Impact of COVID-19 in Minas Gerais, Brazil: Excess deaths, sub-notified cases, geographic and ethnic distribution

New, fast, and precise method of COVID-19 detection in nasopharyngeal and tracheal aspirate samples combining optical spectroscopy and machine learning

Fast, precise, and low-cost diagnostic testing to identify persons infected with SARS-CoV-2 virus is pivotal to control the global pandemic of COVID-19 that began in late 2019. The gold standard method of diagnostic recommended is the RT-qPCR test. However, this method is not universally available, and is time-consuming and requires specialized personnel, as well as sophisticated laboratories. Currently, machine learning is a useful predictive tool for biomedical applications, being able to classify data from diverse nature. Relying on the artificial intelligence learning process, spectroscopic data from nasopharyngeal swab and tracheal aspirate samples can be used to leverage characteristic patterns and nuances in healthy and infected body fluids, which allows to identify infection regardless of symptoms or any other clinical or laboratorial tests. Hence, when new measurements are performed on samples of unknown status and the corresponding data is submitted to such an algorithm, it will be possible to predict whether the source individual is infected or not. This work presents a new methodology for rapid and precise label-free diagnosing of SARS-CoV-2 infection in clinical samples, which combines spectroscopic data acquisition and analysis via artificial intelligence algorithms. Our results show an accuracy of 85% for detection of SARS-CoV-2 in nasopharyngeal swab samples collected from asymptomatic patients or with mild symptoms, as well as an accuracy of 97% in tracheal aspirate samples collected from critically ill COVID-19 patients under mechanical ventilation. Moreover, the acquisition and processing of the information is fast, simple, and cheaper than traditional approaches, suggesting this methodology as a promising tool for biomedical diagnosis vis-à-vis the emerging and re-emerging viral SARS-CoV-2 variant threats in the future.

The COVID-19 pandemic and vaccination abandonment in children: spatial heterogeneity maps

OBJECTIVE

to identify spatial clusters corresponding to abandonment of routine vaccines in children.

METHOD

an ecological study, according to data from the 853 municipalities of a Brazilian state. The records analyzed were those of the multidose pentavalent, pneumococcal 10-valent, inactivated poliomyelitis and oral human rotavirus vaccines of 781,489 children aged less than one year old. The spatial scan statistics was used to identify spatial clusters and assess the relative risk based on the vaccination abandonment indicator.

RESULTS

the spatial scan statistics detected the presence of statistically significant clusters for abandonment regarding the four vaccines in all the years analyzed. However, the highest number of clusters with high relative risk estimates was identified in 2020. The Vale do Aço and West, North and West, and Southwest regions stand out for the pentavalent, poliomyelitis and rotavirus vaccines, respectively.

CONCLUSION

in an attempt to mitigate the devastating impact of the COVID-19 pandemic, the immunization program experienced setbacks. The presence of clusters points to the need to implement integrated strategies that may involve different sectors for an active search for children and prevent outbreaks of vaccine-preventable diseases in the near future.

Pandemia de COVID-19 y abandono de la vacunación en niños: mapas de heterogeneidad espacial

Resumen Objetivo: identificar grupos espaciales que abandonaron la vacunación de rutina de los niños. Método: estudio ecológico, basado en los datos de 853 municipios de un Estado brasileño. Se analizaron los registros de vacunas multidosis pentavalente, antineumocócica 10-valente y antipoliomielítica inactivada y vacuna oral contra el rotavirus humano de 781.489 niños menores de un año de edad. Se utilizó la estadística scan espacial para identificar agrupaciones espaciales y medir el riesgo relativo del indicador abandono de la vacunación. Resultados: la estadística scan espacial detectó la presencia de grupos estadísticamente significativos para el abandono de las cuatro vacunas en todos los años analizados. Sin embargo, el mayor número de grupos con estimaciones altas de riesgos relativos se identificó en 2020. Se destacan las macrorregiones del Vale do Aço y Oeste; Norte y Oeste; y Sudeste para las vacunas pentavalente, antipoliomielítica y contra el rotavirus, respectivamente. Conclusión: mientras se intentaba disminuir el impacto devastador de la pandemia de COVID-19, retrocedió el programa de inmunización. La presencia de grupos indica que es necesario implementar estrategias integradas que puedan involucrar a diferentes sectores para la búsqueda activa de niños y evitar brotes de enfermedades inmunoprevenibles en el futuro próximo.

How many more? Under-reporting of the COVID-19 deaths in Brazil in 2020

Objective

To evaluate the magnitude of under‐reporting the number of deaths due to COVID‐19 in Brazil in 2020, previously shown to occur due to low rate of laboratory testing for SARS‐CoV‐2, reporting delay, inadequate access to medical care, and its poor quality, leading to the low sensitivity of epidemiological surveillance and poor outcomes, often without laboratory confirmation of the cause of death.

Methods

Excess mortality due to COVID‐19 was estimated directly based on various data sources, and indirectly, based on the difference between the observed and expected number of deaths from serious acute respiratory infection (SARI) and all‐natural causes in 2020 had there been no COVID‐19. The absence of laboratory testing for SARS‐CoV‐2 was adjusted based on the proportion of those who tested positive among the tested individuals whose death was attributed to COVID‐19. Least absolute shrinkage and selection operator (lasso) were used to improve prediction of likely mortality without COVID‐19 in 2020.

Results

Under‐reporting of COVID‐19 deaths was 22.62%, with a corresponding mortality rate per 100 000 inhabitants of 115 by the direct method, 71–76 by the indirect methods based on the excess SARI mortality and 95–104 by excess mortality due to natural causes. COVID‐19 was the third cause of mortality that contributed directly with 18%, and indirectly with additional 10–11% to all deaths in Brazil in 2020.

Conclusions

Underestimation of COVID‐19 mortality between 1:5 and 1:4 is likely its lower bound. Timely and accurate surveillance of death causes is of the essence to evaluate the COVID‐19 burden.