EpiSimulator

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

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) | | | | | | | ** | (=0.5,=0.1;[0,0.5]) | Symptomatic fraction on infection for "young" age groups| Davies et al. (2020) | | ** | 0.5 | Symptomatic fraction on infection for "middle" age groups| Davies et al. (2020) | | ** | (=0.1,=0.1;[0.5,1]) | Symptomatic fraction on infection for "old" age groups | Davies et al. (2020) | | | | | | | _S | (=0.5,=0.023;[0,+]) | Transmissibility of symptomatic infectious person | Davies et al. (2020) | | _P | 0.15 _S | Transmissibility of pre-symptomatic infectious person | Aleta et al. (2020) | | _A | 0.5 _S | Transmissibility of a-symptomatic infectious person | Davies et al. (2020) | | | | | |
| d_E | (=3,k=4) | Incubation period | Davies et al. (2020) | | d_P | (=1.5,k=4) | Duration of infectiousness in days during the pre-symptomatic phase | Davies et al. (2020) | | d_A | (=3.5,k=4) | Duration of infectiousness in days during the a-symptomatic phase | Davies et al. (2020) | | d_S | (=5,k=4) | Duration of infectiousness in days during the symptomatic phase | Davies et al. (2020) | | | | | | | ** | 0 | Infection fatality ratio for the 0-50 age group | Poletti et al. (2020) | | ** | 0.46 | Infection fatality ratio for the 50-60 age group | Poletti et al. (2020) | | ** | 1.42 | Infection fatality ratio for the 60-70 age group | Poletti et al. (2020) | | ** | 6.87 | Infection fatality ratio for the 70-80 age group | Poletti et al. (2020) | | | | | | | FNRS | mean(0.20,0.38) | False negative rate in symptomatic phase | Kucirka et al. (2020) | | FNRP | 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

• ISTAT, Confini delle unit amministrative a fini statistici al 1 Gennaio 2020. (2020)
• OpenPolis. Limits of Italian Provinces. GitHub (2019) #### Population
• ISTAT, Resident Population by Sex, Age and Marital Status (2020).
• ISTAT, Population census data (2020). #### Contact
• Mossong J, Hens N, Jit M, Beutels P, Auranen K, Mikolajczyk R, et al. (2008) Social Contacts and Mixing Patterns Relevant to the Spread of Infectious Diseases. PLoS Med 5(3): e74.
• Prem K, Cook AR, Jit M (2017). Projecting social contact matrices in 152 countries using contact surveys and demographic data. PLoS Comput Biol 13(9).
• Kiesha Prem et al. (2020). Projecting contact matrices in 177 geographical regions: an update and comparison with empirical data for the COVID-19 era. medRxiv pre-print. #### Mobility
• Pepe, E., Bajardi, P., Gauvin, L. et al. COVID-19 outbreak response, a dataset to assess mobility changes in Italy following national lockdown. Scientific Data 7, 230 (2020). #### Diagnostic
• ECDC, Diagnostic testing and screening for SARS-CoV-2, 2020
• ECDC, Testing strategies, 2020
• European Commission (EC). Current performance of COVID-19 test methods and devices and proposed performance criteria (16 April 2020). Brussels: EC; [21 April, 2020].
• Jessica Watson et al. Interpreting a covid-19 test result. BMJ, 2020.
• Edward C.Stites, Craig B.Wilen. The Interpretation of SARS-CoV-2 Diagnostic Tests, Med (2020).
• Sethuraman N, Jeremiah SS, Ryo A. Interpreting Diagnostic Tests for SARS-CoV-2. JAMA. 2020;323(22):22492251.
• Diagnostics Committee of the Infectious Diseases Society of America (IDSA), Molecular Testing for Acute Respiratory Tract Infections: Clinical and Diagnostic Recommendations From the IDSAs Diagnostics Committee, Clinical Infectious Diseases, , ciaa508
• False Negative Tests for SARS-CoV-2 Infection Challenges and Implications N Engl J Med 2020; 383:e38
• Saliva or Nasopharyngeal Swab Specimens for Detection of SARS-CoV-2
• Lisboa Bastos Mayara, Tavaziva Gamuchirai, Abidi Syed Kunal, Campbell Jonathon R, Haraoui Louis-Patrick, Johnston James C et al. Diagnostic accuracy of serological tests for covid-19: systematic review and meta-analysis BMJ 2020; 370 :m2516
• Kucirka, Lauren M., et al. Variation in false-negative rate of reverse transcriptase polymerase chain reactionbased SARS-CoV-2 tests by time since exposure. Annals of Internal Medicine (2020).
• ISTAT, First results of the SARS-CoV-2 seroprevalence study (2020) #### Epidemiological
• Our World in Data, Data on COVID-19.
• COVID-19 Routes, Data Fetching for Italy.
• PCM-DPC, COVID-19 Data for Italy.
• ISS, COVID-19 Integrated Surveillance.
• Davies, N.G., Klepac, P., Liu, Y. et al. Age-dependent effects in the transmission and control of COVID-19 epidemics. Nature Medicine 26, 12051211 (2020).
• Kiesha Prem et al. (2020). Projecting contact matrices in 177 geographical regions: an update and comparison with empirical data for the COVID-19 era. medRxiv pre-print.
• Khalili, M., Karamouzian, M., Nasiri, N., Javadi, S., Mirzazadeh, A., & Sharifi, H. (2020). Epidemiological characteristics of COVID-19: A systematic review and meta-analysis. Epidemiology and Infection, 148, E130.
• Oxford University Clinical Research Unit COVID-19 Research Group, The Natural History and Transmission Potential of Asymptomatic Severe Acute Respiratory Syndrome Coronavirus 2 Infection, Clinical Infectious Diseases, ciaa711
• ISTAT, First results of the SARS-CoV-2 seroprevalence study (2020)
• Luca Ferretti et al. The timing of COVID-19 transmission. medRxiv pre-print (2020)
• Poletti Piero et al. Age-specific SARS-CoV-2 infection fatality ratio and associated risk factors, Italy, February to April 2020. Euro Surveill. 2020;25(31):pii=2001383.
• Poletti, Piero, et al. Probability of symptoms and critical disease after SARS-CoV-2 infection. arXiv pre-print arXiv:2006.08471 (2020).
• Andrew T. Levin et al. Assessing the Age Specificity of Infection Fatality Rates for COVID-19: Systematic Review, Meta-Analysis, and Public Policy Implications. medRxiv pre-print (2020)
• Biggerstaff M, Cowling BJ, Cucunub ZM, Dinh L, Ferguson NM, Gao H, et al., for the WHO COVID-19 Modelling Parameters Group. Early insights from statistical and mathematical modeling of key epidemiologic parameters of COVID-19. Emerg Infect Dis (2020. #### Policy
• Desvars-Larrive, A., Dervic, E., Haug, N. et al. A structured open dataset of government interventions in response to COVID-19. Scientific Data 7, 285 (2020). ### Modelling #### Conceptual
• Vespignani, A., Tian, H., Dye, C. et al. Modelling COVID-19. Nat Rev Phys 2, 279281 (2020)
• Metcalf et al. Mathematical models to guide pandemic response. Science Perspective (2020).
• Ernesto Estrada. COVID-19 and SARS-CoV-2. Modeling the present, looking at the future. Physics Reports (2020).
• Bjrnstad, O.N., Shea, K., Krzywinski, M. et al. The SEIRS model for infectious disease dynamics. Nature Methods 17, 557558 (2020). #### Metapopulation
• Alun Lloyd, Vincent Jansenc. Spatiotemporal dynamics of epidemics: synchrony in metapopulation models. Mathematical Biosciences (2004).
• Duncan J. Watts et al. Multiscale, resurgent epidemics in a hierarchical metapopulation model. PNAS (2005)
• Vittoria Colizza, Alessandro Vespignani. Epidemic modeling in metapopulation systems with heterogeneous coupling pattern: Theory and simulations. Journal of Theoretical Biology (2008).
• Balcan, Duygu, Vittoria Colizza, Bruno Gonalves, Hao Hu, Jos J. Ramasco, and Alessandro Vespignani. Multiscale mobility networks and the spatial spreading of infectious diseases. Proceedings of the National Academy of Sciences 106, no. 51 (2009): 21484-21489.
• Balcan, Duygu, Bruno Gonalves, Hao Hu, Jos J. Ramasco, Vittoria Colizza, and Alessandro Vespignani. Modeling the spatial spread of infectious diseases: The GLobal Epidemic and Mobility computational model. Journal of Computational Science 1, no. 3 (2010): 132-145
• Ross JV, House T, Keeling MJ Calculation of Disease Dynamics in a Population of Households. PLoS ONE (2010)
• Salath M, Jones JH (2010) Dynamics and Control of Diseases in Networks with Community Structure. PLoS Comput Biol 6(4): e1000736
• Apolloni, A., Poletto, C., Ramasco, J.J. et al. Metapopulation epidemic models with heterogeneous mixing and travel behaviour. Theor Biol Med Model 11, 3 (2014)
• Daniela Calvetti et al. Metapopulation Network Models for Understanding, Predicting, and Managing the Coronavirus Disease COVID-19. Front. Phys., 2020
• Hassan S, Mughal BJ, Siwiak M, Yasin Z. A modified SEIR meta-population transmission based Modeling and Forecasting of the COVID-19 pandemic in Pakistan. medRxiv, 2020
• Stephen J Beckett et al. Spread of COVID-19 through Georgia, USA. Near-term projections and impacts of social distancing via a metapopulation model. medRxiv pre-print (2020) #### Calibration
• Hazelbag CM, Dushoff J, Dominic EM, Mthombothi ZE, Delva W (2020) Calibration of individual-based models to epidemiological data: A systematic review. PLoS Comput Biol 16(5): e1007893
• Biggerstaff M, Cowling BJ, Cucunub ZM, Dinh L, Ferguson NM, Gao H, et al., for the WHO COVID-19 Modelling Parameters Group. Early insights from statistical and mathematical modeling of key epidemiologic parameters of COVID-19. Emerg Infect Dis (2020. #### Surveillance
• Daniel B Larremore et al. Test sensitivity is secondary to frequency and turnaround time for COVID-19 surveillance, medRxiv (2020)
• Yang Liu et al. A Modelling Study for Designing a Multi-layered Surveillance Approach to Detect the Potential Resurgence of SARS-CoV-2, medRxiv (2020)
• Frank G Sandmann, Peter J White, Mary Ramsay, Mark Jit, Optimizing Benefits of Testing Key Workers for Infection with SARS-CoV-2: A Mathematical Modeling Analysis, Clinical Infectious Diseases, ciaa901
• Colman E, Holme P, Sayama H, Gershenson C (2019) Efficient sentinel surveillance strategies for preventing epidemics on networks. PLoS Comput Biol 15(11): e1007517.
• Grassly NC, Pons-Salort M, Parker EPK, et al. Comparison of molecular testing strategies for COVID-19 control: a mathematical modelling study. Lancet Infect Dis 2020
• Ted Bergstrom, Carl Bergstrom, Haoran Li Frequency and accuracy of proactive testing for COVID-19, medRxiv (2020) #### Interventions
• Josh A Firth, Joel Hellewell, Petra Klepac, Stephen M Kissler et al. Combining fine-scale social contact data with epidemic modelling reveals interactions between contact tracing, quarantine, testing and physical distancing for controlling COVID-19, CMMID pre-print (2020)
• Akira Endo et al. Implication of backward contact tracing in the presence of overdispersed transmission in COVID-19 outbreaks, CMMID pre-print (2020)
• Giordano, G., Blanchini, F., Bruno, R. et al. Modelling the COVID-19 epidemic and implementation of population-wide interventions in Italy. Nat Med 26, 855860 (2020)
• Marino Gatto et al. Spread and dynamics of the COVID-19 epidemic in Italy: Effects of emergency containment measures. PNAS (2020)
• Bertuzzo, E., Mari, L., Pasetto, D. et al. The geography of COVID-19 spread in Italy and implications for the relaxation of confinement measures. Nat Commun 11, 4264 (2020)
• Aleta, A., Martn-Corral, D., Pastore y Piontti, A. et al. Modelling the impact of testing, contact tracing and household quarantine on second waves of COVID-19. Nat Hum Behav (2020) #### Behavioral
• Perra N, Balcan D, Gonalves B, Vespignani A (2011) Towards a Characterization of Behavior-Disease Models. PLoS ONE 6(8): e23084.
• Verelst Frederik, Willem Lander and Beutels Philippe (2016) Behavioural change models for infectious disease transmission: a systematic review (20102015) J. R. Soc. Interface.1320160820
• Granell C, Gmez S, Arenas A. (2013) Dynamical Interplay between Awareness and Epidemic Spreading in Multiplex Networks. Phys Rev Lett. 2013;111: 128701.
• Massaro E, Bagnoli F. (2014) Epidemic spreading and risk perception in multiplex networks: A self-organized percolation method. Phys Rev EStat Nonlinear, Soft Matter Phys. 2014;90: 5364.
• Abdulkareem SA, Augustijn E-W, Filatova T, Musial K, Mustafa YT (2020) Risk perception and behavioral change during epidemics: Comparing models of individual and collective learning. PLoS ONE 15(1): e0226483.
• Hunter E, Mac Namee B, Kelleher J (2018) An open-data-driven agent-based model to simulate infectious disease outbreaks. PLoS ONE 13(12): e0208775. #### Mortality
• WHO, Estimating mortality from COVID-19, Scientific Brief (2020)