Modeling of rotavirus transmission dynamics and impact of vaccination in Ghana

Abstract

Background: Rotavirus incidence remains relatively high in low-income countries (LICs) compared to high-income countries (HICs) after vaccine introduction. Ghana introduced monovalent rotavirus vaccine in April 2012 and despite the high coverage, vaccine performance has been modest compared to developed countries. The predictors of low vaccine effectiveness in LICs are poorly understood, and the drivers of subnational heterogeneity in rotavirus vaccine impact are unknown. Methods: We used mathematical models to investigate variations in rotavirus incidence in children <5 years old in Ghana. We fit models to surveillance and case-control data from three different hospitals: Korle-Bu Teaching Hospital in Accra, Komfo Anokye Teaching Hospital in Kumasi, and War Memorial Hospital in Navrongo. The models were fitted to both pre- and post-vaccine data to estimate parameters describing the transmission rate, waning of maternal immunity, and vaccine response rate. Results: The seasonal pattern and age distribution of rotavirus cases varied among the three study sites in Ghana. Our model was able to capture the spatio-temporal variations in rotavirus incidence across the three sites and showed good agreement with the age distribution of observed cases. The rotavirus transmission rate was highest in Accra and lowest in Navrongo, while the estimated duration of maternal immunity was longer (∼5 months) in Accra and Kumasi and shorter (∼3 months) in Navrongo. The proportion of infants who responded to the vaccine was estimated to be high in Accra and Kumasi and low in Navrongo. Conclusions: Rotavirus vaccine impact varies within Ghana. A low vaccine response rate was estimated for Navrongo, where rotavirus is highly seasonal and incidence limited to a few months of the year. Our findings highlight the need to further explore the relationship between rotavirus seasonality, maternal immunity, and vaccine response rate to determine how they influence vaccine effectiveness and to develop strategies to improve vaccine impact.

Predictive modeling for infectious diarrheal disease in pediatric populations: A systematic review

“…Majority of the studies were conducted in Asia 17 (44.7%) [15][16][17][18][19][20][21][22][23][24][25][26][27][28][29][30][31] with 8 (47.1%) of them conducted in Bangladesh alone. Eight studies (21.1%), 4 (10.5%), and 2 (5.3%) reported data from Africa, [32][33][34][35][36][37][38][39] Americas, [40][41][42][43] and Europe, 44,45 respectively. Seven studies (17.4%) were multi-site [46][47][48][49][50][51][52] of which 4 (57.1%) were reported from the Global Enteric Multicenter Study (GEMS).…”

“…1,54 Consequently, rotavirus vaccines have been incorporated into national immunization programs following recommendations by the World Health Organization (WHO). 55 Nine studies focused on predictions around rotavirus vaccine with seven studies focusing on vaccine impact, 28,35,38,[43][44][45]47 one on vaccine hesitancy, 27 and one on vaccine cost-effectiveness 19 (Table 3).…”

“…The seven studies on vaccine impact used data from Niger, 35 Ghana, 38 United States, 43 Kyrgyzstan, 28 England and Wales, 44,45 and a multi-site study (France, Germany, Italy, Spain, and the United Kingdom). 47 These studies built predictive models using SIRs models, periodic regression models and deterministic age-structured dynamic model (Table 2).…”

Pediatric Norovirus Vaccination: Modeling the Influence of Schedule on Population Health

“…We developed a deterministic, age-structured dynamic compartmental transmission model building on our and others' previously published norovirus vaccination models combined with elements of a pediatric rotavirus vaccination model 22,[35][36][37] . To simulate different pediatric immunization schedules, this model tracks transmission in finer age groups than previous models (Figure 1).…”

The influence of demographic and meteorological factors on temporal patterns of rotavirus infection in Dhaka, Bangladesh

“…In order to evaluate the impact of rotavirus vaccination against changing epidemiological patterns of disease burden, it is necessary to identify potential drivers of rotavirus incidence in the absence of vaccination. Some of these drivers of pre-vaccination rotavirus epidemiology could also interfere with vaccine performance and contribute to the differential rotavirus vaccine impact between and within LMICs and high-income settings [13][14][15][16].…”