Dataset Information

Forecasting hospital demand in metropolitan areas during the current COVID-19 pandemic and estimates of lockdown-induced 2nd waves.

ABSTRACT: We present a forecasting model aim to predict hospital occupancy in metropolitan areas during the current COVID-19 pandemic. Our SEIRD type model features asymptomatic and symptomatic infections with detailed hospital dynamics. We model explicitly branching probabilities and non-exponential residence times in each latent and infected compartments. Using both hospital admittance confirmed cases and deaths, we infer the contact rate and the initial conditions of the dynamical system, considering breakpoints to model lockdown interventions and the increase in effective population size due to lockdown relaxation. The latter features let us model lockdown-induced 2nd waves. Our Bayesian approach allows us to produce timely probabilistic forecasts of hospital demand. We have applied the model to analyze more than 70 metropolitan areas and 32 states in Mexico.

SUBMITTER: Capistran MA

PROVIDER: S-EPMC7822260 | biostudies-literature | 2021

REPOSITORIES: biostudies-literature

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Forecasting hospital demand in metropolitan areas during the current COVID-19 pandemic and estimates of lockdown-induced 2nd waves.

Capistran Marcos A MA Capella Antonio A Christen J Andrés JA

PloS one 20210122 1

We present a forecasting model aim to predict hospital occupancy in metropolitan areas during the current COVID-19 pandemic. Our SEIRD type model features asymptomatic and symptomatic infections with detailed hospital dynamics. We model explicitly branching probabilities and non-exponential residence times in each latent and infected compartments. Using both hospital admittance confirmed cases and deaths, we infer the contact rate and the initial conditions of the dynamical system, considering b ...[more]

PMID: 33481925

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Project description:BackgroundThe delivery of prompt and appropriate antimicrobial therapy for life-threatening infections is an important antimicrobial stewardship measure and a priority for hospitals.ObjectivesTo better understand U.S. hospital pharmacy stocking processes and acquisition of nonstocked antimicrobials and to identify strategies for improving this process.MethodsThis mixed-methods study recruited infectious diseases and antimicrobial stewardship pharmacists. Semistructured interviews with pharmacists in Minnesota were conducted via video conferencing software from January 21, 2021, to March 17, 2021. Audio recordings of the interviews guided survey development and were also transcribed, coded, and qualitatively analyzed. Surveys were distributed throughout the United States via an e-mail listserv, and responses were collected between August 5, 2021, and September 15, 2021.ResultsTen interviews and 78 surveys were included in the analysis. Formulary and stocking practices varied based on institution. Stocking decisions were most frequently based on the frequency of use, clinical utility, and cost of antimicrobials. Nonstocked antimicrobials were often ordered from the wholesale distributor but, if needed urgently, acquired from another local institution. Antibacterial agents were the most frequently needed nonstocked antimicrobials, especially those targeting multidrug-resistant gram-negative bacteria. When acquiring nonstocked antimicrobials, barriers include process inefficiencies, cost, availability, and safety concerns. Improved information sharing between local institutions may help improve this process.ConclusionIn this exploratory study, antimicrobial stocking practices varied within U.S. hospitals. Acquisition of nonstocked, urgently needed antimicrobials from neighboring hospitals may be common; however, this process lacks guidance and is often inefficient. Establishing better mechanisms for information sharing may improve this process and should be explored.

Project description:BackgroundSimple phenomenological growth models can be useful for estimating transmission parameters and forecasting epidemic trajectories. However, most existing phenomenological growth models only support single-peak outbreak dynamics whereas real epidemics often display more complex transmission trajectories.MethodsWe develop and apply a novel sub-epidemic modeling framework that supports a diversity of epidemic trajectories including stable incidence patterns with sustained or damped oscillations to better understand and forecast epidemic outbreaks. We describe how to forecast an epidemic based on the premise that the observed coarse-scale incidence can be decomposed into overlapping sub-epidemics at finer scales. We evaluate our modeling framework using three outbreak datasets: Severe Acute Respiratory Syndrome (SARS) in Singapore, plague in Madagascar, and the ongoing Ebola outbreak in the Democratic Republic of Congo (DRC) and four performance metrics.ResultsThe sub-epidemic wave model outperforms simpler growth models in short-term forecasts based on performance metrics that account for the uncertainty of the predictions namely the mean interval score (MIS) and the coverage of the 95% prediction interval. For example, we demonstrate how the sub-epidemic wave model successfully captures the 2-peak pattern of the SARS outbreak in Singapore. Moreover, in short-term sequential forecasts, the sub-epidemic model was able to forecast the second surge in case incidence for this outbreak, which was not possible using the simple growth models. Furthermore, our findings support the view that the national incidence curve of the Ebola epidemic in DRC follows a stable incidence pattern with periodic behavior that can be decomposed into overlapping sub-epidemics.ConclusionsOur findings highlight how overlapping sub-epidemics can capture complex epidemic dynamics, including oscillatory behavior in the trajectory of the epidemic wave. This observation has significant implications for interpreting apparent noise in incidence data where the oscillations could be dismissed as a result of overdispersion, rather than an intrinsic part of the epidemic dynamics. Unless the oscillations are appropriately modeled, they could also give a false positive, or negative, impression of the impact from public health interventions. These preliminary results using sub-epidemic models can help guide future efforts to better understand the heterogenous spatial and social factors shaping sub-epidemic patterns for other infectious diseases.

Dataset Information

Forecasting hospital demand in metropolitan areas during the current COVID-19 pandemic and estimates of lockdown-induced 2nd waves.

Publications

Forecasting hospital demand in metropolitan areas during the current COVID-19 pandemic and estimates of lockdown-induced 2nd waves.

Similar Datasets

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