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. 2022 Aug 3;2(8):e0000399.
doi: 10.1371/journal.pgph.0000399. eCollection 2022.

Epidemiological and virological factors determining dengue transmission in Sri Lanka during the COVID-19 pandemic

Dinuka Ariyaratne  1 , Laksiri Gomes  1 , Tibutius T P Jayadas  1 , Heshan Kuruppu  1 , Lahiru Kodituwakku  2 , Chandima Jeewandara  1 , Nimalka Pannila Hetti  2 , Anoja Dheerasinghe  2 , Sudath Samaraweera  2 , Graham S Ogg  3 , Gathsaurie Neelika Malavige  1   3
Affiliations

Epidemiological and virological factors determining dengue transmission in Sri Lanka during the COVID-19 pandemic

Dinuka Ariyaratne et al. PLOS Glob Public Health. .

Abstract

With the onset of the COVID-19 pandemic in early 2020 there was a drastic reduction in the number of dengue cases in Sri Lanka, with an increase towards the end of 2021. We sought to study the contribution of virological factors, human mobility, school closure and mosquito factors in affecting these changes in dengue transmission in Sri Lanka during this time. To understand the reasons for the differences in the dengue case numbers in 2020 to 2021 compared to previous years, we determined the association between the case numbers in Colombo (which has continuously reported the highest number of cases) with school closures, stringency index, changes in dengue virus (DENV) serotypes and vector densities. There was a 79.4% drop in dengue cases from 2019 to 2020 in Colombo. A significant negative correlation was seen with the number of cases and school closures (Spearman's r = -0.4732, p <0.0001) and a negative correlation, which was not significant, between the stringency index and case numbers (Spearman's r = -0.3755 p = 0.0587). There was no change in the circulating DENV serotypes with DENV2 remaining the most prevalent serotype by early 2022 (65%), similar to the frequencies observed by end of 2019. The Aedes aegypti premise and container indices showed positive but insignificant correlations with dengue case numbers (Spearman r = 0.8827, p = 0.93). Lockdown measures, especially school closures seemed to have had a significant impact on the number of dengue cases, while the vector indices had a limited effect.

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Conflict of interest statement

The authors have declared that no competing interests exist.

Figures

Fig 1
Fig 1. The number of dengue cases reported from 2018 to 2021.
The weekly reported number of dengue cases for Sri Lanka from 2018 and 2019 (A) and the weekly number of dengue cases reported in the Colombo district (B), was obtained from the National Dengue Control Unit (NDCU) active dengue surveillance platform DenSys, which collects data from sentinel hospitals and epidemiological surveillance.
Fig 2
Fig 2. Changes in the circulating dengue virus serotypes in Colombo from 2018 to 2021.
Serotyping of the DENVs was carried out on sera obtained from patients with acute dengue, in the Colombo district from 2018 to end of 2019 (A) and from June 2021 to December 2021(B). The percentage of each DENV serotype is shown.
Fig 3
Fig 3
The relationship between the stringency index and the number of dengue cases reported from 2020 to 2021(A) and the correlation between the stringency index and number of dengue cases from 2020–2021 (B). The country stringency index during the years 2020 and 2021 was obtained from Our World in Data (6) and plotted against the number of dengue cases reported each month from all regions in Sri Lanka. The Spearman’s correlation coefficient was measured (Spearman’s r = -0.3755 p = 0.0587).
Fig 4
Fig 4
The relationship between number of dengue cases and periods of full and partial school closure in 2021(A) and the correlation between number of dengue cases and periods of full and partial school closure (B). The number of cases of dengue for 2021 were obtained from DenSys (2) and the SARS-CoV2 case numbers were obtained from Health Promotion Bureau (5). The data was plotted against school closure data for 2021 from UNESCO (7). School closure status was classified as 1 = fully open, 2 = partially open and 3 = fully closed/academic break. Spearman correlation was calculated (Spearman’s r = - 0.4732, p = <0.0001).
Fig 5
Fig 5. The relationship between the number of dengue cases with mosquito indices.
Changes in the number of dengue cases with the Aedes aegypti and Aedes albopictus and container and premise indices data obtained from NDCU vector surveillance from January 2019 to December 2021 was plotted. The container and premise indices Aedes aegypti (A) and the correlation between the number of cases in vector indices Aedes aegypti (B) showed no correlation with the number of dengue cases with premise index (Spearman’s r = 0.8827, p = 0.93) and container indices (Spearman’s r = 0.3825, p = 0.4667). The relationship between the number of dengue cases with container and premise indices Aedes albopictus (C) and the correlation between the number of cases in vector indices Aedes albopictus (D) again showed no association seen with the number of dengue cases with premise index (Spearman’s r = 0.2, p = 0.7139) or with the container index (Spearman’s r = -0.2571, p = 0.6583).
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