Short-sighted decision-making by those not vaccinated against COVID-19.
Ontology highlight
ABSTRACT:
SUBMITTER: Halilova JG
PROVIDER: S-EPMC9277980 | biostudies-literature |
REPOSITORIES: biostudies-literature
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Project description:Human behavioural changes are poorly understood, and this limitation has been a serious obstacle to epidemic forecasting. It is generally understood that people change their respective behaviours to reduce the risk of infection in response to the status of an epidemic or government interventions. We must first identify the factors that lead to such decision-making to predict these changes. However, due to an absence of a method to observe decision-making for future behaviour, understanding the behavioural responses to disease is limited. Here, we show that accommodation reservation data could reveal the decision-making process that underpins behavioural changes, travel avoidance, for reducing the risk of COVID-19 infections. We found that the motivation to avoid travel with respect to only short-term future behaviours dynamically varied and was associated with the outbreak status and/or the interventions of the government. Our developed method can quantitatively measure and predict a large-scale population's behaviour to determine the future risk of COVID-19 infections. These findings enable us to better understand behavioural changes in response to disease spread, and thus, contribute to the development of reliable long-term forecasting of disease spread.
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Project description:Policy decision-making for system resilience to a hazard requires the estimation and prediction of the trends of growth and decline of the impacts of the hazard. With focus on the recent worldwide spread of CoVid-19, we take the infection rate as the relevant metric whose trend of evolution to follow for verifying the effectiveness of the countermeasures applied. By comparison with the theories of growth and recovery in coupled socio-medical systems, we find that the data for many countries show infection rate trends that are exponential in form. In particular, the recovery trajectory is universal in trend and consistent with the learning theory, which allows for predictions useful in the assistance of decision-making of emergency recovery actions. The findings are validated by extensive data and comparison to medical pandemic models.
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Project description:Background:The number of cases from the coronavirus disease 2019 (COVID-19) global pandemic has overwhelmed existing medical facilities and forced clinicians, patients, and families to make pivotal decisions with limited time and information. Main body:While machine learning (ML) methods have been previously used to augment clinical decisions, there is now a demand for "Emergency ML." Throughout the patient care pathway, there are opportunities for ML-supported decisions based on collected vitals, laboratory results, medication orders, and comorbidities. With rapidly growing datasets, there also remain important considerations when developing and validating ML models. Conclusion:This perspective highlights the utility of evidence-based prediction tools in a number of clinical settings, and how similar models can be deployed during the COVID-19 pandemic to guide hospital frontlines and healthcare administrators to make informed decisions about patient care and managing hospital volume.
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Project description:The elderly may represent a specific cluster of high-risk patients for developing COVID-19 with rapidly progressive clinical deterioration. Indeed, in older individuals, immunosenescence and comorbid disorders are more likely to promote viral-induced cytokine storm resulting in life-threatening respiratory failure and multisystemic involvement. Early diagnosis and individualized therapeutic management should be developed for elderly subjects based on personal medical history and polypharmacotherapy. Our review examines the pathogenesis and clinical implications of ageing in COVID-19 patients; finally, we discuss the evidence and controversies in the management in the long-stay residential care homes and aspects of end-of-life care for elderly patients with COVID-19.
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Project description:IntroductionThe vaccine distribution for the COVID-19 is a multicriteria decision-making (MCDM) problem based on three issues, namely, identification of different distribution criteria, importance criteria and data variation. Thus, the Pythagorean fuzzy decision by opinion score method (PFDOSM) for prioritising vaccine recipients is the correct approach because it utilises the most powerful MCDM ranking method. However, PFDOSM weighs the criteria values of each alternative implicitly, which is limited to explicitly weighting each criterion. In view of solving this theoretical issue, the fuzzy-weighted zero-inconsistency (FWZIC) can be used as a powerful weighting MCDM method to provide explicit weights for a criteria set with zero inconstancy. However, FWZIC is based on the triangular fuzzy number that is limited in solving the vagueness related to the aforementioned theoretical issues.ObjectivesThis research presents a novel homogeneous Pythagorean fuzzy framework for distributing the COVID-19 vaccine dose by integrating a new formulation of the PFWZIC and PFDOSM methods.MethodsThe methodology is divided into two phases. Firstly, an augmented dataset was generated that included 300 recipients based on five COVID-19 vaccine distribution criteria (i.e., vaccine recipient memberships, chronic disease conditions, age, geographic location severity and disabilities). Then, a decision matrix was constructed on the basis of an intersection of the 'recipients list' and 'COVID-19 distribution criteria'. Then, the MCDM methods were integrated. An extended PFWZIC was developed, followed by the development of PFDOSM.Results(1) PFWZIC effectively weighted the vaccine distribution criteria. (2) The PFDOSM-based group prioritisation was considered in the final distribution result. (3) The prioritisation ranks of the vaccine recipients were subject to a systematic ranking that is supported by high correlation results over nine scenarios of the changing criteria weights values.ConclusionThe findings of this study are expected to ensuring equitable protection against COVID-19 and thus help accelerate vaccine progress worldwide.
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Project description:PurposeTo describe youth sport participation behaviors during the COVID-19 pandemic as reported by parents of youth sport athletes, to examine the association of family demographics with the likelihood of children resuming sports, and to qualitatively report the factors influencing parental decision-making regarding youth sport participation during the COVID-19 pandemic.MethodsA national sample of parents of youth athletes from across the United States (n=236, age: 39.2±8.1 years, 57.2% female) were recruited to complete an online questionnaire by Qualtrics Online Samples (Qualtrics, Provo, UT) using a combination of actively managed, double-opt-in market research panels. The questionnaire focused on changes in child sport participation and family finances as a result of COVID-19 and included both close-ended and open-ended questions.ResultsMost parents (63.1%) reported the time their children spent participating in organized youth sports had decreased because of the COVID-19 pandemic. Three-quarters of parents (75.5%) reported that it was likely that their children would fully resume participating in organized youth sports within the next year. Parents whose financial situation was worsened by COVID-19 were less likely to report that their children would resume sports in the upcoming year. Three domains related to parental decision-making emerged from the open-ended responses: safety, fear, and normalcy.ConclusionsUnderstanding the factors influencing changes in youth sport participation during the pandemic can allow systems to implement strategies for safe participation in youth sport and physical activity among children. Encouraging participation in outdoor sports with appropriate safety precautions such as masking, social distancing, and hygiene may reduce fear of participation among parents.
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Project description:BackgroundThe COVID-19 pandemic has led to a high interest in mathematical models describing and predicting the diverse aspects and implications of the virus outbreak. Model results represent an important part of the information base for the decision process on different administrative levels. The Robert-Koch-Institute (RKI) initiated a project whose main goal is to predict COVID-19-specific occupation of beds in intensive care units: Steuerungs-Prognose von Intensivmedizinischen COVID-19 Kapazitäten (SPoCK). The incidence of COVID-19 cases is a crucial predictor for this occupation.MethodsWe developed a model based on ordinary differential equations for the COVID-19 spread with a time-dependent infection rate described by a spline. Furthermore, the model explicitly accounts for weekday-specific reporting and adjusts for reporting delay. The model is calibrated in a purely data-driven manner by a maximum likelihood approach. Uncertainties are evaluated using the profile likelihood method. The uncertainty about the appropriate modeling assumptions can be accounted for by including and merging results of different modelling approaches. The analysis uses data from Germany describing the COVID-19 spread from early 2020 until March 31st, 2021.ResultsThe model is calibrated based on incident cases on a daily basis and provides daily predictions of incident COVID-19 cases for the upcoming three weeks including uncertainty estimates for Germany and its subregions. Derived quantities such as cumulative counts and 7-day incidences with corresponding uncertainties can be computed. The estimation of the time-dependent infection rate leads to an estimated reproduction factor that is oscillating around one. Data-driven estimation of the dark figure purely from incident cases is not feasible.ConclusionsWe successfully implemented a procedure to forecast near future COVID-19 incidences for diverse subregions in Germany which are made available to various decision makers via an interactive web application. Results of the incidence modeling are also used as a predictor for forecasting the need of intensive care units.
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Project description:The COVID-19 pandemic continues to pose significant health, economic, and social challenges. Given that many of these challenges have moral relevance, the present studies investigate whether the COVID-19 pandemic is influencing moral decision-making and whether moralisation of behaviours specific to the crisis predict adherence to government-recommended behaviours. Whilst we find no evidence that utilitarian endorsements have changed during the pandemic at two separate timepoints, individuals have moralised non-compliant behaviours associated with the pandemic such as failing to physically distance themselves from others. Importantly, our findings show that this moralisation predicts sustained individual compliance with government-recommended behaviours.
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Project description:Severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) is a highly contagious zoonotic pathogen that has exacted heavy public health, social and economic tolls. In February 2020, the World Health Organization acronymed the disease caused by SARS-CoV-2 as COVID-19, for coronavirus disease 2019. The number of confirmed COVID-19 infections, which has been detected in at least 103 countries, has reached 1,970,225 worldwide as of April 14, 2020 with 124,544 deaths, according to the U.S. Centers for Disease Control and Prevention (CDC). Many cases of COVID-19 resolve quickly. However, the disease, which, like other respiratory pathogens that cause common cold symptoms is believed to be transmitted through respiratory droplets. Infection with COVID-19 can also lead to significant morbidity and death; this is particularly the case for cancer patients. Moreover, because the signs and symptoms of COVID-19 are easily misattributed to the sequelae of cancer itself, such as pulmonary embolism, or its treatment, such as nausea and diarrhea, diagnosis may be delayed or missed. Potential COVID-19 rule out criteria, based on the Wells' criteria for pulmonary embolism, another protean disease entity, are provided as a decision-making aid. This review summarizes the current understanding of the transmission, clinical presentation, diagnosis and differential diagnosis, pathogenesis, rationale to treat the cancer or not, treatment and prevention of COVID-19 with an emphasis on implications in cancer.
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