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. 2024 Dec 26;18(12):e0012726.
doi: 10.1371/journal.pntd.0012726. eCollection 2024 Dec.

Impact of the COVID-19 pandemic on dengue in Brazil: Interrupted time series analysis of changes in surveillance and transmission

Kirstin Oliveira Roster  1 , Tiago Martinelli  1 , Colm Connaughton  2   3 , Mauricio Santillana  4   5 , Francisco A Rodrigues  1
Affiliations

Impact of the COVID-19 pandemic on dengue in Brazil: Interrupted time series analysis of changes in surveillance and transmission

Kirstin Oliveira Roster et al. PLoS Negl Trop Dis. .

Erratum in

Abstract

Measures to curb the spread of SARS-CoV-2 impacted not only COVID-19 dynamics, but also other infectious diseases, such as dengue in Brazil. The COVID-19 pandemic disrupted not only transmission dynamics due to changes in mobility patterns, but also several aspects of surveillance, such as care seeking behavior and clinical capacity. However, we lack a clear understanding of the overall impact on dengue in different parts of Brazil and the contribution of individual causal drivers. In this study, we estimated the gap between expected and observed dengue cases in each Brazilian state from March to April 2020 using an interrupted time series design with forecasts from machine learning models. We then decomposed the gap into the contributions of pandemic-induced changes in disease surveillance and transmission dynamics, using proxies for care availability and care seeking behavior. Of 25 states in the analysis, 19 reported fewer dengue cases than predicted and the gap between expected and observed cases was largely explained by excess under-reporting, as illustrated by a reduction in observed cases below expected levels in early March 2020 in several states. A notable exception is the experience in the Southern states, which reported unusually large dengue outbreaks in 2020. These estimates of dengue case counts adjusted for under-reporting help mitigate some of the data gaps from 2020. Reliable estimates of changes in the disease burden are critical for anticipating future outbreaks.

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Conflict of interest statement

The authors have declared that no competing interests exist.

Figures

Fig 1
Fig 1. Study area.
Map of Brazil with state boundaries, colored by region. Map data from OpenStreetMap, Copyright: Creative Commons, https://www.openstreetmap.org/copyright.
Fig 2
Fig 2. Observed dengue cases by epidemiological week and region.
Average and 95% confidence interval of weekly dengue cases from 2014 to 2019 (teal) and weekly observed dengue cases in 2020 (orange). The start of the COVID-19 pandemic is indicated by vertical gray dotted lines.
Fig 3
Fig 3. Observed and expected dengue cases in five sample states.
Observed dengue cases (black) and 1- to 10-week forecasts (teal) with 95% prediction intervals (shaded area).
Fig 4
Fig 4. Proportion of the observed-expected gap explained by under-reporting.
Total gap between expected and observed cases in March and April 2020 (black boxes) and the proportion of the gap that is explained by under-reporting adjustments using HIV-related treatments (orange) and elective hospitalizations (purple) proxies. Columns show (i) states where observed cases are lower than expected cases, and at least one of the two adjustments is within the observed-expected gap (Observed < adjusted < expected) (left column) and (ii) states where the adjustment over-corrects for the gap, such that the adjustment is greater than the prediction (Observed < expected < adjusted) (right column). All x-values are expressed as shares of the observed-expected gap.
Fig 5
Fig 5. Observed, expected, and under-reporting-adjusted dengue cases over time in sample states.
Observed dengue cases (dark gray), predicted dengue cases (teal), and under-reporting-adjusted cases using HIV-related treatments (orange) and elective hospitalizations proxies (purple).
See this image and copyright information in PMC

Update of

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