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1.
EuropePMC; 2020.
Preprint in English | EuropePMC | ID: ppcovidwho-317552

ABSTRACT

Epidemiological simulations as a method are used to better understand and predict the spreading of infectious diseases, for example of COVID-19. This paper presents an approach that combines person-centric data-driven human mobility modelling with a mechanistic infection model and a person-centric disease progression model. The model includes the consequences of disease import, of changed activity participation rates over time (coming from mobility data), of masks, of indoors vs.\ outdoors leisure activities, and of contact tracing. Results show that the model is able to credibly track the infection dynamics in Berlin (Germany). The model can be used to understand the contributions of different activity types to the infection dynamics over time. The model clearly shows the effects of contact reductions, school closures/vacations, or the effect of moving leisure activities from outdoors to indoors in fall. Sensitivity tests show that all ingredients of the model are necessary to track the current infection dynamics. One interesting result from the mobility data is that behavioral changes of the population mostly happened \textit{before} the government-initiated so-called contact ban came into effect. Similarly, people started drifting back to their normal activity patterns \emph{before} the government officially reduced the contact ban. Our work shows that is is possible to build detailed epidemiological simulations from microscopic mobility models relatively quickly. They can be used to investigate mechanical aspects of the dynamics, such as the transmission from political decisions via human behavior to infections, consequences of different lockdown measures, consequences of wearing masks in certain situations, or contact tracing.

2.
PLoS One ; 16(10): e0259037, 2021.
Article in English | MEDLINE | ID: covidwho-1496524

ABSTRACT

Epidemiological simulations as a method are used to better understand and predict the spreading of infectious diseases, for example of COVID-19. This paper presents an approach that combines a well-established approach from transportation modelling that uses person-centric data-driven human mobility modelling with a mechanistic infection model and a person-centric disease progression model. The model includes the consequences of different room sizes, air exchange rates, disease import, changed activity participation rates over time (coming from mobility data), masks, indoors vs. outdoors leisure activities, and of contact tracing. It is validated against the infection dynamics in Berlin (Germany). The model can be used to understand the contributions of different activity types to the infection dynamics over time. It predicts the effects of contact reductions, school closures/vacations, masks, or the effect of moving leisure activities from outdoors to indoors in fall, and is thus able to quantitatively predict the consequences of interventions. It is shown that these effects are best given as additive changes of the reproduction number R. The model also explains why contact reductions have decreasing marginal returns, i.e. the first 50% of contact reductions have considerably more effect than the second 50%. Our work shows that is is possible to build detailed epidemiological simulations from microscopic mobility models relatively quickly. They can be used to investigate mechanical aspects of the dynamics, such as the transmission from political decisions via human behavior to infections, consequences of different lockdown measures, or consequences of wearing masks in certain situations. The results can be used to inform political decisions.


Subject(s)
COVID-19/prevention & control , Communicable Disease Control/methods , Contact Tracing/methods , Berlin , COVID-19/metabolism , Cell Phone/trends , Computer Simulation , Germany , Hand Disinfection/trends , Humans , Masks/trends , Models, Theoretical , Physical Distancing , Population Dynamics/trends , SARS-CoV-2/pathogenicity , Systems Analysis
3.
J Trauma Acute Care Surg ; 89(4): 792-800, 2020 10.
Article in English | MEDLINE | ID: covidwho-616206

ABSTRACT

BACKGROUND: Whole blood is optimal for resuscitation of traumatic hemorrhage. Walking Blood Banks provide fresh whole blood (FWB) where conventional blood components or stored, tested whole blood are not readily available. There is an increasing interest in this as an emergency resilience measure for isolated communities and during crises including the coronavirus disease 2019 pandemic. We conducted a systematic review and meta-analysis of the available evidence to inform practice. METHODS: Standard systematic review methodology was used to obtain studies that reported the delivery of FWB (PROSPERO registry CRD42019153849). Studies that only reported whole blood from conventional blood banking were excluded. For outcomes, odds ratios (ORs) and 95% confidence interval (CI) were calculated using random-effects modeling because of high risk of heterogeneity. Quality of evidence was assessed using the Grading of Recommendations, Assessment, Development, and Evaluation system. RESULTS: Twenty-seven studies published from 2006 to 2020 reported >10,000 U of FWB for >3,000 patients (precise values not available for all studies). Evidence for studies was "low" or "very low" except for one study, which was "moderate" in quality. Fresh whole blood patients were more severely injured than non-FWB patients. Overall, survival was equivalent between FWB and non-FWB groups for eight studies that compared these (OR, 1.00 [95% CI, 0.65-1.55]; p = 0.61). However, the highest quality study (matched groups for physiological and injury characteristics) reported an adjusted OR of 0.27 (95% CI, 0.13-0.58) for mortality for the FWB group (p < 0.01). CONCLUSION: Thousands of units of FWB from Walking Blood Banks have been transfused in patients following life-threatening hemorrhage. Survival is equivalent for FWB resuscitation when compared with non-FWB, even when patients were more severely injured. Evidence is scarce and of relative low quality and may underestimate potential adverse events. Whereas Walking Blood Banks may be an attractive resilience measure, caution is still advised. Walking Blood Banks should be subject to prospective evaluation to optimize care and inform policy. LEVEL OF EVIDENCE: Systematic/therapeutic, level 3.


Subject(s)
Blood Banks , Blood Transfusion/methods , Resuscitation/methods , Shock, Hemorrhagic/therapy , Shock, Traumatic/therapy , Humans , Severity of Illness Index , Shock, Hemorrhagic/diagnosis , Shock, Hemorrhagic/etiology , Shock, Hemorrhagic/mortality , Shock, Traumatic/complications , Shock, Traumatic/diagnosis , Shock, Traumatic/mortality , Survival Analysis , Treatment Outcome
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