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1.
PLoS One ; 15(10): e0240153, 2020.
Artigo em Inglês | MEDLINE | ID: mdl-33007054

RESUMO

The novel coronavirus COVID-19 arrived on Australian shores around 25 January 2020. This paper presents a novel method of dynamically modeling and forecasting the COVID-19 pandemic in Australia with a high degree of accuracy and in a timely manner using limited data; a valuable resource that can be used to guide government decision-making on societal restrictions on a daily and/or weekly basis. The "partially-observable stochastic process" used in this study predicts not only the future actual values with extremely low error, but also the percentage of unobserved COVID-19 cases in the population. The model can further assist policy makers to assess the effectiveness of several possible alternative scenarios in their decision-making processes.


Assuntos
Infecções por Coronavirus/epidemiologia , Modelos Estatísticos , Pneumonia Viral/epidemiologia , Austrália/epidemiologia , Betacoronavirus , Humanos , Pandemias
2.
BMC Infect Dis ; 20(1): 735, 2020 Oct 07.
Artigo em Inglês | MEDLINE | ID: mdl-33028283

RESUMO

BACKGROUND: The pandemic of COVID-19 has occurred close on the heels of a global resurgence of measles. In 2019, an unprecedented epidemic of measles affected Samoa, requiring a state of emergency to be declared. Measles causes an immune amnesia which can persist for over 2 years after acute infection and increases the risk of a range of other infections. METHODS: We modelled the potential impact of measles-induced immune amnesia on a COVID-19 epidemic in Samoa using data on measles incidence in 2018-2019, population data and a hypothetical COVID-19 epidemic. RESULTS: The young population structure and contact matrix in Samoa results in the most transmission occurring in young people < 20 years old. The highest rate of death is the 60+ years old, but a smaller peak in death may occur in younger people, with more than 15% of total deaths in the age group under 20 years old. Measles induced immune amnesia could increase the total number of cases by 8% and deaths by more than 2%. CONCLUSIONS: Samoa, which had large measles epidemics in 2019-2020 should focus on rapidly achieving high rates of measles vaccination and enhanced surveillance for COVID-19, as the impact may be more severe due to measles-induced immune paresis. This applies to other severely measles-affected countries in the Pacific, Europe and elsewhere.


Assuntos
Betacoronavirus , Infecções por Coronavirus/epidemiologia , Infecções por Coronavirus/mortalidade , Sarampo/epidemiologia , Sarampo/mortalidade , Pneumonia Viral/epidemiologia , Pneumonia Viral/mortalidade , Adolescente , Adulto , Distribuição por Idade , Idoso , Criança , Pré-Escolar , Comorbidade , Infecções por Coronavirus/virologia , Feminino , Humanos , Incidência , Lactente , Recém-Nascido , Masculino , Sarampo/imunologia , Sarampo/prevenção & controle , Pessoa de Meia-Idade , Modelos Estatísticos , Pandemias , Pneumonia Viral/virologia , Samoa/epidemiologia , Vacinação , Adulto Jovem
4.
Int J Med Sci ; 17(15): 2257-2263, 2020.
Artigo em Inglês | MEDLINE | ID: mdl-32922189

RESUMO

Background: Corona Virus Disease 2019 (COVID-19) has become a global pandemic. This study established prognostic scoring models based on comorbidities and other clinical information for severe and critical patients with COVID-19. Material and Methods: We retrospectively collected data from 51 patients diagnosed as severe or critical COVID-19 who were admitted between January 29, 2020, and February 18, 2020. The Charlson (CCI), Elixhauser (ECI), and age- and smoking-adjusted Charlson (ASCCI) and Elixhauser (ASECI) comorbidity indices were used to evaluate the patient outcomes. Results: The mean hospital length of stay (LOS) of the COVID-19 patients was 22.82 ± 12.32 days; 19 patients (37.3%) were hospitalized for more than 24 days. Multivariate analysis identified older age (OR 1.064, P = 0.018, 95%CI 1.011-1.121) and smoking (OR 3.696, P = 0.080, 95%CI 0.856-15.955) as positive predictors of a long LOS. There were significant trends for increasing hospital LOS with increasing CCI, ASCCI, and ASECI scores (OR 57.500, P = 0.001, 95%CI 5.687-581.399; OR 71.500, P = 0.001, 95%CI 5.689-898.642; and OR 19.556, P = 0.001, 95%CI 3.315-115.372, respectively). The result was similar for the outcome of critical illness (OR 21.333, P = 0.001, 95%CI 3.565-127.672; OR 13.000, P = 0.009, 95%CI 1.921-87.990; OR 11.333, P = 0.008, 95%CI 1.859-69.080, respectively). Conclusions: This study established prognostic scoring models based on comorbidities and clinical information, which may help with the graded management of patients according to prognosis score and remind physicians to pay more attention to patients with high scores.


Assuntos
Comorbidade , Infecções por Coronavirus/mortalidade , Estado Terminal/mortalidade , Modelos Estatísticos , Pneumonia Viral/mortalidade , Índice de Gravidade de Doença , Adulto , Idoso , Idoso de 80 Anos ou mais , Betacoronavirus/isolamento & purificação , Betacoronavirus/patogenicidade , Tomada de Decisão Clínica , Infecções por Coronavirus/diagnóstico , Infecções por Coronavirus/terapia , Infecções por Coronavirus/virologia , Feminino , Mortalidade Hospitalar , Humanos , Tempo de Internação/estatística & dados numéricos , Masculino , Pessoa de Meia-Idade , Pandemias , Pneumonia Viral/diagnóstico , Pneumonia Viral/terapia , Pneumonia Viral/virologia , Prognóstico , Estudos Retrospectivos , Medição de Risco/métodos
5.
Nat Commun ; 11(1): 4392, 2020 09 01.
Artigo em Inglês | MEDLINE | ID: mdl-32873810

RESUMO

The successful mitigation of emerging wildlife diseases may involve controversial host culling. For livestock, 'preemptive host culling' is an accepted practice involving the removal of herds with known contact to infected populations. When applied to wildlife, this proactive approach comes in conflict with biodiversity conservation goals. Here, we present an alternative approach of 'proactive hunting surveillance' with the aim of early disease detection that simultaneously avoids undesirable population decline by targeting demographic groups with (1) a higher likelihood of being infected and (2) a lower reproductive value. We applied this harvesting principle to populations of reindeer to substantiate freedom of chronic wasting disease (CWD) infection. Proactive hunting surveillance reached 99% probability of freedom from infection (<4 reindeer infected) within 3-5 years, in comparison to ~10 years using ordinary harvest surveillance. However, implementation uncertainties linked to social issues appear challenging also with this kind of host culling.


Assuntos
Abate de Animais/métodos , Animais Selvagens , Conservação dos Recursos Naturais/métodos , Monitoramento Epidemiológico/veterinária , Rena , Doença de Emaciação Crônica/diagnóstico , Fatores Etários , Animais , Simulação por Computador , Feminino , Masculino , Modelos Estatísticos , Dinâmica Populacional , Fatores Sexuais , Doença de Emaciação Crônica/prevenção & controle , Doença de Emaciação Crônica/transmissão
6.
Nat Commun ; 11(1): 4662, 2020 09 16.
Artigo em Inglês | MEDLINE | ID: mdl-32938926

RESUMO

Haplotype reconstruction of distant genetic variants remains an unsolved problem due to the short-read length of common sequencing data. Here, we introduce HapTree-X, a probabilistic framework that utilizes latent long-range information to reconstruct unspecified haplotypes in diploid and polyploid organisms. It introduces the observation that differential allele-specific expression can link genetic variants from the same physical chromosome, thus even enabling using reads that cover only individual variants. We demonstrate HapTree-X's feasibility on in-house sequenced Genome in a Bottle RNA-seq and various whole exome, genome, and 10X Genomics datasets. HapTree-X produces more complete phases (up to 25%), even in clinically important genes, and phases more variants than other methods while maintaining similar or higher accuracy and being up to 10×  faster than other tools. The advantage of HapTree-X's ability to use multiple lines of evidence, as well as to phase polyploid genomes in a single integrative framework, substantially grows as the amount of diverse data increases.


Assuntos
Desequilíbrio Alélico , Haplótipos , Análise de Sequência de RNA , Algoritmos , Bases de Dados Genéticas , Diploide , Humanos , Células K562 , Modelos Genéticos , Modelos Estatísticos , Polimorfismo de Nucleotídeo Único , Poliploidia , RNA-Seq , Análise de Sequência de RNA/métodos , Análise de Sequência de RNA/estatística & dados numéricos
7.
Phys Biol ; 17(6): 065001, 2020 09 22.
Artigo em Inglês | MEDLINE | ID: mdl-32959788

RESUMO

Epidemiological models usually contain a set of parameters that must be adjusted based on available observations. Once a model has been calibrated, it can be used as a forecasting tool to make predictions and to evaluate contingency plans. It is customary to employ only point estimators of model parameters for such predictions. However, some models may fit the same data reasonably well for a broad range of parameter values, and this flexibility means that predictions stemming from them will vary widely, depending on the particular values employed within the range that gives a good fit. When data are poor or incomplete, model uncertainty widens further. A way to circumvent this problem is to use Bayesian statistics to incorporate observations and use the full range of parameter estimates contained in the posterior distribution to adjust for uncertainties in model predictions. Specifically, given an epidemiological model and a probability distribution for observations, we use the posterior distribution of model parameters to generate all possible epidemic curves, whose information is encapsulated in posterior predictive distributions. From these, one can extract the worst-case scenario and study the impact of implementing contingency plans according to this assessment. We apply this approach to the evolution of COVID-19 in Mexico City and assess whether contingency plans are being successful and whether the epidemiological curve has flattened.


Assuntos
Betacoronavirus , Infecções por Coronavirus/epidemiologia , Epidemias , Pneumonia Viral/epidemiologia , Teorema de Bayes , Infecções por Coronavirus/mortalidade , Bases de Dados Factuais , Epidemias/estatística & dados numéricos , Humanos , Conceitos Matemáticos , México/epidemiologia , Modelos Biológicos , Modelos Estatísticos , Pandemias , Pneumonia Viral/mortalidade , Probabilidade , Fatores de Tempo , Incerteza
8.
J Med Internet Res ; 22(9): e20924, 2020 09 22.
Artigo em Inglês | MEDLINE | ID: mdl-32915762

RESUMO

BACKGROUND: SARS-CoV-2, the novel coronavirus that causes COVID-19, is a global pandemic with higher mortality and morbidity than any other virus in the last 100 years. Without public health surveillance, policy makers cannot know where and how the disease is accelerating, decelerating, and shifting. Unfortunately, existing models of COVID-19 contagion rely on parameters such as the basic reproduction number and use static statistical methods that do not capture all the relevant dynamics needed for surveillance. Existing surveillance methods use data that are subject to significant measurement error and other contaminants. OBJECTIVE: The aim of this study is to provide a proof of concept of the creation of surveillance metrics that correct for measurement error and data contamination to determine when it is safe to ease pandemic restrictions. We applied state-of-the-art statistical modeling to existing internet data to derive the best available estimates of the state-level dynamics of COVID-19 infection in the United States. METHODS: Dynamic panel data (DPD) models were estimated with the Arellano-Bond estimator using the generalized method of moments. This statistical technique enables control of various deficiencies in a data set. The validity of the model and statistical technique was tested. RESULTS: A Wald chi-square test of the explanatory power of the statistical approach indicated that it is valid (χ210=1489.84, P<.001), and a Sargan chi-square test indicated that the model identification is valid (χ2946=935.52, P=.59). The 7-day persistence rate for the week of June 27 to July 3 was 0.5188 (P<.001), meaning that every 10,000 new cases in the prior week were associated with 5188 cases 7 days later. For the week of July 4 to 10, the 7-day persistence rate increased by 0.2691 (P=.003), indicating that every 10,000 new cases in the prior week were associated with 7879 new cases 7 days later. Applied to the reported number of cases, these results indicate an increase of almost 100 additional new cases per day per state for the week of July 4-10. This signifies an increase in the reproduction parameter in the contagion models and corroborates the hypothesis that economic reopening without applying best public health practices is associated with a resurgence of the pandemic. CONCLUSIONS: DPD models successfully correct for measurement error and data contamination and are useful to derive surveillance metrics. The opening of America involves two certainties: the country will be COVID-19-free only when there is an effective vaccine, and the "social" end of the pandemic will occur before the "medical" end. Therefore, improved surveillance metrics are needed to inform leaders of how to open sections of the United States more safely. DPD models can inform this reopening in combination with the extraction of COVID-19 data from existing websites.


Assuntos
Infecções por Coronavirus/epidemiologia , Infecções por Coronavirus/transmissão , Política de Saúde , Modelos Biológicos , Modelos Estatísticos , Pneumonia Viral/epidemiologia , Pneumonia Viral/transmissão , Vigilância em Saúde Pública/métodos , Betacoronavirus , Infecções por Coronavirus/prevenção & controle , Humanos , Pandemias/prevenção & controle , Pandemias/estatística & dados numéricos , Pneumonia Viral/prevenção & controle , Reprodutibilidade dos Testes , Estados Unidos/epidemiologia
9.
Rev Esp Salud Publica ; 942020 Sep 23.
Artigo em Espanhol | MEDLINE | ID: mdl-32963218

RESUMO

In December 2019, an acute respiratory disease outbreak from zoonotic origin was detected in the city of Wuhan, China. The outbreak's infectious agent was a type of coronavirus never seen. Thenceforth, the Covid-19 disease has rapidly spread to more than 200 countries around the world. To minimize the devastating effects of the virus, the States have adopted epidemiological measures of various kinds that involved enormous economic expenses and the massive use of the media to explain the measures to the entire population. For the prediction and mitigation of infectious events, various epidemiological models, such as SIR, SEIR, MSIR and MSEIR, are used. Among them, the most widely used is the SIR model, which is based on the analysis of the transition of individuals susceptible to infection (S) to the state of infected individuals that infect (I) and, finally, to that of recovered (cured or deceased) (R), by using differential equations. The objective of this article was the mathematical development of the SIR model and its application to predict the course of the Covid-19 pandemic in the city of Santa Marta (Colombia), in order to understand the reason behind several of the measures of containment adopted by the States of the world in the fight against the pandemic.


Assuntos
Controle de Doenças Transmissíveis , Infecções por Coronavirus/epidemiologia , Modelos Estatísticos , Pneumonia Viral/epidemiologia , Betacoronavirus , Cidades , Colômbia/epidemiologia , Infecções por Coronavirus/prevenção & controle , Surtos de Doenças , Humanos , Pandemias/prevenção & controle , Pneumonia Viral/prevenção & controle
10.
PLoS One ; 15(9): e0239800, 2020.
Artigo em Inglês | MEDLINE | ID: mdl-32970786

RESUMO

The SIR ('susceptible-infectious-recovered') formulation is used to uncover the generic spread mechanisms observed by COVID-19 dynamics globally, especially in the early phases of infectious spread. During this early period, potential controls were not effectively put in place or enforced in many countries. Hence, the early phases of COVID-19 spread in countries where controls were weak offer a unique perspective on the ensemble-behavior of COVID-19 basic reproduction number Ro inferred from SIR formulation. The work here shows that there is global convergence (i.e., across many nations) to an uncontrolled Ro = 4.5 that describes the early time spread of COVID-19. This value is in agreement with independent estimates from other sources reviewed here and adds to the growing consensus that the early estimate of Ro = 2.2 adopted by the World Health Organization is low. A reconciliation between power-law and exponential growth predictions is also featured within the confines of the SIR formulation. The effects of testing ramp-up and the role of 'super-spreaders' on the inference of Ro are analyzed using idealized scenarios. Implications for evaluating potential control strategies from this uncontrolled Ro are briefly discussed in the context of the maximum possible infected fraction of the population (needed to assess health care capacity) and mortality (especially in the USA given diverging projections). Model results indicate that if intervention measures still result in Ro > 2.7 within 44 days after first infection, intervention is unlikely to be effective in general for COVID-19.


Assuntos
Número Básico de Reprodução , Infecções por Coronavirus/epidemiologia , Pneumonia Viral/epidemiologia , Betacoronavirus , Controle de Doenças Transmissíveis , Previsões , Humanos , Modelos Estatísticos , Pandemias
11.
Medicine (Baltimore) ; 99(35): e21897, 2020 Aug 28.
Artigo em Inglês | MEDLINE | ID: mdl-32871921

RESUMO

Allogeneic red blood cell transfusion (ABT) is 1 of the poor prognostic factors for morbidity and mortality in patients with hip fracture, particularly among elderly patients. This study aimed to investigate the risk factors for ABT and 1-year mortality in elderly patients undergoing surgery for femoral neck fracture.A total of 225 elderly patients who underwent femoral neck fracture surgery between May 2013 and November 2015 at a tertiary medical center were retrospectively recruited. Medical records were analyzed.The median patient age was 80 years and 28.4% were men. A total of 113 patients received ABT (50.2%). Multivariate logistic regression analysis showed that female sex (odds ratio [OR] 2.606, 95% confidence interval [CI] 1.283-5.295, P = .008), malignancy (OR 5.098, 95% CI 1.725-15.061, P = .003), chronic kidney disease stage ≥ 3 (OR 3.258, 95% CI 1.603-6.622, P = .001), and anemia (hemoglobin < 12 g/dL) (OR 4.684, 95% CI 2.230-9.837, P < .001) were significantly associated with ABT. The 1-year mortality rate after surgery was 15.1%. Male sex (OR 2.477, 95% CI 1.101-5.575, P = .028), ABT (OR 2.367, 95% CI 1.036-5.410, P = .041), and intensive care unit admission (OR 5.564, 95% CI 1.457-21.249, P = .012) were significantly associated with 1-year mortality.In this study, underlying comorbidities such as chronic kidney disease and malignancy were associated with ABT. Furthermore, ABT was a significant independent risk factor for 1-year mortality. These findings suggest that underlying comorbidities and the need for ABT should be considered in the risk assessment of elderly patients with femoral neck fracture to improve the outcomes after surgery.


Assuntos
Causas de Morte , Transfusão de Eritrócitos , Fraturas do Colo Femoral/cirurgia , Fatores Etários , Idoso , Idoso de 80 Anos ou mais , Anemia/complicações , Feminino , Fraturas do Colo Femoral/complicações , Humanos , Masculino , Modelos Estatísticos , Neoplasias/complicações , Insuficiência Renal Crônica/complicações , Estudos Retrospectivos , Medição de Risco , Fatores de Risco , Fatores Sexuais
12.
BMC Infect Dis ; 20(1): 649, 2020 Sep 03.
Artigo em Inglês | MEDLINE | ID: mdl-32883213

RESUMO

BACKGROUND: More than 80,000 dengue cases including 215 deaths were reported nationally in less than 7 months between 2016 and 2017, a fourfold increase in the number of reported cases compared to the average number over 2010-2016. The region of Negombo, located in the Western province, experienced the greatest number of dengue cases in the country and is the focus area of our study, where we aim to capture the spatial-temporal dynamics of dengue transmission. METHODS: We present a statistical modeling framework to evaluate the spatial-temporal dynamics of the 2016-2017 dengue outbreak in the Negombo region of Sri Lanka as a function of human mobility, land-use, and climate patterns. The analysis was conducted at a 1 km × 1 km spatial resolution and a weekly temporal resolution. RESULTS: Our results indicate human mobility to be a stronger indicator for local outbreak clusters than land-use or climate variables. The minimum daily temperature was identified as the most influential climate variable on dengue cases in the region; while among the set of land-use patterns considered, urban areas were found to be most prone to dengue outbreak, followed by areas with stagnant water and then coastal areas. The results are shown to be robust across spatial resolutions. CONCLUSIONS: Our study highlights the potential value of using travel data to target vector control within a region. In addition to illustrating the relative relationship between various potential risk factors for dengue outbreaks, the results of our study can be used to inform where and when new cases of dengue are likely to occur within a region, and thus help more effectively and innovatively, plan for disease surveillance and vector control.


Assuntos
Dengue/epidemiologia , Clima , Surtos de Doenças , Humanos , Modelos Estatísticos , Fatores de Risco , Sri Lanka/epidemiologia , Temperatura , Viagem
13.
J Korean Med Sci ; 35(34): e317, 2020 Aug 31.
Artigo em Inglês | MEDLINE | ID: mdl-32864913

RESUMO

BACKGROUND: The novel coronavirus (coronavirus disease 2019 [COVID-19]) outbreak began in China in December last year, and confirmed cases began occurring in Korea in mid-February 2020. Since the end of February, the rate of infection has increased greatly due to mass (herd) infection within religious groups and nursing homes in the Daegu and Gyeongbuk regions. This mass infection has increased the number of infected people more rapidly than was initially expected; the epidemic model based on existing studies had predicted a much lower infection rate and faster recovery. METHODS: The present study evaluated rapid infection spread by mass infection in Korea and the high mortality rate for the elderly and those with underlying diseases through the Susceptible-Exposed-Infected-Recovered-Dead (SEIRD) model. RESULTS: The present study demonstrated early infection peak occurrence (-6.3 days for Daegu and -5.3 days for Gyeongbuk) and slow recovery trend (= -1,486.6 persons for Daegu and -223.7 persons for Gyeongbuk) between the actual and the epidemic model for a mass infection region compared to a normal infection region. CONCLUSION: The analysis of the time difference between infection and recovery can help predict the epidemic peak due to mass (or normal) infection and can also be used as a time index to prepare medical resources.


Assuntos
Infecções por Coronavirus/epidemiologia , Infecções por Coronavirus/transmissão , Modelos Estatísticos , Pneumonia Viral/epidemiologia , Pneumonia Viral/transmissão , Adolescente , Adulto , Idoso , Idoso de 80 Anos ou mais , Betacoronavirus , Criança , Pré-Escolar , Infecções por Coronavirus/patologia , Feminino , Humanos , Lactente , Recém-Nascido , Masculino , Pessoa de Meia-Idade , Casas de Saúde/estatística & dados numéricos , Pandemias , Pneumonia Viral/patologia , República da Coreia/epidemiologia , Fatores de Tempo , Adulto Jovem
14.
Eur J Epidemiol ; 35(8): 749-761, 2020 Aug.
Artigo em Inglês | MEDLINE | ID: mdl-32888169

RESUMO

The global pandemic of the 2019-nCov requires the evaluation of policy interventions to mitigate future social and economic costs of quarantine measures worldwide. We propose an epidemiological model for forecasting and policy evaluation which incorporates new data in real-time through variational data assimilation. We analyze and discuss infection rates in the UK, US and Italy. We furthermore develop a custom compartmental SIR model fit to variables related to the available data of the pandemic, named SITR model, which allows for more granular inference on infection numbers. We compare and discuss model results which conducts updates as new observations become available. A hybrid data assimilation approach is applied to make results robust to initial conditions and measurement errors in the data. We use the model to conduct inference on infection numbers as well as parameters such as the disease transmissibility rate or the rate of recovery. The parameterisation of the model is parsimonious and extendable, allowing for the incorporation of additional data and parameters of interest. This allows for scalability and the extension of the model to other locations or the adaption of novel data sources.


Assuntos
Infecções por Coronavirus/epidemiologia , Previsões , Pandemias , Pneumonia Viral/epidemiologia , Informática em Saúde Pública/métodos , Teorema de Bayes , Betacoronavirus , Simulação por Computador , Surtos de Doenças , Humanos , Itália/epidemiologia , Modelos Biológicos , Modelos Estatísticos , Quarentena , Reino Unido/epidemiologia , Estados Unidos/epidemiologia
15.
Medicine (Baltimore) ; 99(33): e21085, 2020 Aug 14.
Artigo em Inglês | MEDLINE | ID: mdl-32871979

RESUMO

The lymph nodal invasion diagnosis is critical for therapeutic-decision and follows up in gastric cancer. However, the number of nodes to be examined for nodal invasion diagnosis is still under controversy, and the model for quantifying risk of missing positive node is currently not reported yet. We analyzed the nodal invasion status of 13,857 gastric cancer samples with records of primary tumor stage, the number of examined and positive lymph nodes in the surveillance, epidemiology, and end results (SEER) database, fitting a beta-binomial model. The nodes need to be examined with different primary tumor stage were determined based on the model. Overall, examining 11 lymph nodes reduces the probability of missing positive nodes to <10%, and the currently median nodes dissected is adequate (12 nodes). While the number of nodes demands to be dissected for T1, T2, T3, and T4 subgroups are 6, 19, 40, and 66, respectively. The currently implemented median value for these samples was 12, 12, 13, and 16, separately. It implies that the number of nodes to be examined is sufficient for early gastric cancer (T1), but it is inadequate for middle and advanced gastric cancer (T2-T3). The clinical significance of nodal staging score was validated with survival information. In summary, we first quantified the lymph nodes to be examined during surgery using a beta-binomial model, and validated with survival information.


Assuntos
Linfonodos/patologia , Metástase Linfática/diagnóstico , Metástase Linfática/patologia , Estadiamento de Neoplasias/métodos , Neoplasias Gástricas/diagnóstico , Neoplasias Gástricas/patologia , Reações Falso-Negativas , Feminino , Humanos , Linfonodos/cirurgia , Masculino , Pessoa de Meia-Idade , Modelos Estatísticos , Probabilidade , Estudos Retrospectivos , Programa de SEER , Sensibilidade e Especificidade , Neoplasias Gástricas/epidemiologia , Neoplasias Gástricas/cirurgia , Análise de Sobrevida
16.
BMC Pediatr ; 20(1): 410, 2020 09 01.
Artigo em Inglês | MEDLINE | ID: mdl-32873269

RESUMO

BACKGROUND: The emerging virus is rampaging globally. A growing number of pediatric infected cases have been reported. Great efforts are needed to cut down the transmission. METHODS: A single-arm meta-analysis was conducted. We searched PubMed, Google Scholar, Web of Science, and several Chinese databases for studies presenting characteristics of children confirmed with Coronavirus Disease 2019 (COVID-19) from December 12, 2019 to May 10, 2020. Quality Appraisal of Case Series Studies Checklist was used to assess quality and publication bias was analyzed by Egger's test. Random-effect model was used to calculate the pooled incidence rate (IR) or mean difference (MD) with 95% confidence intervals (CI), or a fixed model instead when I2 < 50%. We conducted subgroup analysis according to geographic region. Additionally, we searched United Nations Educational Scientific and Cultural Organization to see how different countries act to the education disruption in COVID-19. RESULTS: 29 studies with 4300 pediatric patients were included. The mean age was 7.04 (95% CI: 5.06-9.08) years old. 18.9% of children were asymptomatic (95% CI: 0.121-0.266), 37.4% (95% CI: 0.280-0.474) had no radiographic abnormalities. Besides, a proportion of 0.1% patients were admitted to intensive care units (0, 95% CI: 0.000-0.013) and four deaths were reported (0, 95% CI: 0.000-0.000). Up to 159 countries have implemented nationwide school closures, affecting over 70% of the world's students. CONCLUSION: Children were also susceptible to SARS-CoV-2, while critical cases or deaths were rare. Characterized by mild presentation, the dilemma that children may become a potential spreader in the pandemic, while strict managements like prolonged school closures, may undermine their well-beings. Thus, the public policies are facing challenge.


Assuntos
Betacoronavirus , Técnicas de Laboratório Clínico , Infecções por Coronavirus/diagnóstico , Infecções por Coronavirus/prevenção & controle , Política de Saúde , Pandemias/prevenção & controle , Pneumonia Viral/diagnóstico , Pneumonia Viral/prevenção & controle , Índice de Gravidade de Doença , Adolescente , Criança , Pré-Escolar , Infecções por Coronavirus/epidemiologia , Saúde Global , Humanos , Modelos Estatísticos , Pneumonia Viral/epidemiologia , Instituições Acadêmicas
17.
PLoS One ; 15(8): e0238000, 2020.
Artigo em Inglês | MEDLINE | ID: mdl-32866182

RESUMO

The standard GLM and GAM frequency-severity models assume independence between the claim frequency and severity. To overcome restrictions of linear or additive forms and to relax the independence assumption, we develop a data-driven dependent frequency-severity model, where we combine a stochastic gradient boosting algorithm and a profile likelihood approach to estimate parameters for both of the claim frequency and average claim severity distributions, and where we introduce the dependence between the claim frequency and severity by treating the claim frequency as a predictor in the regression model for the average claim severity. The model can flexibly capture the nonlinear relation between the claim frequency (severity) and predictors and complex interactions among predictors and can fully capture the nonlinear dependence between the claim frequency and severity. A simulation study shows excellent prediction performance of our model. Then, we demonstrate the application of our model with a French auto insurance claim data. The results show that our model is superior to other state-of-the-art models.


Assuntos
Revisão da Utilização de Seguros/estatística & dados numéricos , Modelos Estatísticos , Processos Estocásticos
18.
PLoS One ; 15(8): e0238067, 2020.
Artigo em Inglês | MEDLINE | ID: mdl-32866165

RESUMO

AIMS: To determine the out-of-hospital cardiac arrest (OHCA) rates and occurrences at municipality level through a novel statistical model accounting for temporal and spatial heterogeneity, space-time interactions and demographic features. We also aimed to predict OHCAs rates and number at municipality level for the upcoming years estimating the related resources requirement. METHODS: All the consecutive OHCAs of presumed cardiac origin occurred from 2005 until 2018 in Canton Ticino region were included. We implemented an Integrated Nested Laplace Approximation statistical method for estimation and prediction of municipality OHCA rates, number of events and related uncertainties, using age and sex municipality compositions. Comparisons between predicted and real OHCA maps validated our model, whilst comparisons between estimated OHCA rates in different yeas and municipalities identified significantly different OHCA rates over space and time. Longer-time predicted OHCA maps provided Bayesian predictions of OHCA coverages in varying stressful conditions. RESULTS: 2344 OHCAs were analyzed. OHCA incidence either progressively reduced or continuously increased over time in 6.8% of municipalities despite an overall stable spatio-temporal distribution of OHCAs. The predicted number of OHCAs accounts for 89% (2017) and 90% (2018) of the yearly variability of observed OHCAs with prediction error ≤1OHCA for each year in most municipalities. An increase in OHCAs number with a decline in the Automatic External Defibrillator availability per OHCA at region was estimated. CONCLUSIONS: Our method enables prediction of OHCA risk at municipality level with high accuracy, providing a novel approach to estimate resource allocation and anticipate gaps in demand in upcoming years.


Assuntos
Recursos em Saúde/estatística & dados numéricos , Modelos Estatísticos , Parada Cardíaca Extra-Hospitalar/epidemiologia , Idoso , Teorema de Bayes , Feminino , Geografia , Humanos , Masculino , Sistema de Registros , Análise Espaço-Temporal
19.
Proc Natl Acad Sci U S A ; 117(39): 24575-24580, 2020 09 29.
Artigo em Inglês | MEDLINE | ID: mdl-32887803

RESUMO

In the late stages of an epidemic, infections are often sporadic and geographically distributed. Spatially structured stochastic models can capture these important features of disease dynamics, thereby allowing a broader exploration of interventions. Here we develop a stochastic model of severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) transmission among an interconnected group of population centers representing counties, municipalities, and districts (collectively, "counties"). The model is parameterized with demographic, epidemiological, testing, and travel data from Ontario, Canada. We explore the effects of different control strategies after the epidemic curve has been flattened. We compare a local strategy of reopening (and reclosing, as needed) schools and workplaces county by county, according to triggers for county-specific infection prevalence, to a global strategy of province-wide reopening and reclosing, according to triggers for province-wide infection prevalence. For trigger levels that result in the same number of COVID-19 cases between the two strategies, the local strategy causes significantly fewer person-days of closure, even under high intercounty travel scenarios. However, both cases and person-days lost to closure rise when county triggers are not coordinated and when testing rates vary among counties. Finally, we show that local strategies can also do better in the early epidemic stage, but only if testing rates are high and the trigger prevalence is low. Our results suggest that pandemic planning for the far side of the COVID-19 epidemic curve should consider local strategies for reopening and reclosing.


Assuntos
Controle de Doenças Transmissíveis/organização & administração , Infecções por Coronavirus/prevenção & controle , Infecções por Coronavirus/transmissão , Pandemias/prevenção & controle , Pneumonia Viral/prevenção & controle , Pneumonia Viral/transmissão , Betacoronavirus , Cidades/epidemiologia , Controle de Doenças Transmissíveis/métodos , Infecções por Coronavirus/diagnóstico , Infecções por Coronavirus/epidemiologia , Humanos , Modelos Estatísticos , Ontário/epidemiologia , Pneumonia Viral/diagnóstico , Pneumonia Viral/epidemiologia , Prevalência , Processos Estocásticos , Viagem
20.
Proc Natl Acad Sci U S A ; 117(39): 24180-24187, 2020 09 29.
Artigo em Inglês | MEDLINE | ID: mdl-32913057

RESUMO

Standard epidemiological models for COVID-19 employ variants of compartment (SIR or susceptible-infectious-recovered) models at local scales, implicitly assuming spatially uniform local mixing. Here, we examine the effect of employing more geographically detailed diffusion models based on known spatial features of interpersonal networks, most particularly the presence of a long-tailed but monotone decline in the probability of interaction with distance, on disease diffusion. Based on simulations of unrestricted COVID-19 diffusion in 19 US cities, we conclude that heterogeneity in population distribution can have large impacts on local pandemic timing and severity, even when aggregate behavior at larger scales mirrors a classic SIR-like pattern. Impacts observed include severe local outbreaks with long lag time relative to the aggregate infection curve, and the presence of numerous areas whose disease trajectories correlate poorly with those of neighboring areas. A simple catchment model for hospital demand illustrates potential implications for health care utilization, with substantial disparities in the timing and extremity of impacts even without distancing interventions. Likewise, analysis of social exposure to others who are morbid or deceased shows considerable variation in how the epidemic can appear to individuals on the ground, potentially affecting risk assessment and compliance with mitigation measures. These results demonstrate the potential for spatial network structure to generate highly nonuniform diffusion behavior even at the scale of cities, and suggest the importance of incorporating such structure when designing models to inform health care planning, predict community outcomes, or identify potential disparities.


Assuntos
Infecções por Coronavirus/epidemiologia , Infecções por Coronavirus/transmissão , Pneumonia Viral/epidemiologia , Pneumonia Viral/transmissão , Betacoronavirus , Cidades/epidemiologia , Infecções por Coronavirus/prevenção & controle , Assistência à Saúde , Demografia , Disparidades nos Níveis de Saúde , Humanos , Modelos Estatísticos , Pandemias/prevenção & controle , Pneumonia Viral/prevenção & controle , Rede Social , Estados Unidos/epidemiologia
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