PMC10343065_01
Excess deaths in the Czech Republic.. Excess deaths in weeks of the years 2020 (red thick dashed line) and 2021 (blue thick dashed line) according to average week deaths in years 2011-2019 (thin solid lines show excess deaths in weeks of the years 2011 to 2019), weekly reported SARS-CoV-2 positive subjects that died (red and blue thick solid lines), and weekly reported deaths (red and blue thin solid lines). Our model works with data related to the date of the report, including fatal reports (thick line), and excess deaths (dashed line) and COVID-19 deaths (thin line) are related to the day of death.
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In the Czech Republic, excess mortality during autumn and winter 2020 (from) is highly correlated with the epidemic wave and gave approximately 0.5 excess unreported deaths to each reported patient who died with COVID-19 (using an anonymized dataset created from ISID data that can be downloaded from the MZ CR portal), see Fig 2.
Epidemic monitoring.. Exposed (new cases divided by AR estimate), new cases, hospital admissions and daily deaths (black), and model fit optimized to hospital admissions (blue).
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The need for this modification is also visible in Fig 3. Fig 3 shows the estimated exposed using real new cases divided by AR estimate, real new cases, real daily admissions to hospitals and deaths (black circles) and the optimized fit to hospitalization data (solid blue line) with the AR estimate average baseline . The usefulness of using moving AR is shown in Fig 3.
Comparison of using constant AR and moving AR.. The model (blue) fitted to the data (black) of new hospitalizations (bottom right) reproduces the actual new cases (top black) only when using moving AR, whereas using constant AR does not account for either the number of cases in the first wave or the fluctuation due to the change in testing during the OKD outbreak (Jul).
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This simulated the first outbreak very well from April 4, 2020, until the summer; see Fig 4. AR estimate can be used as an early-warning indicator. As we saw in the previous subsection, retrospective analysis provides a good explanation of the OKD wave: new cases did not significantly affect the dynamics of hospitalized patients and deaths (see Fig 4).
Insufficiency of tracing in the second half of September 2020.. The peak of the second wave is marked on 2020-11-04. The dates in the chart denote: 2020-09-01:the beginning of the school year; 2020-09-15 to 2021-10-01:tracing overload; 2021-11-04:peak of the second wave; and 2021-01-01:New Year.
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AR estimate shows the likely decrease in the effectiveness of tracing by the Regional Public Health Authorities (RPHAs), which occurred in the second half of September (Fig 5, grey period). A significant decrease in the AR estimate was a signal of contact tracing and testing system overload.
Correlation with ICUs.. Proportion of undetected in the community (PU) and number of COVID-19 patients treated in intensive care units after 30 days (ICU30d).
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Between mid-September and mid-December 2020, we observed a high correlation of the proportion of newly hospitalized non-detected in the community 1 - P(Det|H) and the subsequent number of COVID-19 patients treated at intensive care units (Fig 6, correlation coefficient 0.92).
Exposed population and NPIs.. Exposed estimated using new cases divided by AR estimate compared to the introduction of NPIs: 2020-09-01 (beginning of the school year), 2020-10-22 (a partial lockdown), 2020-11-14 (announcement of the first measure releases after 2020-11-18), 2020-12-03 (shops reopening), 2020-12-27 (a partial lockdown), 2021-01-11 (partial school reopening), 2021-02-23 (mandatory respirators).
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Fig 7 shows several specific dates: 2020-09-01 (beginning of the school year), 2020-10-22 (partial lockdown), 2020-11-14 (announcement of the first measure release, partial school reopening 2020-11-18), 2020-12-03 (shops reopening), 2020-12-27 (a partial lockdown), 2021-01-11 (partial school reopening), and 2021-02-23 (mandatory and widespread introduction of respirators followed with partial lockdown).