ABSTRACT
OBJECTIVE: Estimate daily infections of COVID-19 during the first year of the pandemic in the Santiago Metropolitan Region (SRM) in Chile and Chile that are more realistic than those officially registered. METHOD: Retrospective estimate of daily infections from daily data on COVID-19 deaths, a seroprevalence study, and the REMEDID (Retrospective Methodology to Estimate Daily Infections from Deaths) algorithm. RESULTS: In SRM, it is observed that: 1) the maximum peak of infections was more than double that registered in the official statistics; 2) such peak was reached on May 22 (95% CI: 20-24 May), 2022, that is, 24 days before the official date of the peak of infections; and 3) the first estimated contagion took place on January 28, 2020 (95% CI: January 21 to February 16), that is, 36 days before the official date. In Chile, the situation is similar. During the first wave SRM accounted for 70%-76% of those infected in Chile, while from August 2020 onwards it accounted for 36%-39%. CONCLUSIONS: The official records of COVID-19 infections in SRM and Chile underestimated the real number of positives and showed a delay of about a month in the dynamics of infections. This is not an isolated situation, as it is known to have been the case in other countries as well. However, it is important to have reliable estimates for a correct modeling of the spread of the virus.
ABSTRACT
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. Given the incubation period, the time from illness onset to death, and the case fatality ratio, the date of death can be estimated from the date of infection. We apply this idea conversely to estimate infections from deaths. This methodology is applied to Spain and its 19 administrative regions. Our results showed that probable daily 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 the official data were available 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.
Subject(s)
COVID-19/epidemiology , COVID-19/mortality , Communicable Disease Control/methods , Humans , Infectious Disease Incubation Period , Pandemics , Retrospective Studies , Seroepidemiologic Studies , SpainABSTRACT
Current predictive models for cardiovascular disease based on points systems use the baseline situation of the risk factors as independent variables. These models do not take into account the variability of the risk factors over time. Predictive models for other types of disease also exist that do consider the temporal variability of a single biological marker in addition to the baseline variables. However, due to their complexity these other models are not used in daily clinical practice. Bearing in mind the clinical relevance of these issues and that cardiovascular diseases are the leading cause of death worldwide we show the properties and viability of a new methodological alternative for constructing cardiovascular risk scores to make predictions of cardiovascular disease with repeated measures of the risk factors and retaining the simplicity of the points systems so often used in clinical practice (construction, statistical validation by simulation and explanation of potential utilization). We have also applied the system clinically upon a set of simulated data solely to help readers understand the procedure constructed.
