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Correction: Sutthanont et al. Effectiveness of Herbal Essential Oils as Single and Combined Repellents Against <i>Aedes aegypti</i>, <i>Anopheles dirus</i> and <i>Culex quinquefasciatus</i> (Diptera: Culicidae). <i>Insects</i> 2022, <i>13</i>, 658.

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Bayesian spatio-temporal distributed lag modeling for delayed climatic effects on sparse malaria incidence data.

Chawarat Rotejanaprasert1,2, Nattwut Ekapirat3, Prayuth Sudathip4

  • 1Department of Tropical Hygiene, Faculty of Tropical Medicine, Mahidol University, Ratchathewi, Bangkok, 10400, Thailand. chawarat.rot@mahidol.ac.th.

BMC Medical Research Methodology
|December 21, 2021
PubMed
Summary
This summary is machine-generated.

A new Bayesian method estimates climate

Keywords:
BayesianLag effectMalariaSpatiotemporalWeather

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Area of Science:

  • Epidemiology
  • Environmental Health
  • Biostatistics

Background:

  • Malaria transmission is declining in the Greater Mekong Subregion (GMS), with remaining foci characterized by low incidence.
  • Accurate prediction of malaria trends is crucial for achieving regional elimination goals by 2030.
  • Standard analytical methods may be inadequate for low-transmission settings due to climatic variable lags and sparse case data.

Purpose of the Study:

  • To develop and validate a methodology for estimating the spatio-temporal lag effect of climate on malaria incidence in low-transmission areas.
  • To address challenges posed by delayed climate-incidence relationships and sparse data in the GMS.

Main Methods:

  • A Bayesian framework was employed to model the spatio-temporal lag effects of climatic factors on malaria incidence.
  • A simulation study assessed model performance with lagged effects and excess zeros, mimicking sparse malaria case data.
  • A case study in Thailand utilized sub-district level data to estimate lagged environmental effects on malaria.

Main Results:

  • Models incorporating delayed effects and excess zeros demonstrated superior performance in simulations.
  • The case study successfully estimated lagged climatic effects on malaria incidence using real-world data.
  • The developed models effectively estimated the shape of the association between climate variables and malaria incidence.

Conclusions:

  • A novel method for estimating climate's spatiotemporal effect on malaria trends in low-transmission settings has been presented.
  • This methodology can enhance the understanding and prediction of malaria incidence.
  • Further development could aid policymakers in resource allocation and malaria elimination strategies.