Addressing the COVID-19 transmission in inner Brazil by a mathematical model.

In 2020, the world experienced its very first pandemic of the globalized era. A novel coronavirus, SARS-CoV-2, is the causative agent of severe pneumonia and has rapidly spread through many nations, crashing health systems and leading a large number of people to death. In Brazil, the emergence of local epidemics in major metropolitan areas has always been a concern. In a vast and heterogeneous country, with regional disparities and climate diversity, several factors can modulate the dynamics of COVID-19. What should be the scenario for inner Brazil, and what can we do to control infection transmission in each of these locations? Here, a mathematical model is proposed to simulate disease transmission among individuals in several scenarios, differing by abiotic factors, social-economic factors, and effectiveness of mitigation strategies. The disease control relies on keeping all individuals' social distancing and detecting, followed by isolating, infected ones. The model reinforces social distancing as the most efficient method to control disease transmission. Moreover, it also shows that improving the detection and isolation of infected individuals can loosen this mitigation strategy. Finally, the effectiveness of control may be different across the country, and understanding it can help set up public health strategies.

Spatial and sociodemographic factors associated with surgical site infection rates in hospitals in inner São Paulo State, Brazil.

Low-to-middle-income countries often have high incidence of surgical site infection (SSI). To assess spatial and sociodemographic predictors of SSI rates, this study analysed and georeferenced governmental surveillance data from 385 hospitals located in inner São Paulo State, Brazil. In multi-variate models, SSI rates were positively associated with distance from the state capital [incidence rate ratio (IRR) for each 100 km 1.19, 95% confidence interval (CI) 1.07-1.32], and were lower for non-profit (IRR 0.95, 95% CI 0.37-0.85) and private (IRR 0.47, 95% CI 0.31-0.71) facilities compared with public hospitals. Georeferencing results reinforced the need to direct SSI-prevention policies to hospitals located in areas distant from the state capital.

The role of intra and inter-hospital patient transfer in the dissemination of heathcare-associated multidrug-resistant pathogens.

Healthcare-associated infections cause significant patient morbidity and mortality, and contribute to growing healthcare costs, whose effects may be felt most strongly in developing countries. Active surveillance systems, hospital staff compliance, including hand hygiene, and a rational use of antimicrobials are among the important measures to mitigate the spread of healthcare-associated infection within and between hospitals. Klebsiella pneumoniae is an important human pathogen that can spread in hospital settings, with some forms exhibiting drug resistance, including resistance to the carbapenem class of antibiotics, the drugs of last resort for such infections. Focusing on the role of patient movement within and between hospitals on the transmission and incidence of enterobacteria producing the K. pneumoniae Carbapenemase (KPC, an enzyme that inactivates several antimicrobials), we developed a metapopulation model where the connections among hospitals are made using a theoretical hospital network based on Brazilian hospital sizes and locations. The pathogen reproductive number, R0 that measures the average number of new infections caused by a single infectious individual, was calculated in different scenarios defined by both the links between hospital environments (regular wards and intensive care units) and between different hospitals (patient transfer). Numerical simulation was used to illustrate the infection dynamics in this set of scenarios. The sensitivity of R0 to model input parameters, such as hospital connectivity and patient-hospital staff contact rates was also established, highlighting the differential importance of factors amenable to change on pathogen transmission and control.