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<p>Over the past decade, multiple outbreaks of hand, foot, and mouth disease (HFMD) have occurred in East Asia, especially in China. It is crucial to understand the distribution pattern and risk factors of HFMD while also studying the corresponding characteristics of reinfections. This paper aims to jointly analyze the spatiotemporal distribution and influential factors of primary infection and reinfection of HFMD in Jiangsu province, China, under the Bayesian framework. Using county-level monthly HFMD counts from 2009 to 2023, we proposed four spatiotemporal hierarchical models with latent effects shared in the reinfection sub-model to evaluate the influence of air pollution, meteorological factors, and demographic characteristics on primary infection and reinfection of HFMD. The integrated nested Laplace approximation (INLA) approach estimated model parameters and quantified the spatial and temporal random effects. The optimal model with spatial, temporal, and spatiotemporal interaction effects indicated a significant positive influence of NO<InlineEquation ID="IEq1"> <InlineMediaObject> <ImageObject Color="BlackWhite" FileRef="12889_2025_24931_Article_IEq1.gif" Format="GIF" Height="10" Rendition="HTML" Resolution="72" Type="Linedraw" Width="8" /> </InlineMediaObject> <EquationSource Format="TEX">\(_2\)</EquationSource> </InlineEquation>, wind speed, relative humidity, and solar radiation, as well as a significant negative effect of PM<InlineEquation ID="IEq2"> <InlineMediaObject> <ImageObject Color="BlackWhite" FileRef="12889_2025_24931_Article_IEq2.gif" Format="GIF" Height="10" Rendition="HTML" Resolution="72" Type="Linedraw" Width="17" /> </InlineMediaObject> <EquationSource Format="TEX">\(_{2.5}\)</EquationSource> </InlineEquation>, O<InlineEquation ID="IEq3"> <InlineMediaObject> <ImageObject Color="BlackWhite" FileRef="12889_2025_24931_Article_IEq3.gif" Format="GIF" Height="10" Rendition="HTML" Resolution="72" Type="Linedraw" Width="8" /> </InlineMediaObject> <EquationSource Format="TEX">\(_{3}\)</EquationSource> </InlineEquation>, temperature above 27 °C, precipitation, and COVID-19, on both infections. Scattered status and critical primary infection have a significant positive effect on primary infection and reinfection. Positive sharing coefficients revealed similar spatiotemporal patterns of both types of infection. Beyond these fixed effects, non-linear analyses further highlighted comparable exposure-response patterns for primary infection and reinfection, with notable thresholds around 50–70 <InlineEquation ID="IEq4"> <InlineMediaObject> <ImageObject Color="BlackWhite" FileRef="12889_2025_24931_Article_IEq4.gif" Format="GIF" Height="13" Rendition="HTML" Resolution="72" Type="Linedraw" Width="12" /> </InlineMediaObject> <EquationSource Format="TEX">\(\upmu\)</EquationSource> </InlineEquation>g/m<InlineEquation ID="IEq5"> <InlineMediaObject> <ImageObject Color="BlackWhite" FileRef="12889_2025_24931_Article_IEq5.gif" Format="GIF" Height="10" Rendition="HTML" Resolution="72" Type="Linedraw" Width="8" /> </InlineMediaObject> <EquationSource Format="TEX">\(^3\)</EquationSource> </InlineEquation> for PM<InlineEquation ID="IEq6"> <InlineMediaObject> <ImageObject Color="BlackWhite" FileRef="12889_2025_24931_Article_IEq2.gif" Format="GIF" Height="10" Rendition="HTML" Resolution="72" Type="Linedraw" Width="17" /> </InlineMediaObject> <EquationSource Format="TEX">\(_{2.5}\)</EquationSource> </InlineEquation>, 90 <InlineEquation ID="IEq7"> <InlineMediaObject> <ImageObject Color="BlackWhite" FileRef="12889_2025_24931_Article_IEq4.gif" Format="GIF" Height="13" Rendition="HTML" Resolution="72" Type="Linedraw" Width="12" /> </InlineMediaObject> <EquationSource Format="TEX">\(\upmu\)</EquationSource> </InlineEquation>g/m<InlineEquation ID="IEq8"> <InlineMediaObject> <ImageObject Color="BlackWhite" FileRef="12889_2025_24931_Article_IEq5.gif" Format="GIF" Height="10" Rendition="HTML" Resolution="72" Type="Linedraw" Width="8" /> </InlineMediaObject> <EquationSource Format="TEX">\(^3\)</EquationSource> </InlineEquation> for O<InlineEquation ID="IEq9"> <InlineMediaObject> <ImageObject Color="BlackWhite" FileRef="12889_2025_24931_Article_IEq3.gif" Format="GIF" Height="10" Rendition="HTML" Resolution="72" Type="Linedraw" Width="8" /> </InlineMediaObject> <EquationSource Format="TEX">\(_{3}\)</EquationSource> </InlineEquation>, 80 <InlineEquation ID="IEq10"> <InlineMediaObject> <ImageObject Color="BlackWhite" FileRef="12889_2025_24931_Article_IEq4.gif" Format="GIF" Height="13" Rendition="HTML" Resolution="72" Type="Linedraw" Width="12" /> </InlineMediaObject> <EquationSource Format="TEX">\(\upmu\)</EquationSource> </InlineEquation>g/m<InlineEquation ID="IEq11"> <InlineMediaObject> <ImageObject Color="BlackWhite" FileRef="12889_2025_24931_Article_IEq5.gif" Format="GIF" Height="10" Rendition="HTML" Resolution="72" Type="Linedraw" Width="8" /> </InlineMediaObject> <EquationSource Format="TEX">\(^3\)</EquationSource> </InlineEquation> for NO<InlineEquation ID="IEq12"> <InlineMediaObject> <ImageObject Color="BlackWhite" FileRef="12889_2025_24931_Article_IEq12.gif" Format="GIF" Height="10" Rendition="HTML" Resolution="72" Type="Linedraw" Width="8" /> </InlineMediaObject> <EquationSource Format="TEX">\(_{2}\)</EquationSource> </InlineEquation>, 20 °C for temperature, and 55% for relative humidity. Our findings reveal distinct drivers of primary infection and reinfection but also similar spatiotemporal patterns, suggesting the need for both targeted protection of vulnerable groups and integrated surveillance strategies.</p>

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Bayesian joint spatiotemporal modelling of primary infection and reinfection of HFMD at county level in Jiangsu, China, 2009–2023

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  • 发表日期：

    2025年10月28日

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  • Weijia Wang,
  • Hong Ji,
  • Yifan Tang,
  • Hongfei Zhu,
  • Wendong Liu,
  • Kai Wang,
  • Liguo Zhu,
  • Chengxiu Ling,
  • Changjun Bao,
  • Ying Wang

摘要

Over the past decade, multiple outbreaks of hand, foot, and mouth disease (HFMD) have occurred in East Asia, especially in China. It is crucial to understand the distribution pattern and risk factors of HFMD while also studying the corresponding characteristics of reinfections. This paper aims to jointly analyze the spatiotemporal distribution and influential factors of primary infection and reinfection of HFMD in Jiangsu province, China, under the Bayesian framework. Using county-level monthly HFMD counts from 2009 to 2023, we proposed four spatiotemporal hierarchical models with latent effects shared in the reinfection sub-model to evaluate the influence of air pollution, meteorological factors, and demographic characteristics on primary infection and reinfection of HFMD. The integrated nested Laplace approximation (INLA) approach estimated model parameters and quantified the spatial and temporal random effects. The optimal model with spatial, temporal, and spatiotemporal interaction effects indicated a significant positive influence of NO \(_2\) , wind speed, relative humidity, and solar radiation, as well as a significant negative effect of PM \(_{2.5}\) , O \(_{3}\) , temperature above 27 °C, precipitation, and COVID-19, on both infections. Scattered status and critical primary infection have a significant positive effect on primary infection and reinfection. Positive sharing coefficients revealed similar spatiotemporal patterns of both types of infection. Beyond these fixed effects, non-linear analyses further highlighted comparable exposure-response patterns for primary infection and reinfection, with notable thresholds around 50–70 \(\upmu\) g/m \(^3\) for PM \(_{2.5}\) , 90 \(\upmu\) g/m \(^3\) for O \(_{3}\) , 80 \(\upmu\) g/m \(^3\) for NO \(_{2}\) , 20 °C for temperature, and 55% for relative humidity. Our findings reveal distinct drivers of primary infection and reinfection but also similar spatiotemporal patterns, suggesting the need for both targeted protection of vulnerable groups and integrated surveillance strategies.