The Pandemic Experience in Southeast Asia: Interface Between SARS-CoV-2, Malaria, and Dengue

Determinants of exposure to Aedes mosquitoes: A comprehensive geospatial analysis in peri-urban Cambodia

Aedes mosquitoes are some of the most important and globally expansive vectors of disease. Public health efforts are largely focused on prevention of human-vector contact. A range of entomological indices are used to measure risk of disease, though with conflicting results (i.e. larval or adult abundance does not always predict risk of disease). There is a growing interest in the development and use of biomarkers for exposure to mosquito saliva, including for Aedes spp, as a proxy for disease risk. In this study, we conduct a comprehensive geostatistical analysis of exposure to Aedes mosquito bites among a pediatric cohort in a peri‑urban setting endemic to dengue, Zika, and chikungunya viruses. We use demographic, household, and environmental variables (the flooding index (NFI), land type, and proximity to a river) in a Bayesian geostatistical model to predict areas of exposure to Aedes aegypti bites. We found that hotspots of exposure to Ae. aegypti salivary gland extract (SGE) were relatively small (< 500 m and sometimes < 250 m) and stable across the two-year study period. Age was negatively associated with antibody responses to Ae. aegypti SGE. Those living in agricultural settings had lower antibody responses than those living in urban settings, whereas those living near recent surface water accumulation were more likely to have higher antibody responses. Finally, we incorporated measures of larval and adult density in our geostatistical models and found that they did not show associations with antibody responses to Ae. aegypti SGE after controlling for other covariates in the model. Our results indicate that targeted house- or neighborhood-focused interventions may be appropriate for vector control in this setting. Further, demographic and environmental factors more capably predicted exposure to Ae. aegypti mosquitoes than commonly used entomological indices.

Evaluation of the performance of a lateral flow device for quantitative detection of anti-SARS-CoV-2 IgG

INTRODUCTION

The AbC-19™ lateral flow immunoassay (LFIA) performance was evaluated on plasma samples from a SARS-CoV-2 vaccination cohort, WHO international standards for anti-SARS-CoV-2 IgG (human), individuals ≥2 weeks from infection of RT-PCR confirmed SARS-CoV-2 genetic variants, as well as microorganism serology.

METHODS

Pre-vaccination to three weeks post-booster samples were collected from a cohort of 111 patients (including clinically extremely vulnerable patients) from Northern Ireland. All patients received Oxford-AstraZeneca COVID-19 vaccination for the first and second dose, and Pfizer-BioNTech for the third (first booster). WHO international standards, 15 samples from 2 variants of concern (Delta and Omicron) and cross-reactivity with plasma samples from other microorganism infections were also assessed on AbC-19™.

RESULTS

All 80 (100%) participants sampled post-booster had high positive IgG responses, compared to 38/95 (40%) participants at 6 months post-first vaccination. WHO standard results correlated with information from corresponding biological data sheets, and antibodies to all genetic variants were detected by LFIA. No cross-reactivity was found with exception of one (of five) Dengue virus samples.

CONCLUSION

These findings suggest BNT162b2 booster vaccination enhanced humoral immunity to SARS-CoV-2 from pre-booster levels, and that this antibody response was detectable by the LFIA. In combination with cross-reactivity, standards and genetic variant results would suggest LFIA may be a cost-effective measure to assess SARS-CoV-2 antibody status.

Temporal Variations and Spatial Clusters of Dengue in Thailand: Longitudinal Study before and during the Coronavirus Disease (COVID-19) Pandemic

The efforts towards effective control of the COVID-19 pandemic may affect the incidence of dengue. This study aimed to investigate temporal variations and spatial clusters of dengue in Thailand before and during the COVID-19 pandemic. Reported dengue cases before (2011–2019) and during (2020–2021) the COVID-19 pandemic were obtained from the national disease surveillance datasets. The temporal variations were analyzed using graphics, a seasonal trend decomposition procedure based on Loess, and Poisson regression. A seasonal ARIMA model was used to forecast dengue cases. Spatial clusters were investigated using the local indicators of spatial associations (LISA). The cyclic pattern showed that the greatest peak of dengue cases likely changed from every other year to every two or three years. In terms of seasonality, a notable peak was observed in June before the pandemic, which was delayed by one month (July) during the pandemic. The trend for 2011–2021 was relatively stable but dengue incidence decreased dramatically by 7.05% and 157.80% on average in 2020 and 2021, respectively. The forecasted cases in 2020 were slightly lower than the reported cases (2.63% difference), whereas the forecasted cases in 2021 were much higher than the actual cases (163.19% difference). The LISA map indicated 5 to 13 risk areas or hotspots of dengue before the COVID-19 pandemic compared to only 1 risk area during the pandemic. During the COVID-19 pandemic, dengue incidence sharply decreased and was lower than forecasted, and the spatial clusters were much lower than before the pandemic.

Most frequently harboured missense variants of hACE2 across different populations exhibit varying patterns of binding interaction with spike glycoproteins of emerging SARS-CoV-2 of different lineages

Since the emergence of SARS-CoV-2 at Wuhan in the Hubei province of China in 2019, the virus has accumulated various mutations, giving rise to many variants. According to the combinations of mutations acquired, these variants are classified into lineages and greatly differ in infectivity and transmissibility. In 2021 alone, a variant of interest (VoI) Mu (B.1.621), as well as, variants of concern (VoC) Delta (B.1.617.2) and Omicron (BA.1, BA.2) and later in 2022, BA.4, BA.5, and BA.2.12.1 have emerged. Since then, the world has seen prominent surges in the rate of infection during short periods of time. However, not all populations have suffered equally, which suggests a possible role of host genetic factors. Here, we investigated the strength of binding of the spike glycoprotein receptor-binding domain (RBD) of the SARS-CoV-2 variants: Mu, Delta, Delta Plus (AY.1), Omicron sub-variants BA.1, BA.2, BA.4, BA.5, and BA.2.12.1 with the human angiotensin-converting enzyme 2 (hACE2) missense variants prevalent in major populations. In this purpose, molecular docking analysis, as well as, molecular dynamics simulation was performed of the above-mentioned SARS-CoV-2 RBD variants with the hACE2 containing the single amino acid substitutions prevalent in African (E37K), Latin American (F40L), non-Finnish European (D355 N), and South Asian (P84T) populations, in order to predict the effects of the lineage-defining mutations of the viral variants on receptor binding. The effects of these mutations on protein stability were also explored. The protein-protein docking and molecular dynamics simulation analyses have revealed variable strength of attachment and exhibited altered interactions in the case of different hACE2-RBD complexes. In vitro studies are warranted to confirm these findings which may enable early prediction regarding the risk of transmissibility of newly emerging variants across different populations in the future.