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

Abstract

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.

Spatial and Genomic Data to Characterize Endemic Typhoid Transmission

“…Geolocating cases as a part of routine surveillance has become increasingly common, allowing for spatially informed disease control, as for the targeting of polio vaccines [ 27 ], and the investigation of hot spots and transmission routes of Ebola [ 28 ] and severe acute respiratory syndrome coronavirus 2. Geospatial analyses for typhoid fever to date have revealed the spatially heterogeneous nature of the disease at both municipal and national scales [ 29 , 30 ], but generalizable and epidemiologically relevant incidence covariates have yet to be identified [ 31 ]. In the current study, we demonstrated the utility of using statistical modeling to place multidimensional data sets in the context of a classic observational study, and in so doing we provide deeper insight into S .…”

Spatial and temporal clustering of typhoid fever in an urban slum of Dhaka City: Implications for targeted typhoid vaccination

“…Temporal clustering aligns with seasonal variations and is impacted by temperature and rainfall [17]. Several studies have reported on the spatiotemporal trends of typhoid fever in different settings [11,[18][19][20][21] and phylogenetic analyses of Salmonella Typhi isolates have aided in the understanding of modes of transmission in endemic areas [18]. However, these approaches did not…”

Occurrence of human infection withSalmonellaTyphi in sub-Saharan Africa

“…Outbreaks often show district-level variation of typhoid incidence [7][8][9] and country-level surveys show sub-national heterogeneity of incidence [10][11][12][13] . Understanding idiosyncratic behaviour of typhoid transmission between communities will be critical for a country to implement intervention programs such as campaign vaccination against endemic or 4 epidemic typhoid fever more efficiently and effectively.…”