Abstract
To analyze the underdiagnosis of COVID-19 through nowcasting with machine learning in a Southern Brazilian capital city. Observational ecological design and data from 3916 notified cases of COVID-19 from April 14th to June 2nd, 2020 in Florianópolis, Brazil. A machine-learning algorithm was used to classify cases that had no diagnosis, producing the nowcast. To analyze the underdiagnosis, the difference between data without nowcasting and the median of the nowcasted projections for the entire period and for the six days from the date of onset of symptoms were compared. The number of new cases throughout the entire period without nowcasting was 389. With nowcasting, it was 694 (95%CI 496-897). During the six-day period, the number without nowcasting was 19 and 104 (95%CI 60-142) with nowcasting. The underdiagnosis was 37.29% in the entire period and 81.73% in the six-day period. The underdiagnosis was more critical in the six days from the date of onset of symptoms to diagnosis before the data collection than in the entire period. The use of nowcasting with machine learning techniques can help to estimate the number of new disease cases.
Highlights
The World Health Organization has reported more than 10 million cases of SARS-CoV-2 infection and 500,000 deaths[1], a significant part of which have occurred in Brazil
To help overcome this challenge, the present study aimed to analyze the underdiagnosis of COVID-19 cases through nowcasting with machine learning in a Southern Brazilian capital city
The underdiagnosis was analyzed by the difference between the median of the number of cases predicted by the model and the number of cases diagnosed by the Public Health Department of Florianópolis
Summary
The World Health Organization has reported more than 10 million cases of SARS-CoV-2 infection and 500,000 deaths[1], a significant part of which have occurred in Brazil. Nowcasting approaches try to estimate the number of a given event in the present[13,15,22] This strategy has been used to improve surveillance of infectious diseases like AIDS23,24, cholera[25], influenza infections[13,26], and recently, COVID-193,8,17,20,22,27. Monitoring the impact of non-pharmacological actions is essential to optimize the allocation of scarce resources in non-high-income-countries, like Brazil[21]. It is plausible to assume that these challenges are even greater in non-high-income-countries that have healthcare systems that are not widely available to the population To help overcome this challenge, the present study aimed to analyze the underdiagnosis of COVID-19 cases through nowcasting with machine learning in a Southern Brazilian capital city
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Schedule a callMore From: Revista brasileira de epidemiologia = Brazilian journal of epidemiology
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