Jekyll2023-08-25T06:17:56+00:00https://covid-en-wetenschap.github.io//feed.xmlCOVID en wetenschapAchtergrond en commentaar over de COVID-19 situatie in Belgie, door de experts
Jan Aertsjan.aerts@uhasselt.beTechnical note: SARS-CoV-2 variants and vaccination in Belgium (v2023-08-24)2023-08-24T00:00:00+00:002023-08-24T00:00:00+00:00https://covid-en-wetenschap.github.io//2023/08/technical-note-simid-august<p>The SIMID consortium prepared a new technical note (v2023-08-24) containing the estimates of a stochastic dynamic transmission model focusing on the potential impact of the vaccination campaign starting in September 2023 in Belgium.</p>
<p>Study highlights</p>
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<p>We explore the potential impact of a vaccination campaign starting on September 15, 2023 with an XBB.1.5 bivalent booster and with different theoretical uptakes, in addition to the potential impact of increased transmission from September 2023 onward as a result of resuming societal activities after the summer break and seasonality effects (e.g., as a result of a shift from outdoor to indoor contacts).</p>
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<p>Our scenario analysis shows a considerable benefit from the vaccination campaign during November–December 2023. Regarding a possible wave in September–October due to the restart of activities, the benefit increases with an earlier start of the vaccination campaign. The benefit of a campaign targeting the same audience as the flu vaccine is almost double the benefit of a campaign targeting half of that audience. An additional 15% increase in vaccinated people has limited impact.</p>
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<p>Modification of contact rates and seasonality is simulated in the future according to last years collected data and estimated parameters in order to reproduce potential changes in transmission. Therefore, the timing and height of the projected peaks are subject to the model assumptions and are not intended to be predictive. The main value of this work lies in the relative comparison between different strategies and the overall risk assessment.</p>
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<p>We make the implicit assumption that Omicron XBB.1.5 and the close variants currently circulating will remain dominant throughout the simulation period. Nonetheless, other (newly emerging) VOCs may have different transmission probabilities and different probabilities of causing disease, hospitalization, or death, and different vaccine effectiveness characteristics against each of these manifestations. In particular, the still unknown particularities of the very recent EG.5 variant are not taken into account.</p>
</li>
</ul>
<p><a href="/assets/20230824_technical_note_SIMID.pdf">The full document from August 2023 (v2023-08-24) is available here.</a></p>Christel Faes (UHasselt), Lander Willem (UAntwerpen), Maikel Bosschaert (UHasselt), Nicolas Franco (UNamur & UHasselt), Nicolas Herman (UNamur), Niel Hens (UHasselt & UAntwerpen), Philippe Beutels (UAntwerpen), Steven Abrams (UAntwerpen & UHasselt), Toon Braeye (Sciensano)The SIMID consortium prepared a new technical note (v2023-08-24) containing the estimates of a stochastic dynamic transmission model focusing on the potential impact of the vaccination campaign starting in September 2023 in Belgium.Technical note: A venue-specific model for assessing the local impact of the Covid Safe Ticket (v2022-10-29)2023-02-21T00:00:00+00:002023-02-21T00:00:00+00:00https://covid-en-wetenschap.github.io//2023/02/cst-events<p>We wrote a technical note presenting a simulation study on the effect of the Covid Safe Ticket (CST) in limiting the number of transmissions that might occur during events. While the model used to simulate transmission events relies on simplistic assumptions, the set of simulation runs, and the sensitivity analyses performed highlight characteristics that can affect the effectiveness of the CST.</p>
<p>Study highlights</p>
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<p>We explored characteristics related to the event (i.e., number of attendees, number of index cases), SARS-CoV-2 infectiousness (i.e., basic reproduction number), vaccine-induced immunity (i.e., vaccine effectiveness against infectiousness and vaccine effectiveness against susceptibility to infection), test sensitivity and vaccination coverage of attendees.</p>
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<p>For each simulation scenario, we compared three strategies: everyone can join the event (No CST), all people present need to be vaccinated, recently tested or recently infected (CST) and all the individuals (vaccinated and non-vaccinated) need to take a COVID-19 test to attend the event (CST-X).</p>
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<p>Results indicate that while some characteristics such as the event size, the basic reproduction number, and the proportion of index cases affect the total number of infections, other quantities such as the vaccination coverage at the event affect the relative effectiveness of the strategies.</p>
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<p>In the baseline scenario, which is characterized by high vaccination coverage and low effectiveness, the use of CST decreased the number of infections that might take place during an event by 13-15%. However, when the vaccination coverage at an event is low, (i.e., 20%), the effectiveness of a CST strategy substantially increased since a higher proportion of index cases is unvaccinated and therefore tested (relative difference with No CST is 54%).</p>
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<p>In this work we simulated infections that take place during an isolated event at a specific time in the pandemic by adopting disease estimated for the Delta variant. To account for the number of infections generated at the population level, it would be necessary to embed this model in a more general framework that accounts for all human-to-human interactions taking place before and after the events and in the vicinity of the events.</p>
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<p>We assumed a homogeneous population, therefore no individual characteristics (e.g., age) are considered. We also did not distinguish between symptomatic and asymptomatic infections. We did not account for characteristics of the environment, e.g. ventilation, and we assumed that susceptibility to infection is driven exclusively by vaccine-induced immunity. These assumptions might affect the effectiveness of CST measures when different viral load profiles, vaccine effectiveness or coverage values are heterogeneously distributed among the attendees.</p>
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</ul>
<p><a href="/assets/20221029_technical_note_SIMID_CST.pdf">The full report from October 2022 is available here.</a></p>Andrea Torneri (UHasselt) and Niel Hens (UHasselt & UAntwerpen)We wrote a technical note presenting a simulation study on the effect of the Covid Safe Ticket (CST) in limiting the number of transmissions that might occur during events. While the model used to simulate transmission events relies on simplistic assumptions, the set of simulation runs, and the sensitivity analyses performed highlight characteristics that can affect the effectiveness of the CST.Technical note: SARS-CoV-2 variants and vaccination in Belgium (v2022-08-31)2022-08-31T00:00:00+00:002022-08-31T00:00:00+00:00https://covid-en-wetenschap.github.io//2022/08/technical-note-simid-august<p>The SIMID consortium prepared a new technical note (v2022-08-31) containing the estimates of a stochastic dynamic transmission model using observational data up to August 23rd, 2022.</p>
<p>Study highlights</p>
<ul>
<li>
<p>We explored the potential impact of and increasing transmission in September 2022 as a result of resuming societal activities and seasonality. In addition we explore the potential impact of a vaccination campaign starting on September 12, 2022 in different target groups.</p>
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<p>Our scenario analysis shows a new wave in October-November as a result of resuming societal activities and seasonality. However, a booster vaccine campaign with an Omicron dedicated booster and coverage of at least 50% of the oldest population (65 years and older) with already one booster shows a substantial impact on the size thereof. More specifically, including vaccination in the scenario analysis results in a wave moderate in size, near the level of the latest Omicron wave in June. Projections with subsequent vaccination campaign targeting the 18 years and older population show the lowest hospital admission rates in December 2022. While we focus on hospital admissions, high infection rates could lead to significant absenteeism and pressure on primary care.</p>
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<p>While the timing and height of the project peaks are subject to our model assumptions, the main value of this work lies in the relative comparison between different strategies and the overall risk assessment.</p>
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<p>A growing body of evidence shows that individuals who have been infected and/or vaccinated (with or without a booster) lose their protection over time. This implies that infections and therefore hospital admissions could reach a long-term equilibrium, of which the level depends on the waning rates. An equilibrium can be disturbed when a change in contacts and/or transmission dynamics occurs due to (non-)pharmaceutical interventions or seasonality.</p>
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<p>We are making the implicit assumption that Omicron BA.5 will remain dominant throughout the entire simulation period. Nonetheless, other (newly emerging) VOCs may have different transmission probabilities and different probabilities to cause disease, hospitalization or death, and different vaccine effectiveness characteristics against each of these manifestations.</p>
</li>
</ul>
<p><a href="/assets/20220831_technical_note_SIMID.pdf">The full document from August 2022 (v2022-08-31) is available here.</a></p>Christel Faes (UHasselt), Lander Willem (UAntwerpen), Nicolas Franco (UHasselt & UNamur), Niel Hens (UHasselt & UAntwerpen), Philippe Beutels (UAntwerpen), Steven Abrams (UAntwerpen & UHasselt)The SIMID consortium prepared a new technical note (v2022-08-31) containing the estimates of a stochastic dynamic transmission model using observational data up to August 23rd, 2022.Technical note: SARS-CoV-2 variants and vaccination in Belgium (v2022-04-15)2022-04-25T00:00:00+00:002022-04-25T00:00:00+00:00https://covid-en-wetenschap.github.io//2022/04/technical-note-simid-april<p>The SIMID consortium prepared a new technical note (v2022-04-15) containing the estimates of a stochastic dynamic transmission model using observational data up to April 11th, 2022.</p>
<p>Conclusions</p>
<ul>
<li>
<p>We explored the impact of the Omicron Variant of Concern (VOC) for Belgium with a country-level stochastic transmission model that incorporates infection- and vaccine-induced immunity levels in the population. Under a baseline scenario without any future change in currently estimated transmission dynamics or circulating VOCs, the model projects decreasing numbers of infections and hospital admissions and load in the coming weeks. This trend is caused by a persisting decrease in overall susceptibility in the population, even when we account for waning of vaccine-induced and natural immunity over time. Due to uncertainty with regard to the dynamics for March-April 2022, we explored different assumptions on drivers for these dynamics on the projected outcomes in terms of hospital admissions and load for May-June 2022.</p>
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<p>We explored the impact of additional booster doses by increasing the age-specific booster uptake in the population to the uptake level of at least two doses of a COVID-19 vaccine by May 15th, 2022. With increased transmission dynamics for March-April 2022, the model output shows only small differences between the scenarios on booster dose uptake. The incremental effect of the additional booster doses decreases when the circulation of the virus decreases. Note that the model does not account for local differences in immunity and clustered social contact networks. General trends are captured well, though local outbreaks are underestimated and herd immunity effects are overestimated in sub-populations with immunity levels below the national level.</p>
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<p>We explored a counterfactual historical scenario with an overall 50% reduction in booster dose uptake in the past, while all other factors (i.e., social contact behaviour, VOC, etc.) are assumed to remain constant based on the most up to date available information from the literature, empirical observations and model calibration. As expected, the model shows that the peak in hospital admissions and load in January 2022 could have been much higher (as a direct result of lower vaccine-induced immunity levels) while the peak of the second Omicron wave in March could have been lower. The model accounts for substantial waning of immunity against infection with the Omicron VOC after two doses of any vaccine type, though protection against severe disease is considered to remain substantial. The booster dose re-establishes protection against infection and severe disease, which explains the lower hospital load in the scenario based on the reported uptake. A higher number of infections in the first year 2022 (mainly Omicron) wave, hence increased natural immunity levels upon recovery, explains the reduced peak of the second wave in 2022. These conclusions hold in the absence of re-infections with the same VOC.</p>
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<p>We are making the implicit assumption that the current Omicron VOC will remain dominant throughout the entire simulation period. Nonetheless, other (new emerging) VOCs may have different transmission probabilities and probabilities to cause disease, hospitalization, death, and different vaccine effectiveness characteristics against each of these manifestations.</p>
</li>
</ul>
<p><a href="/assets/20220415_technical_note_SIMID.pdf">The full document from April 2022 (v2022-04-15) is available here.</a></p>Christel Faes (UHasselt), Lander Willem (UAntwerpen), Nicolas Franco (UHasselt & UNamur), Niel Hens (UHasselt & UAntwerpen), Philippe Beutels (UAntwerpen), Steven Abrams (UAntwerpen & UHasselt)The SIMID consortium prepared a new technical note (v2022-04-15) containing the estimates of a stochastic dynamic transmission model using observational data up to April 11th, 2022.Technical note: SARS-CoV-2 variants and vaccination in Belgium (v2022-02-08)2022-02-11T00:00:00+00:002022-02-11T00:00:00+00:00https://covid-en-wetenschap.github.io//2022/02/technical-note-simid-february<p>The SIMID consortium prepared a new technical note (v2022-02-08) containing the estimates of a stochastic dynamic transmission model using observational data up to February 4th, 2022.</p>
<p>Conclusions</p>
<ul>
<li>
<p>We explored the impact of the Omicron Variant of Concern (VOC) for Belgium with a country-level stochastic transmission model that incorporates infection- and vaccine-induced immunity levels in the population. By combining data from the baseline genomics surveillance of SARS-COV-2 with estimated transmission dynamics for Belgium, the model projects decreasing numbers of infections and hospitalisations by the Omicron VOC in the coming weeks.</p>
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<p>Exploring different scenarios of increased transmission rates with relatively high and low protection of current vaccines against infection and severe disease by Omicron, we project a limited resurgence of hospital admissions. The rise in transmission potential could be due to adapted social contact behaviour, increased infectiousness of an Omicron sub-strain (e.g., BA.2), or both.</p>
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<p>Given our focus on severe disease and hospital admissions, we assume no waning of vaccine-induced immunity after booster vaccination doses over the time span considered in this note. The incidence of breakthrough-infections in the scenarios could still cause a burden on primary care. This should be revisited when new information becomes available.</p>
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<p>We are making the implicit assumption that the Omicron VOC will remain the dominant strain throughout the entire simulation period. Nonetheless, other (new emerging) VOCs may have different transmission probabilities and probabilities to cause disease, hospitalization, death, and different vaccine effectiveness characteristics against each of these manifestations.</p>
</li>
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<p>The national model does not account for local differences in immunity and clustered social contact networks. General trends are captured well, though local outbreaks are underestimated and herd immunity effects are overestimated in sub-populations with immunity levels below the national level, and the reverse may be true in sub-populations with higher than average immunity.</p>
</li>
</ul>
<p><a href="/assets/20220208_technical_note_SIMID.pdf">The full document from February 2022 (v2022-02-08) is available here.</a></p>Christel Faes (UHasselt), Lander Willem (UAntwerpen), Nicolas Franco (UHasselt & UNamur), Niel Hens (UHasselt & UAntwerpen), Philippe Beutels (UAntwerpen), Steven Abrams (UAntwerpen & UHasselt)The SIMID consortium prepared a new technical note (v2022-02-08) containing the estimates of a stochastic dynamic transmission model using observational data up to February 4th, 2022.Technical note: SARS-CoV-2 variants and vaccination in Belgium (v2022-01-05)2022-01-05T00:00:00+00:002022-01-05T00:00:00+00:00https://covid-en-wetenschap.github.io//2022/01/technical-note-simid-january<p>The SIMID consortium prepared a new technical note (v2022-01-05) containing the model estimates of hospital admissions and load by stochastic dynamic transmission model using observational data up to Janary 3rd, 2022.</p>
<p>Conclusions</p>
<ul>
<li>
<p>We explored the impact of the Omicron VOC for Belgium with a country-specific stochastic transmission model that incorporates infection- and vaccine-induced immunity levels in the population given the ongoing COVID-19 pandemic. By combining estimates on the predicted penetration of Omicron based on S-gene-target failure data for Belgium with current estimated transmission dynamics in Belgium, the model projects increasing numbers of infections and hospitalisations by the Omicron VOC in the coming weeks, with hospital admissions likely exceeding those observed in the fourth wave.</p>
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<p>Although the relative risk of hospital admission per infection is estimated from international observations to be lower for Omicron than for Delta, the expected high incidence of infections penetrating all age groups, could still cause a large burden on the healthcare system, both in primary care and in hospitals.</p>
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<p>We estimated the increase in transmissibility of Omicron relative to Delta based on S-gene-target failure (SGTF) data from Belgium between 30% and 80%, depending on the assumptions for vaccine-related protection against Omicron. In addition, the latest model calibration resulted in a much faster transition to the pre-symptomatic infectious stage after infection with Omicron, relative to Delta. This aligns with a shorter serial interval for Omicron, as reported by Kim et al (2021, preprint), hence the transmission advantage of Omicron in our projections is not only based on immune escape and increased infectiousness.</p>
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<p>In this technical note it is particularly difficult to properly calibrate the model and define scenarios for the following reasons:
(1) The Omicron VOC has only become completely dominant near the end of the Christmas holiday period and the observed hospital admissions (to which the model is calibrated) during that period were a result of (mostly) Delta infections and (to a much lesser extent) Omicron infections acquired before and at the start of the holiday period;
(2) social contact behaviour is known to be very different during holiday versus non-holiday periods, not only in terms of numbers, but also in terms of age-specificity, and we are currently only starting to observe the impact of infections that occurred during the Christmas holidays on hospital admissions (to which the model is calibrated, note also that recent data on serology in unvaccinated persons are not available as an additional source for calibration);
(3) We currently have no observations on hospital admissions caused by infections acquired in a non-holiday period (with associated non-holiday contact behaviour) while Omicron is the dominant VOC in Belgium, which would allow us to establish the relationship between age-specific contacts, infections and hospital admissions that is representative of the period for which projections are made in this technical note;
(4) policy makers made important changes to the rules of quarantine and isolation from 10th January onwards, which is likely to alter the transmission dynamics in comparison to the previous months, over and above the expected changes instigated by the full dominance of Omicron in Belgium.</p>
</li>
</ul>
<p><a href="/assets/20220105_technical_note_SIMID.pdf">The full document from January 2022 (v2022-01-05) is available here.</a></p>
<hr />
<p>The SIMID consortium wrote also a technical note in December 2021 to share with the Superior Health Council on COVID-19 vaccine uptake for 5-11-year old children. <a href="/assets/20211209_technical_note_SIMID.pdf">The full Technical Note from December (v2021-12-09) is available here.</a></p>Christel Faes (UHasselt), Lander Willem (UAntwerpen), Nicolas Franco (UHasselt & UNamur), Niel Hens (UHasselt & UAntwerpen), Philippe Beutels (UAntwerpen), Steven Abrams (UAntwerpen & UHasselt)The SIMID consortium prepared a new technical note (v2022-01-05) containing the model estimates of hospital admissions and load by stochastic dynamic transmission model using observational data up to Janary 3rd, 2022.Technical note: SARS-CoV-2 variants and vaccination in Belgium (v2021-11-16)2021-11-18T00:00:00+00:002021-11-18T00:00:00+00:00https://covid-en-wetenschap.github.io//2021/11/technical-note-simid-november<p>The SIMID consortium prepared a new technical note (v2021-11-16) containing the model estimates of hospital admissions and ICU load by a short term prediction model and a stochastic dynamic transmission model using observational data up to November 16th, 2021.</p>
<p>Conclusions</p>
<ul>
<li>
<p>The short-term prediction model forecasts between 298 and 543 new hospital admissions on November 26th, 2021. The ICU-related prediction model shows over 740 patients in ICU on November 26th. If the same speed of growth continues, there is a chance that 1000 ICU beds for COVID-19 patients are needed by the beginning of December.</p>
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<p>The stochastic transmission model for Belgium shows that if the transmission dynamics, captured by the effective reproduction number Rt, remains stable until November 21st 2021 and subsequently decreases, a peak ICU load around 750 beds could be reached by the beginning of December 2021. If Rt remains at a stable level until December 2021, the peak ICU load will be even higher and it will take longer to gradually decrease to a level below 500 ICU beds.</p>
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<p>If the speed at which new infections occur slows down in the second half of November 2021, implying a decreasing Rt value, an ICU load up to 650 beds could be expected based on the stochastic model projections. A stronger reduction in Rt, would lead to a more rapid decline in ICU load after the 650 bed peak, to less than 500 beds by the second week of December.</p>
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<p>Infections as a result of social interactions drive the projected hospital burden. Whether an hypothesized decrease or increase in transmission rates (translated into a change in Rt) is of the specific magnitude we assumed for the coming period, is impossible to estimate at this point in time. However, we present a range of plausible scenarios for Rt evolution, similar to the scenarios we formulated in our previous reports according to a percentage change in social contact behaviour/transmission rates.</p>
</li>
</ul>
<p><a href="/assets/20211116_technical_note_SIMID_update.pdf">The full document from November (v2021-11-16) is available here.</a></p>
<hr />
<p>The SIMID consortium wrote also technical notes in September and October 2021 to share with the GEMS. We updated the Figures on November 18th, 2021, with the reported data from Sciensano.</p>
<p><a href="/assets/20210914_technical_note_SIMID_update.pdf">The updated Technical Note from September (v2021-09-14) is available here.</a></p>
<p><a href="/assets/20211012_technical_note_SIMID_update.pdf">The updated Technical Note from October (v2021-10-12) is available here.</a></p>Christel Faes (UHasselt), Lander Willem (UAntwerpen), Niel Hens (UHasselt & UAntwerpen), Philippe Beutels (UAntwerpen), Steven Abrams (UAntwerpen & UHasselt)The SIMID consortium prepared a new technical note (v2021-11-16) containing the model estimates of hospital admissions and ICU load by a short term prediction model and a stochastic dynamic transmission model using observational data up to November 16th, 2021.Technical note: SARS-CoV-2 variants and vaccination in Belgium (v2021-09-14)2021-09-20T00:00:00+00:002021-09-20T00:00:00+00:00https://covid-en-wetenschap.github.io//2021/09/technical-note-simid-september<p>The SIMID consortium prepared a new technical note (v2021-09-14) containing the model estimates of hospital admissions and ICU load by a stochastic dynamic transmission model using observational data up to September 2021.</p>
<p><a href="/assets/20210914_technical_note_SIMID_update.pdf">The full document is available here.</a></p>
<p>Conclusions</p>
<ul>
<li>
<p>Social mixing and thus risk behavior still drives the projected burden of disease. An increase of +50% of the risk behavior we estimated for August 2021 would result in a high pressure on hospital capacity on the national level, which is in line with the projections using the “September 2020” behavior. If the increase in risk behavior is only +30% of the August 2021 situation, we project on average only half of the daily hospital admissions and ICU load.</p>
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<p>A regional analysis for the Brussels Capital Region shows that an increase of +50% of the behavior we estimated for August 2021, could result in a high pressure on hospital capacity.</p>
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<p>When we explore behavioral changes from September 1st, 2021, they lead to different outcomes from the second half of September 2021 onward. In combination with the current variability in reported daily COVID-19 related hospital admissions, we cannot select or rule out any scenario at this moment in time. The transmission model we use is suited for scenario analyses to investigate possible future paths, it is not a prediction model.</p>
</li>
</ul>Christel Faes (UHasselt), Lander Willem (UAntwerpen), Niel Hens (UHasselt & UAntwerpen), Steven Abrams (UAntwerpen & UHasselt)The SIMID consortium prepared a new technical note (v2021-09-14) containing the model estimates of hospital admissions and ICU load by a stochastic dynamic transmission model using observational data up to September 2021.Technical note: SARS-CoV-2 variants and vaccination in Belgium (v2021-08-18)2021-08-23T00:00:00+00:002021-08-23T00:00:00+00:00https://covid-en-wetenschap.github.io//2021/08/technical-note-simid-august<p>The SIMID consortium prepared a new technical note (v2021-08-18) containing the model estimates of hospital and ICU admissions and load by a short term prediction model and a stochastic dynamic transmission model using observational data up to December 2021.</p>
<p><a href="/assets/20210818_technical_note_SIMID.pdf">The full document is available here.</a></p>
<p>Conclusions</p>
<ul>
<li>
<p>The short-term prediction model depicts a further increase in new hospitalizations and ICU load, driven by the current trends in positivity ratio. The model predicts between 78 and 136 new hospital admissions and between 242 and 282 patients in ICU on August 28.</p>
</li>
<li>
<p>A further increase in hospital admissions is predicted in all regions (Brussels, Flanders and Wallonia), with a doubling of the number of new hospitalizations from August 16 to August 28 in both Brussels and Wallonia, and an increase of 70% in Flanders.</p>
</li>
<li>
<p>Social mixing and thus risk behaviour still drives the projected burden of disease. An increase of +50% of the behaviour we estimate for August 2021, shows hospital admission levels in line with the projections using the ``September 2020’’ behaviour which would result in a high pressure on hospital capacity. If the increase in risk behaviour is only +30% of the August 2021 situation, we project on average only half of the daily hospital admissions and ICU load.</p>
</li>
</ul>
<p>We wrote also a report with additional results for the Brussels Captial Region, which is <a href="/assets/20210823bxl_technical_note_SIMID.pdf"> available here.</a></p>Christel Faes (UHasselt), Lander Willem (UAntwerpen), Niel Hens (UHasselt & UAntwerpen), Steven Abrams (UAntwerpen & UHasselt)The SIMID consortium prepared a new technical note (v2021-08-18) containing the model estimates of hospital and ICU admissions and load by a short term prediction model and a stochastic dynamic transmission model using observational data up to December 2021.Technical note: SARS-CoV-2 variants and vaccination in Belgium (v2021-05-06)2021-05-06T00:00:00+00:002021-05-06T00:00:00+00:00https://covid-en-wetenschap.github.io//2021/05/technical-note-simid-may<p>The SIMID consortium prepared a third technical note (v2021-05-06) containing the model estimates of hospital and ICU admissions and load by a short term prediction model and a stochastic dynamic transmission model using observational data up to May 1st, 2021.</p>
<p>Conclusions</p>
<ul>
<li>
<p>The age-specific vaccination uptake and the higher transmissibility and severity of variants of concern (VOC), primarily VOC-202012/1 or lineage B.1.1.7, have caused a change in the relation between confirmed COVID-19 cases, daily number of new hospitalizations, hospital load, ICU load and number of COVID-19 related deaths (see e.g. Davies et al. 2021, Patone et al. 2021).</p>
</li>
<li>
<p>The short term prediction model depicts a further decrease in new hospitalizations and ICU load, driven by the current decrease in positivity ratio and the observed mobility patterns. The model predicts between 63 and 116 new hospital admissions and between 441 and 468 patients in ICU on May 19th.</p>
</li>
<li>
<p>Dynamic stochastic modelling of the underlying mechanisms informed by empirical social contact data up to April 18th, 2021, shows also decreasing trends that reach on average 100 hospital admissions and an ICU load of 500 beds by the end of May. However, these projections show large credible intervals and should be interpreted with care.</p>
</li>
<li>
<p>Model scenarios assuming a shift in behavior (and transmission) after the Easter holiday (19-24 April, 2021) in line with the situation before the holidays (1-24 March, 2021) show a plateau in hospital admissions in May-June 2021.</p>
</li>
<li>
<p>Model scenarios assuming also a substantial change in behavior in May 2021, show a resurgence of the hospital admissions and associated occupancy in ICU and non-ICU. This is more pronounced when this behavioural change occurs from the 1st of May, instead of from the 15th of May onwards.</p>
</li>
</ul>
<p><a href="/assets/20210506_technical_note_SIMID.pdf">The full document is available here.</a></p>Christel Faes (UHasselt), Lander Willem (UAntwerpen), Niel Hens (UHasselt & UAntwerpen), Philippe Beutels (UAntwerpen), Steven Abrams (UAntwerpen & UHasselt)The SIMID consortium prepared a third technical note (v2021-05-06) containing the model estimates of hospital and ICU admissions and load by a short term prediction model and a stochastic dynamic transmission model using observational data up to May 1st, 2021.