icumonitoring.ch: a platform for short-term forecasting of intensive care unit occupancy during the COVID-19 epidemic in Switzerland.

In Switzerland, the COVID-19 epidemic is progressively slowing down owing to “social distancing” measures introduced by the Federal Council on 16 March 2020. However, the gradual ease of these measures may initiate a second epidemic wave, the length and intensity of which are difficult to anticipate. In this context, hospitals must prepare for a potential increase in intensive care unit (ICU) admissions of patients with acute respiratory distress syndrome. Here, we introduce icumonitoring.ch, a platform providing hospital-level projections for ICU occupancy. We combined current data on the number of beds and ventilators with canton-level projections of COVID-19 cases from two S-E-I-R models. We disaggregated epidemic projection in each hospital in Switzerland for the number of COVID-19 cases, hospitalisations, hospitalisations in ICU, and ventilators in use. The platform is updated every 3-4 days and can incorporate projections from other modelling teams to inform decision makers with a range of epidemic scenarios for future hospital occupancy.

How New Mexico Leveraged a COVID-19 Case Forecasting Model to Preemptively Address the Health Care Needs of the State: Quantitative Analysis.

BACKGROUND: Prior to the COVID-19 pandemic, US hospitals relied on static projections of future trends for long-term planning and were only beginning to consider forecasting methods for short-term planning of staffing and other resources. With the overwhelming burden imposed by COVID-19 on the health care system, an emergent need exists to accurately forecast hospitalization needs within an actionable timeframe. OBJECTIVE: Our goal was to leverage an existing COVID-19 case and death forecasting tool to generate the expected number of concurrent hospitalizations, occupied intensive care unit (ICU) beds, and in-use ventilators 1 day to 4 weeks in the future for New Mexico and each of its five health regions. METHODS: We developed a probabilistic model that took as input the number of new COVID-19 cases for New Mexico from Los Alamos National Laboratory's COVID-19 Forecasts Using Fast Evaluations and Estimation tool, and we used the model to estimate the number of new daily hospital admissions 4 weeks into the future based on current statewide hospitalization rates. The model estimated the number of new admissions that would require an ICU bed or use of a ventilator and then projected the individual lengths of hospital stays based on the resource need. By tracking the lengths of stay through time, we captured the projected simultaneous need for inpatient beds, ICU beds, and ventilators. We used a postprocessing method to adjust the forecasts based on the differences between prior forecasts and the subsequent observed data. Thus, we ensured that our forecasts could reflect a dynamically changing situation on the ground. RESULTS: Forecasts made between September 1 and December 9, 2020, showed variable accuracy across time, health care resource needs, and forecast horizon. Forecasts made in October, when new COVID-19 cases were steadily increasing, had an average accuracy error of 20.0%, while the error in forecasts made in September, a month with low COVID-19 activity, was 39.7%. Across health care use categories, state-level forecasts were more accurate than those at the regional level. Although the accuracy declined as the forecast was projected further into the future, the stated uncertainty of the prediction improved. Forecasts were within 5% of their stated uncertainty at the 50% and 90% prediction intervals at the 3- to 4-week forecast horizon for state-level inpatient and ICU needs. However, uncertainty intervals were too narrow for forecasts of state-level ventilator need and all regional health care resource needs. CONCLUSIONS: Real-time forecasting of the burden imposed by a spreading infectious disease is a crucial component of decision support during a public health emergency. Our proposed methodology demonstrated utility in providing near-term forecasts, particularly at the state level. This tool can aid other stakeholders as they face COVID-19 population impacts now and in the future.

Toward a New Strategic Public Health Science for Policy, Practice, Impact, and Health Equity.

The COVID-19 pandemic and its social and health impact have underscored the need for a new strategic science agenda for public health. To optimize public health impact, high-quality strategic science addresses scientific gaps that inform policy and guide practice. At least 6 scientific gaps emerge from the US experience with COVID-19: health equity science, data science and modernization, communication science, policy analysis and translation, scientific collaboration, and climate science. Addressing these areas within a strategic public health science agenda will accelerate achievement of public health goals. Public health leadership and scientists have an unprecedented opportunity to use strategic science to guide a new era of improved and equitable public health.

Development of COVIDVax Model to Estimate the Risk of SARS-CoV-2-Related Death Among 7.6 Million US Veterans for Use in Vaccination Prioritization.

Importance: A strategy that prioritizes individuals for SARS-CoV-2 vaccination according to their risk of SARS-CoV-2-related mortality would help minimize deaths during vaccine rollout. Objective: To develop a model that estimates the risk of SARS-CoV-2-related mortality among all enrollees of the US Department of Veterans Affairs (VA) health care system. Design, Setting, and Participants: This prognostic study used data from 7 635 064 individuals enrolled in the VA health care system as of May 21, 2020, to develop and internally validate a logistic regression model (COVIDVax) that predicted SARS-CoV-2-related death (n = 2422) during the observation period (May 21 to November 2, 2020) using baseline characteristics known to be associated with SARS-CoV-2-related mortality, extracted from the VA electronic health records (EHRs). The cohort was split into a training period (May 21 to September 30) and testing period (October 1 to November 2). Main Outcomes and Measures: SARS-CoV-2-related death, defined as death within 30 days of testing positive for SARS-CoV-2. VA EHR data streams were imported on a data integration platform to demonstrate that the model could be executed in real-time to produce dashboards with risk scores for all current VA enrollees. Results: Of 7 635 064 individuals, the mean (SD) age was 66.2 (13.8) years, and most were men (7 051 912 [92.4%]) and White individuals (4 887 338 [64.0%]), with 1 116 435 (14.6%) Black individuals and 399 634 (5.2%) Hispanic individuals. From a starting pool of 16 potential predictors, 10 were included in the final COVIDVax model, as follows: sex, age, race, ethnicity, body mass index, Charlson Comorbidity Index, diabetes, chronic kidney disease, congestive heart failure, and Care Assessment Need score. The model exhibited excellent discrimination with area under the receiver operating characteristic curve (AUROC) of 85.3% (95% CI, 84.6%-86.1%), superior to the AUROC of using age alone to stratify risk (72.6%; 95% CI, 71.6%-73.6%). Assuming vaccination is 90% effective at preventing SARS-CoV-2-related death, using this model to prioritize vaccination was estimated to prevent 63.5% of deaths that would occur by the time 50% of VA enrollees are vaccinated, significantly higher than the estimate for prioritizing vaccination based on age (45.6%) or the US Centers for Disease Control and Prevention phases of vaccine allocation (41.1%). Conclusions and Relevance: In this prognostic study of all VA enrollees, prioritizing vaccination based on the COVIDVax model was estimated to prevent a large proportion of deaths expected to occur during vaccine rollout before sufficient herd immunity is achieved.

Predicting mortality for Covid-19 in the US using the delayed elasticity method.

The evolution of the pandemic caused by COVID-19, its high reproductive number and the associated clinical needs, is overwhelming national health systems. We propose a method for predicting the number of deaths, and which will enable the health authorities of the countries involved to plan the resources needed to face the pandemic as many days in advance as possible. We employ OLS to perform the econometric estimation. Using RMSE, MSE, MAPE, and SMAPE forecast performance measures, we select the best lagged predictor of both dependent variables. Our objective is to estimate a leading indicator of clinical needs. Having a forecast model available several days in advance can enable governments to more effectively face the gap between needs and resources triggered by the outbreak and thus reduce the deaths caused by COVID-19.

The legacy of MeNZB and possible implications for COVID-19 vaccination.

It is now over a decade since the meningococcal B vaccine, MeNZB, was in routine use in New Zealand. From July 2004 until June 2008 it was administered in a three-dose schedule to over a million individuals, aged six weeks to 20 years, to provide protection against the epidemic strain of group B Meningococci. The cost of the campaign, including the development of the vaccine was substantial, in excess of $200M, but it contributed to a reduced incidence of meningococcal infections along with a reduction in morbidity and mortality. The campaign led to the development of a national immunisation register (NIR), which is still in existence today. As well as considering the legacies of the MeNZB vaccination programme, this paper examines whether there are any lessons to be learned, specifically concerning active vaccine safety monitoring, which may be important if, and when, a COVID-19 vaccine is developed and a national immunisation campaign instituted.

We Know Health Is Not Elective: Impacts of COVID-19.

Several months into the impact of the global COVID-19 pandemic, the authors use the framework of "radical uncertainty" and specific regional health care data to understand current and future health and economic impacts. Four key areas of discussion included are: (1) How did structural health care inequality manifest itself during the closure of all elective surgeries and visits?; (2) How can we really calculate the so-called untold burden that resulted from the closure, with a special emphasis on primary care?; (3) The Pennsylvania experience - using observations from the population of one major delivery ecosystem (Jefferson Health), a major accountable care organization (Delaware Valley ACO), and statewide data from Pennsylvania; and (4) What should be the priorities and focus of the delivery system of the future given the dramatic financial and clinical disruption of COVID-19?

Creating a Palliative Care Inpatient Response Plan for COVID-19-The UW Medicine Experience.

CONTEXT: The coronavirus disease 2019 (COVID-19) pandemic is stressing health care systems throughout the world. Significant numbers of patients are being admitted to the hospital with severe illness, often in the setting of advanced age and underlying comorbidities. Therefore, palliative care is an important part of the response to this pandemic. The Seattle area and UW Medicine have been on the forefront of the pandemic in the U.S. METHODS: UW Medicine developed a strategy to implement a palliative care response for a multihospital health care system that incorporates conventional capacity, contingency capacity, and crisis capacity. The strategy was developed by our palliative care programs with input from the health care system leadership. RESULTS: In this publication, we share our multifaceted strategy to implement high-quality palliative care in the context of the COVID-19 pandemic that incorporates conventional, contingency, and crisis capacity and focuses on the areas of the hospital caring for the most patients: the emergency department, intensive care units, and acute care services. The strategy focuses on key content areas, including identifying and addressing goals of care, addressing moderate and severe symptoms, and supporting family members. CONCLUSION: Strategy planning for delivery of high-quality palliative care in the context of the COVID-19 pandemic represents an important area of need for our health care systems. We share our experiences of developing such a strategy to help other institutions conduct and adapt such strategies more quickly.

Planning for a pandemic: Mitigating risk to radiation therapy service delivery in the COVID-19 era.

The novel coronavirus (COVID-19) has rapidly impacted all of our lives following its escalation to pandemic status on 11 March 2020. Government guidelines and restrictions implemented to mitigate the risk of COVID-19 community transmission have forced radiation therapy departments to promptly adjust to the significant impact on our ability to deliver best clinical care. The inherent nature of our tri-partied professions relies heavily on multidisciplinary teamwork and patient-clinician interactions. Teamwork and patient interaction are critical to the role of a radiation therapist. The aim of this paper is to describe the experience of the Peter MacCallum Cancer Centre's (Peter Mac) radiation therapy services during the preliminary stages of the COVID-19 pandemic in minimising risk to patients, staff and our clinical service. Four critical areas were identified in developing risk mitigation strategies across our service: (a) Workforce planning, (b) Workforce communication, (c) Patient safety and wellbeing, and (d) Staff safety and wellbeing. Each of these initiatives had a focus on continuum of clinical care, whilst minimising risk of cross infection for our radiation therapy workforce and patients alike. Initiatives included, but were not limited to, establishing COVID-Eclipse clinical protocols, remote access to local applications, implementation of Microsoft Teams, personal protective equipment (PPE) guidelines and virtual 'Division of Radiation Oncology' briefing/updates. The COVID-19 pandemic has dictated change in conventional radiation therapy practice. It is hoped that by sharing our experiences, the radiation therapy profession will continue to learn, adapt and navigate this period together, to ensure optimal outcomes for ourselves and our patients.

Emergency medicine's COVID future: Facing the triple challenge after flattening the curve.

After successfully avoiding the situations experienced by some countries, Australasian EDs now face a future in which the ongoing threat of COVID-19 is added to the traditional challenges in providing quality emergency care. The contribution of emergency medicine to the national containment strategy adds a new dimension to the demands placed on emergency medicine in Australia and similarly, to the elimination strategy employed in New Zealand. These demands will best be met by a considered, planned and resourced approach that will challenge traditional measures of 'ED efficiency'.

COVID-19 in Vietnam: A lesson of pre-preparation.

BACKGROUND: Vietnam was slowing the spread of COVID-19 to 200 cases by the end of March. From perspective of a relatively vulnerable healthcare systems, timely interventions were implemented to different stage of pandemic progress to limit the spread. METHOD: The authors compiled literature on different public health measures in Vietnam in compared to the progression of COVID-19 from January to March 2020. RESULTS: Three stages of pandemic progression of COVID-19 were recorded in Vietnam. At 213 confirmed cases under treatment and isolation, a range of interventions were enforced including intensive and expansive contact, mass testing, isolation, and sterilization. Many were in place before any case were reported. CONCLUSION: Preparation were key for Vietnam's healthcare system in the ever-changing landscape of COVID-19 pandemic.

Influenza response planning for the centers of excellence for influenza research and surveillance: Science preparedness for enhancing global health security.

BACKGROUND: The Centers of Excellence for Influenza Research and Surveillance (CEIRS) network, funded by the US National Institutes of Health, has been operational since 2007 and is tasked with conducting research to improve understanding of influenza viruses. Recently, CEIRS developed an Influenza Response Plan (IRP) to improve science preparedness for the network. METHODS: Development of the IRP involved a collaborative process between project staff, CEIRS center directors or their designees, and NIAID CEIRS leadership (referred to as the Pandemic Planning Advisory Committee [PPAC]). Project staff identified and summarized the response capabilities of each center and then worked with the PPAC to identify and rank research priorities for an emergency response using a modified Delphi method. RESULTS: Key elements of the response plan include tables of response capabilities for each CEIRS center, a framework that outlines and ranks research priorities for CEIRS during an emergency situation, and an operational strategy for executing the research priorities. CONCLUSIONS: The CEIRS IRP highlights the importance of enhancing science preparedness in advance of an influenza pandemic or other influenza-related zoonotic incident to ensure that research can be carried out expeditiously and effectively in emergency situations and to improve global health security.