270 research outputs found
Forecasting COVID-19 daily cases using phone call data
The need to forecast COVID-19 related variables continues to be pressing as
the epidemic unfolds. Different efforts have been made, with compartmental
models in epidemiology and statistical models such as AutoRegressive Integrated
Moving Average (ARIMA), Exponential Smoothing (ETS) or computing intelligence
models. These efforts have proved useful in some instances by allowing decision
makers to distinguish different scenarios during the emergency, but their
accuracy has been disappointing, forecasts ignore uncertainties and less
attention is given to local areas. In this study, we propose a simple Multiple
Linear Regression model, optimised to use call data to forecast the number of
daily confirmed cases. Moreover, we produce a probabilistic forecast that
allows decision makers to better deal with risk. Our proposed approach
outperforms ARIMA, ETS and a regression model without call data, evaluated by
three point forecast error metrics, one prediction interval and two
probabilistic forecast accuracy measures. The simplicity, interpretability and
reliability of the model, obtained in a careful forecasting exercise, is a
meaningful contribution to decision makers at local level who acutely need to
organise resources in already strained health services. We hope that this model
would serve as a building block of other forecasting efforts that on the one
hand would help front-line personal and decision makers at local level, and on
the other would facilitate the communication with other modelling efforts being
made at the national level to improve the way we tackle this pandemic and other
similar future challenges.Comment: 13 pages, 7 figure
An infrastructure of stream data mining, fusion and management for monitored patients
Paper presented at the 19th IEEE International Symposium on Computer-Based Medical Systems, CBMS 2006, Salt Lake City, UT.This paper proposes an infrastructure for data mining,
fusion and patient care management using continuous
stream data monitored from critically ill patients. Stream
data mining, fusion, and management provide efficient
ways to increase data utilization and to support knowledge
discovery, which can be utilized in many clinical areas to
improve the quality of patient care services. The primary
goal of our work is to establish a customized infrastructure
model designed for critical care services at hospitals.
However this structure can be easily expanded to other
areas of clinical specialties
Bayesian Monitoring of COVID-19 in Sweden
In an effort to provide regional decision support for the public healthcare,
we design a data-driven compartment-based model of COVID-19 in Sweden. From
national hospital statistics we derive parameter priors, and we develop linear
filtering techniques to drive the simulations given data in the form of daily
healthcare demands. We additionally propose a posterior marginal estimator
which provides for an improved temporal resolution of the reproduction number
estimate as well as supports robustness checks via a parametric bootstrap
procedure.
From our computational approach we obtain a Bayesian model of predictive
value which provides important insight into the progression of the disease,
including estimates of the effective reproduction number, the infection
fatality rate, and the regional-level immunity. We successfully validate our
posterior model against several different sources, including outputs from
extensive screening programs. Since our required data in comparison is easy and
non-sensitive to collect, we argue that our approach is particularly promising
as a tool to support monitoring and decisions within public health.Comment: Software for reproducibility:
https://github.com/robineriksson/Bayesian-Monitoring-of-COVID-19-in-Swede
Developing service supply chains by using agent based simulation
The Master thesis present a novel approach to model a service supply chain with agent based simulation. Also, the case study of thesis is related to healthcare services and research problem includes facility location of healthcare centers in Vaasa region by considering the demand, resource units and service quality. Geographical information system is utilized for locating population, agent based simulation for patients and their illness status probability, and discrete event simulation for healthcare services modelling. Health centers are located on predefined sites based on managers’ preference, then each patient based on the distance to health centers, move to the nearest point for receiving the healthcare services. For evaluating cost and services condition, various key performance indicators have defined in the modelling such as Number of patient in queue, patients waiting time, resource utilization, and number of patients ratio yielded by different of inflow and outflow. Healthcare managers would be able to experiment different scenarios based on changing number of resource units or location of healthcare centers, and subsequently evaluate the results without necessity of implementation in real life.fi=Opinnäytetyö kokotekstinä PDF-muodossa.|en=Thesis fulltext in PDF format.|sv=Lärdomsprov tillgängligt som fulltext i PDF-format
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