Objective: To determine whether the SEIR model, associated to mobility changes parameters, can determine the likelihood of establishing control over an epidemic in a city, state or country. Study design and setting: The critical step in the prediction of COVID-19 by a SEIR model are the values of the basic reproduction number (R0) and the infectious period, in days. R0 and the infectious periods were calculated by mathematical constrained optimization, and used to determine the numerically minimum SEIR model errors in a country, based on COVID-19 data until April 11th. The Community Mobility Reports from Google Maps (<https://www.google.com/covid19/mobility>) provided mobility changes on April 5th compared to the baseline (Jan 3th to Feb 6th). The data was used to measure the non-pharmacological intervention adherence. The impact of each mobility component was calculated by logistic regression models. COVID-19 control was defined by SEIR model R0<1.0 in a country. Results: The ECDC has registered 1,653,204 COVID-19 worldwide on April 11th. Sixteen countries presented 78% of all cases. Of the six Google Maps mobility parameters, the “Stay at home” parameter was the strongest one to control COVID-19 in a country: an increase of 50% in mobility trends for places of residence has a 99% chance of outbreak control. Conclusions: Residential mobility restriction presented itself as the most effective measure. The SEIR model associated with mobility parameters proved to be a useful tool in determining the chance of COVID-19 outbreak control.
BackgroundMeningitis after craniotomy can be devastating. The objective of our study is to answer four questions: (a) what is the risk of meningitis after craniotomy? (b) What are the main microorganisms causing meningitis after craniotomy? (c) What is the impact of meningitis in the hospital length of stay and mortality? (d) What are risk factors for meningitis after craniotomy?MethodsSurveillance data based on NHSN/CDC protocols were collected between 2013 and 2017 from nine hospitals at Belo Horizonte, Brazil. Outcome: meningitis, hospital death and total length of hospital stay. Twenty-three independent variables were analyzed using Epi Info and applying statistical two-tailed test hypothesis with significance level of 5%.ResultsA sample of 4,549 patients submitted to craniotomy was analyzed: risk of meningitis = 1.9% (IC 95% = 1.6%; 2.4%). Mortality rate in patients without infection was 9% rising to 33% in infected patients (P < 0.01). Hospital length of stay in non-infected patients (days): mean = 18, median = 7, std. dev. = 36. Hospital stay in infected patients: mean = 56, median = 37, std. dev. = 63 (P < 0.001). The duration of procedure was the main risk factor for meningitis: 1.5% risk of meningitis in surgery less than or equal to 4 hours vs. 2.5% if the duration of procedure was more than 4 hours (relative risk = 1.7; P = 0.041). From 88 meningitis, in 68 (77%) the etiologic agent was identified: Klebsiella pneumoniae (20%), Staphylococcus aureus (16%), Acinetobacter baumannii (13%), Pseudomonas aeruginosa (9%), Staphylococcus sp. (8%), Acinetobacter sp. (7%), Staphylococcus epidermidis (5%), and other (20%).ConclusionThe study showed how much meningitis is devastating, rising the death risk and length of hospital stay.Disclosures All authors: No reported disclosures.
BackgroundApplying benchmarks from high resource countries on low resource countries may result in misleading conclusions, thus improvements can be made in order to refine the precision of external benchmarks in developing countries.MethodsThe NOIS Project uses SACIH software to retrieve data from different hospitals at Belo Horizonte, Brazil. The hospitals use prospective Healthcare-Associated Infections—HAI surveillance according to the NHSN/CDC protocols. The objective is to calculate benchmarks for HAI rates from intensive care units, ICU, and surgical procedures. Benchmarks were defined as the 10 percentile and 90 percentile, considering data from 11 hospitals and 13 ICUs, collected between 2013 and 2017.ResultsHospital-wide and ICUs benchmarks: HAI risk [1.5%; 4.7%]; HAI incidence per 1,000 patient-days [4.4; 12.6]; ICU infection risk [4.0%; 23.8%]; ICU incidence density rate of HAI per 1,000 patient-days [10.8; 35.7]; risk of urinary catheter-associated urinary tract infections[0.0%; 6.3%]; incidence density rate of urinary catheter-associated urinary tract infections per 1,000 urinary catheter-days [0.0; 9.4]; risk of central line-associated primary bloodstream infections [0.0%; 10.3%]; incidence density rate of central line-associated primary bloodstream infections per 1,000 central line-days [0; 16]; risk of ventilator associated pneumonia [0.0%; 13.5%]; incidence density rate of ventilator associated pneumonia per 1,000 ventilator-days [0.0; 20.6]. Surgical site infection benchmarks: Cesarean section [0,6%;0,9%]; open reduction of fracture [3,3%;3,9%]; Gallbladder surgery [0,7%;1%]; herniorrhaphy [1,1%;1,6%]; peripheral vascular bypass surgery [0,6%;1%]; gastric surgery [1,7%;2,4%]; appendix surgery [1,1%;1,8%]; colon surgery [3,0%;4,1%]; exploratory abdominal surgery [4,1%;5,3%]; craniotomy [5%;6,5%]; abdominal hysterectomy [0,7%;1,4%]; limb amputation [4,1%;6,1%]; thoracic surgery [0,8%;1,5%]; hip prosthesis [3%;4,3%]; knee prosthesis [2,3%;3,5%]; pacemaker surgery [1,9%;3,1,0%]; breast surgery [0,3%;0,9%]; bile duct, liver or pancreatic surgery [7%;11%]; ventricular shunt [3,3%;6,5%].ConclusionThe benchmarks proposed can be used by infection preventionists that decide to monitor selected surgical procedures and/or ICUs, especially in developing countries.Disclosures All authors: No reported disclosures.
Background In December 2009, a cluster of patients with pneumonia was reported in the city of Wuhan, capital of Hubei province in China, caused by a novel coronavirus: SARS-CoV-2. The epidemiological compartmental susceptible-exposed-infected-recovered (SEIR) model has been previously used during the initial wave of the H1N1 influenza pandemic in 2009. This study investigates whether the SEIR model, associated to mobility changes parameters, can determine the likelihood of establishing control over an epidemic in a city, state or country. Methods The critical step in the prediction of COVID-19 by a SEIR model are the values of the basic reproduction number (R0) and the infectious period, in days. R0 and the infectious periods were calculated by mathematical constrained optimization, and used to determine the numerically minimum SEIR model errors in a country, based on COVID-19 data until april 11th. The Community Mobility Reports from Google Maps (https://www.google.com/covid19/mobility/) provided mobility changes on april 5th compared to the baseline (Jan 3th to Feb 6th). The data was used to measure the non-pharmacological intervention adherence. The impact of each mobility component was made by logistic regression models. COVID-19 control was defined by R0 of the SEIR model in a country less than 1.0. Algorithm for the SEIR model applied to COVID-19 (initialization) Table 01: Algorithm for the SEIR model applied to COVID-19 (calculation of new COVID-19 cases day-by-day) Results Residential mobility restriction presented the higher logistic coefficient (17.7), meaning higher impact on outbreak control. Workplace mobility restriction was the second most effective measure, considering a restriction minimum of 56% for a 53% chance of outbreak control. Retail and recreation mobility presented 53%, and 86% respectively. Transit stations (96% and 54%) were also assessed. Park mobility restriction demonstrated the lowest effectiveness in outbreak control, considering that absolute (100%) restriction provided the lowest chance of outbreak control (46%). Table 2: The Community Mobility Reports from Google Maps: Mobility changes on April 5 compared to the baseline (5- week period; Jan 3–Feb 6, 2020): T_infectious and R0 obtained by using COVID-19 new cases day-by-day in each country, adjusted to the SEIR model by mathematical constrained optimization Logistic regression models to evaluate the chance of an epidemic control based on the non-pharmacological interventions adherence Simulation of the impact of the mobility component in the chance of outbreak control: analysis by using the logistic regression model summarized in Table 2 Conclusion Residential mobility restriction is the most effective measure. The degree to which mobility restrictions increase or decrease the overall epidemic size depends on the level of risk in each community and the characteristics of the disease. More research is required in order to estimate the optimal balance between mobility restriction, outbreak control, economy and freedom of movement. Disclosures All Authors: No reported disclosures
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