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1.
PLoS One ; 16(3): e0245519, 2021.
Artículo en Inglés | MEDLINE | ID: mdl-33657128

RESUMEN

Since the onset of the COVID-19 pandemic many researchers and health advisory institutions have focused on virus spread prediction through epidemiological models. Such models rely on virus- and disease characteristics of which most are uncertain or even unknown for SARS-CoV-2. This study addresses the validity of various assumptions using an epidemiological simulation model. The contributions of this work are twofold. First, we show that multiple scenarios all lead to realistic numbers of deaths and ICU admissions, two observable and verifiable metrics. Second, we test the sensitivity of estimates for the number of infected and immune individuals, and show that these vary strongly between scenarios. Note that the amount of variation measured in this study is merely a lower bound: epidemiological modeling contains uncertainty on more parameters than the four in this study, and including those as well would lead to an even larger set of possible scenarios. As the level of infection and immunity among the population are particularly important for policy makers, further research on virus and disease progression characteristics is essential. Until that time, epidemiological modeling studies cannot give conclusive results and should come with a careful analysis of several scenarios on virus- and disease characteristics.


Asunto(s)
/epidemiología , Transmisión de Enfermedad Infecciosa/estadística & datos numéricos , Predicción/métodos , /transmisión , Humanos , Modelos Estadísticos , Pandemias , /patogenicidad
2.
Sci Rep ; 11(1): 5106, 2021 03 03.
Artículo en Inglés | MEDLINE | ID: mdl-33658529

RESUMEN

The COVID-19 pandemic has strained hospital resources and necessitated the need for predictive models to forecast patient care demands in order to allow for adequate staffing and resource allocation. Recently, other studies have looked at associations between Google Trends data and the number of COVID-19 cases. Expanding on this approach, we propose a vector error correction model (VECM) for the number of COVID-19 patients in a healthcare system (Census) that incorporates Google search term activity and healthcare chatbot scores. The VECM provided a good fit to Census and very good forecasting performance as assessed by hypothesis tests and mean absolute percentage prediction error. Although our study and model have limitations, we have conducted a broad and insightful search for candidate Internet variables and employed rigorous statistical methods. We have demonstrated the VECM can potentially be a valuable component to a COVID-19 surveillance program in a healthcare system.


Asunto(s)
Predicción/métodos , Hospitalización/tendencias , Motor de Búsqueda/tendencias , /epidemiología , Hospitalización/estadística & datos numéricos , Humanos , Modelos Estadísticos , Pandemias , Asignación de Recursos , Motor de Búsqueda/estadística & datos numéricos , Factores de Tiempo
3.
Sci Rep ; 11(1): 5018, 2021 03 03.
Artículo en Inglés | MEDLINE | ID: mdl-33658593

RESUMEN

One major bottleneck in the ongoing COVID-19 pandemic is the limited number of critical care beds. Due to the dynamic development of infections and the time lag between when patients are infected and when a proportion of them enters an intensive care unit (ICU), the need for future intensive care can easily be underestimated. To infer future ICU load from reported infections, we suggest a simple statistical model that (1) accounts for time lags and (2) allows for making predictions depending on different future growth of infections. We have evaluated our model for three heavily affected regions in Europe, namely Berlin (Germany), Lombardy (Italy), and Madrid (Spain). Before extensive containment measures made an impact, we first estimate the region-specific model parameters, namely ICU rate, time lag between infection, and ICU admission as well as length of stay in ICU. Whereas for Berlin, an ICU rate of 6%, a time lag of 6 days, and a stay of 12 days in ICU provide the best fit of the data, for Lombardy and Madrid the ICU rate was higher (18% and 15%) and the time lag (0 and 3 days) and the stay in ICU (3 and 8 days) shorter. The region-specific models are then used to predict future ICU load assuming either a continued exponential phase with varying growth rates (0-15%) or linear growth. By keeping the growth rates flexible, this model allows for taking into account the potential effect of diverse containment measures. Thus, the model can help to predict a potential exceedance of ICU capacity depending on future growth. A sensitivity analysis for an extended time period shows that the proposed model is particularly useful for exponential phases of the disease.


Asunto(s)
/epidemiología , Predicción/métodos , Unidades de Cuidados Intensivos/tendencias , Cuidados Críticos/estadística & datos numéricos , Cuidados Críticos/tendencias , Europa (Continente)/epidemiología , Alemania/epidemiología , Hospitalización/estadística & datos numéricos , Hospitalización/tendencias , Humanos , Unidades de Cuidados Intensivos/estadística & datos numéricos , Italia/epidemiología , Modelos Estadísticos , Pandemias , España/epidemiología
4.
J Transl Med ; 19(1): 109, 2021 03 16.
Artículo en Inglés | MEDLINE | ID: mdl-33726787

RESUMEN

BACKGROUND: No versatile web app exists that allows epidemiologists and managers around the world to comprehensively analyze the impacts of COVID-19 mitigation. The http://covid-webapp.numerusinc.com/ web app presented here fills this gap. METHODS: Our web app uses a model that explicitly identifies susceptible, contact, latent, asymptomatic, symptomatic and recovered classes of individuals, and a parallel set of response classes, subject to lower pathogen-contact rates. The user inputs a CSV file of incidence and, if of interest, mortality rate data. A default set of parameters is available that can be overwritten through input or online entry, and a user-selected subset of these can be fitted to the model using maximum-likelihood estimation (MLE). Model fitting and forecasting intervals are specifiable and changes to parameters allow counterfactual and forecasting scenarios. Confidence or credible intervals can be generated using stochastic simulations, based on MLE values, or on an inputted CSV file containing Markov chain Monte Carlo (MCMC) estimates of one or more parameters. RESULTS: We illustrate the use of our web app in extracting social distancing, social relaxation, surveillance or virulence switching functions (i.e., time varying drivers) from the incidence and mortality rates of COVID-19 epidemics in Israel, South Africa, and England. The Israeli outbreak exhibits four distinct phases: initial outbreak, social distancing, social relaxation, and a second wave mitigation phase. An MCMC projection of this latter phase suggests the Israeli epidemic will continue to produce into late November an average of around 1500 new case per day, unless the population practices social-relaxation measures at least 5-fold below the level in August, which itself is 4-fold below the level at the start of July. Our analysis of the relatively late South African outbreak that became the world's fifth largest COVID-19 epidemic in July revealed that the decline through late July and early August was characterised by a social distancing driver operating at more than twice the per-capita applicable-disease-class (pc-adc) rate of the social relaxation driver. Our analysis of the relatively early English outbreak, identified a more than 2-fold improvement in surveillance over the course of the epidemic. It also identified a pc-adc social distancing rate in early August that, though nearly four times the pc-adc social relaxation rate, appeared to barely contain a second wave that would break out if social distancing was further relaxed. CONCLUSION: Our web app provides policy makers and health officers who have no epidemiological modelling or computer coding expertise with an invaluable tool for assessing the impacts of different outbreak mitigation policies and measures. This includes an ability to generate an epidemic-suppression or curve-flattening index that measures the intensity with which behavioural responses suppress or flatten the epidemic curve in the region under consideration.


Asunto(s)
/epidemiología , Control de Infecciones , Internet , Aplicaciones Móviles , /etiología , Simulación por Computador , Modificador del Efecto Epidemiológico , Inglaterra/epidemiología , Epidemias , Predicción/métodos , Humanos , Control de Infecciones/métodos , Control de Infecciones/organización & administración , Control de Infecciones/normas , Israel/epidemiología , Cadenas de Markov , Vigilancia de la Población/métodos , Factores de Riesgo , Sudáfrica/epidemiología
5.
Nat Med ; 27(3): 388-395, 2021 03.
Artículo en Inglés | MEDLINE | ID: mdl-33723452

RESUMEN

Epidemic nowcasting broadly refers to assessing the current state by understanding key pathogenic, epidemiologic, clinical and socio-behavioral characteristics of an ongoing outbreak. Its primary objective is to provide situational awareness and inform decisions on control responses. In the event of large-scale sustained emergencies, such as the COVID-19 pandemic, scientists need to constantly update their aims and analytics with respect to the rapidly evolving emergence of new questions, data and findings in order to synthesize real-time evidence for policy decisions. In this Perspective, we share our views on the functional aims, rationale, data requirements and challenges of nowcasting at different stages of an epidemic, drawing on the ongoing COVID-19 experience. We highlight how recent advances in the computational and laboratory sciences could be harnessed to complement traditional approaches to enhance the scope, timeliness, reliability and utility of epidemic nowcasting.


Asunto(s)
/epidemiología , Enfermedades Transmisibles Emergentes/epidemiología , Epidemias , Predicción/métodos , Enfermedades Transmisibles Emergentes/diagnóstico , Brotes de Enfermedades/historia , Epidemias/historia , Historia del Siglo XXI , Humanos , Pandemias , Reproducibilidad de los Resultados
7.
Comput Math Methods Med ; 2021: 6927985, 2021.
Artículo en Inglés | MEDLINE | ID: mdl-33680071

RESUMEN

COVID-19 has led to a pandemic, affecting almost all countries in a few months. In this work, we applied selected deep learning models including multilayer perceptron, random forest, and different versions of long short-term memory (LSTM), using three data sources to train the models, including COVID-19 occurrences, basic information like coded country names, and detailed information like population, and area of different countries. The main goal is to forecast the outbreak in nine countries (Iran, Germany, Italy, Japan, Korea, Switzerland, Spain, China, and the USA). The performances of the models are measured using four metrics, including mean average percentage error (MAPE), root mean square error (RMSE), normalized RMSE (NRMSE), and R 2. The best performance was found for a modified version of LSTM, called M-LSTM (winner model), to forecast the future trajectory of the pandemic in the mentioned countries. For this purpose, we collected the data from January 22 till July 30, 2020, for training, and from 1 August 2020 to 31 August 2020, for the testing phase. Through experimental results, the winner model achieved reasonably accurate predictions (MAPE, RMSE, NRMSE, and R 2 are 0.509, 458.12, 0.001624, and 0.99997, respectively). Furthermore, we stopped the training of the model on some dates related to main country actions to investigate the effect of country actions on predictions by the model.


Asunto(s)
/epidemiología , Aprendizaje Profundo , Pandemias , Biología Computacional , Bases de Datos Factuales , Predicción/métodos , Humanos , Irán/epidemiología , Conceptos Matemáticos , Modelos Estadísticos , Redes Neurales de la Computación , Pandemias/estadística & datos numéricos , Factores de Tiempo
8.
Sci Data ; 8(1): 59, 2021 02 11.
Artículo en Inglés | MEDLINE | ID: mdl-33574342

RESUMEN

Forecasting has emerged as an important component of informed, data-driven decision-making in a wide array of fields. We introduce a new data model for probabilistic predictions that encompasses a wide range of forecasting settings. This framework clearly defines the constituent parts of a probabilistic forecast and proposes one approach for representing these data elements. The data model is implemented in Zoltar, a new software application that stores forecasts using the data model and provides standardized API access to the data. In one real-time case study, an instance of the Zoltar web application was used to store, provide access to, and evaluate real-time forecast data on the order of 108 rows, provided by over 40 international research teams from academia and industry making forecasts of the COVID-19 outbreak in the US. Tools and data infrastructure for probabilistic forecasts, such as those introduced here, will play an increasingly important role in ensuring that future forecasting research adheres to a strict set of rigorous and reproducible standards.


Asunto(s)
Predicción/métodos , Programas Informáticos , /epidemiología , Conjuntos de Datos como Asunto , Brotes de Enfermedades , Humanos , Estándares de Referencia
9.
BMC Med Res Methodol ; 21(1): 34, 2021 02 14.
Artículo en Inglés | MEDLINE | ID: mdl-33583405

RESUMEN

BACKGROUND: Ensemble modeling aims to boost the forecasting performance by systematically integrating the predictive accuracy across individual models. Here we introduce a simple-yet-powerful ensemble methodology for forecasting the trajectory of dynamic growth processes that are defined by a system of non-linear differential equations with applications to infectious disease spread. METHODS: We propose and assess the performance of two ensemble modeling schemes with different parametric bootstrapping procedures for trajectory forecasting and uncertainty quantification. Specifically, we conduct sequential probabilistic forecasts to evaluate their forecasting performance using simple dynamical growth models with good track records including the Richards model, the generalized-logistic growth model, and the Gompertz model. We first test and verify the functionality of the method using simulated data from phenomenological models and a mechanistic transmission model. Next, the performance of the method is demonstrated using a diversity of epidemic datasets including scenario outbreak data of the Ebola Forecasting Challenge and real-world epidemic data outbreaks of including influenza, plague, Zika, and COVID-19. RESULTS: We found that the ensemble method that randomly selects a model from the set of individual models for each time point of the trajectory of the epidemic frequently outcompeted the individual models as well as an alternative ensemble method based on the weighted combination of the individual models and yields broader and more realistic uncertainty bounds for the trajectory envelope, achieving not only better coverage rate of the 95% prediction interval but also improved mean interval scores across a diversity of epidemic datasets. CONCLUSION: Our new methodology for ensemble forecasting outcompete component models and an alternative ensemble model that differ in how the variance is evaluated for the generation of the prediction intervals of the forecasts.


Asunto(s)
Brotes de Enfermedades , Predicción/métodos , Modelos Estadísticos , /epidemiología , Fiebre Hemorrágica Ebola/epidemiología , Humanos , Gripe Humana/epidemiología , Infección por el Virus Zika/epidemiología
10.
Nat Commun ; 12(1): 726, 2021 02 09.
Artículo en Inglés | MEDLINE | ID: mdl-33563980

RESUMEN

Human mobility is a primary driver of infectious disease spread. However, existing data is limited in availability, coverage, granularity, and timeliness. Data-driven forecasts of disease dynamics are crucial for decision-making by health officials and private citizens alike. In this work, we focus on a machine-learned anonymized mobility map (hereon referred to as AMM) aggregated over hundreds of millions of smartphones and evaluate its utility in forecasting epidemics. We factor AMM into a metapopulation model to retrospectively forecast influenza in the USA and Australia. We show that the AMM model performs on-par with those based on commuter surveys, which are sparsely available and expensive. We also compare it with gravity and radiation based models of mobility, and find that the radiation model's performance is quite similar to AMM and commuter flows. Additionally, we demonstrate our model's ability to predict disease spread even across state boundaries. Our work contributes towards developing timely infectious disease forecasting at a global scale using human mobility datasets expanding their applications in the area of infectious disease epidemiology.


Asunto(s)
Predicción/métodos , Gripe Humana/epidemiología , Aprendizaje Automático , Australia/epidemiología , Humanos , Gripe Humana/prevención & control , Gripe Humana/transmisión , Modelos Teóricos , Ciudad de Nueva York/epidemiología , Dinámica Poblacional , Reproducibilidad de los Resultados , Teléfono Inteligente
11.
Emerg Infect Dis ; 27(3): 767-778, 2021.
Artículo en Inglés | MEDLINE | ID: mdl-33622460

RESUMEN

To increase situational awareness and support evidence-based policymaking, we formulated a mathematical model for coronavirus disease transmission within a regional population. This compartmental model accounts for quarantine, self-isolation, social distancing, a nonexponentially distributed incubation period, asymptomatic persons, and mild and severe forms of symptomatic disease. We used Bayesian inference to calibrate region-specific models for consistency with daily reports of confirmed cases in the 15 most populous metropolitan statistical areas in the United States. We also quantified uncertainty in parameter estimates and forecasts. This online learning approach enables early identification of new trends despite considerable variability in case reporting.


Asunto(s)
Infecciones por Coronavirus/epidemiología , Epidemias , Predicción/métodos , Teorema de Bayes , Coronavirus , Infecciones por Coronavirus/prevención & control , Infecciones por Coronavirus/transmisión , Epidemias/prevención & control , Humanos , Incidencia , Modelos Teóricos , Incertidumbre , Estados Unidos/epidemiología
12.
PLoS One ; 16(1): e0244173, 2021.
Artículo en Inglés | MEDLINE | ID: mdl-33411744

RESUMEN

The novel coronavirus (COVID-19) is an emergent disease that initially had no historical data to guide scientists on predicting/ forecasting its global or national impact over time. The ability to predict the progress of this pandemic has been crucial for decision making aimed at fighting this pandemic and controlling its spread. In this work we considered four different statistical/time series models that are readily available from the 'forecast' package in R. We performed novel applications with these models, forecasting the number of infected cases (confirmed cases and similarly the number of deaths and recovery) along with the corresponding 90% prediction interval to estimate uncertainty around pointwise forecasts. Since the future may not repeat the past for this pandemic, no prediction model is certain. However, any prediction tool with acceptable prediction performance (or prediction error) could still be very useful for public-health planning to handle spread of the pandemic, and could policy decision-making and facilitate transition to normality. These four models were applied to publicly available data of the COVID-19 pandemic for both the USA and Italy. We observed that all models reasonably predicted the future numbers of confirmed cases, deaths, and recoveries of COVID-19. However, for the majority of the analyses, the time series model with autoregressive integrated moving average (ARIMA) and cubic smoothing spline models both had smaller prediction errors and narrower prediction intervals, compared to the Holt and Trigonometric Exponential smoothing state space model with Box-Cox transformation (TBATS) models. Therefore, the former two models were preferable to the latter models. Given similarities in performance of the models in the USA and Italy, the corresponding prediction tools can be applied to other countries grappling with the COVID-19 pandemic, and to any pandemics that can occur in future.


Asunto(s)
/epidemiología , Predicción/métodos , Modelos Biológicos , /mortalidad , Control de Enfermedades Transmisibles , Simulación por Computador , Toma de Decisiones , Humanos , Italia/epidemiología , Estados Unidos/epidemiología
14.
Sci Rep ; 11(1): 2147, 2021 01 25.
Artículo en Inglés | MEDLINE | ID: mdl-33495534

RESUMEN

We analyze data from Twitter to uncover early-warning signals of COVID-19 outbreaks in Europe in the winter season 2019-2020, before the first public announcements of local sources of infection were made. We show evidence that unexpected levels of concerns about cases of pneumonia were raised across a number of European countries. Whistleblowing came primarily from the geographical regions that eventually turned out to be the key breeding grounds for infections. These findings point to the urgency of setting up an integrated digital surveillance system in which social media can help geo-localize chains of contagion that would otherwise proliferate almost completely undetected.


Asunto(s)
/epidemiología , Monitoreo Epidemiológico , Pandemias/prevención & control , Medios de Comunicación Sociales/estadística & datos numéricos , /prevención & control , Interpretación Estadística de Datos , Europa (Continente)/epidemiología , Predicción/métodos , Humanos , Pandemias/estadística & datos numéricos , Denuncia de Irregularidades
15.
J Appl Lab Med ; 6(2): 451-462, 2021 03 01.
Artículo en Inglés | MEDLINE | ID: mdl-33463684

RESUMEN

BACKGROUND: Patient surges beyond hospital capacity during the initial phase of the COVID-19 pandemic emphasized a need for clinical laboratories to prepare test processes to support future patient care. The objective of this study was to determine if current instrumentation in local hospital laboratories can accommodate the anticipated workload from COVID-19 infected patients in hospitals and a proposed field hospital in addition to testing for non-infected patients. METHODS: Simulation models predicted instrument throughput and turn-around-time for chemistry, ion-selective-electrode, and immunoassay tests using vendor-developed software with different workload scenarios. The expanded workload included tests from anticipated COVID patients in 2 local hospitals and a proposed field hospital with a COVID-specific test menu in addition to the pre-pandemic workload. RESULTS: Instrumentation throughput and turn-around time at each site was predicted. With additional COVID-patient beds in each hospital, the maximum throughput was approached with no impact on turnaround time. Addition of the field hospital workload led to significantly increased test turnaround times at each site. CONCLUSIONS: Simulation models depicted the analytic capacity and turn-around times for laboratory tests at each site and identified the laboratory best suited for field hospital laboratory support during the pandemic.


Asunto(s)
/instrumentación , Asignación de Recursos para la Atención de Salud/métodos , Laboratorios de Hospital/organización & administración , Pandemias/estadística & datos numéricos , /epidemiología , /estadística & datos numéricos , Servicios de Laboratorio Clínico/organización & administración , Servicios de Laboratorio Clínico/estadística & datos numéricos , Simulación por Computador , Conjuntos de Datos como Asunto , Predicción/métodos , Asignación de Recursos para la Atención de Salud/estadística & datos numéricos , Asistencia Técnica a la Planificación en Salud , Capacidad de Camas en Hospitales/estadística & datos numéricos , Humanos , Unidades de Cuidados Intensivos/organización & administración , Unidades de Cuidados Intensivos/estadística & datos numéricos , Unidades de Cuidados Intensivos/tendencias , Laboratorios de Hospital/provisión & distribución , Laboratorios de Hospital/tendencias , Modelos Estadísticos , Juego de Reactivos para Diagnóstico/provisión & distribución , Juego de Reactivos para Diagnóstico/tendencias , Saskatchewan/epidemiología , Programas Informáticos , Factores de Tiempo , Carga de Trabajo/estadística & datos numéricos
16.
Commun Biol ; 4(1): 126, 2021 01 25.
Artículo en Inglés | MEDLINE | ID: mdl-33495509

RESUMEN

It is important to forecast the risk of COVID-19 symptom onset and thereby evaluate how effectively the city lockdown measure could reduce this risk. This study is a first comprehensive, high-resolution investigation of spatiotemporal heterogeneities on the effect of the Wuhan lockdown on the risk of COVID-19 symptom onset in all 347 Chinese cities. An extended Weight Kernel Density Estimation model was developed to predict the COVID-19 onset risk under two scenarios (i.e., with and without the Wuhan lockdown). The Wuhan lockdown, compared with the scenario without lockdown implementation, in general, delayed the arrival of the COVID-19 onset risk peak for 1-2 days and lowered risk peak values among all cities. The decrease of the onset risk attributed to the lockdown was more than 8% in over 40% of Chinese cities, and up to 21.3% in some cities. Lockdown was the most effective in areas with medium risk before lockdown.


Asunto(s)
/epidemiología , Modelos Estadísticos , Pandemias/prevención & control , Cuarentena/métodos , Análisis Espacial , /virología , China/epidemiología , Ciudades/epidemiología , Exactitud de los Datos , Predicción/métodos , Humanos , Pronóstico , Factores de Riesgo , Migrantes/estadística & datos numéricos
17.
J Theor Biol ; 509: 110501, 2021 01 21.
Artículo en Inglés | MEDLINE | ID: mdl-32980371

RESUMEN

We model the COVID-19 coronavirus epidemics in China, South Korea, Italy, France, Germany and the United Kingdom. We identify the early phase of the epidemics, when the number of cases grows exponentially, before government implementation of major control measures. We identify the next phase of the epidemics, when these social measures result in a time-dependent exponentially decreasing number of cases. We use reported case data, both asymptomatic and symptomatic, to model the transmission dynamics. We also incorporate into the transmission dynamics unreported cases. We construct our models with comprehensive consideration of the identification of model parameters. A key feature of our model is the evaluation of the timing and magnitude of implementation of major public policies restricting social movement. We project forward in time the development of the epidemics in these countries based on our model analysis.


Asunto(s)
/epidemiología , Epidemias , Predicción/métodos , Modelos Estadísticos , /transmisión , China/epidemiología , Francia/epidemiología , Alemania/epidemiología , Implementación de Plan de Salud/normas , Humanos , Italia/epidemiología , Pandemias , Política Pública , Cuarentena , República de Corea/epidemiología , Aislamiento Social , Reino Unido/epidemiología
19.
Sci Rep ; 10(1): 22365, 2020 12 22.
Artículo en Inglés | MEDLINE | ID: mdl-33353964

RESUMEN

COVID-19 pandemic in Italy displayed a spatial distribution that made the tracking of its time course quite difficult. The most relevant anomaly was the marked spatial heterogeneity of COVID-19 diffusion. Lombardia region accounted for around 60% of fatal cases (while hosting 15% of Italian population). Moreover, 86% of fatalities concentrated in four Northern Italy regions. The 'explosive' outbreak of COVID-19 in Lombardia at the very beginning of pandemic fatally biased the R-like statistics routinely used to control the disease dynamics. To (at least partially) overcome this bias, we propose a new index RI = dH/dI (daily derivative ratio of H and I, given H = Healed and I = Infected), corresponding to the ratio between healed and infected patients relative daily changes. The proposed index is less flawed than R by the uncertainty related to the estimated number of infected persons and allows to follow (and possibly forecast) epidemic dynamics in a largely model-independent way. To analyze the dynamics of the epidemic, starting from the beginning of the virus spreading-when data are insufficient to make an estimate by adopting SIR model-a "sigmoidal family with delay" logistic model was introduced. That approach allowed in estimating the epidemic peak using the few data gathered even before mid-March. Based on this analysis, the peak was correctly predicted to occur by end of April. Analytical methodology of the dynamics of the epidemic we are proposing herein aims to forecast the time and intensity of the epidemic peak (forward prediction), while allowing identifying the (more likely) beginning of the epidemic (backward prediction). In addition, we established a relationship between hospitalization in intensive care units (ICU) versus deaths daily rates by avoiding the necessity to rely on precise estimates of the infected fraction of the population The joint evolution of the above parameters over time allows for a trustworthy and unbiased estimation of the dynamics of the epidemic, allowing us to clearly detect the qualitatively different character of the 'so-called' second wave with respect to the previous epidemic peak.


Asunto(s)
/epidemiología , Pandemias , /mortalidad , Predicción/métodos , Hospitalización , Humanos , Unidades de Cuidados Intensivos , Italia/epidemiología , Modelos Logísticos , Análisis de Regresión
20.
Rev. medica electron ; 42(6): 2560-2574, nov.-dic. 2020. tab, graf
Artículo en Español | LILACS, CUMED | ID: biblio-1150037

RESUMEN

RESUMEN Introducción: la neumonía adquirida en la comunidad es la enfermedad infecciosa que conlleva una mayor mortalidad en los países desarrollados. El diagnóstico pasa por varios momentos, el cuadro clínico, la analítica y las imágenes. Objetivos: realizar la validación externa de un modelo matemático predictivo de mortalidad en pacientes ingresados por neumonía grave adquirida en la comunidad. Material y métodos: estudio longitudinal prospectivo (cohorte) con un grupo, con todos los pacientes que ingresaron en la Unidad de Cuidados intensivos emergentes con el diagnóstico de neumonía adquirida en la comunidad en el Hospital Militar Dr. Carlos J. Finlay, de febrero de 2018 hasta marzo del 2019. El universo estuvo constituido por 160 pacientes y no se tomó muestra alguna. Resultados: índice de Kappa K=1. Test Hosmer Lemenshow 0,650 con elevado ajuste. Resultados del modelo con sensibilidad= 79%. Especificidad: 91% con (VPP): 80 y (VPN)= 91. RR: 9,1. Área bajo la Curva = 0997. Porcentaje de aciertos en la regresión logística de 88,4 %. Conclusiones: el modelo propuesto constituyo una herramienta útil en la detección temprana de pacientes con riesgo de muerte a corto plazo. Permitió unificar en una sola variable el resultado de otras que aparentemente no tienen relación entre ellas; con lo que se hace más fácil la interpretación de los resultados, toda vez que este refleja, el conjunto y no la individualidad (AU).


SUMMARY Introduction: community-acquired pneumonia is the infectious disease leading to higher mortality in developed countries. The diagnosis goes through several moments, clinical symptoms, analytics, and images. Objective: to perform the external validation of a predictive mathematical model of mortality in patients admitted by serious community-acquired pneumonia. Methods: longitudinal prospective (cohort) study with a group formed with all patients who were admitted to the Emergent Intensive Care Unit in the Military Hospital ¨Dr. Carlos Juan Finlay¨ with the diagnosis of community-acquired pneumonia, from February 2018 to March 2019. The universe was formed by 160 patients and no sample was chosen. Results: Kappa index K= 1. Hosmer Lemenshow test= 0.650 with a high adjustment. Result of the model with sensibility= 79 %. Specificity= 91 % with (APV) = 80 and (NPV) = 91. RR= 9.1. Area under the curve= 0997. Percentage of correctness in logistic regression of 88.4 %. Conclusions: The proposed model was a useful tool in the early detection of patients at near-term death risk. It allowed to unite in an only variant the result of others that apparently are not related one to another, making it easier the interpretation of the results, since it reflects the whole and not the individuality (AU).


Asunto(s)
Humanos , Masculino , Femenino , Anciano , Neumonía/mortalidad , Anciano/fisiología , Neumonía/complicaciones , Neumonía/diagnóstico , Cuidados Críticos/métodos , Predicción/métodos , Atención al Paciente/métodos
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