Multivariate phenomenological models for real-time short-term forecasts of hospital capacity for COVID-19 in Belgium from March to June 2020
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ABSTRACT:
SUBMITTER: Nguyen M
PROVIDER: S-EPMC8755551 | biostudies-literature |
REPOSITORIES: biostudies-literature
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Project description:BackgroundCOVID-19 mortality, excess mortality, deaths per million population (DPM), infection fatality ratio (IFR) and case fatality ratio (CFR) are reported and compared for many countries globally. These measures may appear objective, however, they should be interpreted with caution.AimWe examined reported COVID-19-related mortality in Belgium from 9 March 2020 to 28 June 2020, placing it against the background of excess mortality and compared the DPM and IFR between countries and within subgroups.MethodsThe relation between COVID-19-related mortality and excess mortality was evaluated by comparing COVID-19 mortality and the difference between observed and weekly average predictions of all-cause mortality. DPM were evaluated using demographic data of the Belgian population. The number of infections was estimated by a stochastic compartmental model. The IFR was estimated using a delay distribution between infection and death.ResultsIn the study period, 9,621 COVID-19-related deaths were reported, which is close to the excess mortality estimated using weekly averages (8,985 deaths). This translates to 837 DPM and an IFR of 1.5% in the general population. Both DPM and IFR increase with age and are substantially larger in the nursing home population.DiscussionDuring the first pandemic wave, Belgium had no discrepancy between COVID-19-related mortality and excess mortality. In light of this close agreement, it is useful to consider the DPM and IFR, which are both age, sex, and nursing home population-dependent. Comparison of COVID-19 mortality between countries should rather be based on excess mortality than on COVID-19-related mortality.
| S-EPMC8855510 | biostudies-literature
Project description:BackgroundCOVID-19-related mortality in Belgium has drawn attention for two reasons: its high level, and a good completeness in reporting of deaths. An ad hoc surveillance was established to register COVID-19 death numbers in hospitals, long-term care facilities (LTCF) and the community. Belgium adopted broad inclusion criteria for the COVID-19 death notifications, also including possible cases, resulting in a robust correlation between COVID-19 and all-cause mortality.AimTo document and assess the COVID-19 mortality surveillance in Belgium.MethodsWe described the content and data flows of the registration and we assessed the situation as of 21 June 2020, 103 days after the first death attributable to COVID-19 in Belgium. We calculated the participation rate, the notification delay, the percentage of error detected, and the results of additional investigations.ResultsThe participation rate was 100% for hospitals and 83% for nursing homes. Of all deaths, 85% were recorded within 2 calendar days: 11% within the same day, 41% after 1 day and 33% after 2 days, with a quicker notification in hospitals than in LTCF. Corrections of detected errors reduced the death toll by 5%.ConclusionBelgium implemented a rather complete surveillance of COVID-19 mortality, on account of a rapid investment of the hospitals and LTCF. LTCF could build on past experience of previous surveys and surveillance activities. The adoption of an extended definition of 'COVID-19-related deaths' in a context of limited testing capacity has provided timely information about the severity of the epidemic.
| S-EPMC8641068 | biostudies-literature
Project description:.RAW files and Compound Discoverer peak lists used for a manuscript regarding changes the chemical fingerprint of sewage sludge during the COVID-19 pandemic
2021-01-08 | MSV000086676 | MassIVE
Project description:In March 2020, a severe respiratory syndrome developed in a cat, 1 week after its owner received positive test results for severe acute respiratory syndrome coronavirus 2. Viral RNA was detected in the cat's nasopharyngeal swab samples and vomitus or feces; immunoglobulin against the virus was found in convalescent-phase serum. Human-to-cat transmission is suspected.
| S-EPMC7706966 | biostudies-literature
Project description:Mexico has experienced one of the highest COVID-19 mortality rates in the world. A delayed implementation of social distancing interventions in late March 2020 and a phased reopening of the country in June 2020 has facilitated sustained disease transmission in the region. In this study we systematically generate and compare 30-day ahead forecasts using previously validated growth models based on mortality trends from the Institute for Health Metrics and Evaluation for Mexico and Mexico City in near real-time. Moreover, we estimate reproduction numbers for SARS-CoV-2 based on the methods that rely on genomic data as well as case incidence data. Subsequently, functional data analysis techniques are utilized to analyze the shapes of COVID-19 growth rate curves at the state level to characterize the spatiotemporal transmission patterns of SARS-CoV-2. The early estimates of the reproduction number for Mexico were estimated between Rt ~1.1-1.3 from the genomic and case incidence data. Moreover, the mean estimate of Rt has fluctuated around ~1.0 from late July till end of September 2020. The spatial analysis characterizes the state-level dynamics of COVID-19 into four groups with distinct epidemic trajectories based on epidemic growth rates. Our results show that the sequential mortality forecasts from the GLM and Richards model predict a downward trend in the number of deaths for all thirteen forecast periods for Mexico and Mexico City. However, the sub-epidemic and IHME models perform better predicting a more realistic stable trajectory of COVID-19 mortality trends for the last three forecast periods (09/21-10/21, 09/28-10/27, 09/28-10/27) for Mexico and Mexico City. Our findings indicate that phenomenological models are useful tools for short-term epidemic forecasting albeit forecasts need to be interpreted with caution given the dynamic implementation and lifting of social distancing measures.
| S-EPMC8294497 | biostudies-literature
Project description: Not available
| S-EPMC7092506 | biostudies-literature
Project description:Undetected infection with SARS-CoV-2, the virus that causes coronavirus disease 2019 (COVID-19) contributes to transmission in nursing homes, settings where large outbreaks with high resident mortality have occurred (1,2). Facility-wide testing of residents and health care personnel (HCP) can identify asymptomatic and presymptomatic infections and facilitate infection prevention and control interventions (3-5). Seven state or local health departments conducted initial facility-wide testing of residents and staff members in 288 nursing homes during March 24-June 14, 2020. Two of the seven health departments conducted testing in 195 nursing homes as part of facility-wide testing all nursing homes in their state, which were in low-incidence areas (i.e., the median preceding 14-day cumulative incidence in the surrounding county for each jurisdiction was 19 and 38 cases per 100,000 persons); 125 of the 195 nursing homes had not reported any COVID-19 cases before the testing. Ninety-five of 22,977 (0.4%) persons tested in 29 (23%) of these 125 facilities had positive SARS-CoV-2 test results. The other five health departments targeted facility-wide testing to 93 nursing homes, where 13,443 persons were tested, and 1,619 (12%) had positive SARS-CoV-2 test results. In regression analyses among 88 of these nursing homes with a documented case before facility-wide testing occurred, each additional day between identification of the first case and completion of facility-wide testing was associated with identification of 1.3 additional cases. Among 62 facilities that could differentiate results by resident and HCP status, an estimated 1.3 HCP cases were identified for every three resident cases. Performing facility-wide testing immediately after identification of a case commonly identifies additional unrecognized cases and, therefore, might maximize the benefits of infection prevention and control interventions. In contrast, facility-wide testing in low-incidence areas without a case has a lower proportion of test positivity; strategies are needed to further optimize testing in these settings.
| S-EPMC7440119 | biostudies-literature
Project description:Guangdong province, located in South China, is an important economic hub with a large domestic migrant population and was among the earliest areas to report COVID-19 cases outside of Wuhan. We conducted a cross-sectional, age-stratified serosurvey to determine the seroprevalence of antibodies against SARS-CoV-2 after the emergence of COVID-19 in Guangdong. We tested 14,629 residual serum samples that were submitted for clinical testing from 21 prefectures between March and June 2020 for SARS-CoV-2 antibodies using a magnetic particle based chemiluminescent enzyme immunoassay and validated the results using a pseudovirus neutralization assay. We found 21 samples positive for SARS-CoV-2 IgG, resulting in an estimated age- and sex-weighted seroprevalence of 0.15% (95% CI: 0.06-0.24%). The overall age-specific seroprevalence was 0.07% (95% CI: 0.01-0.24%) in persons up to 9 years old, 0.22% (95% CI: 0.03-0.79%) in persons aged 10-19, 0.16% (95% CI: 0.07-0.33%) in persons aged 20-39, 0.13% (95% CI: 0.03-0.33%) in persons aged 40-59 and 0.18% (95% CI: 0.07-0.40%) in persons ≥60 years old. Fourteen (67%) samples had pseudovirus neutralization titers to S-protein, suggesting most of the IgG-positive samples were true-positives. Seroprevalence of antibodies to SARS-CoV-2 was low, indicating that there were no hidden epidemics during this period. Vaccination is urgently needed to increase population immunity to SARS-CoV-2.
| S-EPMC8619097 | biostudies-literature
Project description:ObjectiveTo describe case rates, testing rates and percent positivity of COVID-19 among children aged 0-18 years by school-age grouping.DesignWe abstracted data from Georgia's State Electronic Notifiable Disease Surveillance System on all 10 437 laboratory-confirmed COVID-19 cases among children aged 0-18 years during 30 March 2020 to 6 June 2021. We examined case rates, testing rates and percent positivity by school-aged groupings, namely: preschool (0-4 years), elementary school (5-10 years), middle school (11-13 years), and high school (14-18 years) and compared these data among school-aged children with those in the adult population (19 years and older).SettingFulton County, Georgia.Main outcome measuresCOVID-19 case rates, testing rates and percent positivity.ResultsOver time, the proportion of paediatric cases rose substantially from 1.1% (April 2020) to 21.6% (April 2021) of all cases in the county. Age-specific case rates and test rates were consistently highest among high-school aged children. Test positivity was similar across school-age groups, with periods of higher positivity among high-school aged children.ConclusionsLow COVID-19 testing rates among children, especially early in the pandemic, likely underestimated the true burden of disease in this age group. Despite children having lower measured incidence of COVID-19, we found when broader community incidence increased, incidence also increased among all paediatric age groups. As the COVID-19 pandemic continues to evolve, it remains critical to continue learning about the incidence and transmissibility of COVID-19 in children.
| S-EPMC8671844 | biostudies-literature
Project description:ObjectivesGiven finite ICU bed capacity, knowledge of ICU bed utilization during the coronavirus disease 2019 pandemic is critical to ensure future strategies for resource allocation and utilization. We sought to examine ICU census trends in relation to ICU bed capacity during the rapid increase in severe coronavirus disease 2019 cases early during the pandemic.DesignObservational cohort study.SettingThirteen geographically dispersed academic medical centers in the United States.Patients/subjectsWe obtained daily ICU censuses from March 26 to June 30, 2020, as well as prepandemic ICU bed capacities. The primary outcome was daily census of ICU patients stratified by coronavirus disease 2019 and mechanical ventilation status in relation to ICU capacity.InterventionsNone.Measurements and main resultsPrepandemic overall ICU capacity ranged from 62 to 225 beds (median 109). During the study period, the median daily coronavirus disease 2019 ICU census per hospital ranged from 1 to 84 patients, and the daily ICU census exceeded overall ICU capacity for at least 1 day at five institutions. The number of critically ill patients exceeded ICU capacity for a median (interquartile range) of 17 (12-50) of 97 days at these five sites. All 13 institutions experienced decreases in their noncoronavirus disease ICU population, whereas local coronavirus disease 2019 cases increased. Coronavirus disease 2019 patients reached their greatest proportion of ICU capacity on April 12, 2020, when they accounted for 44% of ICU patients across all participating hospitals. Maximum ICU census ranged from 52% to 289% of overall ICU capacity, with three sites less than 80%, four sites 80-100%, five sites 100-128%, and one site 289%.ConclusionsFrom March to June 2020, the coronavirus disease 2019 pandemic led to ICU censuses greater than ICU bed capacity at fives of 13 institutions evaluated. These findings demonstrate the short-term adaptability of U.S. healthcare institutions in redirecting limited resources to accommodate a public health emergency.
| S-EPMC7994039 | biostudies-literature