Projecting the future incidence and burden of dengue in Southeast Asia

Association between short-term exposure to PM2.5 and its components and mumps incidence in Lanzhou, China

To date, a limited number of studies have assessed the impact of individual and combined PM2.5 components on mumps incidence. Between 2013 and 2019, we collected data on 6270 mumps cases in Lanzhou, along with corresponding PM2.5 and its components, to analyze their temporal and spatial distributions. A generalized additive mixed model was constructed to examine the association between PM2.5 components and mumps incidence. Additionally, Bayesian kernel machine regression was used to evaluate the combined and interactive effects of co-exposure to PM2.5 components on mumps incidence and to identify key contributing components. A significant linear correlation was found between PM2.5 and mumps incidence at lag 1 month, with a relative risk (RR) of 1.85 (95 % CI: 1.14, 3.02) for each unit increase in PM2.5 (log-transformed PM2.5 concentration). Organic matter (OM) at lag 0 and 1 month, as well as black carbon (BC) at lag 1 month, were significantly positively correlated with mumps incidence. Furthermore, the joint exposure-effect curve for PM2.5 components and mumps incidence displayed an approximate V-shape. The effects of PM2.5 and its components on mumps incidence were more pronounced during the warm season. These findings suggest a significant association between short-term exposure to PM2.5 and its components and mumps incidence in Lanzhou, with potential variations in effect depending on the specific components of PM2.5.

Assessing the influencing factors of dengue fever in Chinese mainland based on causal analysis

Previous studies have identified various factors affecting dengue fever, but most focus on correlations within specific regions, not establishing causality. This study uses Convergent Cross Mapping (CCM) to explore the causal relationships between nine meteorological factors and reported dengue fever cases in 14 Chinese provinces with the highest incidence. Results show that temperature and pressure have causal links with case numbers in more provinces. In Guangdong, which has the most reported cases, Partial Cross Mapping (PCM) reveals a direct causal relationship only between GDP and reported dengue fever cases, while meteorological factors influence dengue fever via their impact on mosquito populations. Principal Component Analysis (PCA) from 30 provinces further confirms the importance of temperature and pressure. Given the significant negative correlation between temperature and pressure, separate models were developed for each province using the Distributed Lag Nonlinear Model (DLNM) combined with the Generalized Additive Model (GAM), with GDP as a covariate. The results indicate that the Relative Risk (RR) increases significantly under high temperatures and low pressure within a shorter lag period. GDP significantly promotes case numbers in all provinces.

Unraveling varying spatiotemporal patterns of Dengue Fever and associated exposure-response relationships with environmental variables in three Southeast Asian countries before and during COVID-19

The enforcement of COVID-19 interventions by diverse governmental bodies, coupled with the indirect impact of COVID-19 on short-term environmental changes (e.g., plant shutdowns lead to lower greenhouse gas emissions), influences the Dengue Fever (DF) vector. This provides a unique opportunity to investigate the indirect impact of COVID-19 on DF transmission and generate insights for targeted prevention measures. We aim to compare DF transmission patterns and the exposure-response relationship of environmental variables and DF incidence in the pre- and during-COVID-19 to identify variations and assess the indirect impact of COVID-19 on DF transmission. We initially visualized the overall trend of DF transmission from 2017-2022, then conducted two quantitative analyses to compare DF transmission pre-COVID-19 (2017-2019) and during-COVID-19 (2020-2022). These analyses included time series analysis to assess DF seasonality, and a Distributed Lag Non-linear Model (DLNM) to quantify the exposure-response relationship between environmental variables and DF incidence. We observed a notable surge in Singapore during-COVID-19, particularly from May to August in 2020 and 2022, with cases multiplying several times compared to pre-COVID-19. All subregions in Thailand exhibited remarkable synchrony with a similar annual trend except 2021. Cyclic patterns remained generally consistent, but seasonal variability in Singapore has become increasingly pronounced. Monthly DF incidence in three countries varied significantly. Exposure-response relationships of DF and environmental variables show varying degrees of change, notably in Northern Thailand, where the peak relative risk for the maximum temperature-DF relationship rose from about 3-17, and the max RR of overall cumulative association 0-3 months of relative humidity increased from around 4-40. Our study is the first to compare DF transmission patterns and their relationship with environmental variables before and during COVID-19, demonstrating that the pandemic has affected DF transmission and altered the exposure-response relationship at both national and regional levels.

HLA alleles and dengue susceptibility across populations in the era of climate change: a comprehensive review

Dengue, a viral infection transmitted by Aedes mosquitoes, is an emerging global health threat exacerbated by climate change. Rising temperatures and altered precipitation patterns create favourable conditions for vector proliferation and extended transmission periods, increasing the risk of dengue in endemic regions and facilitating its spread to non-endemic areas. Understanding the interplay between critical genetic factors and dengue susceptibility is crucial for developing effective public health strategies. The Human Leukocyte Antigen (HLA) genes encode proteins essential for an effective immune response against pathogens, and their genetic variations influence susceptibility to severe dengue. In this study, we conducted a comprehensive meta-analysis of HLA alleles associated with dengue infection and dengue severity. We analysed 19 case-control studies on dengue infections in populations worldwide to infer HLA associations with various pathological forms of dengue and to examine differences across different populations. Our findings indicate that HLA-A*02 increases susceptibility to dengue fever (DF), while HLA-A*03 increases the risk of Dengue Haemorrhagic Fever (DHF), with these increased susceptibilities primarily observed in Southeast Asian populations. Additionally, HLA-A*24 is associated with DHF and all symptomatic dengue infections (DEN), contributing to dengue risk in both Southeast Asia and the Caribbean. Conversely, HLA-A*33 and HLA-B*44 show a protective effect against DHF but show significant regional heterogeneity, highlighting divergent, population-specific susceptibility profiles. This study underscores the importance of population-specific genetic risk assessments for dengue infection and emphasizes the need for targeted medical interventions and improved predictive models to mitigate dengue's impact, especially as climate change accelerates disease spread.

Assessing dengue forecasting methods: a comparative study of statistical models and machine learning techniques in Rio de Janeiro, Brazil

BACKGROUND

Dengue is a mosquito-borne viral disease that poses a significant public health threat in tropical and subtropical regions worldwide. Accurate forecasting of dengue outbreaks is crucial for effective public health planning and intervention. This study aims to assess the predictive performance and computational efficiency of a number of statistical models and machine learning techniques for dengue forecasting, both with and without the inclusion of climate factors, to inform the design of dengue surveillance systems.

METHODS

The dengue forecasting methods comparison in this study considers dengue cases in Rio de Janeiro, Brazil, as well as climate factors known to affect disease transmission. Employing a dynamic window approach, various statistical methods and machine learning techniques were used to generate weekly forecasts at several time horizons. Error measures, uncertainty intervals, and computational efficiency obtained with each method were compared. Statistical models considered were Autoregressive (AR), Moving Average (MA), Autoregressive Integrated Moving Average (ARIMA), and Exponential Smoothing State Space Model (ETS). In addition, models incorporating temperature and humidity as covariates, such as Vector Autoregression (VAR) and Seasonal ARIMAX (SARIMAX), were employed. Machine learning techniques evaluated were Random Forest, XGBoost, Support Vector Machine (SVM), Long-Short-Term Memory (LSTM) networks, and Prophet. Ensemble approaches that integrated the top performing methods were also considered. The evaluated methods also incorporated lagged climatic variables to account for delayed effects.

RESULTS

Among the statistical models, ARIMA demonstrated the best performance using only historical case data, while SARIMAX significantly improved predictive accuracy by incorporating climate covariates. In general, the LSTM model, particularly when combined with climate covariates, proved to be the most accurate machine learning model, despite being slower to train and predict. For long-term forecasts, Prophet with climate covariates was the most effective. Ensemble models, such as the combination of LSTM and ARIMA, showed substantial improvements over individual models.

CONCLUSIONS

This study demonstrates the strengths and limitations of various methods for dengue forecasting across multiple timeframes. It highlights the best-performing statistical and machine learning methods, including their computational efficiency, underscoring the significance of machine learning techniques and the integration of climate covariates to improve forecasts. These findings offer valuable insights for public health officials, facilitating the development of dengue surveillance systems for more accurate forecasting and timely allocation of resources to mitigate dengue outbreaks.

Establishing clinical research networks for future infectious disease outbreak responses in Southeast Asia: Report of a workshop on challenges and opportunities

The COVID-19 pandemic highlighted the critical need for well-established clinical research networks capable of rapid response during infectious disease outbreaks. In Southeast Asia, the absence of active research networks at the onset of the COVID-19 contributed to gaps in regional preparedness. This manuscript discusses the challenges and opportunities identified during a regional workshop held in Singapore (February 26 to March 1, 2024), which brought together 130 stakeholders from across the region. The workshop aimed to build consensus on strategies to strengthen existing networks by developing human capacity, overcoming resource limitations, and sustaining collaborative research during inter-pandemic periods. Key challenges identified included the variability in research infrastructure across countries, complex regulatory requirements, and insufficient funding during non-outbreak periods. Recommendations include the development of standardized research protocols, the creation of a regional data sharing platform, and fostering sustained collaboration to ensure these networks remain functional and responsive. Addressing these gaps is critical for enhancing Southeast Asia's capacity to conduct high-quality clinical trials and effectively respond to future infectious disease outbreaks.

Climate warming is expanding dengue burden in the Americas and Asia

Climate change is expected to pose significant threats to public health, particularly including vector-borne diseases. Despite dramatic recent increases in the burden of dengue that many anecdotally connect with climate change, the effect of past and future anthropogenic climate change on dengue remains poorly quantified. To assess the link between climate warming and dengue we assembled a dataset covering 21 countries in Asia and the Americas, and found a nonlinear relationship between temperature and dengue incidence with the largest impact of warming at lower temperatures (below about 20°C), peak incidence at 27.8°C, and subsequent declines at higher temperatures. Using this inferred temperature response, we estimate that historical climate change has increased dengue incidence by 18% (11 - 27%) on average across our study countries, and that future warming could further increase it by 49% (16 - 136%) to 76% (27 - 239%) by mid-century for low or high emissions scenarios, respectively, with some cooler regions projected to see dengue doubling due to warming and other currently hot regions seeing no impact or even small declines. Under the highest emissions scenario, we estimate that 262 million people are currently living in places in these 21 countries where dengue incidence is expected to more than double due to climate change by mid-century. These insights highlight the major impacts of anthropogenic warming on dengue burden across most of its endemic range, providing a foundation for public health planning and the development of strategies to mitigate future risks due to climate change.

Identification of climate-sensitive disease incidences in vietnam: A longitudinal retrospective analysis of infectious disease rates between 2014 and 2022

Objective

There is a growing correlation between the rise in infectious diseases and climate change; however, little is known about the interactions and mixed effects of climate factors on infectious diseases.

Method

We conducted a retrospective longitudinal study spanning 108 consecutive months from 2014 to 2022 in Can Tho, Vietnam to identify common infectious diseases (excluding tuberculosis, HIV, and COVID-19) and their associations with climate change and determine which common diseases presented concurrently with the COVID-19 period using multivariate linear regression, receiver operating characteristic (ROC) curve analysis, Bayesian kernel machine regression (BKMR) and orthogonal partial least squares discriminant analysis.

Result

The five infectious diseases with the highest average incidence rates per 100,000 people were diarrhea; hand, foot, and mouth disease (HFMD); dengue fever; viral hepatitis; and influenza. Positive associations with humidity were observed for dengue fever and HFMD. Temperature was positively associated with malaria. Negative associations were found between humidity and both chickenpox and tetanus. Diarrhea (AUC = 0.79; 95 % CL = 0.70-0.87) and dengue fever (AUC = 0.74; 95 % CL = 0.62-0.83) emerged as the most influential diseases both before and during the COVID-19 period. In our BKMR analysis, we found a significant association between the combined influence of temperature and humidity and the occurrence of dengue fever and HFMD, especially when all climate factors were at or above their 60th percentile relative to their values at the 50th percentile. Temperature emerged as the primary driver associated with the occurrence of infectious diseases.

Conclusion

These findings underscore the importance of implementing robust surveillance, prevention, and control measures by public health authorities in Can Tho. Initiatives like vaccination campaigns, vector control programs, public education on hygiene practices, and strengthening healthcare infrastructure are crucial for mitigating the spread of infectious diseases and safeguarding public health in the region.

Impact of Climate Change on the Global Dynamics of Vector-Borne Infectious Diseases: A Narrative Review

Climate change has significantly altered the dynamics of vector-borne infectious diseases, favoring their proliferation and geographic expansion. Factors such as rising temperatures, the frequency of extreme weather events, and uncontrolled urbanization have increased the incidence of diseases such as dengue, Zika, chikungunya, malaria, and Lyme disease, especially in vulnerable regions with limited infrastructure. This article presents a narrative review based on recent scientific literature (2018-2025) to assess the impact of climate change on vector distribution, co-infections, and control strategies. The evidence collected highlights how changing climate conditions, combined with socioeconomic, political, and demographic factors, exacerbate public health crises and complicate mitigation efforts. It is concluded that facing this challenge requires a comprehensive strategy that combines environmental management, technological innovation, epidemiological surveillance, and community educational programs, promoting a coordinated global response to reduce the associated risks.

Harmonizing Multisource Data to Inform Vector-Borne Disease Risk Management Strategies

In the last few decades, we have witnessed the emergence of new vector-borne diseases (VBDs), the globalization of endemic VBDs, and the urbanization of previously rural VBDs. Data harmonization forms the basis of robust decision-support systems designed to protect at-risk communities from VBD threats. Strong interdisciplinary partnerships, protocols, digital infrastructure, and capacity-building initiatives are essential for facilitating the coproduction of robust multisource data sets. This review provides a foundation for researchers and practitioners embarking on data harmonization efforts to (a) better understand the links among environmental degradation, climate change, socioeconomic inequalities, and VBD risk; (b) conduct risk assessments, health impact attribution, and projection studies; and (c) develop robust early warning and response systems. We draw upon best practices in harmonizing data for two well-studied VBDs, dengue and malaria, and provide recommendations for the evolution of research and digital technology to improve data harmonization for VBD risk management.

Impact of compound warm and wet events on dengue fever infection in South and Southeast Asian countries

BACKGROUND

Multiple studies have reported the profound influence of various climate factors on dengue fever infection, while the effects of joint exposure to warm and wet environment, a condition favouring dengue vectors, on disease transmission were less evaluated. This study aims to investigate the impact of various compound temperature, rainfall, and relative humidity exposures on dengue fever infection in the South and Southeast Asia regions.

METHODS

Weekly dengue fever surveillance data from 2012 to 2020 were collected from 48 locations in four countries named Singapore (1 location), Sri Lanka (15 locations), Malaysia (9 locations), and Thailand (23 locations, with 11 locations having different study periods). The distributed lag non-linear models were built to assess the impacts of compound temperature, rainfall, and relative humidity exposures on dengue fever infection risks.

RESULTS

A total of 1,359,993 dengue fever cases were reported with 9.33%, 24.02%, 48.73%, and 17.91% cases contributed by Singapore, Sri Lanka, Malaysia, and Thailand, respectively. Compared to non-warm-non-wet, compound warm-wet was associated with an increased dengue risk (RR:1.32, 95% CI:1.21-1.44). Compared to moderate temperature-humidity, warm-wet environment was also associated with an increase in dengue risk (RR:1.37, 95% CI:1.22-1.55). In comparison to weeks with moderate temperature-rainfall, warm-wet weeks was linked to an elevated dengue risk (RR:1.39, 95% CI:1.27-1.52), whereas cold-dry weather would significantly reduce the infection risk (RR:0.70, 95% CI:0.62-0.80). Modification effects showed that the hot effect on dengue infection was more pronounced under higher humidity, while the impact of rainfall increased with warmer temperature.

CONCLUSION

Warm-wet events were associated with an increased dengue fever risk, while the infection risk would decline in cold-dry environment, and modification effects exist among exposures. Findings from this study highlight the importance of considering joint temperature, humidity, and rainfall dependency of dengue fever infection in disease prevention and control.

Global, regional, and national burden of dengue infection in children and adolescents: an analysis of the Global Burden of Disease Study 2021

BACKGROUND

Dengue remains a global threat to public health, however, its disease burden among children and adolescents has always been poorly quantified. Therefore, we aimed to systematically estimate the global, regional, and national burden and temporal trends of dengue infection in children and adolescents from 1990 to 2021.

METHODS

Data on the number and rate of incidence and disability-adjusted life-years (DALYs) of dengue infection among children and adolescents under 20 years were obtained from the Global Burden of Disease Study (GBD) 2021. Estimated annual percentage changes (EAPCs) of the age-standardised incidence rate (ASIR) and age-standardised DALYs rate (ASDR), by age, sex, and different countries and regions, were calculated to quantify the temporal trends of dengue burden. The association between development levels (measured using the socio-demographic index [SDI]) and dengue burden was also determined.

FINDINGS

From 1990 to 2021, the global burden of dengue incidence and its associated DALYs was consistently higher in children and adolescents than in the entire population. Globally, among children and adolescents, there were 21,641,016 incident cases in 2021, an increase of 64.43% compared to 13,241,719 in 1990. The ASIR per 100,000 population increased from 587.81 in 1990 to 812.16 in 2021, with an EAPC of 1.39% (95% confidence interval [CI], 1.13%-1.65%). Besides, the number of dengue-associated DALYs among children and adolescents increased by 16.36%, from 910,458.60 in 1990 to 1,059,428.31 in 2021. The increase in DALYs was less pronounced than incidence, with the ASDR per 100,000 population increasing from 40.17 in 1990 to 41.50 in 2021, and the EAPC was only 0.67% (95% CI, 0.40%-0.95%). The incidence and DALYs burden of dengue in children and adolescents was highest in middle SDI regions, followed by low-middle SDI regions, with the lowest burden in high SDI regions. Furthermore, Tropical Latin America had an extremely high ASIR (6040.29 per 100,000 population in 2021), and Southeast Asia had an extremely high ASDR (298.20 per 100,000 population in 2021), much higher than other regions around the world.

INTERPRETATION

The global dengue burden in children and adolescents is high and has been increasing from 1990 to 2021, even though the distribution patterns vary across different countries and territories. This study first reported the global disease burden and temporal trends of children and adolescents, which has significant implications for policymakers and public health officials, as it underscores the need for age- and region-specific strategies to mitigate the growing global burden of dengue.

FUNDING

National Natural Science Foundation of China.

Global burden of viral infectious diseases of poverty based on Global Burden of Diseases Study 2021

BACKGROUND

Viral infectious diseases of poverty (vIDPs) remain a significant global health challenge. Despite their profound impact, the burden of these diseases is not comprehensively quantified. This study aims to analyze the global burden of six major vIDPs, including coronavirus disease 2019 (COVID-19), HIV/AIDS, acute hepatitis, dengue, rabies, and Ebola virus disease (EVD), using data from the Global Burden of Diseases, Injuries, and Risk Factors Study 2021 (GBD 2021).

METHODS

Following the GBD 2021 framework, we analyzed the incidence, mortality, and disability-adjusted life years (DALYs) of the six vIDPs across 204 countries and territories from 1990 to 2021. We examined the association between the Socio-Demographic Index (SDI) and the burden of vIDPs. All estimates were reported as numbers and rates per 100,000 population, calculated using the Bayesian statistical model employed by GBD 2021, with 95% uncertainty intervals (UI).

RESULTS

In 2021, vIDPs caused approximately 8.7 million deaths and 259.2 million DALYs, accounting for 12.8% and 9.0% of the global all-cause totals, respectively. Globally, the burden of vIDPs varied significantly: COVID-19 caused around 7.9 million (95% UI: 7.5, 8.4) deaths and 212.0 million (95% UI 197.9, 234.7) DALYs in 2021. Acute hepatitis had the second-highest age-standardized incidence rate, with 3411.5 (95% UI: 3201.8, 3631.3) per 100,000 population, while HIV/AIDS had a high age-standardized prevalence rate, with 483.1 (95% UI: 459.0, 511.4) per 100,000 population. Dengue incidence cases rose from 26.5 million (95% UI: 3.9, 51.9) in 1990 to 59.0 million (95% UI: 15.5, 106.9) in 2021. Rabies, although reduced in prevalence, continued to pose a significant mortality risk. EVD had the lowest overall burden but significant outbreak impacts. Age-standardized DALY rates for vIDPs were significantly negatively correlated with SDI: acute hepatitis (r = -0.8, P < 0.0001), rabies (r = -0.7, P < 0.0001), HIV/AIDS (r = -0.6, P < 0.0001), COVID-19 (r = -0.5, P < 0.0001), dengue (r = -0.4, P < 0.0001), and EVD (r = -0.2, P < 0.005).

CONCLUSIONS

VIDPs pose major public health challenges worldwide, with significant regional, age, and gender disparities. The results underscore the need for targeted interventions and international cooperation to mitigate the burden of these diseases. Policymakers can use these findings to implement cost-effective interventions and improve health outcomes, particularly in regions with high or increasing burdens.

Resurgence of Dengue Virus Serotype 2: Findings from the 2023 Bangladesh Outbreak

Bangladesh experienced the largest and deadliest dengue outbreak in 2023, after the virus had reappeared in the country 2 decades earlier. A total of 1,705 people died in Bangladesh, representing the highest case fatality rate (0.5%) due to dengue in the world for that year. The severity of dengue infection is to some extent related to the emergence of new circulating serotypes. To identify the possible predominant serotype in 2023, the reverse transcription polymerase chain reaction-based identification technique was used on stored serum samples of suspected dengue patients during the period between July and December 2023. The overall result of molecular serotyping showed that dengue virus (DENV-2) reappeared as the predominant serotype (74.1%), followed by a moderate number of samples with DENV-1 (19.8%) and DENV-3 (6.1%), in 2023. However, DENV-1 was found to be dominant in a few rural areas of Cox's Bazar districts. During the 2019 outbreak, DENV-3 was the dominant serotype, which seemed to be replaced by the DENV-2 serotype; this may have impacted the increased case fatality in 2023.

The global incidence and trends of three common flavivirus infections (Dengue, yellow fever, and Zika) from 2011 to 2021

Background

Flavivirus pose a continued threat to global health, yet their worldwide burden and trends remain poorly quantified. We aimed to evaluate the global, regional, and national incidence of three common flavivirus infections (Dengue, yellow fever, and Zika) from 2011 to 2021.

Methods

Data on the number and rate of incidence for the three common flavivirus infection in 204 countries and territories were retrieved from the Global Burden of Diseases, Injuries, and Risk Factors Study (GBD) 2021. The estimated annual percent change (EAPC) was calculated to quantify the temporal trend during 2011-2016, 2016-2019, and 2019-2021, respectively.

Results

In 2021, an estimated 59,220,428 individuals were infected globally, comprising 58,964,185 cases of dengue, 86,509 cases of yellow fever, and 169,734 cases of Zika virus infection. The age-standardized incidence rate (ASIR) of the three common flavivirus infections increased by an annual average of 5.08% (95% CI 4.12 to 6.05) globally from 2011 to 2016, whereas decreased by an annual average of -8.37% (95% CI -12.46 to -4.08) per year between 2016 to 2019. The ASIR remained stable during 2019-2021, with an average change of 0.69% (95% CI -0.96 to 2.37) per year globally for the three common flavivirus infections. Regionally, the burden of the three common flavivirus infections was primarily concentrated in those regions with middle income, such as South Asia, Southeast Asia, and Tropical Latin America. Additionally, at the country level, there was an inverted "U" relationship between the SDI level and the ASI. Notably, an increase in the average age of infected cases has been observed worldwide, particularly in higher-income regions.

Conclusion

Flavivirus infections are an expanding public health concern worldwide, with considerable regional and demographic variation in the incidence. Policymakers and healthcare providers must stay vigilant regarding the impact of COVID-19 and other environmental factors on the risk of flavivirus infection and be prepared for potential future outbreaks.

The Role of TIM-1 and CD300a in Zika Virus Infection Investigated with Cell-Based Electrical Impedance

Orthoflaviviruses cause a major threat to global public health, and no antiviral treatment is available yet. Zika virus (ZIKV) entry, together with many other viruses, is known to be enhanced by phosphatidylserine (PS) receptors such as T-cell immunoglobulin mucin domain protein 1 (TIM-1). In this study, we demonstrate for the first time, using cell-based electrical impedance (CEI) biosensing, that ZIKV entry is also enhanced by expression of CD300a, another PS receptor. Furthermore, inhibiting CD300a in immature monocyte-derived dendritic cells partially but significantly inhibits ZIKV replication. As we have previously demonstrated that CEI is a useful tool to study Orthoflavivirus infection in real time, we now use this technology to determine how these PS receptors influence the kinetics of in vitro ZIKV infection. Results show that ZIKV entry is highly sensitive to minor changes in TIM-1 expression, both after overexpression of TIM-1 in infection-resistant HEK293T cells, as well as after partial knockout of TIM-1 in susceptible A549 cells. These results are confirmed by quantification of viral copy number and viral infectivity, demonstrating that CEI is highly suited to study and compare virus-host interactions. Overall, the results presented here demonstrate the potential of targeting this universal viral entry pathway.

A comprehensive review on targeting cluster of differentiation: An attractive strategy for inhibiting viruses through host proteins

Viral infections continue to pose a significant global public health threat. Targeting host proteins, such as cluster of differentiation (CD) macromolecules, may offer a promising alternative approach to developing antiviral treatments. CDs are cell-surface biological macromolecules mainly expressed on leukocytes that viruses can use to enter cells, thereby evading immune detection and promoting their replication. The manipulation of CDs by viruses may represent an effective and clever means of survival through the prolonged co-evolution of hosts and viruses. Targeting of CDs is anticipated to hinder the invasion of related viruses, modulate the body's immune system, and diminish the incidence of subsequent inflammation. They have become crucial for biomedical diagnosis, and some have been used as valuable tools for resisting viral infections. However, a summary of the structures and functions of CDs involved in viral infection is currently lacking. The development of drugs targeting these biological macromolecules is restricted both in terms of their availability and the number of compounds currently identified. This review provides a comprehensive analysis of the critical role of CD proteins in virus invasion and a list of relevant targeted antiviral agents, which will serve as a valuable reference for future research in this field.

In-vitro assessment of cutaneous immune responses to aedes mosquito salivary gland extract and dengue virus in Cambodian individuals

Dengue virus (DENV) poses a global health threat, affecting millions individuals annually with no specific therapy and limited vaccines. Mosquitoes, mainly Aedes aegypti and Aedes albopictus worldwide, transmit DENV through their saliva during blood meals. In this study, we aimed to understand how Aedes mosquito saliva modulate skin immune responses during DENV infection in individuals living in mosquito-endemic regions. To accomplish this, we dissociated skin cells from Cambodian volunteers and incubated them with salivary gland extract (SGE) from three different mosquito strains: Ae. aegypti USDA strain, Ae. aegypti and Ae. albopictus wild type (WT) in the presence/absence of DENV. We observed notable alterations in skin immune cell phenotypes subsequent to exposure to Aedes salivary gland extract (SGE). Specifically, exposure lead to an increase in the frequency of macrophages expressing chemokine receptor CCR2, and neutrophils expressing CD69. Additionally, we noted a substantial increase in the percentage of macrophages that became infected with DENV in the presence of Aedes SGE. Differences in cellular responses were observed when Aedes SGE of three distinct mosquito strains were compared. Our findings deepen the understanding of mosquito saliva's role in DENV infection and skin immune responses in individuals regularly exposed to mosquito bites. This study provides insights into skin immune cell dynamics that could guide strategies to mitigate DENV transmission and other arbovirus diseases.

Projection of dengue fever transmissibility under climate change in South and Southeast Asian countries

Vector-borne infectious disease such as dengue fever (DF) has spread rapidly due to more suitable living environments. Considering the limited studies investigating the disease spread under climate change in South and Southeast Asia, this study aimed to project the DF transmission potential in 30 locations across four South and Southeast Asian countries. In this study, weekly DF incidence data, daily mean temperature, and rainfall data in 30 locations in Singapore, Sri Lanka, Malaysia, and Thailand from 2012 to 2020 were collected. The effects of temperature and rainfall on the time-varying reproduction number (Rt) of DF transmission were examined using generalized additive models. Projections of location-specific Rt from 2030s to 2090s were determined using projected temperature and rainfall under three Shared Socioeconomic Pathways (SSP126, SSP245, and SSP585), and the peak DF transmissibility and epidemic duration in the future were estimated. According to the results, the projected changes in the peak Rt and epidemic duration varied across locations, and the most significant change was observed under middle-to-high greenhouse gas emission scenarios. Under SSP585, the country-specific peak Rt was projected to decrease from 1.63 (95% confidence interval: 1.39-1.91), 2.60 (1.89-3.57), and 1.41 (1.22-1.64) in 2030s to 1.22 (0.98-1.51), 2.09 (1.26-3.47), and 1.37 (0.83-2.27) in 2090s in Singapore, Thailand, and Malaysia, respectively. Yet, the peak Rt in Sri Lanka changed slightly from 2030s to 2090s under SSP585. The epidemic duration in Singapore and Malaysia was projected to decline under SSP585. In conclusion, the change of peak DF transmission potential and disease outbreak duration would vary across locations, particularly under middle-to-high greenhouse gas emission scenarios. Interventions should be considered to slow down global warming as well as the potential increase in DF transmissibility in some locations of South and Southeast Asia.

Unraveling varying spatiotemporal patterns of dengue and associated exposure-response relationships with environmental variables in Southeast Asian countries before and during COVID-19

The enforcement of COVID-19 interventions by diverse governmental bodies, coupled with the indirect impact of COVID-19 on short-term environmental changes (e.g. plant shutdowns lead to lower greenhouse gas emissions), influences the dengue vector. This provides a unique opportunity to investigate the impact of COVID-19 on dengue transmission and generate insights to guide more targeted prevention measures. We aim to compare dengue transmission patterns and the exposure-response relationship of environmental variables and dengue incidence in the pre- and during-COVID-19 to identify variations and assess the impact of COVID-19 on dengue transmission. We initially visualized the overall trend of dengue transmission from 2012-2022, then conducted two quantitative analyses to compare dengue transmission pre-COVID-19 (2017-2019) and during-COVID-19 (2020-2022). These analyses included time series analysis to assess dengue seasonality, and a Distributed Lag Non-linear Model (DLNM) to quantify the exposure-response relationship between environmental variables and dengue incidence. We observed that all subregions in Thailand exhibited remarkable synchrony with a similar annual trend except 2021. Cyclic and seasonal patterns of dengue remained consistent pre- and during-COVID-19. Monthly dengue incidence in three countries varied significantly. Singapore witnessed a notable surge during-COVID-19, particularly from May to August, with cases multiplying several times compared to pre-COVID-19, while seasonality of Malaysia weakened. Exposure-response relationships of dengue and environmental variables show varying degrees of change, notably in Northern Thailand, where the peak relative risk for the maximum temperature-dengue relationship rose from about 3 to 17, and the max RR of overall cumulative association 0-3 months of relative humidity increased from around 5 to 55. Our study is the first to compare dengue transmission patterns and their relationship with environmental variables before and during COVID-19, showing that COVID-19 has affected dengue transmission at both the national and regional level, and has altered the exposure-response relationship between dengue and the environment.