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Rev Med Suisse ; 18(802): 2084, 2022 11 02.
Article in French | MEDLINE | ID: covidwho-2207108

Thinking , Humans , Forecasting
Neuroepidemiology ; 56(3): 147-150, 2022.
Article in English | MEDLINE | ID: covidwho-2194286

Forecasting , Humans
Arch Dis Child ; 107(12): 1129-1130, 2022 Dec.
Article in English | MEDLINE | ID: covidwho-2193632
Infect Control Hosp Epidemiol ; 43(1): 1-2, 2022 01.
Article in English | MEDLINE | ID: covidwho-2185225

Forecasting , Humans
Lancet ; 400(10355): 787, 2022 09 10.
Article in English | MEDLINE | ID: covidwho-2184634
Lancet ; 400(10353): 641-643, 2022 08 27.
Article in English | MEDLINE | ID: covidwho-2184627
JMIR Public Health Surveill ; 7(6): e27888, 2021 06 09.
Article in English | MEDLINE | ID: covidwho-2197908


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.

COVID-19/therapy , Delivery of Health Care , Health Planning/methods , Hospitalization , Intensive Care Units , Pandemics , Respiration, Artificial , COVID-19/mortality , Equipment and Supplies , Forecasting , Hospitals , Humans , Length of Stay , Models, Statistical , New Mexico , Public Health , SARS-CoV-2 , Surge Capacity
JMIR Public Health Surveill ; 7(6): e26267, 2021 06 18.
Article in English | MEDLINE | ID: covidwho-2197896


In March 2020, the World Health Organization declared COVID-19 as a global pandemic. The COVID-19 pandemic has affected various public health functions and essential services in different ways and magnitudes. Although all countries have witnessed the effect of COVID-19, the impact differed based on many factors including the integrity and resiliency of the countries' health systems. This paper presents opinions and expectations of the authors about the anticipated changes in the future of public health at the global, regional, and national levels. The viewpoint is based on the current efforts and challenges that various stakeholders have carried out to control COVID-19 and the contribution from the literature on the future of public health. Numerous agencies and actors are involved in the fight against COVID-19 with variations in their effectiveness. The public health services showed weaknesses in most of the countries, in addition to the lack of adequate curative medicine settings. The pandemic highlighted the need for better governance and stronger and more resilient health systems and capacities. The COVID-19 experience has also emphasized the importance of coordination and collaboration among the countries and stakeholders. The COVID-19 pandemic might lead to a wide discussion to improve international and national approaches to prepare for and respond to similar events in terms of preparedness and response mechanisms and tools. Public health will not be the same as before COVID-19. New health priorities, approaches, and new agendas will be on the table of the global platforms and initiatives. More investment in research and technology to meet the demand for new vaccines and medicines, innovative methods like distance learning and working, more respect and remuneration to health professionals, and normalization of the public health and social measures that were induced during the COVID-19 pandemic are expected to be seen in future.

COVID-19 , Forecasting , Global Health/trends , Public Health/trends , Health Priorities/trends , Humans , SARS-CoV-2
J Infect Dis ; 224(12 Suppl 2): S910-S914, 2021 12 20.
Article in English | MEDLINE | ID: covidwho-2189128


Informal slums are growing exponentially in the developing world and these will serve as the breeding ground for a future global pandemic. Virtually every sustainable development goal is unmet in slums around the globe thus we must act now to divert a global humanitarian crisis.

Communicable Diseases , Pandemics , Poverty Areas , Forecasting , Humans , Urban Population
Br J Surg ; 108(7): 740-741, 2021 07 23.
Article in English | MEDLINE | ID: covidwho-2188274
Infect Dis Poverty ; 11(1): 57, 2022 May 22.
Article in English | MEDLINE | ID: covidwho-1849786


BACKGROUND: A One Health approach has been increasingly mainstreamed by the international community, as it provides for holistic thinking in recognizing the close links and inter-dependence of the health of humans, animals and the environment. However, the dearth of real-world evidence has hampered application of a One Health approach in shaping policies and practice. This study proposes the development of a potential evaluation tool for One Health performance, in order to contribute to the scientific measurement of One Health approach and the identification of gaps where One Health capacity building is most urgently needed. METHODS: We describe five steps towards a global One Health index (GOHI), including (i) framework formulation; (ii) indicator selection; (iii) database building; (iv) weight determination; and (v) GOHI scores calculation. A cell-like framework for GOHI is proposed, which comprises an external drivers index (EDI), an intrinsic drivers index (IDI) and a core drivers index (CDI). We construct the indicator scheme for GOHI based on this framework after multiple rounds of panel discussions with our expert advisory committee. A fuzzy analytical hierarchy process is adopted to determine the weights for each of the indicators. RESULTS: The weighted indicator scheme of GOHI comprises three first-level indicators, 13 second-level indicators, and 57 third-level indicators. According to the pilot analysis based on the data from more than 200 countries/territories the GOHI scores overall are far from ideal (the highest score of 65.0 out of a maximum score of 100), and we found considerable variations among different countries/territories (31.8-65.0). The results from the pilot analysis are consistent with the results from a literature review, which suggests that a GOHI as a potential tool for the assessment of One Health performance might be feasible. CONCLUSIONS: GOHI-subject to rigorous validation-would represent the world's first evaluation tool that constructs the conceptual framework from a holistic perspective of One Health. Future application of GOHI might promote a common understanding of a strong One Health approach and provide reference for promoting effective measures to strengthen One Health capacity building. With further adaptations under various scenarios, GOHI, along with its technical protocols and databases, will be updated regularly to address current technical limitations, and capture new knowledge.

One Health , Forecasting , Global Health
PLoS Comput Biol ; 18(9): e1010405, 2022 09.
Article in English | MEDLINE | ID: covidwho-2162508


Forecasts based on epidemiological modelling have played an important role in shaping public policy throughout the COVID-19 pandemic. This modelling combines knowledge about infectious disease dynamics with the subjective opinion of the researcher who develops and refines the model and often also adjusts model outputs. Developing a forecast model is difficult, resource- and time-consuming. It is therefore worth asking what modelling is able to add beyond the subjective opinion of the researcher alone. To investigate this, we analysed different real-time forecasts of cases of and deaths from COVID-19 in Germany and Poland over a 1-4 week horizon submitted to the German and Polish Forecast Hub. We compared crowd forecasts elicited from researchers and volunteers, against a) forecasts from two semi-mechanistic models based on common epidemiological assumptions and b) the ensemble of all other models submitted to the Forecast Hub. We found crowd forecasts, despite being overconfident, to outperform all other methods across all forecast horizons when forecasting cases (weighted interval score relative to the Hub ensemble 2 weeks ahead: 0.89). Forecasts based on computational models performed comparably better when predicting deaths (rel. WIS 1.26), suggesting that epidemiological modelling and human judgement can complement each other in important ways.

COVID-19 , Communicable Diseases , COVID-19/epidemiology , Forecasting , Humans , Pandemics , Poland/epidemiology
Int J Environ Res Public Health ; 19(13)2022 06 23.
Article in English | MEDLINE | ID: covidwho-2154973


In a world with an increasingly aging population, design researchers and practitioners can play an essential role in shaping better future societies, by designing environments, tools, and services that positively influence older adults' everyday experiences. The World Health Organization (WHO) has proposed a framework called Healthy Ageing, which can be adopted as the basis for designing for an aging society. There are, however, many challenges in achieving this goal. This article addresses one of these challenges identified by WHO, which is overcoming ageism as a form of discrimination based on age. In contrast with most other types of discrimination, ageism is not always easy to detect and overcome because of its generally implicit nature. This paper investigates adopting storytelling as a method for detecting implicit ageism and proposes a co-design process that utilizes this method to better address older adults' needs and requirements. The use of this method is discussed through two example case studies aimed at improving the design of assistive services and technologies for aging people. The findings from these case studies indicate that the proposed method can help co-design teams better identify possible implicit ageist biases and, by doing so, try to overcome them in the design process.

Ageism , Healthy Aging , Aged , Aging , Forecasting , Humans , World Health Organization
J Burn Care Res ; 42(2): 135-140, 2021 03 04.
Article in English | MEDLINE | ID: covidwho-2152044


Coronavirus disease 2019 obliged many countries to apply lockdown policies to contain the spread of infection. The restrictions in Israel included limitations on movement, reduction of working capacity, and closure of the educational system. The present study focused on patients treated at a referral center for burns in northern Israel. Their goal was to investigate temporal variations in burn injuries during this period. Data were retrospectively extracted from the medical records of burn patients treated at our hospital between March 14, 2020 and April 20, 2020 (ie, the period of aggravated lockdown). Data from this period were compared with that from paralleling periods between 2017 and 2019. During the lockdown and paralleling periods, 178 patients were treated for burn injuries, of whom 44% were under 18. Although no restrictions were enforced during the virus outbreak period with regard to seeking medical care, we noticed a decrease in the number of patients admitted to the emergency room for all reasons. Of particular interest was a 66% decrease in the number of adult burn patients (P < .0001). Meanwhile, among the pediatric population, no significant decrease was observed. Nonetheless, subgroups with higher susceptibility to burn injuries included children aged 2 to 5 years (56.3% vs 23.8%, P = .016) and female patients from all pediatric age groups (57.1% vs 25%, P = .027). These findings may be explained by the presumably busier kitchen and dining areas during the lockdown. Overall, the study results can assist with building a stronger understanding of varying burn injuries and with developing educational and preventive strategies.

Burns/epidemiology , COVID-19/epidemiology , Intensive Care Units/organization & administration , Length of Stay/statistics & numerical data , Adolescent , Adult , Burn Units/organization & administration , Burns/therapy , Child , Child, Preschool , Emergency Service, Hospital/organization & administration , Female , Forecasting , Humans , Infant , Israel , Male , Retrospective Studies , Treatment Outcome
Elife ; 92020 08 13.
Article in English | MEDLINE | ID: covidwho-2155738


As of 1 May 2020, there had been 6808 confirmed cases of COVID-19 in Australia. Of these, 98 had died from the disease. The epidemic had been in decline since mid-March, with 308 cases confirmed nationally since 14 April. This suggests that the collective actions of the Australian public and government authorities in response to COVID-19 were sufficiently early and assiduous to avert a public health crisis - for now. Analysing factors that contribute to individual country experiences of COVID-19, such as the intensity and timing of public health interventions, will assist in the next stage of response planning globally. We describe how the epidemic and public health response unfolded in Australia up to 13 April. We estimate that the effective reproduction number was likely below one in each Australian state since mid-March and forecast that clinical demand would remain below capacity thresholds over the forecast period (from mid-to-late April).

Betacoronavirus , Coronavirus Infections/epidemiology , Pandemics , Pneumonia, Viral/epidemiology , Adolescent , Adult , Age Distribution , Aged , Aged, 80 and over , Australia/epidemiology , COVID-19 , Child , Child, Preschool , Communicable Disease Control/methods , Communicable Disease Control/organization & administration , Communicable Disease Control/statistics & numerical data , Coronavirus Infections/prevention & control , Female , Forecasting , Geography, Medical , Hospitalization/statistics & numerical data , Humans , Infant , Infant, Newborn , Male , Middle Aged , Pandemics/prevention & control , Pneumonia, Viral/prevention & control , Public Health , Quarantine , SARS-CoV-2 , Travel , Young Adult
J Healthc Eng ; 2022: 4864920, 2022.
Article in English | MEDLINE | ID: covidwho-2138235


COVID-19 continues to pose a dangerous global health threat, as cases grow rapidly and deaths increase day by day. This increasing phenomenon does not only affect economic policy but also international policy around the world. In this paper, Pakistan daily death cases of COVID-19, from February 25, 2020, to March 23, 2022, have been modeled using the long-established autoregressive-integrated moving average (ARIMA) model and the machine learning multilayer perceptron (MLP) model. The most befitting model is selected based on the root mean square error (RMSE), mean square error (MSE), and mean absolute error (MAE). Values of the key performance indicator (KPI) showed that the MLP model outperformed the ARIMA model. The MLP model with 20 hidden layers, which emerged as the overall most apt model, was used to predict future daily COVID-19 deaths in Pakistan to enable policymakers and health professionals to put in place systematic measures to reduce death cases. We encourage the Government of Pakistan to intensify its vaccination campaign and encourage everyone to get vaccinated.

COVID-19 , Humans , Incidence , Models, Statistical , Neural Networks, Computer , Forecasting
Comput Intell Neurosci ; 2022: 4307708, 2022.
Article in English | MEDLINE | ID: covidwho-2138230


The COVID-19 virus continues to generate waves of infections around the world. With major areas in developing countries still lagging behind in vaccination campaigns, the risk of new variants that can cause re-infections worldwide makes the monitoring and forecasting of the evolution of the virus a high priority. Having accurate models able to forecast the incidence of the spread of the virus provides help to policymakers and health professionals in managing the scarce resources in an optimal way. In this paper, a new machine learning model is proposed to forecast the spread of the virus one-week ahead in a geographic area which combines mobility and COVID-19 incidence data. The area is divided into zones or districts according to the location of the COVID-19 measuring points. A traffic-driven mobility estimate among adjacent districts is proposed to capture the spatial spread of the virus. Traffic-driven mobility in adjacent districts will be used together with COVID-19 incidence data to feed a new deep learning LSTM-based model which will extract patterns from mobility-modulated COVID-19 incidence spatiotemporal data in order to optimize one-week ahead estimations. The model is trained and validated with open data available for the city of Madrid (Spain) for 3 different validation scenarios. A baseline model based on previous literature able to extract temporal patterns in COVID-19 incidence time series is also trained with the same dataset. The results show that the proposed model, based on the combination of traffic and COVID-19 incidence data, is able to outperform the baseline model in all the validation scenarios.

COVID-19 , Humans , Incidence , COVID-19/epidemiology , Machine Learning , Forecasting , Cities
Sci Justice ; 62(6): 667-668, 2022 11.
Article in English | MEDLINE | ID: covidwho-2132300