SDL-PAND KPIs Model actual
Introduction
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Detected new cases model 2.11
Model 2.11 with a parametretrization for reinfection due to new BA,5 variant of about 100% (worse scenario). Data regarding the spread across data ranges can be reviewed at http://pand.sdlps.com/Pandemic-drivers.html (*From the version 2.10 of the model the forecast tries to find the current number of real cases and not a pesimistic scenario like previous models.)
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Information
This panel starts with the SDL-PAND project.
SDL-PAND aim is to become a Minimum Viable Digital Twin of the Catalonia pandemic situation to enhance the discussion based on models.
SDL-PAND: PANDEMIC SIMULATION TO TEST THE EFFECTIVENESS OF CONTAINMENT STRATEGIES THROUGH CELLULAR AUTOMATA AND INTELLIGENT AGENTS USING FORMAL LANGUAGES |
With the collaboration of:
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All Catalunya
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New cases
The simulation model allows to have a forecast of the evolution of the pandemic. It is corrected based on the actual data of the evolution of the pandemic. The current model is model 2.10. The R is calculated with the EpiEstim package of the statistical language “R”.
R value (Digital Shadow)
Accumulated curve (Digital Shadow)
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KPI details
C3: 3 days moving average; C7: 7 days moving average; C14: 14 days moving average. C7 and C14 are more stable than C3, picking up trend variations in full week. Especially C3 has bias, an increase (very unstable) may represent a quick warning of possible worsening. Calculated for 100,000 inhabitants.
Effective growth. Digital Shadow
Detected cases.
Incidence. Digital Shadow.
Detected cases.
Effective growth. Digital Twin
True cases.
Incidence. Digital Twin.
True cases.
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Last day of system update
2024-06-17New cases from the last day. Digital Shadow.
5515Total number of cases (detected, with reinfections). Digital Shadow.
3806331Last day of system executed.
2024-06-25Actual number of cases (estimated, not considering reinfections). Digital Twin.
7275200Actual number of cases at the end of the wave (estimated, not considering reinfections). Digital Twin.
7473330Row
Infections
Health system incidence
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Hospitalizations
Hospitalizations.
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UCIs
Critical hospitalizations.
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Exitus
Daily deaths.
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Vaccinations First dose
Daily vaccinations (first dose, cumulative)
Vaccinations Second dose
Daily vaccinations (second dose, cumulative)
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Vaccinations% First Dose
87.94Vaccinations% Second Dose
80.11Health regions
Catalonia Health Regions
The aim of modeling by health regions is not the forecast but the validation of the global forecast.
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SDL-PAND: PANDEMIC SIMULATION TO TEST THE EFFECTIVENESS OF CONTAINMENT STRATEGIES THROUGH CELLULAR AUTOMATA AND INTELLIGENT AGENTS USING FORMAL LANGUAGES
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EDAR
Catalonia EDAR
The aim of modeling by EDAR is to perform a cross validation of the model previsions.
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Catalonia EDAR PUIGCERDÀ - DPUI
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Catalonia EDAR BESÒS - DBSS
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Catalonia EDAR GAVÀ/VILADECANS - DGVC
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Catalonia EDAR PRAT DE LLOBREGAT, EL - DPDL
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Catalonia EDAR FALSET - DFAL
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Catalonia EDAR REUS - DRUS
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Catalonia EDAR TARRAGONA - DTAR
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Catalonia EDAR 9
Edar IGUALADA - DIGU
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Catalonia EDAR MANRESA - DMAS
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Catalonia EDAR SOLSONA - DSOL
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Catalonia EDAR 12
Edar BANYOLES - DBAY
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Catalonia EDAR 13
Edar GIRONA - DGIR
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Catalonia EDAR 14
Edar PALAMÓS - DPAM
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Catalonia EDAR 15
Edar BALAGUER - DBAL
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Catalonia EDAR LLEIDA - DLLE
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Catalonia EDAR MONTFERRER - DMOF
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Catalonia EDAR 18
Edar AMPOSTA - DAMP