A methodology to generate epidemic scenarios for emerging infectious diseases based on the use of key calendar events
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Abstract
This work presents a methodology to recreate the observed dynamics of emerging infectious diseases and to generate short-term forecasts for their evolution based on superspreading events occurring on key calendar dates. The method is illustrated by the COVID-19 pandemic dynamics in Mexico and Peru up to January 31, 2022. We also produce scenarios obtained through the estimation of a time-dependent contact rate, with the main assumption that the dynamic of the disease is determined by the mobility and social activity of the population during holidays and other important calendar dates. First, historical changes in the effective contact rate on predetermined dates are estimated. Then, this information is used to forecast scenarios under the assumption that the trends of the effective contact rate observed in the past will be similar on the same but future key calendar dates. All other conditions are assumed to remain constant in the time scale of the projections. One of the main features of the methodology is that it avoids the necessity of fixing values of the dynamic parameters for the whole prediction period. Results show that considering the key dates as reference information is useful to recreate the different outbreaks, slow or fast-growing, that an epidemic can present and, in most cases, make good short-term predictions.
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SciScore for 10.1101/2022.04.29.22274465: (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
Software and Algorithms Sentences Resources We analyze the posterior distribution using an MCMC algorithm called t-walk [15] since it is implemented in the programming language Python and works well with highly correlated parameters, which typically appear in this type of estimation problems. Pythonsuggested: NoneResults 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: An explicit section about the limitations of the techniques employed in this study was not found. We encourage authors to …SciScore for 10.1101/2022.04.29.22274465: (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
Software and Algorithms Sentences Resources We analyze the posterior distribution using an MCMC algorithm called t-walk [15] since it is implemented in the programming language Python and works well with highly correlated parameters, which typically appear in this type of estimation problems. Pythonsuggested: NoneResults 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: An explicit section about the limitations of the techniques employed in this study was not found. We encourage authors to address study limitations.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.
Results from scite Reference Check: We found no unreliable references.
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