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1.
Chaos ; 32(7): 073123, 2022 Jul.
Article in English | MEDLINE | ID: covidwho-1978070

ABSTRACT

In this study, we examine the impact of information-driven awareness on the spread of an epidemic from the perspective of resource allocation by comprehensively considering a series of realistic scenarios. A coupled awareness-resource-epidemic model on top of multiplex networks is proposed, and a Microscopic Markov Chain Approach is adopted to study the complex interplay among the processes. Through theoretical analysis, the infection density of the epidemic is predicted precisely, and an approximate epidemic threshold is derived. Combining both numerical calculations and extensive Monte Carlo simulations, the following conclusions are obtained. First, during a pandemic, the more active the resource support between individuals, the more effectively the disease can be controlled; that is, there is a smaller infection density and a larger epidemic threshold. Second, the disease can be better suppressed when individuals with small degrees are preferentially protected. In addition, there is a critical parameter of contact preference at which the effectiveness of disease control is the worst. Third, the inter-layer degree correlation has a "double-edged sword" effect on spreading dynamics. In other words, when there is a relatively lower infection rate, the epidemic threshold can be raised by increasing the positive correlation. By contrast, the infection density can be reduced by increasing the negative correlation. Finally, the infection density decreases when raising the relative weight of the global information, which indicates that global information about the epidemic state is more efficient for disease control than local information.


Subject(s)
Epidemics , Resource Allocation , Epidemics/prevention & control , Epidemics/statistics & numerical data , Humans , Markov Chains , Models, Biological , Monte Carlo Method , Resource Allocation/statistics & numerical data , Resource Allocation/trends
4.
Dtsch Med Wochenschr ; 146(13-14): 894-898, 2021 Jul.
Article in German | MEDLINE | ID: covidwho-1324449

ABSTRACT

Nobody supposed that after one year of the pandemia, the SARS-CoV-2 Virus and its emerging mutants dominates the press, our lives and the health system as a whole. As for Geriatric Medicine, many things have also changed: The majority of COVID-19 patients are no more the (oldest) old and mortality is less observed in multimorbid persons, as most of them have been vaccinated. (Oldest) old persons are still especially vulnerable to die due to a COVD-19 infection. In longterm care, a significant higher mortality was seen in the former waves, but now, some longterm care facilities have more places that they can fill. This is a situation that many European countries would never have anticipated.Ressource allocationin stormy times is now more openly discussed, especially who should be admitted to intensive care units. This has led to more detailed and new guidelines which may help even when the pandemia is over. Here, some thoughts regarding the care of older adults in times of the pandemia are discussed.


Subject(s)
COVID-19 Vaccines/administration & dosage , COVID-19/epidemiology , Frailty/complications , Geriatrics , Resource Allocation/trends , Aged , Aged, 80 and over , COVID-19/complications , COVID-19/prevention & control , COVID-19/therapy , Frail Elderly/statistics & numerical data , Geriatrics/trends , Germany/epidemiology , Humans , Intensive Care Units/trends , Protein-Energy Malnutrition/complications
5.
Am J Obstet Gynecol MFM ; 2(3): 100127, 2020 08.
Article in English | MEDLINE | ID: covidwho-1064732

ABSTRACT

Background: The ongoing coronavirus disease 2019 pandemic has severely affected the United States. During infectious disease outbreaks, forecasting models are often developed to inform resource utilization. Pregnancy and delivery pose unique challenges, given the altered maternal immune system and the fact that most American women choose to deliver in the hospital setting. Objective: This study aimed to forecast the first pandemic wave of coronavirus disease 2019 in the general population and the incidence of severe, critical, and fatal coronavirus disease 2019 cases during delivery hospitalization in the United States. Study Design: We used a phenomenological model to forecast the incidence of the first wave of coronavirus disease 2019 in the United States. Incidence data from March 1, 2020, to April 14, 2020, were used to calibrate the generalized logistic growth model. Subsequently, Monte Carlo simulation was performed for each week from March 1, 2020, to estimate the incidence of coronavirus disease 2019 for delivery hospitalizations during the first pandemic wave using the available data estimate. Results: From March 1, 2020, our model forecasted a total of 860,475 cases of coronavirus disease 2019 in the general population across the United States for the first pandemic wave. The cumulative incidence of coronavirus disease 2019 during delivery hospitalization is anticipated to be 16,601 (95% confidence interval, 9711-23,491) cases, 3308 (95% confidence interval, 1755-4861) cases of which are expected to be severe, 681 (95% confidence interval, 1324-1038) critical, and 52 (95% confidence interval, 23-81) fatal. Assuming similar baseline maternal mortality rate as the year 2018, we projected an increase in maternal mortality rate in the United States to at least 18.7 (95% confidence interval, 18.0-19.5) deaths per 100,000 live births as a direct result of coronavirus disease 2019. Conclusion: Coronavirus disease 2019 in pregnant women is expected to severely affect obstetrical care. From March 1, 2020, we forecast 3308 severe and 681 critical cases with about 52 coronavirus disease 2019-related maternal mortalities during delivery hospitalization for the first pandemic wave in the United States. These results are significant for informing counseling and resource allocation.


Subject(s)
COVID-19 , Delivery, Obstetric , Health Care Rationing , Hospitalization , Obstetrics , Pregnancy Complications, Infectious , Resource Allocation , Adult , COVID-19/epidemiology , COVID-19/prevention & control , Delivery, Obstetric/methods , Delivery, Obstetric/statistics & numerical data , Delivery, Obstetric/trends , Female , Forecasting , Health Care Rationing/methods , Health Care Rationing/trends , Hospitalization/statistics & numerical data , Hospitalization/trends , Humans , Incidence , Maternal Mortality/trends , Monte Carlo Method , Obstetrics/organization & administration , Obstetrics/statistics & numerical data , Obstetrics/trends , Patient Acceptance of Health Care , Pregnancy , Pregnancy Complications, Infectious/epidemiology , Pregnancy Complications, Infectious/prevention & control , Resource Allocation/methods , Resource Allocation/trends , SARS-CoV-2 , United States/epidemiology
8.
Disaster Med Public Health Prep ; 14(5): 677-683, 2020 10.
Article in English | MEDLINE | ID: covidwho-65416

ABSTRACT

The aim of this systematic review was to locate and analyze United States state crisis standards of care (CSC) documents to determine their prevalence and quality. Following PRISMA guidelines, Google search for "allocation of scarce resources" and "crisis standards of care (CSC)" for each state. We analyzed the plans based on the 2009 Institute of Medicine (IOM) report, which provided guidance for establishing CSC for use in disaster situations, as well as the 2014 CHEST consensus statement's 11 core topic areas. The search yielded 42 state documents, and we excluded 11 that were not CSC plans. Of the 31 included plans, 13 plans were written for an "all hazards" approach, while 18 were pandemic influenza specific. Eighteen had strong ethical grounding. Twenty-one plans had integrated and ongoing community and provider engagement, education, and communication. Twenty-two had assurances regarding legal authority and environment. Sixteen plans had clear indicators, triggers, and lines of responsibility. Finally, 28 had evidence-based clinical processes and operations. Five plans contained all 5 IOM elements: Arizona, Colorado, Minnesota, Nevada, and Vermont. Colorado and Minnesota have all hazards documents and processes for both adult and pediatric populations and could be considered exemplars for other states.


Subject(s)
Pandemics/prevention & control , Resource Allocation/methods , State Government , Disaster Planning/methods , Humans , Resource Allocation/supply & distribution , Resource Allocation/trends , Standard of Care/ethics , Standard of Care/standards , United States
10.
Oncologist ; 25(6): e936-e945, 2020 06.
Article in English | MEDLINE | ID: covidwho-31492

ABSTRACT

The outbreak of coronavirus disease 2019 (COVID-19) has rapidly spread globally since being identified as a public health emergency of major international concern and has now been declared a pandemic by the World Health Organization (WHO). In December 2019, an outbreak of atypical pneumonia, known as COVID-19, was identified in Wuhan, China. The newly identified zoonotic coronavirus, severe acute respiratory syndrome coronavirus-2 (SARS-CoV-2), is characterized by rapid human-to-human transmission. Many cancer patients frequently visit the hospital for treatment and disease surveillance. They may be immunocompromised due to the underlying malignancy or anticancer therapy and are at higher risk of developing infections. Several factors increase the risk of infection, and cancer patients commonly have multiple risk factors. Cancer patients appear to have an estimated twofold increased risk of contracting SARS-CoV-2 than the general population. With the WHO declaring the novel coronavirus outbreak a pandemic, there is an urgent need to address the impact of such a pandemic on cancer patients. This include changes to resource allocation, clinical care, and the consent process during a pandemic. Currently and due to limited data, there are no international guidelines to address the management of cancer patients in any infectious pandemic. In this review, the potential challenges associated with managing cancer patients during the COVID-19 infection pandemic will be addressed, with suggestions of some practical approaches. IMPLICATIONS FOR PRACTICE: The main management strategies for treating cancer patients during the COVID-19 epidemic include clear communication and education about hand hygiene, infection control measures, high-risk exposure, and the signs and symptoms of COVID-19. Consideration of risk and benefit for active intervention in the cancer population must be individualized. Postponing elective surgery or adjuvant chemotherapy for cancer patients with low risk of progression should be considered on a case-by-case basis. Minimizing outpatient visits can help to mitigate exposure and possible further transmission. Telemedicine may be used to support patients to minimize number of visits and risk of exposure. More research is needed to better understand SARS-CoV-2 virology and epidemiology.


Subject(s)
Betacoronavirus/pathogenicity , Coronavirus Infections/prevention & control , Medical Oncology/organization & administration , Neoplasms/therapy , Pandemics/prevention & control , Patient Care/standards , Pneumonia, Viral/prevention & control , Betacoronavirus/isolation & purification , COVID-19 , Coronavirus Infections/epidemiology , Coronavirus Infections/transmission , Coronavirus Infections/virology , Hand Hygiene/organization & administration , Hand Hygiene/trends , Humans , Infection Control/organization & administration , Infection Control/trends , International Cooperation , Intersectoral Collaboration , Medical Oncology/economics , Medical Oncology/standards , Medical Oncology/trends , Patient Care/economics , Patient Care/trends , Patient Education as Topic , Pneumonia, Viral/epidemiology , Pneumonia, Viral/transmission , Pneumonia, Viral/virology , Resource Allocation/economics , Resource Allocation/organization & administration , Resource Allocation/standards , Resource Allocation/trends , SARS-CoV-2 , Telemedicine/economics , Telemedicine/organization & administration , Telemedicine/standards , Telemedicine/trends , World Health Organization
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