Hasil untuk "South Asia. Southeast Asia. East Asia"

A.R.K. Kullan, E.G. Neumann, O. Alsheblak et al.

This study was conducted to access the genetic diversity and phylogenetic relationship of United Arab Emirates (UAE) native chickens to support conservation of their genetic resources. A total of 161 native chickens were examined for genetic diversity and phylogenetic analysis using partial mitochondrial d-loop sequences (758 bp). The observed haplotype and nucleotide diversities were 0.670 and 0.0028 respectively, A total of 31 polymorphic site and 20 haplotypes were identified. These haplotypes were classified into four distinct haplogroups (A, B, C, and E), with E (95.6%) being the prominent maternal lineages of UAE native chickens. Phylogenetic analysis revealed that the haplogroup E of UAE native chickens clustered with the Indian Red Junglefowl (Gallus gallus murghi), suggesting a South Asian maternal origin. This lineage may have arrived in the country via an ancient trading network, especially through the maritime routes of the Indian Ocean. Other haplogroups were associated with East Asia and Southeast Asia origin. These results suggest that UAE native chickens have multiple maternal origins. The genetic diversity identified in this research will aid in the conservation and breeding of these chickens, focusing on economically advantageous traits.

Minghui Wu, Xueling Liu, Jiahuan Fan et al.

Dysarthric speech exhibits abnormal prosody and significant speaker variability, presenting persistent challenges for automatic speech recognition (ASR). While text-to-speech (TTS)-based data augmentation has shown potential, existing methods often fail to accurately model the pathological rhythm and acoustic style of dysarthric speech. To address this, we propose DARS, a dysarthria-aware rhythm-style synthesis framework based on the Matcha-TTS architecture. DARS incorporates a multi-stage rhythm predictor optimized by contrastive preferences between normal and dysarthric speech, along with a dysarthric-style conditional flow matching mechanism, jointly enhancing temporal rhythm reconstruction and pathological acoustic style simulation. Experiments on the TORGO dataset demonstrate that DARS achieves a Mean Cepstral Distortion (MCD) of 4.29, closely approximating real dysarthric speech. Adapting a Whisper-based ASR system with synthetic dysarthric speech from DARS achieves a 54.22% relative reduction in word error rate (WER) compared to state-of-the-art methods, demonstrating the framework's effectiveness in enhancing recognition performance.

Mohd Arshad Ansari, S. Haider, N. Khan

Abstract The study employed ecological and material footprint from the consumption perspective as a holistic measure of human pressure on the environment to examine the environment-economic growth nexus. Particularly, Environmental Kuznets curve hypothesis has been tested for the group of thirty-seven Asian countries. These are further analyzed into five Asian sub-regions, namely; West-, Central-, South-, East-and Southeast Asian countries over the period of 1991 to 2017. Panel cointegration, Pooled mean group, dynamic ordinary least square and differenced panel generalized methods of moments have been applied. The analysis reveals a mixture of results for the presence of EKC when using ecological footprint. EKC exists for Central-and East Asian countries, but not in case of West-, South-and Southeast Asian countries. Whereas results support EKC when we used material footprint indicator except central Asia. Energy consumption increases the ecological and material footprint. In addition, overall globalization and urbanization enhances ecological and material footprint. From the outcome of this empirical work, a number of policy recommendations have been discussed.

Pradeep Adhikari, Yong Ho Lee, Prabhat Adhikari et al.

Abstract Background Global risk assessment of invasive weeds is a proactive strategy for identifying high-risk species and regions, predicting invasion rates and extents, and evaluating harmful impacts on native biodiversity, agriculture, and ecosystems. In this study, species distribution modeling was used to assess the global invasion risk of Ardisia elliptica, a highly invasive tropical shrub native to South and Southeast Asia that is harmful in other parts of the world, under the current climate and future climate change scenarios [shared socioeconomic pathways (SSPs) SSP1-2.6, SSP2-4.5, SSP3-7.0, and SSP5-8.5] and other environmental variables, including land use and land cover change, soil moisture, soil carbon, soil pH, and human influence index. Results Our study revealed that annual precipitation, human influence index, and precipitation in the wettest month contributed significantly to the MaxEnt model, with estimated contributions of 31.35%, 22.76%, and 14.77%, respectively. These findings suggest that the global distribution of A. elliptica is limited primarily by climatic variables, whereas anthropogenic factors also play an important role in its habitat expansion. The current invasion risk was highest in South America, Oceania (east), and Africa, affecting up to 24.51% of the total land surface area. A risk assessment of 165 countries revealed a risk of invasion in 41 countries with no records of species occurrence. Under future climate change scenarios, a significant global expansion of the distribution was predicted, with invasion in South America covering up to 48.97% of the land surface area by 2061–2080. Habitat suitability analysis revealed that 21 countries under the current climate and 47 countries under SSP5-8.5 had extremely suitable habitats for A. elliptica. Additionally, the species has already invaded at least 115 countries, while 15 countries, including Benin, Burundi, Japan, Uruguay, Swaziland, and South Korea, are predicted to shift categories from having unsuitable or poor invasion risk to having high invasion risk. Conclusions These findings are crucial for understanding the global invasion risk of A. elliptica under substantial climate change and anthropogenic activities and support the development of effective biosecurity measures and sustainable management strategies for this harmful species at the global and national levels.

Robin Singh Mutum, Abhik Das, Sankar Kumar Ghosh et al.

Notably, poultry animals—particularly chickens—are recognized globally for their valuable contributions to the food, ornamental, and game economies. Further, more robust local and regional breeds can be parental donors for these area-specific consumable breeds' resilient traits. Game birds that are locally significant economically or on a much smaller scale are frequently excluded from the procedure. One such breed is the fighting chicken of Manipur, India, known locally as the Kaunayen breed and listed as the 17th breed at the ICAR, the National Bureau of Animal Genetic Resources, India. When Kaunayen fowl from throughout Manipur are considered, they have anatomical characteristics and common behavioural traits despite the breed's extreme genetic heterogeneity. With this gap in mind, we attempted to use mitochondrial D-loop sequences to characterize Manipur's Kaunayen fowls concerning the global breeds of nearly similar molecular characteristics. We found that Kaunayen fowls share evolutionary traits such as a similar transition/transversion ratio with some Southeast Asian breeds, including a few red jungle fowls. Overall Kaunayen are also more closely related to Southeast Asian birds phylogenetically, after which with a few breeds from East Asian, Bangladesh, North-East India, and the Indian island of Nicobar. The global database including our query has 19 haplotypes, and majority of the Kaunayen fowls share haplotypes with North East Indian fowls; the remaining haplotypes are primarily associated with South East Asia and East Asia. The findings additionally indicated that Kaunayen's and the global breed's D-loop region tended to fixed neutral substitution, contributing to the distinct varieties. Further, migration research demonstrated that Kaunayen fowls originated from a substantial maternal genome influx from Southeast Asia, which may have later made a substantial contribution to East Asian and South Asian breeds.We also display a portion of the D-loop that demonstrates the majority of the substitution diversity across all breeds, and we suggest using sequence stretch to create miniature breed-specific identifying barcodes.

Gelila Yitageasu, Kassaw Chekole Adane, Amensisa Hailu Tesfaye et al.

Abstract Background Diarrhea remains a significant health burden among populations affected by inadequate water and sanitation, particularly in internally displaced persons (IDPs) and refugee camp populations. This systematic review and meta-analysis aim to quantify the global prevalence of diarrhea in these vulnerable groups. The findings will inform targeted interventions and policy decisions to mitigate the health impacts in these settings. Methods Systematic searches were conducted from PubMed, Epistemonikos, ScienceDirect, Scopus, Web of Science, and Embase, encompassing studies published up to June 10, 2024. Microsoft Excel 2021 was used for data extraction and STATA version 17 for statistical analyses. Newcastle-Ottawa Scale (NOS) assessed the methodological quality of included studies. The random-effects model was utilized to estimate the pooled prevalence. Publication bias was evaluated through funnel plots and Egger’s regression test, while heterogeneity was measured using the I² statistic. The review protocol is registered with PROSPERO (registration number: CRD42024554686). Results The comprehensive search yielded 23 eligible articles, representing 121,037 samples. The pooled prevalence of diarrhea was 29% (95% CI: 24%, 33%). Subgroup analyses by continents revealed notable regional variations, the pooled prevalence in Asia was 35% (95% CI: 24%, 45%), Africa 28% (95% CI: 21%, 35%), and North America 9% (95% CI: 7%, 11%). Subgroup analysis by subcontinent showed that Southeast Asia with 74% (95% CI: 66%, 81%) and South-central Asia with 54% (95% CI: 44%, 63%) has the highest pooled prevalence from Asia. From Africa, East Africa with 35% (95% CI: 19%, 51%) and Southeastern Africa with 35% (95% CI: 32%, 38%) has highest pooled prevalence. Furthermore, by study setting, the pooled prevalence of diarrhea was 27% (95% CI: 22%, 31%) in IDPs and 32% (95% CI: 22%, 42%) in refugee camps. Conclusions The results of this study underscore the significant global burden of diarrhea among IDPs and refugees living in camps. The implementation of robust health programs specifically tailored to IDPs and refugee camps, including regular screening, deworming, and comprehensive care initiatives, is critical. A multifaceted, holistic approach that addresses socio-economic, behavioral, and environmental determinants is essential to reduce the health burden of this vulnerable population.

Htet Myet Aung, Keiichi Ogawa

This study examines the sources of variability in the academic achievements of primary students in Myanmar, focusing on both demand- and supply-side factors. Using data from the South East Asia Primary Learning Metrics (SEA-PLM) 2019, a comprehensive learning assessment conducted among Grade 5 students in six Southeast Asian countries, this study employs the hierarchical linear modeling (HLM) approach. The findings reveal that approximately one-third of the variability in academic achievement can be attributed to differences between schools, whereas the remaining variance occurs within schools. Demand-side factors contribute to one-fifth of the student-level differences and two-fifths of the school-level differences. Conversely, supply-side factors explain only one-fifth of the school-level differences. When both the demand and supply sides are integrated, half of the variance is explained between schools, and nearly one-fifth is explained within schools. The key predictors on the demand side include repetition status, positive attitudes toward school, parental education, and involvement, whereas teacher absenteeism, the student-teacher ratio, and shortages of instructional materials emerge as significant factors on the supply side.

Tim Schlippe, Matthias Wölfel, Koena Ronny Mabokela

This study investigates how cultural proximity affects the ability to detect AI-generated fake news by comparing South African participants with those from other nationalities. As large language models increasingly enable the creation of sophisticated fake news, understanding human detection capabilities becomes crucial, particularly across different cultural contexts. We conducted a survey where 89 participants (56 South Africans, 33 from other nationalities) evaluated 10 true South African news articles and 10 AI-generated fake versions. Results reveal an asymmetric pattern: South Africans demonstrated superior performance in detecting true news about their country (40% deviation from ideal rating) compared to other participants (52%), but performed worse at identifying fake news (62% vs. 55%). This difference may reflect South Africans' higher overall trust in news sources. Our analysis further shows that South Africans relied more on content knowledge and contextual understanding when judging credibility, while participants from other countries emphasised formal linguistic features such as grammar and structure. Overall, the deviation from ideal rating was similar between groups (51% vs. 53%), suggesting that cultural familiarity appears to aid verification of authentic information but may also introduce bias when evaluating fabricated content. These insights contribute to understanding cross-cultural dimensions of misinformation detection and inform strategies for combating AI-generated fake news in increasingly globalised information ecosystems where content crosses cultural and geographical boundaries.

Taimur Khan

The South Asian Smog refers to the recurring annual air pollution events marked by high contaminant levels, reduced visibility, and significant socio-economic impacts, primarily affecting the Indo-Gangetic Plains (IGP) from November to February. Over the past decade, increased air pollution sources such as crop residue burning, motor vehicles, and changing weather patterns have intensified these smog events. However, real-time forecasting systems for increased particulate matter concentrations are still not established at regional scale. The Aerosol Index, closely tied to smog formation and a key component in calculating the Air Quality Index (AQI), reflects particulate matter concentrations. This study forecasts aerosol events using Sentinel-5P air constituent data (2019-2023) and a Convolutional Long-Short Term Memory (ConvLSTM) neural network, which captures spatial and temporal correlations more effectively than previous models. Using the Ultraviolet (UV) Aerosol Index at 340-380 nm as the predictor, results show the Aerosol Index can be forecasted at five-day intervals with a Mean Squared Error of ~0.0018, loss of ~0.3995, and Structural Similarity Index of ~0.74. While effective, the model can be improved by integrating additional data and refining its architecture.

Koena Ronny Mabokela, Tim Schlippe, Matthias Wölfel

Sentiment analysis can aid in understanding people's opinions and emotions on social issues. In multilingual communities sentiment analysis systems can be used to quickly identify social challenges in social media posts, enabling government departments to detect and address these issues more precisely and effectively. Recently, large-language models (LLMs) have become available to the wide public and initial analyses have shown that they exhibit magnificent zero-shot sentiment analysis abilities in English. However, there is no work that has investigated to leverage LLMs for sentiment analysis on social media posts in South African languages and detect social challenges. Consequently, in this work, we analyse the zero-shot performance of the state-of-the-art LLMs GPT-3.5, GPT-4, LlaMa 2, PaLM 2, and Dolly 2 to investigate the sentiment polarities of the 10 most emerging topics in English, Sepedi and Setswana social media posts that fall within the jurisdictional areas of 10 South African government departments. Our results demonstrate that there are big differences between the various LLMs, topics, and languages. In addition, we show that a fusion of the outcomes of different LLMs provides large gains in sentiment classification performance with sentiment classification errors below 1%. Consequently, it is now feasible to provide systems that generate reliable information about sentiment analysis to detect social challenges and draw conclusions about possible needs for actions on specific topics and within different language groups.

Mehrab Hosain, Sabbir Alom Shuvo, Matthew Ogbe et al.

The modern web stack, which is dominated by browser-based applications and API-first backends, now operates under an adversarial equilibrium where automated, AI-assisted attacks evolve continuously. Content Delivery Networks (CDNs) and edge computing place programmable defenses closest to users and bots, making them natural enforcement points for machine-learning (ML) driven inspection, throttling, and isolation. This survey synthesizes the landscape of AI-enhanced defenses deployed at the edge: (i) anomaly- and behavior-based Web Application Firewalls (WAFs) within broader Web Application and API Protection (WAAP), (ii) adaptive DDoS detection and mitigation, (iii) bot management that resists human-mimicry, and (iv) API discovery, positive security modeling, and encrypted-traffic anomaly analysis. We add a systematic survey method, a threat taxonomy mapped to edge-observable signals, evaluation metrics, deployment playbooks, and governance guidance. We conclude with a research agenda spanning XAI, adversarial robustness, and autonomous multi-agent defense. Our findings indicate that edge-centric AI measurably improves time-to-detect and time-to-mitigate while reducing data movement and enhancing compliance, yet introduces new risks around model abuse, poisoning, and governance.

Janina Krambrich, Filip Mihalič, Michael W Gaunt et al.

In 2018-2019, Thailand experienced a nationwide spread of chikungunya virus (CHIKV), with approximately 15,000 confirmed cases of disease reported. Here, we investigated the evolutionary and molecular history of the East/Central/South African (ECSA) genotype to determine the origins of the 2018-2019 CHIKV outbreak in Thailand. This was done using newly sequenced clinical samples from travellers returning to Sweden from Thailand in late 2018 and early 2019 and previously published genome sequences. Our phylogeographic analysis showed that before the outbreak in Thailand, the Indian Ocean lineage (IOL) found within the ESCA, had evolved and circulated in East Africa, South Asia, and Southeast Asia for about 15 years. In the first half of 2017, an introduction occurred into Thailand from another South Asian country, most likely Bangladesh, which subsequently developed into a large outbreak in Thailand with export to neighbouring countries. Based on comparative phylogenetic analyses of the complete CHIKV genome and protein modelling, we identified several mutations in the E1/E2 spike complex, such as E1 K211E and E2 V264A, which are highly relevant as they may lead to changes in vector competence, transmission efficiency and pathogenicity of the virus. A number of mutations (E2 G205S, Nsp3 D372E, Nsp2 V793A), that emerged shortly before the outbreak of the virus in Thailand in 2018 may have altered antibody binding and recognition due to their position. This study not only improves our understanding of the factors contributing to the epidemic in Southeast Asia, but also has implications for the development of effective response strategies and the potential development of new vaccines.

Y. Yang, S. Mou, H. Wang et al.

<p>Anthropogenic emissions of aerosols and precursor gases have changed significantly in the past few decades around the world. In this study, the Explicit Aerosol Source Tagging (EAST) system is merged into the Energy Exascale Earth System Model version 1 (E3SMv1) to quantify the variations in anthropogenic aerosol concentrations, source contributions, and their subsequent radiative impact in four major emission regions across the globe during 1850–1980, 1980–2010, and 2010–2017. In North America and Europe, changes in anthropogenic <span class="inline-formula">PM_2.5</span> were mainly caused by changes in emissions from local energy and industrial sectors. The local industrial sector caused the largest increase in <span class="inline-formula">PM_2.5</span> in East Asia during 1980–2010 and decrease during 2010–2017. In South Asia, the increase in energy-related emissions dominated the rise in <span class="inline-formula">PM_2.5</span> levels during 1980–2017. During 1850–1980, the increases in emissions from North America contributed to the increase in the European <span class="inline-formula">PM_2.5</span> burden by 1.7 <span class="inline-formula">mg m⁻²</span> and the sources from the Europe were also responsible for the <span class="inline-formula">PM_2.5</span> burden increase in East Asia and South Asia by about 1.0 <span class="inline-formula">mg m⁻²</span>. During 1980–2010, East Asia contributed to an increase of 0.4–0.6 <span class="inline-formula">mg m⁻²</span> in the <span class="inline-formula">PM_2.5</span> burden in North America and Europe, while South Asia contributed about 0.3 <span class="inline-formula">mg m⁻²</span>. During 2010–2017, the contributions from East Asia to the <span class="inline-formula">PM_2.5</span> burdens in the North America, Europe, and South Asia declined by 0.3–0.6 <span class="inline-formula">mg m⁻²</span> due to the clean air actions in China, while the contributions from South Asia still increased due to the continuous increase in emissions in South Asia. The historical changes in aerosols had an impact on effective radiative forcing through aerosol–radiation interactions (<span class="inline-formula">ERF_ari</span>). During 1980–2010, a decline in North American aerosols resulted in a positive <span class="inline-formula">ERF_ari</span> change (warming effect) in Europe and a decline in aerosols in Europe caused a warming effect in Russia and northern China. The changes in <span class="inline-formula">ERF_ari</span> from the increase and decrease in aerosols in China during 1980–2010 and 2010–2017, respectively, are comparable in magnitude. The continuous aerosol increases in South Asia from 1980 to 2017 resulted in negative <span class="inline-formula">ERF_ari</span> (cooling) changes in South Asia, Southeast Asia, and southern China.</p>

Rusham Elahi, Zia Tahseen, Tehreem Fatima et al.

Primary healthcare is a crucial strategy for achieving universal health coverage. South Asian countries are working to improve their primary healthcare system through their country specific policies designed in line with WHO health system framework using the six thematic pillars: Health Financing, Health Service delivery, Human Resource for Health, Health Information Systems, Governance, Essential Medicines and Technology, and an addition area of Cross-Sectoral Linkages. Measuring the current accessibility of healthcare facilities and workforce availability is essential for improving healthcare standards and achieving universal health coverage in developing countries. Data-driven surveillance approaches are required that can provide rapid, reliable, and geographically scalable solutions to understand a) which communities and areas are most at risk of inequitable access and when, b) what barriers to health access exist, and c) how they can be overcome in ways tailored to the specific challenges faced by individual communities. We propose to harness current breakthroughs in Earth-observation (EO) technology, which provide the ability to generate accurate, up-to-date, publicly accessible, and reliable data, which is necessary for equitable access planning and resource allocation to ensure that vaccines, and other interventions reach everyone, particularly those in greatest need, during normal and crisis times. This requires collaboration among countries to identify evidence based solutions to shape health policy and interventions, and drive innovations and research in the region.

Kelian Baert, Shrisha Bharadwaj, Fabien Castan et al.

Feedforward monocular face capture methods seek to reconstruct posed faces from a single image of a person. Current state of the art approaches have the ability to regress parametric 3D face models in real-time across a wide range of identities, lighting conditions and poses by leveraging large image datasets of human faces. These methods however suffer from clear limitations in that the underlying parametric face model only provides a coarse estimation of the face shape, thereby limiting their practical applicability in tasks that require precise 3D reconstruction (aging, face swapping, digital make-up, ...). In this paper, we propose a method for high-precision 3D face capture taking advantage of a collection of unconstrained videos of a subject as prior information. Our proposal builds on a two stage approach. We start with the reconstruction of a detailed 3D face avatar of the person, capturing both precise geometry and appearance from a collection of videos. We then use the encoder from a pre-trained monocular face reconstruction method, substituting its decoder with our personalized model, and proceed with transfer learning on the video collection. Using our pre-estimated image formation model, we obtain a more precise self-supervision objective, enabling improved expression and pose alignment. This results in a trained encoder capable of efficiently regressing pose and expression parameters in real-time from previously unseen images, which combined with our personalized geometry model yields more accurate and high fidelity mesh inference. Through extensive qualitative and quantitative evaluation, we showcase the superiority of our final model as compared to state-of-the-art baselines, and demonstrate its generalization ability to unseen pose, expression and lighting.

Attrayee Chakraborty, Mandar Karhade

Artificial Intelligence (AI) is being adopted across the world and promises a new revolution in healthcare. While AI-enabled medical devices in North America dominate 42.3% of the global market, the use of AI-enabled medical devices in other countries is still a story waiting to be unfolded. We aim to delve deeper into global regulatory approaches towards AI use in healthcare, with a focus on how common themes are emerging globally. We compare these themes to the World Health Organization's (WHO) regulatory considerations and principles on ethical use of AI for healthcare applications. Our work seeks to take a global perspective on AI policy by analyzing 14 legal jurisdictions including countries representative of various regions in the world (North America, South America, South East Asia, Middle East, Africa, Australia, and the Asia-Pacific). Our eventual goal is to foster a global conversation on the ethical use of AI in healthcare and the regulations that will guide it. We propose solutions to promote international harmonization of AI regulations and examine the requirements for regulating generative AI, using China and Singapore as examples of countries with well-developed policies in this area.

Woon-Seng Gan, Santi Peksi, Chung Kwan Lai et al.

This paper introduces a novel portable and scalable Active Noise Mitigation (PSANM) system designed to reduce low-frequency noise from construction machinery. The PSANM system consists of portable units with autonomous capabilities, optimized for stable performance within a specific power range. An adaptive control algorithm with a variable penalty factor prevents the adaptive filter from over-driving the anti-noise actuators, avoiding non-linear operation and instability. This feature ensures the PSANM system can autonomously control noise at its source, allowing for continuous operation without human intervention. Additionally, the system includes a web server for remote management and is equipped with weather-resistant sensors and actuators, enhancing its usability in outdoor conditions. Laboratory and in-situ experiments demonstrate the PSANM system's effectiveness in reducing construction-related low-frequency noise on a global scale. To further expand the noise reduction zone, additional PSANM units can be strategically positioned in front of noise sources, enhancing the system's scalability.The PSANM system also provides a valuable prototyping platform for developing adaptive algorithms prior to deployment. Unlike many studies that rely solely on simulation results under ideal conditions, this paper offers a holistic evaluation of the effectiveness of applying active noise control techniques directly at the noise source, demonstrating realistic and perceptible noise reduction. This work supports sustainable urban development by offering innovative noise management solutions for the construction industry, contributing to a quieter and more livable urban environment.

Raphael Baer-Way, Asia DeGraw, Weikang Zheng et al.

Over recent decades, robotic (or highly automated) searches for supernovae (SNe) have discovered several thousand events, many of them in quite nearby galaxies (distances < 30 Mpc). Most of these SNe, including some of the best-studied events to date, were found before maximum brightness and have associated with them extensive follow-up photometry and spectroscopy. Some of these discoveries are so-called SN impostors, thought to be superoutbursts of luminous blue variable stars, although possibly a new, weak class of massive-star explosions. We conducted a Snapshot program with the Hubble Space Telescope(HST) and obtained images of the sites of 31 SNe and four impostors, to acquire late-time photometry through two filters. The primary aim of this project was to reveal the origin of any lingering energy for each event, whether it is the result of radioactive decay or, in some cases, ongoing late-time interaction of the SN shock with pre-existing circumstellar matter, or the presence of a light echo. Alternatively, lingering faint light at the SN position may arise from an underlying stellar population (e.g., a host star cluster, companion star, or a chance alignment). The results from this study complement and extend those from Snapshot programs by various investigators in previous HST cycles.

Sarawut Khongwichit, J. Chansaenroj, C. Chirathaworn et al.

Chikungunya virus (CHIKV) is a re-emerging mosquito-borne human pathogen that causes chikungunya fever, which is typically accompanied by severe joint pain. In Asia, serological evidence indicated that CHIKV first emerged in 1954. From the 1950’s to 2005, sporadic CHIKV infections were attributed to the Asian genotype. However, the massive outbreak of CHIKV in India and the Southwest Indian Ocean Islands in 2005 has since raised chikungunya as a worldwide public health concern. The virus is spreading globally, but mostly in tropical and subtropical regions, particularly in South and Southeast Asia. The emergence of the CHIKV East/Central/South African genotype-Indian Ocean lineage (ECSA-IOL) has caused large outbreaks in South and Southeast Asia affected more than a million people over a decade. Notably, the massive CHIKV outbreaks before 2016 and the more recent outbreak in Asia were driven by distinct ECSA lineages. The first significant CHIKV ECSA strains harbored the Aedes albopictus-adaptive mutation E1: A226V. More recently, another mass CHIKV ECSA outbreak in Asia started in India and spread beyond South and Southeast Asia to Kenya and Italy. This virus lacked the E1: A226V mutation but instead harbored two novel mutations (E1: K211E and E2: V264A) in an E1: 226A background, which enhanced its fitness in Aedes aegypti. The emergence of a novel ECSA strain may lead to a more widespread geographical distribution of CHIKV in the future. This review summarizes the current CHIKV situation in Asian countries and provides a general overview of the molecular virology, disease manifestation, diagnosis, prevalence, genotype distribution, evolutionary relationships, and epidemiology of CHIKV infection in Asian countries over the past 65 years. This knowledge is essential in guiding the epidemiological study, control, prevention of future CHIKV outbreaks, and the development of new vaccines and antivirals targeting CHIKV.

A. Dai