Publications
13 results found
Christen P, van Elsland S, Saulo D, et al., 2024, Advanced analytics to inform decision making during public health emergencies, Advanced Analytics to Inform Decision Making During Public Health Emergencies, Publisher: WHO, Imperial College London
The COVID-19 pandemic has led to significant understanding of evidence-informed decision making during public health emergencies. Imperial College London and the World Health Organization (WHO) Hub for Pandemic and Epidemic Intelligence (WHO Hub) jointly organized a workshop to generate an understanding of the context and ways in which advanced analytics were used for decision making during the COVID-19 pandemic and to identify opportunities to strengthen the data-to-decisions pathways. Held on 9 – 10 May 2023 at the WHO Hub in Berlin, Germany, the workshop brought together mathematical modellers specialized in infectious disease modelling and scientists based at academic institutions, public health agencies, or Ministries of Health, and public health decision makers. The workshop was conducted in four interactive group activities. The dialogue among participants led to the identification of potential opportunities for support and actions to strengthen the use of outputs from advanced analytics for decision making. These opportunities could be actions to strengthen processes and structures, improve workflows, find consensus on ways of working together, establish a knowledge foundation for support, and jointly drive evidence-based decision-making priorities for epidemic and pandemic preparedness. Workshop participants highlighted further need to capture additional perspectives held by actors from diverse geographical areas, contexts, and roles who were not present at the workshop, and political decision makers to enrich the understanding of the different priorities for advanced analytics for decision making in different regional and local contexts. The workshop highlighted the benefit in bringing together experts from around the globe to share experience and lessons learned to identify priority activities to tackling challenges and improve the way advanced analysis is perceived and used for policy and response decision making. Overall, this workshop has contr
Utunen H, Mattar L, Piroux C, et al., 2022, Superusers of Self-Paced Online Learning on OpenWHO., Pages: 16-19
INTRODUCTION: OpenWHO provides open-access, online, free and real-time learning responses to health emergencies, which includes capacitating healthcare providers, first liners, medical students and even the general public. During the pandemic and to date, an additional 40 courses for COVID-19 response have led to a massive increase in the number of learners and a change in user's trends. This paper presents initial findings on enrollment trends, use and completion rates of health emergency courses offered on OpenWHO. METHODS: The enrolment data statistics were drawn from OpenWHO's built-in reporting system, which tracks learners' enrolments, completion rates, demographics and other key course-related data, This information was collected from the beginning of the OpenWHO launch in 2017 up until October 2021. RESULTS: Average course completion rate on OpenWHO including all courses and languages was equal to 45.9%. Nearly half (46.4%) of all OpenWHO learners have enrolled in at least 2 courses and 71 000 superusers have completed at least 10 courses on the platform. CONCLUSION: WHO's learning platform during the pandemic registered record high completion rates and repeat learners enrollment. This highlights the massive impact of the OpenWHO online learning platform for health emergencies and the tangible knowledge transfer and access to health literacy.
Haw D, Forchini G, Doohan P, et al., 2022, Optimizing social and economic activity while containing SARS-CoV-2 transmission using DAEDALUS, Nature Computational Science, Vol: 2, Pages: 223-233, ISSN: 2662-8457
To study the trade-off between economic, social and health outcomes in the management of a pandemic, DAEDALUS integrates a dynamic epidemiological model of SARS-CoV-2 transmission with a multi-sector economic model, reflecting sectoral heterogeneity in transmission and complex supply chains. The model identifies mitigation strategies that optimize economic production while constraining infections so that hospital capacity is not exceeded but allowing essential services, including much of the education sector, to remain active. The model differentiates closures by economic sector, keeping those sectors open that contribute little to transmission but much to economic output and those that produce essential services as intermediate or final consumption products. In an illustrative application to 63 sectors in the United Kingdom, the model achieves an economic gain of between £161 billion (24%) and £193 billion (29%) compared to a blanket lockdown of non-essential activities over six months. Although it has been designed for SARS-CoV-2, DAEDALUS is sufficiently flexible to be applicable to pandemics with different epidemiological characteristics.
Utunen H, Ndiaye N, Mattar L, et al., 2021, Changes in Users Trends Before and During the COVID-19 Pandemic on WHO's Online Learning Platform., Stud Health Technol Inform, Vol: 287, Pages: 163-164
OpenWHO provides open access, online, free and real time learning responses to health emergencies. Before the pandemic, courses on 18 diseases were provided. The increase to 38 courses in response to COVID-19 have led to a massive increase in the number of new learners. As a result, the COVID-19 pandemic affected learners' trends. This paper presents initial findings of changes perceived in the use and user groups' attendance to the World Health Organization's (WHO) health emergency learning platform OpenWHO. Enrolment statistics were based on data collected in December 2019 and March 2021. A descriptive analysis was conducted to explore changes in the usage pattern of the platform. Several user characteristics shifted between before and during the pandemic. More women, younger and older learners joined the learning during the pandemic. Public health education leaned toward a more equitable reach including previously underrepresented groups.
Christen P, Conteh L, 2021, How are mathematical models and results from mathematical models of vaccine-preventable diseases used, or not, by global health organizations?, BMJ Global Health, Vol: 6, Pages: 1-10, ISSN: 2059-7908
While epidemiological and economic evidence has the potential to provide answers to questions, guide complex programs and inform allocation decisions, how this evidence is used by global health organizations who commission it and what organizational actions are generated from the evidence remains unclear. This study applies analytical tools from organizational science to understand how evidence produced by infectious disease epidemiologists and health economists is used by global health organizations. A conceptual framework that embraces evidence use typologies and relates findings to the organizational process of action generation informs and structures the research. Between March and September 2020, we conducted in-depth interviews with mathematical modellers (evidence producers) and employees at global health organizations, who are involved in decision-making processes (evidence consumers). We found that commissioned epidemiological and economic evidence is used to track progress and provides a measure of success, both, in terms of health outcomes and the organisations’ mission. Global health organizations predominantly use this evidence to demonstrate accountability and solicit funding from external partners. We find common understanding and awareness across consumers and producers about the purposes and uses of these commissioned pieces of work and how they are distinct from more academic explorative research outputs. Conceptual evidence use best describes this process. Evidence is slowly integrated into organizational processes and is one of many influences on global health organizations’ actions. Relationships developed over time and trust guide the process, which may lead to quite a concentrated cluster of those producing and commissioning models. These findings raise several insights relevant to the literature of research utilization in organizations and evidence-based management. The study extends our understanding of how evidence is used and w
Christen P, Conteh L, 2021, HOW ARE MATHEMATICAL MODELS OF VACCINE PREVENTABLE DISEASES USED, OR NOT, BY DECISION MAKERS AT A GLOBAL LEVEL?, Publisher: ELSEVIER SCIENCE INC, Pages: S118-S119, ISSN: 1098-3015
Christen P, D'Aeth J, Lochen A, et al., 2021, The J-IDEA pandemic planner: a framework for implementing hospital provision interventions during the COVID-19 pandemic, Medical Care, Vol: 59, Pages: 371-378, ISSN: 0025-7079
Background : Planning for extreme surges in demand for hospital care of patientsrequiring urgent life-saving treatment for COVID-19, whilst retaining capacity for otheremergency conditions, is one of the most challenging tasks faced by healthcareproviders and policymakers during the pandemic. Health systems must be wellpreparedto cope with large and sudden changes in demand by implementinginterventions to ensure adequate access to care. We developed the first planning toolfor the COVID-19 pandemic to account for how hospital provision interventions (suchas cancelling elective surgery, setting up field hospitals, or hiring retired staff) will affectthe capacity of hospitals to provide life-saving care.Methods : We conducted a review of interventions implemented or considered in 12 European countries in March-April 2020, an evaluation of their impact on capacity, anda review of key parameters in the care of COVID-19 patients. This information wasused to develop a planner capable of estimating the impact of specific interventions ondoctors, nurses, beds and respiratory support equipment. We applied this to ascenario-based case study of one intervention, the set-up of field hospitals in England,under varying levels of COVID-19 patients.Results : The J-IDEA pandemic planner is a hospital planning tool that allows hospitaladministrators, policymakers and other decision-makers to calculate the amount ofcapacity in terms of beds, staff and crucial medical equipment obtained byimplementing the interventions. Flexible assumptions on baseline capacity, the numberof hospitalisations, staff-to-beds ratios, and staff absences due to COVID-19 make theplanner adaptable to multiple settings. The results of the case study show that whilefield hospitals alleviate the burden on the number of beds available, this intervention isfutile unless the deficit of critical care nurses is addressed first.Discussion : The tool supports decision-makers in delivering a fast and effectiveresponse to
Haw D, Christen P, Forchini G, et al., 2020, DAEDALUS: An economic-epidemiological model to optimize economic activity while containing the SARS-CoV-2 pandemic
Haw D, Forchini G, Christen P, et al., 2020, Report 35: How can we keep schools and universities open? Differentiating closures by economic sector to optimize social and economic activity while containing SARS-CoV-2 transmission
There is a trade-off between the education sector and other economic sectors in the control of SARS-Cov-2 transmission. Here we integrate a dynamic model of SARS-CoV-2 transmission with a 63-sector economic model reflecting sectoral heterogeneity in transmission and economic interdependence between sectors. We identify COVID-19 control strategies which optimize economic production while keeping schools and universities operational and constraining infections such that emergency hospital capacity is not exceeded. The model estimates an economic gain of between £163bn and £205bn for the United Kingdom compared to a blanket lockdown of non-essential activity over six months, depending on hospital capacity. Sectors identified as potential priorities for closure are contact-intensive and/or less economically productive.
McCabe R, Schmit N, Christen P, et al., 2020, Adapting hospital capacity to meet changing demands during the COVID-19 pandemic, BMC Medicine, Vol: 18, Pages: 1-12, ISSN: 1741-7015
BackgroundTo calculate hospital surge capacity, achieved via hospital provision interventions implemented for the emergency treatment of coronavirus disease 2019 (COVID-19) and other patients through March to May 2020; to evaluate the conditions for admitting patients for elective surgery under varying admission levels of COVID-19 patients.MethodsWe analysed National Health Service (NHS) datasets and literature reviews to estimate hospital care capacity before the pandemic (pre-pandemic baseline) and to quantify the impact of interventions (cancellation of elective surgery, field hospitals, use of private hospitals, deployment of former medical staff and deployment of newly qualified medical staff) for treatment of adult COVID-19 patients, focusing on general and acute (G&A) and critical care (CC) beds, staff and ventilators.ResultsNHS England would not have had sufficient capacity to treat all COVID-19 and other patients in March and April 2020 without the hospital provision interventions, which alleviated significant shortfalls in CC nurses, CC and G&A beds and CC junior doctors. All elective surgery can be conducted at normal pre-pandemic levels provided the other interventions are sustained, but only if the daily number of COVID-19 patients occupying CC beds is not greater than 1550 in the whole of England. If the other interventions are not maintained, then elective surgery can only be conducted if the number of COVID-19 patients occupying CC beds is not greater than 320. However, there is greater national capacity to treat G&A patients: without interventions, it takes almost 10,000 G&A COVID-19 patients before any G&A elective patients would be unable to be accommodated.ConclusionsUnless COVID-19 hospitalisations drop to low levels, there is a continued need to enhance critical care capacity in England with field hospitals, use of private hospitals or deployment of former and newly qualified medical staff to allow some or all elective surge
McCabe R, Schmit N, Christen P, et al., 2020, Report 27 Adapting hospital capacity to meet changing demands during the COVID-19 pandemic
To meet the growing demand for hospital care due to the COVID-19 pandemic, England implemented a range of hospital provision interventions including the procurement of equipment, the establishment of additional hospital facilities and the redeployment of staff and other resources. Additionally, to further release capacity across England’s National Health Service (NHS), elective surgery was cancelled in March 2020, leading to a backlog of patients requiring care. This created a pressure on the NHS to reintroduce elective procedures, which urgently needs to be addressed. Population-level measures implemented in March and April 2020 reduced transmission of SARS-CoV-2, prompting a gradual decline in the demand for hospital care by COVID-19 patients after the peak in mid-April. Planning capacity to bring back routine procedures for non-COVID-19 patients whilst maintaining the ability to respond to any potential future increases in demand for COVID-19 care is the challenge currently faced by healthcare planners.In this report, we aim to calculate hospital capacity for emergency treatment of COVID-19 and other patients during the pandemic surge in April and May 2020; to evaluate the increase in capacity achieved via five interventions (cancellation of elective surgery, field hospitals, use of private hospitals, and deployment of former and newly qualified medical staff); and to determine how to re-introduce elective surgery considering continued demand from COVID-19 patients. We do this by modelling the supply of acute NHS hospital care, considering different capacity scenarios, namely capacity before the pandemic (baseline scenario) and after the implementation of capacity expansion interventions that impact available general and acute (G&A) and critical care (CC) beds, staff and ventilators. Demand for hospital care is accounted for in terms of non-COVID-19 and COVID-19 patients. Our results suggest that NHS England would not have had sufficient daily capacity
Christen P, D'Aeth J, Lochen A, et al., 2020, Report 15: Strengthening hospital capacity for the COVID-19 pandemic
Planning for extreme surges in demand for hospital care of patients requiring urgent life-saving treatment for COVID-19, and other conditions, is one of the most challenging tasks facing healthcare commissioners and care providers during the pandemic. Due to uncertainty in expected patient numbers requiring care, as well as evolving needs day by day, planning hospital capacity is challenging. Health systems that are well prepared for the pandemic can better cope with large and sudden changes in demand by implementing strategies to ensure adequate access to care. Thereby the burden of the pandemic can be mitigated, and many lives saved. This report presents the J-IDEA pandemic planner, a hospital planning tool to calculate how much capacity in terms of beds, staff and ventilators is obtained by implementing healthcare provision interventions affecting the management of patient care in hospitals. We show how to assess baseline capacity, and then calculate how much capacity is gained by various healthcare interventions using impact estimates that are generated as part of this study. Interventions are informed by a rapid review of policy decisions implemented or being considered in 12 European countries over the past few months , an evaluation of the impact of the interventions on capacity using a variety of research methods, and by a review of key parameters in the care of COVID-19 patients.The J-IDEA planner is publicly available, interactive and adaptable to different and changing circumstances and newly emerging evidence. The planner estimates the additional number of beds, medical staff and crucial medical equipment obtained under various healthcare interventions using flexible inputs on assumptions of existing capacities, the number of hospitalisations, beds-to-staff ratios, and staff absences due to COVID-19. A detailed user guide accompanies the planner. The planner was developed rapidly and has limitations which we will address in future iterations. It support
Utunen H, Christen P, Gamhewage GM, et al., 2018, Knowledge transfer for Ebola outbreak - Production and use of OpenWHO.org online learning resources, Pages: 20-27, ISSN: 2161-9646
WHO's Transfer of Knowledge (TOK) Team under the Infectious Hazard Management Department of the WHO Health Emergencies Programme (WHE) aims to meet the evolving needs of frontline responders to the health emergencies of the 21st century. Epidemics increasingly occur in more complex, rapid and challenging contexts.Frontline workers and decision-makers need the latest science and knowledge to fight disease outbreaks and other health emergencies. This knowledge must be in usable formats and delivered via suitable channels, accessible even in remote areas. If possible, WHO health technical expertise materials in infectious disease outbreaks are also produced in local languages, given that knowledge acquisition in one's mother tongue is crucial to containing epidemics in vulnerable settings. The Transfer of Knowledge team of the WHE launched in June 2017 an online learning platform for disease outbreaks, epidemics, pandemics and health emergencies: OpenWHO.org. The aim of the platform is to ensure that frontline health responders have access to lifesaving knowledge anytime from anywhere during public health emergencies. The Transfer of Knowledge team together with infectious disease expert teams adapt and customize key technical information and package it into usable products easily accessible in the field.This knowledge transfer involves various product types and includes materials targeted for health-care and community workers on the front line of the outbreak response. Materials include epidemiological knowledge and health interventions on infectious diseases like Ebola virus disease (Ebola), yellow fever, plague and cholera. These materials are also produced in the local languages most commonly used by local responders in the outbreak areas. Most recently, a number of materials were targeted, adjusted and translated for the 2018 Ebola outbreak in the Democratic Republic of the Congo. Key materials on Ebola, the production process and initial user analytics are presen
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