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. 2025 Dec 2;20(12):e0332607.
doi: 10.1371/journal.pone.0332607. eCollection 2025.

JSTMapp: A web-based joint spatiotemporal modelling and mapping application for epidemiologists

Alfred Ngwira  1   2   3 , Samuel O M Manda  4 , Esron D Karimuribo  1   3 , Sharadhuli I Kimera  1   3
Affiliations

JSTMapp: A web-based joint spatiotemporal modelling and mapping application for epidemiologists

Alfred Ngwira et al. PLoS One. .

Abstract

Disease mapping models help create disease risk maps, which public health policymakers can use to design disease control and monitoring programmes. These models are now routinely implemented using spatial statistical software packages that use frequentist estimation methods, such as SaTScan and HDSpatialScan, and Bayesian estimation methods, such as the Windows version of Bayesian inference using Gibbs sampling (WinBUGS) and R integrated nested Laplace approximation (INLA). We aimed to develop a user-friendly joint disease spatiotemporal modelling and mapping application (JSTMapp) for epidemiologists and health statistics analysts based on Bayesian methods. Using the R package Shiny and utilising the proven and embedded joint spatial modelling technology in the Bayesian statistical software INLA, we developed the JSTMapp. To illustrate its usage, we used cattle bovine tuberculosis (BTB) and human extrapulmonary tuberculosis (EPTB) data in Africa. The application enables the estimation, mapping, and visualisation of both disease-specific and general spatial and temporal risk factors. It also can evaluate spatial, temporal and spatiotemporal correlations. Additionally, exploratory analyses can be performed, such as mapping the standardised disease incidence ratio. The application showed improved performance when launched from GitHub R as opposed to online from the Shiny server. Improving performance from online servers may seek to use personal servers other than Shiny.

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Conflict of interest statement

The authors have declared that no competing interests exist.

Figures

Fig 1
Fig 1. “Explore” tab showing map of raw incidence ratio and time series of cattle BTB and human EPTB in Africa (Source of shapefiles: ArcGIS [43]).
Fig 2
Fig 2. “Model estimation” tab showing summaries of the estimated model of cattle BTB and human EPTB in Africa.
Fig 3
Fig 3. “Spatial and temporal risk” tab showing spatial and temporal risk of cattle BTB and human EPTB in Africa (Source of shapefiles: ArcGIS [43]).
Fig 4
Fig 4. “Spatiotemporal risk” tab showing estimated spatiotemporal risk of cattle BTB and human EPTB in Africa (Source of shapefiles: ArcGIS [43]).
Fig 5
Fig 5. “Prediction” tab showing the predicted raw incidence ratio and model-based risk of cattle BTB and human EPTB in Africa (Source of shapefiles: ArcGIS [43]).
Fig 6
Fig 6. “Correlation” tab showing spatial, temporal and spatiotemporal correlation of cattle BTB and human EPTB in Africa.
See this image and copyright information in PMC

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