Spatial variation in lymphatic filariasis risk factors of hotspot zones in Ghana.

BACKGROUND: Lymphatic Filariasis (LF), a parasitic nematode infection, poses a huge economic burden to affected countries. LF endemicity is localized and its prevalence is spatially heterogeneous. In Ghana, there exists differences in LF prevalence and multiplicity of symptoms in the country's northern and southern parts. Species distribution models (SDMs) have been utilized to explore the suite of risk factors that influence the transmission of LF in these geographically distinct regions. METHODS: Presence-absence records of microfilaria (mf) cases were stratified into northern and southern zones and used to run SDMs, while climate, socioeconomic, and land cover variables provided explanatory information. Generalized Linear Model (GLM), Generalized Boosted Model (GBM), Artificial Neural Network (ANN), Surface Range Envelope (SRE), Multivariate Adaptive Regression Splines (MARS), and Random Forests (RF) algorithms were run for both study zones and also for the entire country for comparison. RESULTS: Best model quality was obtained with RF and GBM algorithms with the highest Area under the Curve (AUC) of 0.98 and 0.95, respectively. The models predicted high suitable environments for LF transmission in the short grass savanna (northern) and coastal (southern) areas of Ghana. Mainly, land cover and socioeconomic variables such as proximity to inland water bodies and population density uniquely influenced LF transmission in the south. At the same time, poor housing was a distinctive risk factor in the north. Precipitation, temperature, slope, and poverty were common risk factors but with subtle variations in response values, which were confirmed by the countrywide model. CONCLUSIONS: This study has demonstrated that different variable combinations influence the occurrence of lymphatic filariasis in northern and southern Ghana. Thus, an understanding of the geographic distinctness in risk factors is required to inform on the development of area-specific transmission control systems towards LF elimination in Ghana and internationally.

Spatial variation and hot-spots of district level diarrhea incidences in Ghana: 2010-2014.

BACKGROUND: Diarrhea is a public health menace, especially in developing countries. Knowledge of the biological and anthropogenic characteristics is abundant. However, little is known about its spatial patterns especially in developing countries like Ghana. This study aims to map and explore the spatial variation and hot-spots of district level diarrhea incidences in Ghana. METHODS: Data on district level incidences of diarrhea from 2010 to 2014 were compiled together with population data. We mapped the relative risks using empirical Bayesian smoothing. The spatial scan statistics was used to detect and map spatial and space-time clusters. Logistic regression was used to explore the relationship between space-time clustering and urbanization strata, i.e. rural, peri-urban, and urban districts. RESULTS: We observed substantial variation in the spatial distribution of the relative risk. There was evidence of significant spatial clusters with most of the excess incidences being long-term with only a few being emerging clusters. Space-time clustering was found to be more likely to occur in peri-urban districts than in rural and urban districts. CONCLUSION: This study has revealed that the excess incidences of diarrhea is spatially clustered with peri-urban districts showing the greatest risk of space-time clustering. More attention should therefore be paid to diarrhea in peri-urban districts. These findings also prompt public health officials to integrate disease mapping and cluster analyses in developing location specific interventions for reducing diarrhea.

Predicting COVID-19 hospitalizations: The importance of healthcare hotlines, test positivity rates and vaccination coverage.

In this study, we developed a negative binomial regression model for one-week ahead spatio-temporal predictions of the number of COVID-19 hospitalizations in Uppsala County, Sweden. Our model utilized weekly aggregated data on testing, vaccination, and calls to the national healthcare hotline. Variable importance analysis revealed that calls to the national healthcare hotline were the most important contributor to prediction performance when predicting COVID-19 hospitalizations. Our results support the importance of early testing, systematic registration of test results, and the value of healthcare hotline data in predicting hospitalizations. The proposed models may be applied to studies modeling hospitalizations of other viral respiratory infections in space and time assuming count data are overdispersed. Our suggested variable importance analysis enables the calculation of the effects on the predictive performance of each covariate. This can inform decisions about which types of data should be prioritized, thereby facilitating the allocation of healthcare resources.

Evolution of spatial disease clusters via a Bayesian space-time variability modelling.

This study proposes to use exceedance posterior probabilities of a space-time random-effects model to study the temporal dynamics of clusters. The local time trends specified for each area is further smoothed over space. We modelled the common spatial and the space-varying temporal trend using a multivariate Markov Random field to incorporate within-area correlations. We estimate the model parameters within a fully Bayesian framework. The exceedance posterior probabilities are further used to classify the common spatial trend into hot-spots, cold-spots, and neutral-spots. The local time trends are classified into increasing, decreasing, and stable trends. The results is a 3×3 table depicting the time trends within clusters. As a demonstration, we apply the proposed methodology to study the evolution of spatial clustering of intestinal parasite infections in Ghana. We find the methodology presented in this paper applicable and extendable to other or multiple tropical diseases which may have different space-time conceptualizations.

Geospatial Health (GeoHealth): Current Trends, Methods, and Applications.

As an emerging field, Geospatial Health (GeoHealth) integrates geospatial technologies, (spatial) epidemiology, and health services/resource allocations (health accessibility), with a focus to fight the burden of diseases [...].

Modeling schistosomiasis spatial risk dynamics over time in Rwanda using zero-inflated Poisson regression.

The recorded clinical cases of S. mansoni at primary health facility level contain an excessive number of zero records. This could mean that no S. mansoni infection occurred (a true zero) in the health facility service area but it could also that at least one infection occurred but none were reported or diagnosed (a false zero). Standard statistical analysis, using exploratory or confirmatory spatial regression, fail to account for this type of data insufficiency. This study developed a zero-inflated Poisson model to explore the spatiotemporal variation in schistosomiasis risk at a fine spatial scale. We used environmental data generated at primary health facility service area level as explanatory variables affecting transmission risk. Identified risk factors were subsequently used to project the spatial variability of S. mansoni infection risk for 2050. The zero-inflated Poisson model shows a considerable increase of relative risk of the schistosomiasis over one decade. Furthermore, the changes between the risk in 2009 and forecasted risk by 2050 indicated both persistent and emerging areas with high relative risk of schistosomiasis infection. The risk of schistosomiasis transmission is 69%, 29%, and 50% higher in areas with rice cultivation, proximity to rice farms, and proximity to a water body respectively. The prediction and forecasting maps provide a valuable tool for monitoring schistosomiasis risk in Rwanda and planning future disease control initiatives in wetland ecosystem development context.

Bayesian Random Effect Modeling for analyzing spatial clustering of differential time trends of diarrhea incidences.

In 2012, nearly 644,000 people died from diarrhea in sub-Saharan Africa. This is a significant obstacle towards the achievement of the Sustainable Development Goal 3 of ensuring a healthy life and promoting the wellbeing at all ages. To enhance evidence-based site-specific intervention and mitigation strategies, especially in resource-poor countries, we focused on developing differential time trend models for diarrhea. We modeled the logarithm of the unknown risk for each district as a linear function of time with spatially varying effects. We induced correlation between the random intercepts and slopes either by linear functions or bivariate conditional autoregressive (BiCAR) priors. In comparison, models which included correlation between the varying intercepts and slopes outperformed those without. The convolution model with the BiCAR correlation prior was more competitive than the others. The inclusion of correlation between the intercepts and slopes provided an epidemiological value regarding the response of diarrhea infection dynamics to environmental factors in the past and present. We found diarrhea risk to increase by 23% yearly, a rate far exceeding Ghana's population growth rate of 2.3%. The varying time trends widely varied and clustered, with the majority of districts with at least 80% chance of their rates exceeding the previous years. These findings can be useful for active site-specific evidence-based planning and interventions for diarrhea.

Poisson-Gamma Mixture Spatially Varying Coefficient Modeling of Small-Area Intestinal Parasites Infection.

Understanding the spatially varying effects of demographic factors on the spatio-temporal variation of intestinal parasites infections is important for public health intervention and monitoring. This paper presents a hierarchical Bayesian spatially varying coefficient model to evaluate the effects demographic factors on intestinal parasites morbidities in Ghana. The modeling relied on morbidity data collected by the District Health Information Management Systems. We developed Poisson and Poisson-gamma spatially varying coefficient models. We used the demographic factors, unsafe drinking water, unsafe toilet, and unsafe liquid waste disposal as model covariates. The models were fitted using the integrated nested Laplace approximations (INLA). The overall risk of intestinal parasites infection was estimated to be 10.9 per 100 people with a wide spatial variation in the district-specific posterior risk estimates. Substantial spatial variation of increasing multiplicative effects of unsafe drinking water, unsafe toilet, and unsafe liquid waste disposal occurs on the variation of intestinal parasites risk. The structured residual spatial variation widely dominates the unstructured component, suggesting that the unaccounted-for risk factors are spatially continuous in nature. The study concludes that both the spatial distribution of the posterior risk and the associated exceedance probability maps are essential for monitoring and control of intestinal parasites.

Temporal trend and spatial clustering of cholera epidemic in Kumasi-Ghana.

Knowledge of the temporal trends and spatial patterns will have significant implications for effective preparedness in future epidemics. Our objective was to investigate the temporal trends and the nature of the spatial interaction of cholera incidences, dwelling on an outbreak in the Kumasi Metropolis, Ghana. We developed generalized nonparametric and segmented regression models to describe the epidemic curve. We used the pair correlation function to describe the nature of spatial clustering parameters such as the maximum scale of interaction and the scale of maximal interaction. The epidemic rose suddenly to a peak with 40% daily increments of incidences. The decay, however, was slower with 5% daily reductions. Spatial interaction occurred within 1 km radius. Maximal interaction occurred within 0.3 km, suggesting a household level of interactions. Significant clustering during the first week suggests secondary transmissions sparked the outbreak. The nonparametric and segmented regression models, together with the pair correlation function, contribute to understanding the transmission dynamics. The issue of underreporting remains a challenge we seek to address in future. These findings, however, will have innovative implications for developing preventive measures during future epidemics.

Spatial and temporal heterogeneities of district-level typhoid morbidities in Ghana: A requisite insight for informed public health response.

Typhoid fever is estimated to cause between 9.9-24.2 million cases and 75,000-208,000 deaths per year globally. Low-income and middle-income countries report the majority of cases, especially those in sub-Saharan Africa. The epidemiology of typhoid fever is poorly understood, particularly in Ghana where there has been no study of the within-country variation. Our objective was to explore and analyze the spatial and temporal patterns of typhoid fever morbidities in Ghana. We used the global and local Moran's indices to uncover the existence of global and local spatial patterns, respectively. Generalized linear autoregressive moving average (glarma) models were developed to explore the overall and regional level temporal patterns of morbidities. The overall index of spatial association was 0.19 (p < 0.001). The global Moran's monthly indices of clustering ranged from ≈ 0 - 0.28, with few non-significant (p > 0.05) estimates. The yearly estimates were all significant (p < 0.001) and ranged from 0.1-0.19, suggesting spatial clustering of typhoid. The local Moran's maps indicated isolated high contributions of clustering within the Upper West and Western regions. The overall and regional level glarma models indicated significant first and second-order serial correlation as well as quarterly trends. These findings can provide relevant epidemiological insight into the spatial and temporal patterns of typhoid epidemiology and useful to complement the development of control strategies by public health managers.

Spatio-temporal dynamics of schistosomiasis in Rwanda between 2001 and 2012: impact of the national Neglected Tropical Disease control programme.

Schistosomiasis is recognised as a major public health problem in Rwanda. We aimed to identify the spatio-temporal dynamics of its distribution at a fine-scale spatial resolution and to explore the impact of control programme interventions. Incidence data of Schistosoma mansoni infection at 367 health facilities were obtained for the period 2001-2012. Disease cluster analyses were conducted using spatial scan statistics and geographic information systems. The impact of control interventions was assessed for three distinct sub-periods. Findings demonstrated persisting, emerging and re-emerging clusters of schistosomiasis infection across space and time. The control programme initially caused an abrupt increase in incidence rates during its implementation phase. However, this was followed by declining and disappearing clusters when the programme was fully in place. The findings presented should contribute to a better understanding of the dynamics of schistosomiasis distribution to be used when implementing future control activities, including prevention and elimination efforts.