Systematic literature review and meta-analysis on the prevalence of rotavirus genotypes in Asia from 2015 to 2021

ABSTRACT

Understanding rotavirus prevalence by genotype can inform disease prevention and immunization decision-making. This systematic literature review summarized the genotype-specific prevalence of rotavirus in Asia from 2015 to 2021. We identified surveillance studies using PubMed, Embase, and Scopus databases, and used proportional meta-analysis (with the generic inverse variance method with arcsine transformation and generalized linear mixed models) to summarize genotype prevalence by region. A total of 7,601 studies were screened, and 73 studies from 16 countries were included. Data from 19,935 rotavirus samples reveal that the most common rotavirus genotypes circulating in Asia from 2015 to 2021 were G3P[8] (24%; 95% CI: 19%, 30%), G9P[8] (11%; 95% CI: 7%, 16%), G1P[8] (11%; 95% CI: 8%, 14%) and G2P[4] (8%; 95% CI: 6%, 10%). The results are comparable to estimates from the pre-vaccine licensure period. Surveillance based on sequencing data could help detect more subtle changes and further characterize the rotavirus burden in Asia.

SUMMARY

This study synthesizes data on rotavirus prevalence by genotype in Asia for the period 2015–2021. It finds G3P[8], G9P[8], G1P[8] and G2P[4] to be the most prevalent genotypes across the included countries.

Background

Rotaviruses possess a double-stranded ribonucleic acid (dsRNA) genome and belong to the Sedoreoviridae family within the Reovirales order.¹ The most clinically relevant rotavirus species in humans is Rotavirus alphagastroenteritidis, formerly known as Rotavirus A (RVA).² Its genome is comprised of 11 segments that encode six structural proteins (VP1–VP4 and VP6–VP7) and six nonstructural proteins (NSP1–NSP6).^3,4 Rotavirus genotypes are classified based on their glycoprotein VP7 (which defines the G type) and their protease-sensitive VP4 protein (which defines the P type) together forming the outer shell.^5–7

The six genotypes most commonly identified in humans are G1P[8], G2P[4], G3P[8], G4P[8], G9P[8], and G12P[8]. A recent review found that these genotypes accounted for over 60% of detections globally, except in the African region, where they represented 48% of identified genotypes.⁷ There is also evidence that humans can also be infected with rotavirus genotypes typically circulating in a large variety of domestic and wild animals, including pigs, cattle, cats and dogs; although these have been observed less frequently, and are usually not able to transmit between humans.^6,8–15

Rotavirus is transmitted via the oral-fecal route, and a leading cause of diarrhea and dehydration in young children worldwide, with most children being infected at least once by the age of 5 years.¹⁶ The rotavirus morbidity and mortality burden in Asia is significant. Troeger et al. estimated that, in 2016, there were approximately 35 million rotavirus cases (and 13.4 thousand rotavirus deaths) in South Asia; 30 million rotavirus cases (3.8 thousand rotavirus deaths) in Southeast Asia, and 8 million rotavirus cases (and 0.6 thousand rotavirus deaths) in East Asia among children younger than 5 years.¹⁷

To reduce global disease burden and improve child health, the World Health Organization (WHO) recommends that rotavirus vaccines are included in national immunization plans, particularly in countries with high rotavirus-associated fatality rates, which includes many in Asia.¹⁶ Safe and effective rotavirus vaccines were first licensed in 2006.¹⁸ As of 2024, four WHO-prequalified rotavirus vaccines were available in Asia, including Rotarix™, a monovalent vaccine developed by GSK Biologicals, Belgium, containing a single attenuated human G1P[8] rotavirus strain; RotaTeq™, a pentavalent vaccine developed by Merck & Co., Inc., Kenilworth, NJ, USA, containing five human bovine reassortants possessing the human G1, G2, G3, G4 and P[8] genotypes; Rotavac™, developed by Bharat Biotech, which contains naturally-occurring bovine-human reassortant neonatal G9P[11]; and RotaSiil™, developed by the Serum Institute of India, a pentavalent bovine-human reassortant containing human G1, G2, G3, G4 and G9 reassorted into a bovine UK G6P[5] backbone.^18,19 There are also oral, live attenuated rotavirus vaccines available on the private market that are licensed locally within country, with low uptake rates, including the human-derived G1P[8] Rotavin-M1 in Vietnam, developed by POLYVAC; and, in China, the Lanzhou lamb rotavirus vaccine, a monovalent G10P[15] derived from a lamb strain developed by Lanzhou Institute of Biological Products.^20,21

As of 2024, 12 out of the 32 countries included in this review have incorporated rotavirus vaccination into their national immunization plans (Table 1).¹⁹ While cross-protective effects have been documented for rotavirus vaccines, recent evidence suggests the superiority of homotypic or partially heterotypic protection.^22–24 Understanding the regional or national rotavirus prevalence by genotype is thus of public health interest and can guide vaccination policy.

Table 1.

Vaccination status for national immunization plans.

Country Name	Vaccine in NIP	Year of introduction
Afghanistan	Yes	2018
Bangladesh	No	N.A
Bhutan	No	N.A
Brunei Darussalam	No	N.A
Cambodia	No	N.A
China	No	N.A
Democratic People’s Republic of Korea	No	N.A
India	Yes	2019
Indonesia	Yes	2023
Japan	Yes	2020
Kazakhstan	No	N.A
Kyrgyzstan	Yes	2019
Lao People’s Democratic Republic	No	N.A
Malaysia	No	N.A
Maldives	No	N.A
Mongolia	No	N.A
Myanmar	Yes	2020
Nepal	Yes	2020
Pakistan	Yes	2018
Philippines	No	N.A
Republic of Korea	No	N.A
Singapore	No	N.A
Sri Lanka	No	N.A
Tajikistan	Yes	2015
Thailand	Yes	2020
Timor-Leste	Yes	2019
Turkmenistan	Yes	2019
Uzbekistan	Yes	2014
Viet Nam	No	N.A

Data unavailable for: Macao, Hong Kong, Taiwan. Source: WHO Immunization Portal. Accessed 25 June 2024.

The last systematic literature review to synthesize Asia-specific genotype prevalence used data from January 2000 through August 2011 and identified G1P[8] (24%), G3P[8] (19%), G2P[4] (12%) and G9P[8] (7%) as the most prevalent genotypes.⁸ We examined the evidence from 2015 to 2021 to provide an updated view of rotavirus genotype circulation in Asia; a period that we chose to maximize data availability to allow for a meta-analyses. The findings will be of value for epidemiologists, public health practitioners, policy-makers, and researchers interested in vaccine policy, programs or modeling viral trends.

Methods

This systematic literature review with meta-analysis was carried out in accordance with best practice as outlined in the Cochrane Handbook and guidance for systematic reviews published by the Center for Reviews and Dissemination (CRD) of York University.²⁵ The primary outcome was the prevalence of circulating rotavirus genotypes in Asia between 2015 and 2021. Secondary outcomes were the prevalence of genotypes by country during the same time period (Table 2).

Table 2.

Inclusion and exclusion criteria.

Picots	Descriptions
Inclusion criteria
Population(s)	All populations in Asia* *Afghanistan, Bangladesh, Bhutan, Brunei, Cambodia, China, East Timor, Hong Kong, India, Indonesia, Japan, Kazakhstan, Kyrgyzstan, Laos, Macau, Malaysia, Maldives, Mongolia, Myanmar, Nepal, North Korea, Pakistan, Philippines, Singapore, South Korea, Sri Lanka, Taiwan, Tajikistan, Thailand, Turkmenistan, Uzbekistan, Vietnam*
Interventions	All rotavirus vaccines
Comparators	Not applicable
Outcomes	Primary outcome: prevalence of rotavirus genotypes in Asia Secondary outcome: genotype dominance by country
Timing	All studies with data collection period between 2015 and August 2021
Study design	Empirical epidemiological studies using sample serology testing
Exclusion criteria
Any study with the following characteristic	Studies published before 2014 or that did not contain extractable post-2015 data
	Less than 30 positive rotavirus samples
	Countries not in Asia
	Studies lacking at least six consecutive months of surveillance or studies with two cross-sections fewer than not 6 months apart

Data sources and searches

A systematic literature search of PubMed, Embase and Scopus databases was carried out to identify epidemiological surveillance studies presenting genotype-specific prevalence data of rotavirus in Asia (Appendix I). Surveillance reports and other gray literature were identified using Google and Google Scholar.

The population of interest was people living in Asia (all ages). Consistent with Kawai et al. 2012, this included Afghanistan, Bangladesh, Bhutan, Brunei, Cambodia, China, East Timor, Hong Kong, India, Indonesia, Japan, Kazakhstan, Kyrgyzstan, Laos, Macau, Malaysia, Maldives, Mongolia, Myanmar, Nepal, North Korea, Pakistan, Philippines, Singapore, South Korea, Sri Lanka, Taiwan, Tajikistan, Thailand, Turkmenistan, Uzbekistan, and Vietnam.⁸ All regions were eligible for inclusion, regardless of vaccine availability and whether vaccines were included in the region’s national immunization plan or accessed via the private market.

Empirical epidemiological studies were included; that is, longitudinal studies and cross-sectional studies evaluating a given time period in the same setting, published from January 1, 2015, to August 25, 2021. No language restriction was applied and, where necessary, records were translated using Google Translate.²⁶ Epidemiological studies published or only reporting data prior to 2015 were excluded, as were studies that did not include genotype-specific data, other study designs (such as editorials), or studies that could not be accessed through database searches. Included studies were required to include at least six consecutive months of surveillance data. Studies with fewer than 30 positive rotavirus samples were also excluded. These measures were to allow for annual rotavirus circulation patterns and to prioritize the most robust genotype data that was representative of the wider country or region. Conference proceedings and abstract-only publications were excluded.

Study selection

All titles and abstracts retrieved by the search were screened by a single reviewer for potential applicability to rotavirus epidemiology to remove articles clearly not relevant to scope. Two reviewers then performed a duplicate second sift of the filtered titles and abstracts against the inclusion and exclusion criteria. Publications selected for full-text review were independently appraised against the inclusion criteria by two reviewers. Any disagreement over inclusions was resolved through discussion, and results were documented using the PRISMA checklist.²⁵

Data extraction

A bespoke data extraction form was used to capture all study characteristics, primary and secondary outcomes, including genotype-specific data by geographic, seasonality and age distribution (Appendix II). A single reviewer extracted data for each included study, with the results independently verified by a second reviewer. Discrepancies were discussed until a resolution was reached.

Quality assessment of included studies

Study quality was assessed based on items included in the Strengthening the Reporting of Observational Studies in Epidemiology (STROBE) checklist.²⁷ Quality appraisals were performed by one reviewer and verified by a second, with any disagreements resolved through discussion.

Analyses

Study data on the frequency of specific rotavirus genotype (G and P designation) was pooled in meta-analyses to estimate the overall prevalence for each country and the Asia region. Studies that did not provide data on specific genotype by sample size were summarized descriptively. When available, meta-analyses incorporated annual prevalence estimates. When data overlapped between two calendar years, data was allocated to the year with the longest period of surveillance. Proportional meta-analyses were conducted using the generic inverse variance method with arcsine transformation. Generalized linear mixed models were used for sensitivity analysis,²⁸ the results of which are reported in the supplementary material. For each country, it was assumed that the true prevalence will vary between studies depending on setting and population characteristics; therefore, random effects models were used.²⁹ Heterogeneity was evaluated using the I-squared (I²) statistic, which was interpreted using thresholds as published in the Cochrane Handbook (0–40% unimportant, 30–60% moderate, 50–90% substantial and 75–100% considerable heterogeneity).²⁵

Results were presented as numerical data estimates, with the aid of bar plots to visualize the prevalence of the six most common genotypes in each country. Rarer genotypes were grouped into an ‘other’ category in the bar plots, and the prevalence of genotypes with zoonotic origins was also summarized. The summed prevalence of the six most common genotypes and ‘other’ genotypes does not always equal exactly 1 due to non-linearities which arise from transforming and back-transforming the prevalences for meta-analysis and heterogeneous reporting of prevalences between studies.³⁰ Following recommendations for multi-category prevalence estimates,²⁸ the estimates were normalized so that they equaled 1 before producing the bar plots, thus there may be a small difference compared to the tables.³⁰ Results were produced using the R version 4.2.1 or above³¹ and the meta package version 6.2–1 or above.³²

Results

Search results

Databases searches retrieved 17,297 global citations (Figure 1) which were exported to EndNote. Following deduplication, 7,601 articles remained for first-pass screening by title and abstract. At second sift, 120 Asia-specific publications of potential applicability to the research question were acquired for full-text appraisal (Appendix III, IV). Reasons for exclusion at full text were: 1) small sample (<30) (n = 2); 2) non-empirical epidemiological studies with no data on genotype circulation (n = 1); 3) pre-2015 data (n = 42); 4) less than 6 months’ consecutive data (n = 1); and 5) non-applicable study design (n = 1). A total of 73 studies covering 16 of the 32 eligible Asian countries met inclusion criteria and were included in the review. Sixty-one studies were included in the meta-analyses, and 12 were summarized descriptively.

Figure 1.

PRISMA flow diagram.

Meta-analyses

Meta-analyses were informed by genotype prevalence data from 19,935 positive rotavirus samples (Figure 2), drawn from 136 observation periods (61 studies) across 16 countries. The largest sample sizes were from India (38% of all samples), followed by China (16% of all samples), and the Philippines (13% of all samples). The full meta-analysis results including country and regional analysis can be found in Appendix V and VI. A quarter of all rotavirus samples included in the meta-analysis were of the G3P[8] genotype (24%; 95% confidence interval [CI]: 19%, 30%), followed by G9P[8] (11%; 95% CI: 7%, 16%), G1P[8] (11%; 95% CI: 8%, 14%) and G2P[4] (8%; 95% CI: 6%, 10%) (Figure 3). However, estimates of genotype-specific prevalence varied considerably between countries (Figure 4).

Figure 2.

Number of samples per country.

The following samples were available per country: Bangladesh – 126 (1% of total samples); China – 3252 (16% of total samples); India – 7600 (38% of total samples); Indonesia – 229 (1% of total samples); Japan – 538 (3% of total samples); Lao PDT – 151 samples (1% of total samples); Malaysia – 41 samples (<1% of total samples); Myanmar – 50 (<1% of total samples); Nepal – 51 (<1% of total samples); Pakistan – 445 (2% of total samples); Philippines – 2555 (13% of total samples); South Korea – 496 (2% of total samples); Sri Lanka – 20 samples (<1% of total samples); Taiwan – 408 (2% of total samples); Thailand – 2206 (11% of total samples); Vietnam – 1767 (9% of total samples).

Figure 3.

Prevalence of the most common rotavirus strains in Asia.

*The bar plot shows normalized genotype-specific prevalence (see methods), whereas elsewhere in the manuscript genotype-specific prevalence is reported. “n” indicates the number of time periods of data included in the meta-analysis. The number of time periods is calculated as the number of calendar years of data available across all studies per country.

Figure 4.

Prevalence of the most common rotavirus strains by country, Asia.

*The bar plot shows normalized genotype-specific prevalence (see methods), whereas elsewhere in the manuscript genotype-specific prevalence is reported. “n” indicates the number of time periods of data included in the meta-analysis. The number of time periods is calculated as the number of calendar years of data available across all studies per country.

G3P[8] was the most common genotype in seven countries (India, Indonesia, Malaysia, Pakistan, Taiwan, Thailand, and Vietnam). Indonesia had the highest prevalence of G3P[8], accounting for 86% of genotypes (95% CI: 74%, 95%), followed by Malaysia with 76% (95% CI: 60%, 88%) and Taiwan with 69% (95% CI 54%, 82%). G3P[8] accounted for just over half of genotypes in Thailand (54%; 95% CI: 37%, 70%), and 40% of genotypes in Vietnam (95% CI: 20%, 63%). In India, G3P[8] accounted for around a third of genotypes (32%; 95% CI: 25%, 39%) and in Pakistan G3P[8] had a prevalence of 27% (95% CI: 20%, 34%).

G1P[8] was the most common strain in Bangladesh, Lao PDR, Philippines and Sri Lanka. In Lao PDR, G1P[8] dominated with 96% prevalence (95% CI: 92%, 99%). This was also the case in Sri Lanka at 80% prevalence (95% CI: 56%, 94%), followed by G3P[8] at 15% (95% CI: 3%, 38%). In the Philippines, the prevalence of G1P[8] and G2P[4] was comparable at 45% (95% CI: 43%, 47%) and 43% (95% CI: 41%, 45%), respectively. In Bangladesh G1P[8] accounted for 35% of samples (95% CI: 11%, 64%), and G2P[4] was the second most common genotype at 17% (95% CI: 3%, 38%).

In three countries (China, Japan and Myanmar), G9P[8] was the most common genotype. It accounted for around three-quarters of genotypes in China (78%; 95% CI: 73%, 83%) and one-third in Japan (31%; 95% CI: 9%, 59%). In both countries, G2P[4] was the second most common genotype at 22% in Japan (95% CI: 6%, 45%) and 7% prevalence in China (95% CI: 5%, 9%). In Myanmar, G9P[8] accounted for 54% of genotypes (95% CI: 39%, 68%), followed by G3P[8] at 36% (95% CI: 23%, 51%).

G12P[6] was the most prevalent strain in Nepal at 37% (95% CI: 24%, 52%), with G1P[8] second most common (29%; 95% CI: 17%, 44%). In the Republic of Korea, G2P[4] accounted for 34% (95% CI: 14%, 58%) of rotavirus genotypes and G8P[8] for 20% (95% CI: 2%, 50%).

G1P[8] was the second most common genotype in both India and Malaysia (16% [95% CI: 12%, 21%] and 15% [95% CI: 6%, 29%], respectively); in Thailand this was G9P[8] (13%; 95% CI: 4%, 27%). The second most prevalent genotype was G3P[6] in Indonesia (7%; 95% CI: 1%, 17%) and G2P[4] in Taiwan (9%; 95% CI: 4%, 16%). In Pakistan, G12P[6] was the second most common genotype at 16% (95% CI: 8%, 25%), which is Vietnam G8P[8] made up a quarter of samples (25%; 95% CI: 12%, 40%).

Zoonotic genotype analysis

Genotypes with zoonotic origins were identified in seven countries (Bangladesh, China, India, Japan, Lao PDR, Republic of Korea, and Thailand) (Table 3). G3P[9] was the most consistently observed genotype, having been identified in six countries (China, India, Japan, Lao PDR, Republic of Korea, and Thailand). In these countries, its prevalence ranged from 0% to 2%. Other zoonotic genotypes identified in these countries included G11P[25] in Bangladesh with a prevalence of 0% (95% CI: 0%–10%) and G1P[11] in India with prevalences of 0% (95% CI: 0%, 1%). A full list of zoonotic genotypes is available in Table 2. The full prevalence estimates of zoonotic genotypes are available in the supplementary material.

Table 3.

Zoonotic genotypes identified by country.

	Generalized linear mixed model	Generic inverse variance (arcsine transformation)
Country/genotype	Estimate of proportion and 95% CI	Estimate of proportion and 95% CI2
Bangladesh
G11P[25]	<0.01	0.01 (0.00, 0.10)
China
G10P[15]	<0.01	<0.01
G3P[9]	<0.01	<0.01
India
G10P[11]	<0.01	<0.01
G10P[6]	<0.01	<0.01
G10P[8]	<0.01	<0.01
G12P[11]	<0.01	<0.01
G1P[11]	<0.01	<0.01
G1P[9]	<0.01	<0.01
G3P[11]	<0.01	<0.01
G3P[9]	<0.01	<0.01
Japan
G3P[9]	<0.01	<0.01
Lao PDR
G3P[9]	<0.01	<0.01
Republic of Korea
G3P[9]	0.02 (0.01, 0.05)	0.02 (0.00, 0.04)
Thailand
G10P[14]	<0.01	<0.01
G3P[10]	<0.01	<0.01
G3P[9]	<0.01	<0.01

Descriptive summary

A total of 12 studies in six countries (Bangladesh, China, India, Indonesia, Mongolia, Nepal) were excluded from the meta-analysis for three reasons: i) two studies reported only G and P types separately;^33,34 ii) 10 studies did not provide enough information to extract post-2015 data;^{11,33,35–42} and one study did not report the number of samples genotyped.⁴³ In the two studies that only reported G types, G1 was the most frequently identified type in both India (36%) and Indonesia (57%).^33,34 Of the 10 studies that included both pre- and post 2015 data and did contain enough information to extract post-2015 data,^{11,33,35–42} G1P[8] was the most common genotype in five studies from Bangladesh and India, with prevalences of 62%,¹¹ 59%,³⁵ 43%,³⁷ and 27%.²⁸ G1P[6] was the most dominant genotype in one study from India at 63% prevalence,³⁸ while another study found that G3P[8] was most common in Mongolia at 48%.³⁹ One study from Nepal found the highest prevalence of the G12P[6] genotype at 45.3%.⁴² One study in India did not give the total sample size but reported G3P[8] to be the most prevalent genotype (46%), followed by G1P[8] (19%).⁴³

Discussion and conclusion

This systematic literature review and meta-analysis demonstrated marked diversity in the rotavirus samples genotyped in Asia between 2015 and 2021. G3P[8] was the most prevalent genotype in seven countries, G1P[8] was the most common strain in four countries, and G9P[8] in three countries. China and India contribute approximately 60% of the population of Asia and contributed the most samples in this study, they have distinctly different genotype distributions, with China dominated by G9P[8] (78%) and India showing no clear dominant strain. Nepal and the Republic of Korea were the two outliers that did not share a dominant genotype in common with another country, with G12P[6] most frequent in Nepal, and G2P[4] in the Republic of Korea.

These findings are similar to the ones obtained in the last systematic review of rotavirus prevalence in Asia which found the most dominant genotypes to be G1P[8] (23.6%), G3P[8] (18.9%), G2P[4] (11.8%), and G9P[8] (7.4%).⁸ However, post-2015 estimates suggest a decline in G1P[8] (10%) and increase in G3P[8] prevalence (25%), with still noted circulation of G2P[4] (8%) and G9P[8] (12%).¹⁸ Furthermore, this review finds that G12P[6], described by Kawai et al. as a rare strain, continues to circulate in Nepal, and at slightly lower levels in Pakistan and India. Other genotypes found in Asia, and less frequently elsewhere include G8P[8], which was found in Japan with a prevalence of 7% (95% CI: 0%, 25%), Lao PDR with a prevalence of 1% (95% CI: 0% 4%), Republic of Korea with a prevalence of 20% (95% CI: 2%, 50%), and Thailand with a prevalence of 5% (2%, 10%).

Consistent with other studies, the results of this study show limited prevalence of genotypes derived from animal origins.^{6,8–12,14,15} G3P[9], a genotype commonly found in cats and dogs, was the most consistently identified genotype, having been identified in six countries. However, in many countries, the overall number of available samples was small, and its prevalence ranged from 0% to 2%. G3P[9] showed the highest prevalence in the Republic of Korea, with a reported prevalence of 2% (95% CI: 0%, 4%) based on a total of 496 samples. While the number of samples available makes it difficult to draw conclusions in several countries, the relatively low prevalence of zoonotic genotypes in India supports the hypothesis that the prevalence of zoonotic genotypes remains low. India included 7,600 samples and eight zoonotic genotypes, all with a prevalence of <1%.

The predominant rotavirus genotypes identified in Asia – G3P[8], G9P[8], G1P[8], and G2P[4]— align to a variable extent with the formulation and types of protection afforded by the four WHO-prequalified vaccines available in the region. Rotarix™ (whose formulation includes G1P[8]) provides fully homotypic protection against G1P[8], and partially heterotypic protection against G3P[8] and G9P[8].⁴⁴ Rotarix^TM’s fully heterotypic protection against G2P[4] has been shown to be slightly lower than that offered to fully homotypic and partially heterotypic strains.^7,23,45–48 RotaTeq™ (whose formulation includes G1, G2, G3, G4, P[8]) provides fully homotypic or partially heterotypic protection to all circulating strains (both types of protection were shown to be similar and high).^45,49 Rotavac™ (whose formulation includes G9P[11]) may offer partially heterotypic protection to G9P[8] and fully heterotypic protection (which has been suggested to be inferior to other types of protection) to all other strains.^7,22,23,50 RotaSiil™ (whose formulation includes G1, G2, G3, G4, G9) may offer partially heterotypic protection to the prevalent circulating strains.⁵¹

This analysis has several limitations, the first of which was the variable sample sizes. The data for some countries, including China and India, was drawn from a relatively large number of studies and rotavirus samples collected during the 2015–2021 time-period. For other countries, such as Bangladesh, Lao PDR and Malaysia, prevalence estimates were based on a single study, while data for Sri Lanka were extracted from a multi-country study which met the minimum sample size for inclusion but only included 20 samples for the country. Practices of clinical sampling and laboratory methods for diagnostics in cases of gastrointestinal illness may vary considerably between countries and settings (e.g. hospital or community). Increased, and more uniform, sampling and genotype surveillance across countries and settings would be beneficial and may give a more representative indication of the true rotavirus burden.

Full genome sequencing to detect lineages within genotypes or genome reassortments may also provide further insight into strain evolution beyond the outer capsid proteins (VP7 and VP4).⁵² A Belgian surveillance study (2009–2023), which used whole genome sequencing, found that strains with an equine like G3 VP7, but a DS-1 like genotype constellation were more common in vaccinated than unvaccinated rotavirus cases.⁵²

Additionally, studies have shown that genotype prevalence within a country may vary by age, but we were unable to analyze rotavirus genotype prevalence by age, as these data are not uniformly reported.⁵³ Finally, rotavirus genotype circulation was not analyzed in conjunction with immunization policy, implementation, or rates of uptake. A prior systematic review found transient genotype changes after rotavirus vaccine introduction.⁷

Limitations aside, the findings of this systematic review and meta-analysis are aligned with prevalence estimates from the period 2000–2011, with most prevalent genotypes including G3P[8], G1P[8], G2P[4], and G9P[8]. The results may help shape vaccination policy and inform ways to further address the burden of rotavirus across Asia.

Biographies

Tim Jesudason is a global health consultant specializing in evidence synthesis, health economics, and communications. He has a strong background in research focused on the control of infectious diseases and childhood illnesses. Tim holds bachelor’s degrees in international relations from the University of Melbourne and in Philosophy from Victoria University of Wellington, as well as a Master of Public Health from the London School of Hygiene & Tropical Medicine.

Kelly Fleetwood is a statistician with 20 years’ experience across industry and academia. She has worked in a statistical consultancy providing input to a broad range of projects in the life sciences, from gene expression studies to evaluations of the cost-effectiveness of new drugs. She currently works for the University of Edinburgh where her research uses large health datasets to better understand the relationship between physical and mental health conditions. Her areas of expertise include meta-analysis and the analysis of observational data, including electronic health records. Kelly holds a bachelor’s degree in Mathematics from the University of Queensland, Australia, and a master’s degree in Statistics from the University of Sheffield, UK.

Maria Bermudez is a senior analyst with a Master’s degree in Health Policy, Planning, and Financing, as well as a medical degree. She has a background in health technology assessment, health policy analysis, and clinical medicine. Maria has worked with a wide range of organizations in both the public and private sectors and currently works with Triangulate Health Ltd on projects involving health policy analysis, systematic reviews, and stakeholder engagement. Her areas of interest include public health policies and population health in low- and middle-income countries.

Oluwaseun Sharomi is a Principal Scientist and Health Economist at Merck, where he has led modeling activities for RotaTeq and is currently leading and supporting modeling activities for Epstein-Barr Virus and Pneumococcal Disease. Prior to joining Merck, Dr Sharomi served as an Associate Director of Modeling at CHEORS and as an Assistant Professor in the Department of Mathematics at Khalifa University in the United Arab Emirates. Dr Sharomi’s work focuses on using mathematical modeling approaches and analysis to understand the transmission dynamics and control of emerging and re-emerging diseases of public health interest. He has designed, analyzed, and simulated novel mathematical models for the spread of various diseases, including respiratory diseases (H1N1, Mycobacterium tuberculosis), sexually transmitted infections (Chlamydia, HIV, HPV, syphilis), and their coinfections (e.g., HIV-TB coinfection). Dr Sharomi has used these models to provide realistic assessments of various intervention strategies, such as vaccines (e.g., for H1N1 and HPV) and drug treatments (e.g., for HIV). His statistical skills include proficiency in parameter estimation, uncertainty and sensitivity analysis of model parameters, optimal control analysis, and fitting models to data. Dr Sharomi’s expertise also encompasses the design and analysis of mathematical models of infectious diseases, simulation of electrical activity in myocardial tissue, parallel computing, problem-solving environments, and numerical algorithms and software for exascale computer architectures.

Hannah Schirrmacher is a health economist with a background in development economics. She has several years of consultancy experience at Triangulate Health Ltd and the Office of Health Economics, where she has worked on research projects related to vaccines, advanced therapy medicinal products, and broader value in health technology assessments. Her research experience includes literature reviews, qualitative research methods, and economic modeling, and she has published in leading journals such as Value in Health, Vaccines, and PharmacoEconomics. Hannah has presented her research at several conferences, including the Health Economics Study Group and the International Health Economics Association. Her areas of interest include global health, infectious diseases, and public health policies.

Christian Hauck holds a Bachelor of Arts in Political Science and Global Studies and a Master of Arts in International Public Policy, with specializations in Human Security and Global Governance. Christian worked as a research analyst with Triangulate Health, where he primarily focused on qualitative analysis, report writing, and data analysis. During his time at Triangulate, Christian was involved in developing health policy recommendations related to smoking cessation and assisted the team in conducting a cost-effectiveness analysis on incontinence in Europe. His research interests include healthcare policy, non-communicable diseases, health technology assessments, and global issues surrounding tuberculosis. Christian has completed courses and obtained certifications through the academic consortium R for Health Technology Assessment, exploring the use of R for cost-effectiveness analysis.

Daniel Hungerford is an infectious disease epidemiologist with expertise in gastrointestinal infections and vaccines, in relation to health inequalities. Previously, he worked for the Health Protection Agency and then Public Health England, specializing in infectious disease surveillance and outbreak investigation; and the LJMU, Centre for Public Health, developing the Trauma and Injury Intelligence Group surveillance system. He is a Senior lecturer in the NIHR Health Protection Research Unit in Gastrointestinal Infections at the University of Liverpool and has recently completed the NIHR Future Focused Leaders – Emerging Leaders Programme. He currently leads the European Rotavirus Surveillance Network (EuroRotaNet) and is Theme lead for Measuring the Value of Vaccines at the Centre for Global Vaccine Research. His main research interests involve the use of “real world” big data for vaccine evaluations and effectiveness studies, focusing on respiratory and diarrheal disease.

David Tordrup is a health economist with a background in life sciences. He has led multiple projects on pharmaceutical policies, financing, health technology assessment (HTA), and reimbursement, working with global clients, including biopharma companies and multilateral organizations. He serves as an external expert for the European Commission and the Journal of Pharmaceutical Policy and Practice and is currently a doctoral researcher at the WHO Collaborating Centre for Pharmaceutical Policy and Regulation. With over a decade of experience, he has managed and implemented numerous pharmaceutical and health economics research projects, publishing extensively in reputable international journals.

Jelle Matthijnssens serves as a professor at the KU Leuven in Belgium and has performed research on rotavirus for two decades. He developed a comprehensive classification system encompassing all 11 rotavirus gene segments. This classification system has been instrumental to study of rotavirus genetic diversity, reassortment, zoonosis, evolution, and epidemiology. He is a member of EuroRotaNet, runs the national reference center (NRC) for rotavirus in Belgium together with prof. Marc Van Ranst and has been member and chair of the Reoviridae family study group of the ICTV for many years. In recent years, his research has shifted towards viral metagenomics, for which he optimized and developed the NetoVIR protocol to purify viral particles from biological samples to be analyzed using deep sequencing, in combination with dedicated bio-informatics pipelines. This technique is being used to investigating a wide range of scientific question ranging from: how does the gut virome development of infants? How does the gut virome associate with human disease? What is the role of the virome in honeybee health and disease? What is the role of insect-specific viruses in mosquito vector competence? or can viral metagenomics be used on environmental samples such as indoor air?

Cristina Carias is a Technological Physics Engineer with a PhD in Strategy, Entrepreneurship, and Technological Change, and advanced training in Econometrics. She has experience in both the public and private sectors and has worked on health economics, outcomes research, emergency preparedness, and public health modeling.

Funding Statement

The work was supported by Merck Sharp & Dohme LLC, a subsidiary of Merck & Co., Inc., Rahway, NJ, USA.

Disclosure statement

Sharomi Oluwaseun and Cristina Carias are employed by Merck Sharp & Dohme LLC, a subsidiary of Merck & Co., Inc., Rahway, NJ, USA and shareholders of Merck & Co., Inc., Rahway, NJ, USA, which manufactures RotaTeq^©. Triangulate Health Ltd received funding from by Merck Sharp & Dohme LLC, a subsidiary of Merck & Co., Inc., Rahway, NJ, USA to complete this study and employed Chrissy Bishop, Christian Hauck, Hannah Schirrmacher, Maria Bermudez and Tim Jesudason during the study. Triangulate Health Ltd also provided funding for Kelly Fleetwood through a sub-contract. Daniel Hungerford received consulting fees and honorarium from Merck & Co., Inc., Rahway, NJ, USA, and institutional grants from Seqirus UK Ltd, Merck & Co., Inc., Rahway, NJ, USA, and GlaxoSmithKline Biologicals. Jelle Matthijnssens also received consulting fees from GlaxoSmithKline and Merck & Co., Inc., Rahway, NJ, USA.

Ethical approval

This study did not involve human participants, and as such, ethical approval was not required.

Supplementary material

Supplemental data for this article can be accessed online at https://doi.org/10.1080/21645515.2025.2500261