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Retrospective epidemiological models are powerful tools to understand its transmission dynamics and to assess the efficacy of different control measures. This study summarises key epidemiological parameters of COVID-19 for retrospective mathematical and clinical modeling. A review of scientific papers and preprints published in English between 1 January and 15 April 2020 in PubMed, MedRxiv and BioRxiv was performed to obtain epidemiological parameters of the initial stage of COVID-19 pandemic in Asia. After excluding articles with unacceptable risks of bias and those that remained as preprints as of 15 November 2021, meta-analyses were performed to derive summary effect estimates from the data collected using the statistical software R. Out of 4,893 articles identified, 88 provided data for 22 parameters for the overall population and 7 specifically for children. Meta-analyses were conducted considering time period as a categorical moderator when it was statistically significant. The results obtained are essential for building more reliable models to help clinicians and policymakers improve their knowledge on COVID-19 and apply it in future decisions.
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BACKGROUND: A unique policy of perimeter closures of Basic Health Zones (small administrative health units) was implemented in the Autonomous Community of Madrid from September 21st 2020 to May 23rd 2021 to face the COVID-19 pandemic. AIM: To assess the impact of local perimeter confinements on the 14-days cumulative incidence of SARS-CoV-2 during the second wave of the pandemic in Madrid, Spain. METHODS: We compare the errors in estimation of two families of mathematical models: ones that include the perimeter closures as explanatory covariables and ones that do not, in search of a significant improvement in estimation of one family over the other. We incorporate leave-one-out cross-validation, and at each step of this process we select the best model in AIC score from a family of 15 differently tuned ones. RESULTS: The two families of models provided very similar estimations, for a 1- to 3-weeks delay in observed cumulative incidence, and also when restricting the analysis to only those Basic Health Zones that were subject to at least one closure during the time under study. In all cases the correlation between the errors yielded by both families of models was higher than 0.98 (±10- 3 95% CI), and the average difference of estimated 14-days cumulative incidence was smaller than 1.49 (±0.33 95% CI). CONCLUSION: Our analysis suggests that the perimeter closures by Basic Health Zone did not have a significant effect on the epidemic curve in Madrid.
Assuntos
COVID-19 , Humanos , Incidência , Pandemias , SARS-CoV-2 , Espanha/epidemiologiaRESUMO
The number of new daily infections is one of the main parameters to understand the dynamics of an epidemic. During the COVID-19 pandemic in 2020, however, such information has been underestimated. Here, we propose a retrospective methodology to estimate daily infections from daily deaths, because those are usually more accurately documented. This methodology is applied to Spain and its 19 administrative regions. Our results showed that probable infections during the first wave were between 35 and 42 times more than those officially documented on 14 March, when the national government decreed a national lockdown and 9 times more than those documented by the updated version of the official data. The national lockdown had a strong effect on the growth rate of virus transmission, which began to decrease immediately. Finally, the first inferred infection in Spain is about 43 days before it was officially reported during the first wave. The current official data show delays of 15-30 days in the first infection relative to the inferred infections in 63% of the regions. In summary, we propose a methodology that allows reinterpretation of official daily infections, improving data accuracy in infection magnitude and dates because it assimilates valuable information from the National Seroprevalence Studies.
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We present a literature review and meta-analysis of relevant epidemiological parameters (24 for adults, 7 for children) of COVID-19. Standardization of these parameters is key to performing valid clinical and mathematical modeling, as well as forecasts, helping us to improve our understanding about the characteristics and impact of the pandemic.
