Modeling Early Phases of COVID-19 Pandemic in Northern Italy and Its Implication for Outbreak Diffusion
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Abstract
The COVID-19 pandemic has sparked an intense debate about the hidden factors underlying the dynamics of the outbreak. Several computational models have been proposed to inform effective social and healthcare strategies. Crucially, the predictive validity of these models often depends upon incorporating behavioral and social responses to infection. Among these tools, the analytic framework known as “dynamic causal modeling” (DCM) has been applied to the COVID-19 pandemic, shedding new light on the factors underlying the dynamics of the outbreak. We have applied DCM to data from northern Italian regions, the first areas in Europe to contend with the outbreak, and analyzed the predictive validity of the model and also its suitability in highlighting the hidden factors governing the pandemic diffusion. By taking into account data from the beginning of the pandemic, the model could faithfully predict the dynamics of outbreak diffusion varying from region to region. The DCM appears to be a reliable tool to investigate the mechanisms governing the spread of the SARS-CoV-2 to identify the containment and control strategies that could efficiently be used to counteract further waves of infection.
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SciScore for 10.1101/2020.08.20.20178798: (What is this?)
Please note, not all rigor criteria are appropriate for all manuscripts.
Table 1: Rigor
Institutional Review Board Statement not detected. Randomization not detected. Blinding not detected. Power Analysis not detected. Sex as a biological variable not detected. Table 2: Resources
No key resources detected.
Results from OddPub: We did not detect open data. We also did not detect open code. Researchers are encouraged to share open data when possible (see Nature blog).
Results from LimitationRecognizer: We detected the following sentences addressing limitations in the study:Model strength and limitations: The proposed study, despite the advantages given by DCM, suffers from some limitations, which have already been mentioned in [Friston et al. 2020d]. i) The DCM does not consider interactions with …
SciScore for 10.1101/2020.08.20.20178798: (What is this?)
Please note, not all rigor criteria are appropriate for all manuscripts.
Table 1: Rigor
Institutional Review Board Statement not detected. Randomization not detected. Blinding not detected. Power Analysis not detected. Sex as a biological variable not detected. Table 2: Resources
No key resources detected.
Results from OddPub: We did not detect open data. We also did not detect open code. Researchers are encouraged to share open data when possible (see Nature blog).
Results from LimitationRecognizer: We detected the following sentences addressing limitations in the study:Model strength and limitations: The proposed study, despite the advantages given by DCM, suffers from some limitations, which have already been mentioned in [Friston et al. 2020d]. i) The DCM does not consider interactions with seasonal flu or other annual fluctuations [Kissler et al 2020]. ii) As with other modeling approaches, the outcomes of the Bayesian model comparison and posterior inferences are strictly model-dependent. iii) Updating the model with available data could change the posterior predictions. iv) The model does not include geospatial aspects, but rather each outbreak is treated as a point process [Chinazzi et al 2020]. Nevertheless, i) annual fluctuations like seasonal flu can alter the overall statistics but are normally distributed throughout the country without preferential geographical localizations, therefore the eventual error is systematic and homogeneously distributed; ii) the outcomes of every model are limited to the approach that is actually employed; iii-iv) the continuous updating of the model and therefore of the posterior predictions is inherently one of the main advantage of this kind of approaches. The flexibility of the model takes into account unexpected and sudden events that can promptly change the pandemic dynamics allowing therefore to continuously update the prediction generating different future scenarios; iv) once more, treating each outbreak as a point process provides the model with the capability to interpret single and spotted e...
Results from TrialIdentifier: No clinical trial numbers were referenced.
Results from Barzooka: We did not find any issues relating to the usage of bar graphs.
Results from JetFighter: We did not find any issues relating to colormaps.
Results from rtransparent:- Thank you for including a conflict of interest statement. Authors are encouraged to include this statement when submitting to a journal.
- Thank you for including a funding statement. Authors are encouraged to include this statement when submitting to a journal.
- No protocol registration statement was detected.
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SciScore for 10.1101/2020.08.20.20178798: (What is this?)
Please note, not all rigor criteria are appropriate for all manuscripts.
Table 1: Rigor
NIH rigor criteria are not applicable to paper type.Table 2: Resources
No key resources detected.
Results from OddPub: Thank you for sharing your code.
Results from LimitationRecognizer: We detected the following sentences addressing limitations in the study:
Model strength and limitations The proposed study, despite the advantages given by DCM, suffers from some limitations, which have already been mentioned in [Friston et al. 2020d]. i) The DCM does not consider interactions with seasonal flu or other annual fluctuations [Kissler et al 2020]. ii) As with other modeling approaches, the outcomes of the Bayesian model comparison and posterior inferences are strictly model-dependent. iii) Updating the …
SciScore for 10.1101/2020.08.20.20178798: (What is this?)
Please note, not all rigor criteria are appropriate for all manuscripts.
Table 1: Rigor
NIH rigor criteria are not applicable to paper type.Table 2: Resources
No key resources detected.
Results from OddPub: Thank you for sharing your code.
Results from LimitationRecognizer: We detected the following sentences addressing limitations in the study:
Model strength and limitations The proposed study, despite the advantages given by DCM, suffers from some limitations, which have already been mentioned in [Friston et al. 2020d]. i) The DCM does not consider interactions with seasonal flu or other annual fluctuations [Kissler et al 2020]. ii) As with other modeling approaches, the outcomes of the Bayesian model comparison and posterior inferences are strictly model-dependent. iii) Updating the model with available data could change the posterior predictions. iv) The model does not include geospatial aspects, but rather each outbreak is treated as a point process [Chinazzi et al 2020]. Nevertheless, i) annual fluctuations like seasonal flu can alter the overall statistics but are normally distributed throughout the country without preferential geographical localizations, therefore the eventual error is systematic and homogeneously distributed; ii) the outcomes of every model are limited to the approach that is actually employed; iii-iv) the continuous updating of the model and therefore of the posterior predictions is inherently one of the main advantage of this kind of approaches. The flexibility of the model takes into account unexpected and sudden events that can promptly change the pandemic dynamics allowing therefore to continuously update the prediction generating different future scenarios; iv) once more, treating each outbreak as a point process provides the model with the capability to interpret single and spotted ev...
Results from TrialIdentifier: No clinical trial numbers were referenced.
Results from Barzooka: We did not find any issues relating to the usage of bar graphs.
Results from JetFighter: We did not find any issues relating to colormaps.
About SciScore
SciScore is an automated tool that is designed to assist expert reviewers by finding and presenting formulaic information scattered throughout a paper in a standard, easy to digest format. SciScore checks for the presence and correctness of RRIDs (research resource identifiers), and for rigor criteria such as sex and investigator blinding. For details on the theoretical underpinning of rigor criteria and the tools shown here, including references cited, please follow this link.
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