Early detection of new pandemic waves. Control chart and a new surveillance index.

The COVID-19 pandemic highlights the pressing need for constant surveillance, updating of the response plan in post-peak periods and readiness for the possibility of new waves of the pandemic. A short initial period of steady rise in the number of new cases is sometimes followed by one of exponential growth. Systematic public health surveillance of the pandemic should signal an alert in the event of change in epidemic activity within the community to inform public health policy makers of the need to control a potential outbreak. The goal of this study is to improve infectious disease surveillance by complementing standardized metrics with a new surveillance metric to overcome some of their difficulties in capturing the changing dynamics of the pandemic. At statistically-founded threshold values, the new measure will trigger alert signals giving early warning of the onset of a new pandemic wave. We define a new index, the weighted cumulative incidence index, based on the daily new-case count. We model the infection spread rate at two levels, inside and outside homes, which explains the overdispersion observed in the data. The seasonal component of real data, due to the public surveillance system, is incorporated into the statistical analysis. Probabilistic analysis enables the construction of a Control Chart for monitoring index variability and setting automatic alert thresholds for new pandemic waves. Both the new index and the control chart have been implemented with the aid of a computational tool developed in R, and used daily by the Navarre Government (Spain) for virus propagation surveillance during post-peak periods. Automated monitoring generates daily reports showing the areas whose control charts issue an alert. The new index reacts sooner to data trend changes preluding new pandemic waves, than the standard surveillance index based on the 14-day notification rate of reported COVID-19 cases per 100,000 population.

Gestión de camas hospitalarias durantela pandemia en Navarra con el apoyo demétodos matemáticos de predicción / Management of hospital beds during the pandemic in Navarra with the support of mathematical prediction methods

Durante la pandemia por coronavirus, en Navarra se utilizaron modelos matemáticos depredicción para estimar las camas necesarias, convencionales y de críticos, para atender alos pacientes COVID-19. Las seis ondas pandémicas presentaron distinta incidencia en la población, ocasionandovariabilidad en los ingresos hospitalarios y en la ocupación hospitalaria. La respuesta a laenfermedad de los pacientes no fue constante en cada onda, por lo que, para la predicción decada una, se utilizaron los datos correspondientes de esa onda.El método de predicción constó de dos partes: una describió la entrada de pacientes alhospital y la otra su estancia dentro del mismo. El modelo requirió de la alimentación a tiempo real de los datos actualizados. Los resultados delos modelos de predicción fueron posteriormente volcados al sistema de información corporativotipo Business Intelligence. Esta información fue utilizada para planificar el recurso cama y lasnecesidades de profesionales asociadas a la atención de estos pacientes en el ámbito hospitalario.En la cuarta onda se realizó un análisis para cuantificar el grado de acierto de los modelospredictivos. Los modelos predijeron adecuadamente el pico, la meseta y el cambio detendencia, pero sobreestimaron los recursos necesarios para la atención de los pacientes enla parte descendente de la curva. El principal punto fuerte de la sistemática utilizada para la construcción de modelospredictivos fue proporcionar modelos en tiempo real con datos recogidos con precisión porlos sistemas de información que consiguieron un grado de acierto aceptable permitiendo unautilización inmediata.(AU)

Estimation of patient flow in hospitals using up-to-date data. Application to bed demand prediction during pandemic waves.

Hospital bed demand forecast is a first-order concern for public health action to avoid healthcare systems to be overwhelmed. Predictions are usually performed by estimating patients flow, that is, lengths of stay and branching probabilities. In most approaches in the literature, estimations rely on not updated published information or historical data. This may lead to unreliable estimates and biased forecasts during new or non-stationary situations. In this paper, we introduce a flexible adaptive procedure using only near-real-time information. Such method requires handling censored information from patients still in hospital. This approach allows the efficient estimation of the distributions of lengths of stay and probabilities used to represent the patient pathways. This is very relevant at the first stages of a pandemic, when there is much uncertainty and too few patients have completely observed pathways. Furthermore, the performance of the proposed method is assessed in an extensive simulation study in which the patient flow in a hospital during a pandemic wave is modelled. We further discuss the advantages and limitations of the method, as well as potential extensions.

Hospital preparedness during epidemics using simulation: the case of COVID-19.

This paper presents a discrete event simulation model to support decision-making for the short-term planning of hospital resource needs, especially Intensive Care Unit (ICU) beds, to cope with outbreaks, such as the COVID-19 pandemic. Given its purpose as a short-term forecasting tool, the simulation model requires an accurate representation of the current system state and high fidelity in mimicking the system dynamics from that state. The two main components of the simulation model are the stochastic modeling of patient admission and patient flow processes. The patient arrival process is modelled using a Gompertz growth model, which enables the representation of the exponential growth caused by the initial spread of the virus, followed by a period of maximum arrival rate and then a decreasing phase until the wave subsides. We conducted an empirical study concluding that the Gompertz model provides a better fit to pandemic-related data (positive cases and hospitalization numbers) and has superior prediction capacity than other sigmoid models based on Richards, Logistic, and Stannard functions. Patient flow modelling considers different pathways and dynamic length of stay estimation in several healthcare stages using patient-level data. We report on the application of the simulation model in two Autonomous Regions of Spain (Navarre and La Rioja) during the two COVID-19 waves experienced in 2020. The simulation model was employed on a daily basis to inform the regional logistic health care planning team, who programmed the ward and ICU beds based on the resulting predictions.