Corosim finnish flag

Historical Estimates & Model Predictions for COVID-19 in Finland

Sorry! This web app is not optimized for a mobile experience or small screens. If you can, please come back on a desktop device.

Scenario outcome
  • 17,860 fatalities in first 977 days.
  • Peak ICU on 26.05.2022.
  • 1278 ICU patients at peak.
  • 57% remain susceptible.
Highlighted day
27.07.2020
✓
Infected
∑ 581 (0.01%)
Δ +23 on day 168
Active infections (incl. incubating, undiagnosed) (excl. hosp, icu)
✓
Hospitalized
∑ 5 (0.00%)
Δ +1 on day 168
Active hospitalizations (excluding ICU)
✓
ICU
∑ 0 (0.00%)
Δ -4 on day 168
Patients in intensive care, active
Recovered
∑ 43,182 (0.78%)
Δ +64 on day 168
Number of full recoveries, cumulative
✓
Fatalities
∑ 411 (0.01%)
Δ +0 on day 168
Number of deaths, cumulative
0 20,00040,00060,00080,000100,000120,000140,000FebMarchAprilMayJuneJulyAugSepOctNovDec2021FebMarchAprilMayJuneJulyAugSepOctNovDecDecJanMarchMarch
Historical estimates
Model predictions
→R0=1.15→ \mathcal{R}_0=1.15
Vaccinations on 24.02.2021
-20% transmission (R0=0.92\mathcal{R}_0=0.92)
Configure
New strain on 02.10.2021
+50% transmission (R0=1.38\mathcal{R}_0=1.38)
Configure
First death
28.07.2020

Parameter configuration

Basic Reproduction Number R0\mathcal{R}_0 (initially)
1.15
Please note that R0 is affected by action markers (those vertical things on the chart).
Undetected infections
83.00 %
Unrecorded deaths
20.00 %
Length of incubation period TincT_{\text{inc}}
4.00 days
Duration patient is infectious TinfT_{\text{inf}}
2.00 days
Length of hospital stay
11.60 days
Recovery time for mild cases
4.00 days
Hospitalization rate
2.15 %
Infection fatality rate
0.75 %
ICU rate
(visualization only)
30.00 %
ICU capacity
(visualization only)
700
Introduction
Corosim combines historical estimates & model predictions to provide a complete overview of the Coronavirus epidemic in Finland. This means you can use Corosim to get some insight towards questions such as "how many Finns have been infected so far" or "when will the epidemic peak".
Our model is a classical epidemiological model (deterministic SEIR). The model is initialized with the latest historical estimate (you can drag the historical estimate "curtain" on the graph if you want the model to start from a different timepoint). Parameter choices impact both historical estimates and model predictions. You have the possibility of tuning parameters by yourself (under the graph). You can also set your own action markers to model the effects of different policy changes (those vertical lines on top of the graph). You can drag action markers, you can add new action markers, you can delete old action markers, and you can configure the name and effect of an action (effect on disease transmissions, in percentage).
Historical estimates
Model predictions
Attribution