of
**relaxing the current mitigation measures on April 19, 2021**on the daily maximum number of COVID-19 cases, hospitalizations, and deaths as follows:

Strengths of the mitigation measures during April-May 2021 | 0.35 | 0.40 | 0.50 | 0.60 | 0.70 |
---|---|---|---|---|---|

Mitigation measures with similar strength were applied on | 6-30 September 2020 | 28 February - 15 March 2020 | 24 June - 20 August 2020 | 12-29 October 2020 | 28 November - 22 December 2020 |

Predicted daily number of cases | 1'512-18'267 | 1'520-9'471 | 564-3'567 | 114-2'684 | 29-1'479 |

Predicted daily number of hospitalizations | 42-507 | 42-263 | 15-99 | 3-74 | 1-41 |

Predicted daily number of deaths | 25-276 | 26-150 | 23-62 | 5-53 | 1-29 |

#### Raw Data and Visualizations

Other data:
January-February 2021 |
February-March 2021 |
April-May 2021

Other visualizations:
January-February 2021 |
February-March 2021 |
April-May 2021

To reproduce the exact same results for the Switzerland case study, please follow these instructions.

#### Mitigation Measure Strength

###### February 2020 - August 2021

#### Daily Reproduction Number, R

###### February 2020 - August 2021

#### Daily Number of COVID-19 Cases

###### February 2020 - August 2021

#### Daily Number of COVID-19 Hospitalizations

###### February 2020 - August 2021

#### Daily Number of COVID-19 Deaths

###### February 2020 - August 2021

#### Table of Abbreviations

Abbreviation | Description | Data Source |
---|---|---|

Observed R | The reproduction number, R, reported officially by the Federal Office of Public Health (FOPH) of the Swiss Confederation. The R number describes how a pathogen spreads in a particular population by quantifying the average number of new infections caused by each infected person at a given point in time. | Link |

Expected Cases | The expected true number of cases based on the number of hospitalizations, assuming that FOPH announces the true number of hospitalizations | Link |

Observed Hospitalizations | The number of COVID-19 hospitalizations reported officially by the FOPH of the Swiss Confederation | Link |

Observed Excess Deaths | The number of excess deaths, which we calculate as the difference between the observed number of deaths reported by the FOPH during 2020 and the expected number of deaths during 2020 (the average number of deaths for the last 5 years, 2015, 2016, 2017, 2018, and 2019). | Link |

ICL | The corresponding number as calculated by the Imperial College London (ICL) model (https://mrc-ide.github.io/global-lmic-reports/). The prediction plot is for maintaining the same mitigation measures. | Link |

ICL+50% | The corresponding predicted number as calculated by the ICL model when strengthening mitigation measures by 50%. | Link |

ICL-50% | The corresponding predicted number as calculated by the ICL model when relaxing mitigation measures by 50%. | Link |

IBZ | The corresponding number as calculated by the Theoretical Biology Group at ETH Zurich (IBZ) model (https://ibz-shiny.ethz.ch/covid-19-re-international/). | Link |

IHME | The corresponding number as calculated by the Institute for Health Metrics and Evaluation (IHME) model (https://mrc-ide.github.io/global-lmic-reports/) | Link |

LSHTM | The corresponding number as calculated by the London School of Hygiene & Tropical Medicine (LSHTM) model (https://cmmid.github.io/topics/covid19/global_cfr_estimates.html) | Link |

COVIDHunter | The corresponding number as calculated by the COVIDHunter model when using three configurations: 1) CRW or CTC environmental change approach, 2) a certainty rate level of 50% or 100%, and 3) using mitigation measures with a strength of 0.7 or 0.3 on a scale from 0 to 1, where 1 refers to the strongest mitigation measure. We explain each of these terms in the following rows below. | Link |

CRW | Harvard CRW (https://projects.iq.harvard.edu/covid19/home) environmental condition approach that considers both weather changes and air pollution. | |

CTC | CTC environmental condition approach that considers only weather changes. The CTC approach refers to our statistical analysis for the relationship between temperature and the number of COVID-19 cases in Switzerland. We find that for each 1 Celsius degree rise in daytime temperature, there is a 3.67% decrease in the daily number of confirmed cases. | |

100% | A certainty rate level of 100%, which means that FOPH reports 100% of the corresponding true number. | |

50% | A certainty rate level of 50%, which means that FOPH reports only 50% of the corresponding true number. | |

M(t)=0.7 | It means that we maintain the same strength of the currently applied mitigation measures. M(t)=0.7 means that the strength of the mitigation measures applied from 22 January to 22 February 2021 is 0.7 on a scale from 0 to 1, where 1 refers to the strongest mitigation measure. | |

M(t)=0.35 | The mitigation measures from 22 January to 22 February 2021 are relaxed by 50% compared to these mitigation measures that are applied right before 22 January 2021. | |

WithoutCTC_50% | This plot represents the effect of excluding environmental changes from the COVIDHunter model, by setting Ce(t)=1 in Equation 1, which leads to an inaccurate evaluation of the mitigation measures. For example, during the summer of 2020 (between the two major waves of 2020), COVIDHunter (WithoutCTC_50%) evaluates the mitigation coefficient to be as high as 0.6. This means that the mitigation measures (only mandatory of wearing mask on public transport) applied during the summer of 2020 are only 14% more relaxed compared to the mitigation measures (e.g., closure of schools, restaurants, and borders, ban on small and large events) applied during the first wave, which is implausible. This highlights the importance of considering the effect of external environmental changes on simulating the spread of COVID-19. Unfortunately, environmental change effects are not considered by any of the IBZ, LSHTM, ICL, and IHME models, which we believe is a serious shortcoming of these prior models. |