Impact of dosing schedules on performance of rotavirus vaccines in Ghana.

Background: Available live-oral rotavirus vaccines are associated with low to moderate performance in low- and middle-income settings. There is limited evidence relating to how the vaccine dosing schedule might be adjusted to improve vaccine performance in these settings. Methods: We used mathematical models fitted to rotavirus surveillance data for children <5 years of age from three different hospitals in Ghana (Korle-Bu Teaching Hospital in Accra, Komfo Anokye Teaching Hospital in Kumasi and War Memorial Hospital in Navrongo) to project the impact of rotavirus vaccination over a 10-year period (April 2012-March 2022). We quantified and compared the impact of the previous vaccination program in Ghana to the model-predicted impact for other vaccine dosing schedules across the three hospitals and the entire country, under different assumptions about vaccine protection. To project the rotavirus vaccine impact over Ghana, we sampled from the range of model parameters for Accra and Navrongo, assuming that these two settings represent the "extremes" of rotavirus epidemiology within Ghana. Results: For the previously implemented 6/10-week monovalent Rotarix vaccine (RV1) schedule, the model-estimated average annual incidence of moderate-to-severe rotavirus-associated gastroenteritis (RVGE) ranged between 1,151 and 3,002 per 100,000 people per year over the 10-year period for the three sites. Compared to no vaccination, the model-estimated median percentage reductions in RVGE ranged from 28-85% and 12-71% among children <1 year and <5 years of age respectively, with the highest and lowest percentage reductions predicted using model parameters estimated for Accra and Navrongo, respectively. The median predicted reductions in RVGE for the whole country ranged from 57-66% and 35-45% among children <1 year and <5 years of age, respectively. The 1/6/10- and 6/10/14-week schedules provided the best and comparable reductions in RVGE compared to the original 6/10-week schedule, whereas there was no improvement in impact for the 10/14-week schedule. Conclusions: We found that administering an additional dose of RV1 might be an effective strategy to improve rotavirus vaccine impact, particularly in settings with low vaccine effectiveness. The results could be extrapolated to other countries using a 2-dose vaccine schedule with low to moderate vaccine performance.

Impact of rotavirus vaccination in Malawi from 2012 to 2022 compared to model predictions before, during, and after the COVID-19 pandemic.

Background: Rotarix® rotavirus vaccine was introduced into the Malawi national immunization program in October 2012. We used a previously developed mathematical models to estimate overall vaccine effectiveness over a 10-year period following rotavirus vaccine introduction. Methods: We analyzed data on children <5 years old hospitalized with acute gastroenteritis in Blantyre, Malawi from January 2012 to June 2022, compared to pre-vaccination data. We estimated vaccine coverage before, during, and after the COVID-19 pandemic using data from rotavirus-negative children. We compared model predictions for the weekly number of rotavirus-associated gastroenteritis (RVGE) cases to the observed number by age to validate model predictions and estimate overall vaccine effectiveness. Results: The number of RVGE and rotavirus-negative acute gastroenteritis cases declined substantially following vaccine introduction. Vaccine coverage among rotavirus-negative controls was >90% with two doses by July 2014, and declined to a low of ~80% in October 2020, before returning to pre-pandemic levels by July 2021. Our models captured the post-vaccination trends in RVGE incidence, with 5.4% to 19.4% of observed weekly RVGE cases falling outside of the 95% prediction intervals. Comparing observed RVGE cases to the model-predicted incidence without vaccination, overall vaccine effectiveness was estimated to be 36.0% (95% prediction interval: 33.6%, 39.9%) peaking in 2014 and was highest in infants (52.5%; 95% prediction interval: 50.1%, 54.9%). Conclusions: Overall effectiveness of rotavirus vaccination in Malawi is modest despite high vaccine coverage and has plateaued since 2016. Our mathematical models provide a validated platform for assessing strategies to improve rotavirus vaccine impact.

Floods and Diarrhea Risk in Young Children in Low- and Middle-Income Countries.

Importance: Climate change is associated with more frequent and intense floods. Current research on the association between flood exposure and diarrhea risk is limited mainly to short-term and event-specific analyses. Moreover, how prior drought or water, sanitation, and hygiene (WaSH) practices influence this association remains largely unknown. Objective: To examine the association between flood exposure and diarrhea risk among children younger than 5 years and to evaluate the compounding influence of prior drought and effect modification by WaSH. Design, Setting, and Participants: This cross-sectional study included multicluster surveys conducted by the Demographic and Health Surveys Program in 43 low- and middle-income countries during 2009 through 2019. This study included children younger than 5 years in all households from each survey cluster. Collected data were analyzed between September 1 and December 31, 2022. Exposures: Historical flood events during 2009 through 2019 were obtained from the Dartmouth Flood Observatory. Main Outcome and Measures: The main outcome was diarrhea prevalence among children younger than 5 years in the 2 weeks before the survey was conducted. Results were analyzed by binomial generalized linear mixed-effects logistic regression models with nested random intercepts for country and survey cluster. Results: Among 639 250 children making up the complete data series (excluding 274 847 children with missing values for diarrhea or baseline characteristics), 6365 (mean [SD] age, 28.9 [17.2] months; 3214 boys [50.5%]; 3151 girls [49.5%]) were exposed to floods during the 8 weeks after a flood started. The prevalence of diarrhea was 13.2% (n = 839) among exposed children and 12.7% (n = 80 337) among unexposed children. Exposure to floods was associated with increased diarrhea risk, with the highest odds ratio (OR) observed during the second to fourth weeks after floods started (OR, 1.35; 95% CI, 1.05-1.73). When floods were stratified by severity and duration, significant associations were observed only for extreme floods (OR during the third to fifth weeks, 2.07; 95% CI, 1.37-3.11) or floods lasting more than 2 weeks (OR during the second to fourth weeks, 1.47; 95% CI, 1.13-1.92), with significantly stronger associations than for less extreme floods or shorter-duration floods, respectively. The OR during the first 4 weeks after the start of floods was significantly higher for floods preceded by a 6-month or longer drought (12-month drought OR, 1.96; 95% CI, 1.53-2.52) than for floods not preceded by a 6-month or longer drought (12-month drought OR, 1.00; 95% CI, 0.79-1.27). Conclusions: These findings suggest that floods, especially severe floods, long-duration floods, and floods preceded by drought, are associated with an increased risk of diarrhea among children younger than 5 years living in low- and middle-income countries. With the projected increasing frequency and intensity of floods and drought under climate change, greater collective efforts are needed to protect children's health from these compounding events.

Climate Drivers of Malaria Transmission Seasonality and Their Relative Importance in Sub-Saharan Africa.

A new database of the Entomological Inoculation Rate (EIR) was used to directly link the risk of infectious mosquito bites to climate in Sub-Saharan Africa. Applying a statistical mixed model framework to high-quality monthly EIR measurements collected from field campaigns in Sub-Saharan Africa, we analyzed the impact of rainfall and temperature seasonality on EIR seasonality and determined important climate drivers of malaria seasonality across varied climate settings in the region. We observed that seasonal malaria transmission was within a temperature window of 15°C-40°C and was sustained if average temperature was well above 15°C or below 40°C. Monthly maximum rainfall for seasonal malaria transmission did not exceed 600 in west Central Africa, and 400 mm in the Sahel, Guinea Savannah, and East Africa. Based on a multi-regression model approach, rainfall and temperature seasonality were found to be significantly associated with malaria seasonality in all parts of Sub-Saharan Africa except in west Central Africa. Topography was found to have significant influence on which climate variable is an important determinant of malaria seasonality in East Africa. Seasonal malaria transmission onset lags behind rainfall only at markedly seasonal rainfall areas such as Sahel and East Africa; elsewhere, malaria transmission is year-round. High-quality EIR measurements can usefully supplement established metrics for seasonal malaria. The study's outcome is important for the improvement and validation of weather-driven dynamical mathematical malaria models that directly simulate EIR. Our results can contribute to the development of fit-for-purpose weather-driven malaria models to support health decision-making in the fight to control or eliminate malaria in Sub-Saharan Africa.

Malaria metrics distribution under global warming: assessment of the VECTRI malaria model over Cameroon.

Malaria is a critical health issue across the world and especially in Africa. Studies based on dynamical models helped to understand inter-linkages between this illness and climate. In this study, we evaluated the ability of the VECTRI community vector malaria model to simulate the spread of malaria in Cameroon using rainfall and temperature data from FEWS-ARC2 and ERA-interim, respectively. In addition, we simulated the model using five results of the dynamical downscaling of the regional climate model RCA4 within two time frames named near future (2035-2065) and far future (2071-2100), aiming to explore the potential effects of global warming on the malaria propagation over Cameroon. The evaluated metrics include the risk maps of the entomological inoculation rate (EIR) and the parasite ratio (PR). During the historical period (1985-2005), the model satisfactorily reproduces the observed PR and EIR. Results of projections reveal that under global warming, heterogeneous changes feature the study area, with localized increases or decreases in PR and EIR. As the level of radiative forcing increases (from 2.6 to 8.5 W.m-2), the magnitude of change in PR and EIR also gradually intensifies. The occurrence of transmission peaks is projected in the temperature range of 26-28 °C. Moreover, PR and EIR vary depending on the three agro-climatic regions of the study area. VECTRI still needs to integrate other aspects of disease transmission, such as population mobility and intervention strategies, in order to be more relevant to support actions of decision-makers and policy makers.

Spatiotemporal patterns of diarrhea incidence in Ghana and the impact of meteorological and socio-demographic factors.

Background: Diarrhea remains a significant public health problem and poses a considerable financial burden on Ghana's health insurance scheme. In order to prioritize district-level hotspots of diarrhea incidence for effective targeted interventions, it is important to understand the potential drivers of spatiotemporal patterns of diarrhea. We aimed to identify the spatiotemporal heterogeneity of diarrhea incidence in Ghana and explore how meteorological and socio-demographic factors influence the patterns. Methods: We used monthly district-level clinically diagnosed diarrhea data between 2012 and 2018 obtained from the Centre for Health Information and Management of the Ghana Health Services. We utilized a hierarchical Bayesian spatiotemporal modeling framework to evaluate potential associations between district-level monthly diarrhea incidence and meteorological variables (mean temperature, diurnal temperature range, surface water presence) and socio-demographic factors (population density, Gini index, District League Table score) in Ghana. In addition, we investigated whether these associations were consistent across the four agro-ecological zones. Results: There was considerable spatial heterogeneity in diarrhea patterns across the districts, with clusters of high diarrhea risk areas mostly found in the transition and savannah zones. The average monthly temporal patterns of diarrhea revealed a weak biannual seasonality with major and minor peaks in June and October, respectively, coinciding with the major and minor rainy seasons. We found a significant association between both meteorological and socio-demographic factors and diarrhea risk, but the strength and direction of associations differed across the four agro-ecological zones. Surface water presence demonstrated consistently positive, while diurnal temperature range and population density demonstrated consistently negative associations with diarrhea both overall and across the agro-ecological zones. Conclusions: Although overall diarrhea incidence is declining in Ghana, our results revealed high-risk districts that could benefit from district-specific tailored intervention strategies to improve control efforts. Ghana health sector policy-makers can use these results to assess the effectiveness of ongoing interventions at the district level and prioritize resource allocation for diarrhea control.

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

To quantify the potential impact of rotavirus vaccines and identify strategies to improve vaccine performance in Bangladesh, a better understanding of the drivers of pre-vaccination rotavirus patterns is required. We developed and fitted mathematical models to 23 years (1990-2012) of weekly rotavirus surveillance data from Dhaka with and without incorporating long-term and seasonal variation in the birth rate and meteorological factors. We performed external model validation using data between 2013 and 2019 from the regions of Dhaka and Matlab. The models showed good agreement with the observed age distribution of rotavirus cases and captured the observed shift in seasonal patterns of rotavirus hospitalizations from biannual to annual peaks. The declining long-term trend in the birth rate in Bangladesh was the key driver of the observed shift from biannual to annual winter rotavirus patterns. Meteorological indices were also important: a 1°C, 1% and 1 mm increase in diurnal temperature range, surface water presence and degree of wetness were associated with a 19%, 3.9% and 0.6% increase in the transmission rate, respectively. The model demonstrated reasonable predictions for both Dhaka and Matlab, and can be used to evaluate the impact of rotavirus vaccination in Bangladesh against changing patterns of disease incidence.

Case fatality risk of diarrhoeal pathogens: a systematic review and meta-analysis.

BACKGROUND: Estimates of the relative contribution of different pathogens to all-cause diarrhoea mortality are needed to inform global diarrhoea burden models and prioritize interventions. We aimed to investigate and estimate heterogeneity in the case fatality risk (CFR) of different diarrhoeal pathogens. METHODS: We conducted a systematic review and meta-analysis of studies that reported cases and deaths for 15 enteric pathogens published between 1990 and 2019. The primary outcome was the pathogen-specific CFR stratified by age group, country-specific under-5 mortality rate, setting, study year and rotavirus vaccine introduction status. We developed fixed-effects and multilevel mixed-effects logistic regression models to estimate the pooled CFR overall and for each pathogen, controlling for potential predictors of heterogeneity. RESULTS: A total of 416 studies met review criteria and were included in the analysis. The overall crude CFR for all pathogens was 0.65%, but there was considerable heterogeneity between and within studies. The overall CFR estimated from a random-effects model was 0.04% (95% CI: 0.026%-0.062%), whereas the pathogen-specific CFR estimates ranged from 0% to 2.7%. When pathogens were included as predictors of the CFR in the overall model, the highest and lowest odds ratios were found for enteropathogenic Escherichia coli (EPEC) [odds ratio (OR) = 3.0, 95% CI: 1.28-7.07] and rotavirus (OR = 0.23, 95% CI: 0.13-0.39), respectively. CONCLUSION: We provide comprehensive estimates of the CFR across different diarrhoeal pathogens and highlight pathogens for which more studies are needed. The results motivate the need for diarrhoeal interventions and could help prioritize pathogens for vaccine development.

Modeling of rotavirus transmission dynamics and impact of vaccination in Ghana.

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.

Modelled and observed mean and seasonal relationships between climate, population density and malaria indicators in Cameroon.

BACKGROUND: A major health burden in Cameroon is malaria, a disease that is sensitive to climate, environment and socio-economic conditions, but whose precise relationship with these drivers is still uncertain. An improved understanding of the relationship between the disease and its drivers, and the ability to represent these relationships in dynamic disease models, would allow such models to contribute to health mitigation and adaptation planning. This work collects surveys of malaria parasite ratio and entomological inoculation rate and examines their relationship with temperature, rainfall, population density in Cameroon and uses this analysis to evaluate a climate sensitive mathematical model of malaria transmission. METHODS: Co-located, climate and population data is compared to the results of 103 surveys of parasite ratio (PR) covering 18,011 people in Cameroon. A limited set of campaigns which collected year-long field-surveys of the entomological inoculation rate (EIR) are examined to determine the seasonality of disease transmission, three of the study locations are close to the Sanaga and Mefou rivers while others are not close to any permanent water feature. Climate-driven simulations of the VECTRI malaria model are evaluated with this analysis. RESULTS: The analysis of the model results shows the PR peaking at temperatures of approximately 22 °C to 26 °C, in line with recent work that has suggested a cooler peak temperature relative to the established literature, and at precipitation rates at 7 mm day-1, somewhat higher than earlier estimates. The malaria model is able to reproduce this broad behaviour, although the peak occurs at slightly higher temperatures than observed, while the PR peaks at a much lower rainfall rate of 2 mm day-1. Transmission tends to be high in rural and peri-urban relative to urban centres in both model and observations, although the model is oversensitive to population which could be due to the neglect of population movements, and differences in hydrological conditions, housing quality and access to healthcare. The EIR follows the seasonal rainfall with a lag of 1 to 2 months, and is well reproduced by the model, while in three locations near permanent rivers the annual cycle of malaria transmission is out of phase with rainfall and the model fails. CONCLUSION: Malaria prevalence is maximum at temperatures of 24 to 26 °C in Cameroon and rainfall rates of approximately 4 to 6 mm day-1. The broad relationships are reproduced in a malaria model although prevalence is highest at a lower rainfall maximum of 2 mm day-1. In locations far from water bodies malaria transmission seasonality closely follows that of rainfall with a lag of 1 to 2 months, also reproduced by the model, but in locations close to a seasonal river the seasonality of malaria transmission is reversed due to pooling in the transmission to the dry season, which the model fails to capture.

A breeding site model for regional, dynamical malaria simulations evaluated using in situ temporary ponds observations.

Daily observations of potential mosquito developmental habitats in a suburb of Kumasi in central Ghana reveal a strong variability in their water persistence times, which ranged between 11 and 81 days. The persistence of the ponds was strongly tied with rainfall, location and size of the puddles. A simple power-law relationship is found to fit the relationship between the average pond depth and area well. A prognostic water balance model is derived that describes the temporal evolution of the pond area and depth, incorporating the power-law geometrical relation. Pond area increases in response to rainfall, while evaporation and infiltration act as sink terms. Based on a range of evaluation metrics, the prognostic model is judged to provide a good representation of the pond coverage evolution at most sites. Finally, we demonstrate that the prognostic equation can be generalised and equally applied to a grid-cell to derive a fractional pond coverage, and thus can be implemented in spatially distributed models for relevant vector- borne diseases such as malaria.

Mosquito breeding site water temperature observations and simulations towards improved vector-borne disease models for Africa.

An energy budget model is developed to predict water temperature of typical mosquito larval developmental habitats. It assumes a homogeneous mixed water column driven by empirically derived fluxes. The model shows good agreement at both hourly and daily time scales with 10-min temporal resolution observed water temperatures, monitored between June and November 2013 within a peri-urban area of Kumasi, Ghana. There was a close match between larvae development times calculated using either the model-derived or observed water temperatures. The water temperature scheme represents a significant improvement over assuming the water temperature to be equal to air temperature. The energy budget model requires observed minimum and maximum temperatures, information that is generally available from weather stations. Our results show that hourly variations in water temperature are important for the simulation of aquatic-stage development times. By contrast, we found that larval development is insensitive to sub-hourly variations. Modelling suggests that in addition to water temperature, accurate estimation of degree-day development time is very important to correctly predict the larvae development times. The results highlight the potential of the model to predict water temperature of temporary bodies of surface water. Our study represents an important contribution towards the improvement of weatherdriven dynamical disease models, including those designed for malaria early forecasting systems.