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

BackgroundWith 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. Methods and findingsTo 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 (Spearmans r=-0.4732, p=<0.0001) and a negative correlation, which was not significant, between the stringency index and case numbers (Spearmans 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). ConclusionsLockdown 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.

Comparison of the Immunogenicity of five COVID-19 vaccines in Sri Lanka

We assessed antibody responses 3 months post-vaccination in those who received mRNA-1273 (n=225), Sputnik V (n=128) or the first dose of Gam-COVID-Vac (n=184) and compared the results with previously reported data of Sinopharm and AZD1222 vaccinees. 99.5% of Moderna >94% of AZD1222 or Sputnik V, 72% to 76% of Gam-COVID-Vac (first dose) and 38.1% to 68.3% of Sinopharm vaccinees had ACE2 blocking antibodies above the positive threshold. The ACE2 blocking antibody levels were highest to lowest was Moderna > Sputnik V/ AZD1222 (had equal levels)> first dose of Gam-COVID-Vac > Sinopharm. All Moderna recipients had antibodies above the positive threshold to the ancestral (WT), B.1.1.7, B.1.351.1 and 80% positivity rate for B.1.617.2. Positivity rates of Sputnik V vaccinees for WT and variants, were higher than AZD1222 vaccinees, while Sinopharm vaccinees had the lowest positivity rates (<16.7%). These findings highlight the need for further studies to understand the effects on clinical outcomes.

Comparison of different sequencing techniques with multiplex real-time PCR for detection to identify SARS-CoV-2 variants of concern

As different SARS-CoV-2 variants emerge and with the continuous evolution of sub-lineages of the delta and other variants, it is crucial that all countries carry out sequencing of at least >1% of their infections, in order to detect emergence of variants with higher transmissibility and with ability to evade immunity. However, as many resource-poor countries are unable to sequence adequate number of viruses, we compared to usefulness of a commercially available multiplex real-time PCR assay to detect important single nucleotide polymorphisms (SNPs) associated with the variants and compared the sensitivity, accuracy and cost effectiveness of the Illumina sequencing platform and the Oxford Nanopore Technologies (ONT) platform. 138/143 (96.5%) identified as the alpha and 36/39 (92.3%) samples identified as the delta variants due to the presence of lineage defining SNPs by the multiplex real time PCR, were assigned to the same lineage by either of the two sequencing platforms. 34/37 of the samples sequenced by ONT had <5% ambiguous bases, while 21/37 samples sequenced using the Illumina generated <15% ambiguous bases. However, the mean PHRED scores averaged at 32.35 by Illumina reads but 10.78 in ONT. Sub-consensus single nucleotide variations (SNV) were highly correlated between both platforms (R2=0.79) while indels showed a weaker correlation (R2=0.13). Although the ONT had a slightly higher error rate compared to the Illumina technology, it achieved higher coverage with a lower number of reads, generated less ambiguous bases and was significantly cheaper than Illumina sequencing technology.

Kinetics of immune responses to the AZD1222/Covishield vaccine with varying dose intervals in Sri Lankan individuals

BackgroundTo understand the kinetics of immune responses with different dosing gaps of the AZD1222 vaccine, we compared antibody and T cell responses in two cohorts with two different dosing gaps. MethodsAntibodies to the SARS-CoV-2 virus were assessed in 297 individuals with a dosing gap of 12 weeks, sampled at 12 weeks post second dose (cohort 1) and in 77 individuals with a median dosing gap of 21.4 weeks (cohort 2) sampled 6 weeks post second dose. ACE2 receptor blocking antibodies (ACE2R-Abs), antibodies to the receptor binding domain (RBD) of the virus and variants of concern (VOC) and ex vivo T cell responses were assessed in a sub cohort. ResultsAll individuals (100%) had SARS-CoV-2 specific total antibodies and 94.2% of cohort 1 and 97.1% of cohort 2 had ACE2R-blocking Abs. There was no difference in antibody titres or positivity rates in different age groups in both cohorts. The ACE2R-blocking Abs (p<0.0001) and antibodies to the RBD of the VOCs were significantly higher in cohort 2, compared to cohort 1. 41.2% to 65.8% of different age groups gave a positive response by the haemagglutination assay to the RBD of the ancestral virus and VOCs in cohort 1, while 53.6% to 90% gave a positive response in cohort 2. 17/57 (29.8%) of cohort 1 and 17/29 (58.6%) of cohort 2 had ex vivo IFN{gamma} ELISpot responses above the positive threshold. The ACE2R-blocking antibodies and ex vivo IFN{gamma} ELISpot responses at 12 weeks post-first dose, significantly correlated with levels 12 weeks post second dose (Spearmans r=0.46, p=0.008) and (Spearmans r=0.71, p<0.0001) respectively. ConclusionsBoth dosing schedules resulted in high levels of antibody and T cell responses post vaccination, although those with a longer dosing gap had a higher magnitude of responses, possibly as immune responses were measured 6 weeks post second dose compared to 12 weeks post second dose.

Comparison of kinetics of immune responses to SARS-CoV-2 proteins in individuals with varying severity of infection and following a single dose of the AZD1222

BackgroundWhile there have been many studies characterizing the IgG and IgA responses to different SARS-CoV-2 proteins in individuals with natural infection, the induction of IgG and IgA to different viral proteins in vaccinees have not been extensively studied. Therefore, we sought to investigate the antibody responses to SARS-CoV-2 following natural infection and following a single dose of AZD2221, in Sri Lankan individuals. MethodsUsing Luminex assays, we characterized the IgG and IgA responses in patients with varying severity of illness and following a single dose of the vaccine at 4 weeks and 12 weeks since onset of illness or following vaccination. Haemagglutination test (HAT) was used to assess the antibodies to the receptor binding domain of SARS-CoV-2 wild type (WT), B.1.1.7, B.1.351 and B.1.617.2 (VOCs) and surrogate neutralizing test to measure ACE2 receptor blocking antibodies. ResultsThose with mild illness and in vaccinees, the IgG responses to S1, S2, RBD and N protein increased from 4 weeks to 12 weeks, while it remained unchanged in those with moderate/severe illness. Those who had a febrile illness in 2017 and 2018 (controls) also gave IgG and IgA high responses to the S2 subunit. In the vaccinees, the most significant rise was seen for the IgG antibodies to the S2 subunit (p<0.0001). Vaccinees had several fold lower IgA antibodies to all the SARS-CoV-2 proteins tested than those with mild and moderate/severe illness at 4 weeks and 12 weeks. At 12 weeks the HAT titres were significantly lower to the B.1.1.7 in vaccinees and significantly lower in those with mild illness, and in vaccinees to B.1.351 and for B.1.617.2. No such difference was seen in those with moderate/severe illness. ConclusionsVaccinees had significantly less IgA to SARS-CoV-2, but comparable IgG responses to those with natural infection. However, following a single dose, vaccinees had reduced antibody levels to the variants of concern (VOC), which further declined with time, compared to natural infection.

Genomic and epidemiological analysis of SARS-CoV-2 viruses in Sri Lanka

BackgroundIn order to understand the molecular epidemiology of SARS-CoV-2 in Sri Lanka, since March 2020, we carried out genomic sequencing overlaid on available epidemiological data until April 2021. MethodsWhole genome sequencing was carried out on diagnostic sputum or nasopharyngeal swabs from 373 patients with COVID-19. Molecular clock phylogenetic analysis was undertaken to further explore dominant lineages. ResultsThe B.1.411 lineage was most prevalent, which was established in Sri Lanka and caused outbreaks throughout the country until March 2021. The estimated time of the most recent common ancestor of this lineage was 29th June 2020 (95% lower and upper bounds 23rd May to 30th July), suggesting cryptic transmission may have occurred, prior to a large epidemic starting in October 2020. Returning travellers were identified with infections caused by lineage B.1.258, as well as the more transmissible B.1.1.7 lineage, which has replaced B.1.411 to fuel the ongoing large outbreak in the country. ConclusionsThe large outbreak that started in early October, is due to spread of a single virus lineage, B.1.411 until the end of March 2021, when B.1.1.7 emerged and became the dominant lineage.