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      PREDICTIVE MODEL FOR COVID-19 INCIDENCE IN A MEDIUM-SIZED MUNICIPALITY IN BRAZIL (PONTA GROSSA, PARANÁ) Translated title: MODELO PREDICTIVO DE LA OCURRENCIA DE COVID-19 EN UN MUNICIPIO DE TAMAÑO MEDIO EN BRASIL (PONTA GROSSA-PARANÁ) Translated title: MODELO PREDITIVO DA OCORRÊNCIA DE COVID-19 EM MUNICÍPIO DE MÉDIO PORTE NO BRASIL (PONTA GROSSA-PARANÁ)

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          Abstract

          ABSTRACT Objective: to produce a predictive model for the incidence of COVID-19 cases, severity and deaths in Ponta Grossa, state of Paraná. Methods: this is an ecological study with data from confirmed cases of COVID-19 reported between March 21, 2020 and May 3, 2020 in Ponta Grossa and proportion of severity, hospitalization and lethality in the literature. A susceptible-infected-recovered (SIR) epidemic model was developed, and reproduction rate (R0), duration of epidemic, peak period, number of cases, hospitalized patients and deaths were estimated. Deaths were calculated by age group and in three scenarios: at day 24, at day 34, and at day 44 of the epidemic. Results: in the three scenarios assessed in this study, the variation in the number of cases was explained by an exponential curve (r2=0.74, 0.79 and 0.89, respectively, p<0.0001 in all scenarios). The SIR model estimated that, in the best scenario, the peak period will be around 120 days after the first case (between July 11, 2020 and July 25, 2020), estimated R0 will be 1.07 and will infect 0.23% of the population. In the worst scenario, peak period will involve 4,375 (95% CI; 4156-4594) cases and 825 (95% CI; 700-950) cases in the best scenario. Most cases and hospital admissions will involve patients aged 20 to 39 years, the number of deaths will be higher among the elderly and more pronounced among patients aged ≥80 years. Conclusion: this is the first study that provides COVID-19 projections for a municipality that is not a large capital. It shows a peak period at a later moment; therefore, the municipality will have more time to prepare and adopt protective measures to reduce the number of simultaneous cases.

          Translated abstract

          RESUMEN Objetivo: obtener un modelo predictivo para la ocurrencia de casos, severidad y muertes por COVID-19 en Ponta Grossa-Paraná. Métodos: estudio ecológico con datos de casos confirmados de COVID-19 notificados del 21/03/2020 al 3/3/2020 en Ponta Grossa y proporción de severidad, hospitalización y letalidad en la literatura. Se construyó un modelo epidemiológico (SIR) infectado-recuperado susceptible y tasa de reproducción estimada (R0), duración de la epidemia, fecha pico, número de casos, hospitalizaciones y muertes. Este último por grupo de edad y en tres escenarios: a los 24 días, a los 34 días y a los 44 días de epidemia. Resultados: en los tres escenarios evaluados, la variación en el número de casos se explicó por una curva exponencial (r2 = 0.74, 0.79 y 0.89, respectivamente y p <0.0001 en total). El modelo SIR estimó que, en el mejor escenario, el pico ocurrirá alrededor de 120 días después del primer caso (entre el 7/11/2020 y el 25/7/2020), el R0 estimado será de 1.07 y alcanzará 0.23 % de habitantes infectados. En el peor de los casos, el pico estimado será de 4375 (IC del 95%: 4156-4594) y 825 (IC del 95%: 700-950) en el mejor de los casos. El mayor número estimado de casos y hospitalizaciones estará en el rango entre 20 y 39 años, el número de muertes será mayor entre los ancianos y más pronunciado entre ≥ 80 años. Conclusión: este es el primer estudio con proyecciones para COVID-19 en un municipio fuera de las grandes capitales y demostró que el pico llegará tarde, por lo tanto, el municipio tendrá más tiempo de preparación y que las medidas de protección pueden reducir el número simultáneo de casos.

          Translated abstract

          RESUMO Objetivo: obter um modelo preditivo da ocorrência de casos, gravidade e óbitos por COVID-19 em Ponta Grossa-Paraná. Métodos: estudo ecológico com dados de casos confirmados de COVID-19 notificados de 21/03/2020 a 03/05/2020 em Ponta Grossa e proporção de gravidade, hospitalização e letalidade da literatura. Um modelo epidemiológico suscetível-infectado-recuperado (SIR) foi construído e estimadas taxa de reprodução (R0), duração da epidemia, data do pico, número de casos, hospitalizações e óbitos. Estas últimas por faixa etária e em três cenários: aos 24 dias, aos 34 dias e aos 44 dias de epidemia. Resultados: nos três cenários avaliados, a variação no número de casos foi explicada por uma curva exponencial (r2=0,74, 0,79 e 0,89, respectivamente e p<0,0001 em todos). O modelo SIR estimou que, no melhor cenário, o pico ocorrerá em torno de 120 dias após o primeiro caso (entre 11/07/2020 e 25/07/2020), o R0 estimado será 1,07 e chegará a 0,23% dos habitantes infectados. No pior cenário, o pico estimado será de 4375 (IC 95% 4156-4594) casos e 825 (IC 95% 700-950) no melhor cenário. O maior número estimado de casos e hospitalizações será na faixa entre 20 e 39 anos, o número de óbitos será maior entre idosos e mais acentuado entre ≥ 80 anos. Conclusão: este é o primeiro estudo com projeções para a COVID-19 em um município fora das grandes capitais e mostrou que o pico será tardio, portanto, o município terá mais tempo de preparo e que medidas protetivas podem reduzir o número simultâneo de casos.

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          Most cited references8

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          A Novel Coronavirus from Patients with Pneumonia in China, 2019

          Summary In December 2019, a cluster of patients with pneumonia of unknown cause was linked to a seafood wholesale market in Wuhan, China. A previously unknown betacoronavirus was discovered through the use of unbiased sequencing in samples from patients with pneumonia. Human airway epithelial cells were used to isolate a novel coronavirus, named 2019-nCoV, which formed a clade within the subgenus sarbecovirus, Orthocoronavirinae subfamily. Different from both MERS-CoV and SARS-CoV, 2019-nCoV is the seventh member of the family of coronaviruses that infect humans. Enhanced surveillance and further investigation are ongoing. (Funded by the National Key Research and Development Program of China and the National Major Project for Control and Prevention of Infectious Disease in China.)
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            COVID-19 and Italy: what next?

            Summary The spread of severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) has already taken on pandemic proportions, affecting over 100 countries in a matter of weeks. A global response to prepare health systems worldwide is imperative. Although containment measures in China have reduced new cases by more than 90%, this reduction is not the case elsewhere, and Italy has been particularly affected. There is now grave concern regarding the Italian national health system's capacity to effectively respond to the needs of patients who are infected and require intensive care for SARS-CoV-2 pneumonia. The percentage of patients in intensive care reported daily in Italy between March 1 and March 11, 2020, has consistently been between 9% and 11% of patients who are actively infected. The number of patients infected since Feb 21 in Italy closely follows an exponential trend. If this trend continues for 1 more week, there will be 30 000 infected patients. Intensive care units will then be at maximum capacity; up to 4000 hospital beds will be needed by mid-April, 2020. Our analysis might help political leaders and health authorities to allocate enough resources, including personnel, beds, and intensive care facilities, to manage the situation in the next few days and weeks. If the Italian outbreak follows a similar trend as in Hubei province, China, the number of newly infected patients could start to decrease within 3–4 days, departing from the exponential trend. However, this cannot currently be predicted because of differences between social distancing measures and the capacity to quickly build dedicated facilities in China.
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              Prediction models for diagnosis and prognosis of covid-19 infection: systematic review and critical appraisal

              Abstract Objective To review and critically appraise published and preprint reports of prediction models for diagnosing coronavirus disease 2019 (covid-19) in patients with suspected infection, for prognosis of patients with covid-19, and for detecting people in the general population at risk of being admitted to hospital for covid-19 pneumonia. Design Rapid systematic review and critical appraisal. Data sources PubMed and Embase through Ovid, Arxiv, medRxiv, and bioRxiv up to 24 March 2020. Study selection Studies that developed or validated a multivariable covid-19 related prediction model. Data extraction At least two authors independently extracted data using the CHARMS (critical appraisal and data extraction for systematic reviews of prediction modelling studies) checklist; risk of bias was assessed using PROBAST (prediction model risk of bias assessment tool). Results 2696 titles were screened, and 27 studies describing 31 prediction models were included. Three models were identified for predicting hospital admission from pneumonia and other events (as proxy outcomes for covid-19 pneumonia) in the general population; 18 diagnostic models for detecting covid-19 infection (13 were machine learning based on computed tomography scans); and 10 prognostic models for predicting mortality risk, progression to severe disease, or length of hospital stay. Only one study used patient data from outside of China. The most reported predictors of presence of covid-19 in patients with suspected disease included age, body temperature, and signs and symptoms. The most reported predictors of severe prognosis in patients with covid-19 included age, sex, features derived from computed tomography scans, C reactive protein, lactic dehydrogenase, and lymphocyte count. C index estimates ranged from 0.73 to 0.81 in prediction models for the general population (reported for all three models), from 0.81 to more than 0.99 in diagnostic models (reported for 13 of the 18 models), and from 0.85 to 0.98 in prognostic models (reported for six of the 10 models). All studies were rated at high risk of bias, mostly because of non-representative selection of control patients, exclusion of patients who had not experienced the event of interest by the end of the study, and high risk of model overfitting. Reporting quality varied substantially between studies. Most reports did not include a description of the study population or intended use of the models, and calibration of predictions was rarely assessed. Conclusion Prediction models for covid-19 are quickly entering the academic literature to support medical decision making at a time when they are urgently needed. This review indicates that proposed models are poorly reported, at high risk of bias, and their reported performance is probably optimistic. Immediate sharing of well documented individual participant data from covid-19 studies is needed for collaborative efforts to develop more rigorous prediction models and validate existing ones. The predictors identified in included studies could be considered as candidate predictors for new models. Methodological guidance should be followed because unreliable predictions could cause more harm than benefit in guiding clinical decisions. Finally, studies should adhere to the TRIPOD (transparent reporting of a multivariable prediction model for individual prognosis or diagnosis) reporting guideline. Systematic review registration Protocol https://osf.io/ehc47/, registration https://osf.io/wy245.
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                Author and article information

                Journal
                tce
                Texto & Contexto - Enfermagem
                Texto contexto - enferm.
                Universidade Federal de Santa Catarina, Programa de Pós Graduação em Enfermagem (, SC, Brazil )
                0104-0707
                1980-265X
                2020
                : 29
                : e20200154
                Affiliations
                [2] Ponta Grossa Paraná orgnameUniversidade Estadual de Ponta Grossa orgdiv1Departamento de Enfermagem e Saúde Pública Brazil
                [3] Curitiba PR orgnameAAC&T Consultoria em Pesquisa LTDA Brasil
                [1] Ponta Grossa Paraná orgnameUniversidade Estadual de Ponta Grossa orgdiv1Departamento de Medicina Brazil
                Article
                S0104-07072020000100204 S0104-0707(20)02900000204
                10.1590/1980-265x-tce-2020-0154
                e3a38a37-fb6a-4014-9056-fa9178ba8faf

                This work is licensed under a Creative Commons Attribution 4.0 International License.

                History
                : 15 June 2020
                : 06 May 2020
                Page count
                Figures: 0, Tables: 0, Equations: 0, References: 25, Pages: 0
                Product

                SciELO Brazil

                Categories
                Special Section COVID-19

                Coronavirus,Letalidade,Epidemics,SARS-CoV-2,Hospitalización,Número reprodutivo basal,Epidemia,Previsão,Letalidad,Hospitalização,Basic reproduction number,Epidemias Número reproductivo basal,Estimación,Hospitalization,Mortality,Estimate

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