# Mathematical modeling to optimize rotavirus vaccination in sub-Saharan Africa > **NIH NIH K01** · UNIVERSITY OF MARYLAND BALTIMORE · 2020 · $129,939 ## Abstract Rotavirus is the leading cause of childhood diarrheal disease, responsible for an estimated 146,000 annual deaths across the globe. A large majority of these deaths occur in low-resource settings where life-saving rehydration therapy is often inaccessible. A challenge to mitigating this burden arises from the reduced efficacy and durability of rotavirus vaccines observed in low-resource settings compared to high-resource settings. Proposed explanations include the higher force of infection in these settings, host factors such as poor nutrition or intestinal co-morbidities, and interference from maternal antibodies or the concomitantly administered oral poliovirus vaccine. These mechanisms may not be mutually exclusive. Depending on the contribution of each potential factor, optimization of the vaccination schedule, through addition of an age-based booster dose, a seasonal booster, or a delay in the initiation of the primary vaccine series, could improve vaccine protection. Each proposed schedule change carries advantages and trade-offs. An age-based booster would have straightforward roll-out within existing infrastructure, while a seasonal booster may have greater impact on incidence. Seasonal boosters may be particularly effective if maternal antibody interference is heightened for infants born during or following the peak of the rotavirus season. Delayed series initiation could improve vaccine protection for older infants, but may extend the period during which infants are susceptible to primary infection. To inform policy development based on these potential interventions, mathematical modeling and cost-effectiveness analyses are tools to evaluate their relative epidemiological and economic impacts. I will use data-driven mathematical modeling to optimize the rotavirus vaccination strategy for Mali based on anticipated health benefits and associated costs, through the following aims: (1) Characterize seasonal variation in maternal and infant antibody titers. Using pairs of mother and infant sera collected by CVD-Mali prior to rotavirus vaccine introduction, I will compare titers during high and low exposure seasons to identify differences in anti-rotavirus IgA and rotavirus neutralizing antibody levels. (2) Analyze rotavirus vaccine efficacy and waning. I will build a dynamic rotavirus transmission model explicitly parameterized and validated for the Malian context. Among other key factors, I will incorporate any seasonal trends in maternal antibody interference identified in Aim 1. (3) Optimize rotavirus vaccination schedule. Applying the transmission model developed in Aim 2, I will compare severe rotavirus incidence under the status quo three-dose (primary) schedule against four proposed schedule changes: delayed primary series initiation, introduction of a booster for 9-month-olds, seasonal booster for children aged 6 through 18 months, and the replacement of the third primary dose with a dose for older children. I will incorporate pr... ## Key facts - **NIH application ID:** 9830583 - **Project number:** 5K01AI141576-02 - **Recipient organization:** UNIVERSITY OF MARYLAND BALTIMORE - **Principal Investigator:** Meagan Colleen Fitzpatrick - **Activity code:** K01 (R01, R21, SBIR, etc.) - **Funding institute:** NIH - **Fiscal year:** 2020 - **Award amount:** $129,939 - **Award type:** 5 - **Project period:** 2018-12-01 → 2023-11-30 ## Primary source NIH RePORTER: https://reporter.nih.gov/project-details/9830583 ## Citation > US National Institutes of Health, RePORTER application 9830583, Mathematical modeling to optimize rotavirus vaccination in sub-Saharan Africa (5K01AI141576-02). Retrieved via AI Analytics 2026-05-22 from https://api.ai-analytics.org/grant/nih/9830583. Licensed CC0. --- *[NIH grants dataset](/datasets/nih-grants) · CC0 1.0*