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EpiSimulator: A Data-Driven Stochastic Hybrid Model for COVID-19 in Italy.

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License: MIT Docs: Report DOI: Zenodo

EpiSimulator

A Data-Driven Stochastic Hybrid Model for COVID-19 in Italy

Multiplex Proximity Graph

Authors

Name Contacts Contribution
Pietro Monticone Mail Geospatial data exploration, selection and processing
GitHub Contact data exploration, selection and processing
Twitter Mobility data exploration, selection and processing
Epidemiological data exploration, selection and processing
Policy data exploration, selection and processing
Age-specific IFR calibration
Epidemiological module design and implementation (50%)
Surveillance module design and implementation
Contact-tracing module design and implementation
Geospatial static and dynamic visualization of simulated data
DigitalEpidemiology.jl package development (50%)
Davide Orsenigo Mail Population data exploration, selection and processing
GitHub Diagnostic data exploration, selection and processing
Twitter Age-specific symptomatic fraction calibration
Inter-compartmental transition delays calibration
Epidemiological module design and implementation (50%)
Contact-tracing static and dynamic visualization of simulated data
DigitalEpidemiology.jl package development (50%)

Computational Framework

Language Activity
Python Data collection
Data wrangling
Data visualization
Julia Modelling
Scenario Analysis

Parameters

Name Value Description References
y 0-29 (1-6) Range of "young" age groups Davies et al. (2020)
m 30-59 (7-12) Range of "middle" age groups Davies et al. (2020)
o 60-80 (13-16) Range of "old" age groups Davies et al. (2020)
σ₁ 𝒩(μ=0.5,σ=0.1;[0,0.5]) Symptomatic fraction on infection for "young" age groups Davies et al. (2020)
σ₂ 0.5 Symptomatic fraction on infection for "middle" age groups Davies et al. (2020)
σ₃ 𝒩(μ=0.1,σ=0.1;[0.5,1]) Symptomatic fraction on infection for "old" age groups Davies et al. (2020)
β_S 𝒩(μ=0.5,σ=0.023;[0,+∞]) Transmissibility of symptomatic infectious person Davies et al. (2020)
β_P 0.15 ⨉ β_S Transmissibility of pre-symptomatic infectious person Aleta et al. (2020)
β_A 0.5 ⨉ β_S Transmissibility of a-symptomatic infectious person Davies et al. (2020)
d_E Γ(μ=3,k=4) Incubation period Davies et al. (2020)
d_P Γ(μ=1.5,k=4) Duration of infectiousness in days during the pre-symptomatic phase Davies et al. (2020)
d_A Γ(μ=3.5,k=4) Duration of infectiousness in days during the a-symptomatic phase Davies et al. (2020)
d_S Γ(μ=5,k=4) Duration of infectiousness in days during the symptomatic phase Davies et al. (2020)
δ 0 Infection fatality ratio for the 0-50 age group Poletti et al. (2020)
δ 0.46 Infection fatality ratio for the 50-60 age group Poletti et al. (2020)
δ 1.42 Infection fatality ratio for the 60-70 age group Poletti et al. (2020)
δ 6.87 Infection fatality ratio for the 70-80 age group Poletti et al. (2020)
FNR_S mean(0.20,0.38) False negative rate in symptomatic phase Kucirka et al. (2020)
FNR_P mean(0.38,0.67) False negative rate in pre-symptomatic phase Kucirka et al. (2020)
FNR_E mean(0.67,1) False negative rate in incubation phase Kucirka et al. (2020)

Diagnostic Strategies

Role Scale Priority Distribution Contact-Tracing
Passive National Random Uniform No
Yes
Targeted Centrality-based Yes
Targeted Age-based / Ex-Ante IFR No
Yes
Symptom-based / Ex-Post IFR No
Yes
Regional Random Uniform No
Yes
Targeted Centrality-based Yes
Targeted Age-based / Ex-Ante IFR No
Yes
Symptom-based / Ex-Post IFR No
Yes
Provincial Random Uniform No
Yes
Targeted Centrality-based Yes
Targeted Age-based / Ex-Ante IFR No
Yes
Symptom-based / Ex-Post IFR No
Yes
Active National Random Uniform No
Yes
Targeted Centrality-based Yes
Targeted Age-based / Ex-Ante IFR No
Yes
Symptom-based / Ex-Post IFR No
Yes
Regional Random Uniform No
Yes
Targeted Centrality-based Yes
Targeted Age-based / Ex-Ante IFR No
Yes
Symptom-based / Ex-Post IFR No
Yes
Provincial Random Uniform No
Yes
Targeted Centrality-based Yes
Targeted Age-based / Ex-Ante IFR No
Yes
Symptom-based / Ex-Post IFR No
Yes
  • All the above with behavioral module: endogenous, individual-based physical distancing (local and global)
  • All the above with behavioral module: exogenous, enforced physical distancing (local and global lockdown)
  • Special one: Active, provincial, targeted, symptom-based, symptomatic-is-positive, contact-tracing, endogenous & exogenous distancing: assume all symptomatic patients to be positive ($I_s$) without testing them (accepting the uncertainty of the symptom-based MD diagnosis) in order to allocate more diagnostic resources to the active surveillance of exposed, asymptomatic, vulnerable patients.

Data

Geospatial

Administrative

Population

Contact

Mobility

Model

Epidemiological Module

Surveillance Module

References

Data

Geospatial

Population

Contact

Mobility

Diagnostic

Epidemiological

Policy

Modelling

Conceptual

Metapopulation

Calibration

Surveillance

Interventions

Behavioral

Mortality

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