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Bayesian spatio-temporal model with INLA for dengue fever risk prediction in Costa Rica

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Abstract

Due to the rapid geographic spread of the Aedes mosquito and the increase in dengue incidence, dengue fever has been an increasing concern for public health authorities in tropical and subtropical countries worldwide. Significant challenges such as climate change, the burden on health systems, and the rise of insecticide resistance highlight the need to introduce new and cost-effective tools for developing public health interventions. Various and locally adapted statistical methods for developing climate-based early warning systems have increasingly been an area of interest and research worldwide. Costa Rica, a country with microclimates and endemic circulation of the dengue virus (DENV) since 1993, provides ideal conditions for developing projection models with the potential to help guide public health efforts and interventions to control and monitor future dengue outbreaks. Climate information was incorporated to model and forecast the dengue cases and relative risks using a Bayesian spatio-temporal model, from 2000 to 2021, in 32 Costa Rican municipalities. This approach is capable of analyzing the spatio-temporal behavior of dengue and also producing reliable predictions.

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Code availability

The code and datasets is available online under https://github.com/shuwei325/DengueCR_Bayesian_ST_Prediction.git

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Funding

The authors did not receive support from any organization for the submitted work.

Author information

Authors and Affiliations

  1. Centro de Investigación en Matematica Pura y Aplicada, Universidad de Costa Rica, San José, Costa Rica

    Shu Wei Chou-Chen, Luis A. Barboza, Paola Vásquez, Juan G. Calvo & Fabio Sanchez

  2. Escuela de Estadística, Universidad de Costa Rica, San José, Costa Rica

    Shu Wei Chou-Chen

  3. Escuela de Matemática, Universidad de Costa Rica, San José, Costa Rica

    Luis A. Barboza, Juan G. Calvo & Fabio Sanchez

  4. Department of Public Health Sciences, University of California Davis, Davis, CA, USA

    Yury E. García

  5. Centro de Investigaciones Geofísicas and Escuela de Física, Universidad de Costa Rica, San José, Costa Rica

    Hugo G. Hidalgo

Authors
  1. Shu Wei Chou-Chen
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  2. Luis A. Barboza
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  3. Paola Vásquez
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  4. Yury E. García
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  5. Juan G. Calvo
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  6. Hugo G. Hidalgo
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  7. Fabio Sanchez
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Contributions

S.W.C. proposed the main conceptual ideas, performed formal analysis, results validations and writing original draft preparation. L.A.B. worked out in data curation, formal analysis, and writing original draft preparation. P.V. helped with the writing original draft preparation and the public health contextualization. Y.E.G. work out in the original writing draft. J.G.C. helped write a review and editing the final version. H.G.H. work out in writing, review and editing. And F.S. supervises the research team and works out in writing, review and editing the final version.

Corresponding author

Correspondence to Shu Wei Chou-Chen.

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The authors declare that they have no known competing financial interests or personal relationships that could have appeared to influence the work reported in this paper.

Additional information

Handling Editor: Luiz Duczmal.

Appendices

Appendix 1

Naïve methods’ predictive metrics of the testing period (from January to March 2021)

Table 4 Predictive metrics of testing data set of the naïve forecasting and negative binomial null model
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Appendix 2

Dengue cases modelling and prediction (see Figs. 8, 9).

Fig. 8
figure 8

Observed (black) and 95% posterior predictive dengue cases (red) over the training period. Upper six panels: best municipalities according to NIS metric. Lower three panels: worst municipalities according to NIS metric

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Fig. 9
figure 9

Observed (black) and 95% posterior predictive dengue cases (red) over the testing period. Upper six panels: best municipalities according to NIS metric. Lower three panels: worst municipalities according to NIS metric

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Appendix 3

Relative risk prediction maps in 2002, 2011 and 2020 (see Figs. 10, 11, 12).

Fig. 10
figure 10

Posterior mean of relative risks from January to December 2002 for 81 municipalities in Costa Rica

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Fig. 11
figure 11

Posterior mean of relative risks from January to December 2011 for 81 municipalities in Costa Rica

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Fig. 12
figure 12

Posterior mean of relative risks from January to December 2020 for 81 municipalities in Costa Rica

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Appendix 4

Absolute percentage error maps in 2002, 2011 and 2020 (see Figs. 13, 14, 15)

Fig. 13
figure 13

Absolute percentage error from January to December 2002 for 81 municipalities in Costa Rica

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Fig. 14
figure 14

Absolute percentage error from January to December 2011 for 81 municipalities in Costa Rica

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Fig. 15
figure 15

Absolute percentage error from January to December 2020 for 81 municipalities in Costa Rica

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Chou-Chen, S.W., Barboza, L.A., Vásquez, P. et al. Bayesian spatio-temporal model with INLA for dengue fever risk prediction in Costa Rica. Environ Ecol Stat 30, 687–713 (2023). https://doi.org/10.1007/s10651-023-00580-9

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