| Name | Contacts | Contribution |
|---|---|---|
| Pietro Monticone | Geospatial data exploration, selection and processing | |
| GitHub | Contact data exploration, selection and processing | |
| 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 | Population data exploration, selection and processing | |
| GitHub | Diagnostic data exploration, selection and processing | |
| 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%) |
| Language | Activity |
|---|---|
| Python | Data collection |
| Data wrangling | |
| Data visualization | |
| Julia | Modelling |
| Scenario Analysis |
| 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) |
| δ₁ | 0 | Infection fatality ratio for the 0-50 age group | Poletti et al. (2020) |
| δ₂ | 0.46 | Infection fatality ratio for the 50-60 age group | Poletti et al. (2020) |
| δ₃ | 1.42 | Infection fatality ratio for the 60-70 age group | Poletti et al. (2020) |
| δ₄ | 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) |
| 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.
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