Towards participatory multi-modeling for policy support across domains and scales: a systematic procedure for integral multi-model design (preprint)

Policymaking for complex challenges such as pandemics necessitates the consideration of intricate implications across multiple domains and scales. Computational models can support policymaking, but a single model is often insufficient for such multidomain and scale challenges. Multi-models comprising several interacting computational models at different scales or relying on different modeling paradigms offer a potential solution. Such multi-models can be assembled from existing computational models (i.e., integrated modeling) or be designed conceptually as a whole before their computational implementation (i.e., integral modeling). Integral modeling is particularly valuable for novel policy problems, such as those faced in the early stages of a pandemic, where relevant models may be unavailable or lack standard documentation. Designing such multi-models through an integral approach is, however, a complex task requiring the collaboration of modelers and experts from various domains. In this collaborative effort, modelers must precisely define the domain knowledge needed from experts and establish a systematic procedure for translating such knowledge into a multi-model. Yet, these requirements and systematic procedures are currently lacking for multi-models that are both multiscale and multi-paradigm. We address this challenge by introducing a procedure for developing multi-models with an integral approach based on clearly defined domain knowledge requirements derived from literature. We illustrate this procedure using the case of school closure policies in the Netherlands during the COVID-19 pandemic, revealing their potential implications in the short and long term and across the healthcare and educational domains. The requirements and procedure provided in this article advance the application of integral multi-modeling for policy support in multiscale and multidomain contexts.

Evaluation of the Abbott PanbioTM COVID-19 antigen detection rapid diagnostic test among healthcare workers in elderly care (preprint)

BackgroundCoronavirus disease 2019 (COVID-19) has been especially dangerous for elderly people. To reduce the risk of transmission from healthcare workers to elderly people, it is of utmost importance to detect possible severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) positive healthcare workers as early as possible. We aimed to determine whether the Abbott Panbio COVID-19 antigen detection rapid diagnostic test (Ag-RDT) could be used as an alternative to reverse transcription-quantitative polymerase chain reaction (RT-qPCR). The second aim was to compare the cycle threshold (Ct) in RT-qPCR with the results of the Ag-RDT. MethodsA prospective diagnostic evaluation of the Abbott Panbio COVID-19 Ag-RDT among healthcare workers across three elderly care facilities as well as home-based elderly care workers who met clinical criteria for COVID-19 during the second wave of the COVID-19 pandemic. Per healthcare worker, the first nasopharyngeal swab was obtained to perform the Ag-RDT and the second swab for RT-qPCR. A Ct-value of < 40 was interpreted as positive, [≥] 40 as negative. ResultsA total of 683 healthcare workers with COVID-19 symptoms were sampled for detection of SARS-CoV-2 by both Ag-RDT and RT-qPCR. Sixty-three healthcare workers (9.2%) tested positive for SARS-CoV-2 by RT-qPCR. The overall sensitivity of Ag-RDT was 81.0% sensitivity (95%CI: 69.6-88.8%) and 100% specificity (95%CI: 99.4-100%). Using a cut-off Ct-value of 32, the sensitivity increased to 92.7% (95% CI: 82.7-97.1%). Negative Ag-RDT results were moderately associated with higher Ct-values (r = 0.62) compared to positive Ag-RDT results. ConclusionThe Panbio COVID-19 Ag-RDT can be used to quickly detect positive SARS-CoV-2 healthcare workers. Negative Ag-RDT should be confirmed by RT-qPCR. In case of severe understaffing and with careful consideration, fully vaccinated healthcare workers with Ag-RDT negative results could work with a mask pending PCR results.

Virological characteristics of SARS-CoV-2 vaccine breakthrough infections in health care workers (preprint)

BackgroundSARS-CoV-2 vaccines are highly effective at preventing COVID-19-related morbidity and mortality. As no vaccine is 100% effective, breakthrough infections are expected to occur. MethodsWe analyzed the virological characteristics of 161 vaccine breakthrough infections in a population of 24,706 vaccinated healthcare workers (HCWs), using RT-PCR and virus culture. ResultsThe delta variant (B.1.617.2) was identified in the majority of cases. Despite similar Ct-values, we demonstrate lower probability of infectious virus detection in respiratory samples of vaccinated HCWs with breakthrough infections compared to unvaccinated HCWs with primary SARS-CoV-2 infections. Nevertheless, infectious virus was found in 68.6% of breakthrough infections and Ct-values decreased throughout the first 3 days of illness. ConclusionsWe conclude that rare vaccine breakthrough infections occur, but infectious virus shedding is reduced in these cases.

The drop in reported invasive pneumococcal disease among adults during the first COVID-19 wave in the Netherlands explained. (preprint)

Streptococcus pneumoniae is the main bacterial pathogen causing respiratory infections. Since the COVID-19 pandemic emerged, less pneumococcal disease was identified by surveillance systems around the world. Measures to prevent transmission of SARS-CoV-2 also reduce transmission of pneumococci, but this would gradually lead to lower disease rates. Here, we explore additional factors that have contributed to the instant drop in pneumococcal disease cases captured in surveillance. Our observations on referral practices and other impediments to diagnostic testing indicate that residual IPD has likely occurred but remained undetected by conventional hospital-based surveillance. Depending on setting, we discuss alternative monitoring strategies that could improve sight on pneumococcal disease dynamics.