2023
DOI: 10.1016/j.idnow.2022.12.004
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Description of an outbreak of hemorrhagic fever with renal syndrome in the southern Jura Mountains, France, in 2021

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Cited by 3 publications
(3 citation statements)
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“…Puumala orthohantavirus (PUUV) is the hantavirus species most commonly detected in patients in metropolitan France, whereas Seoul orthohantavirus (SEOV) and Tula orthohantavirus (TULV) are detected sporadically. As in some other European countries [ 1 , 2 ], the spatial distribution of HFRS cases is not homogenous in metropolitan France, with detection of human cases being restricted to the northeastern area, including the highly endemic regions of Nord-Pas-de-Calais, Picardie, Franche-Comté [ 3 , 4 ]. There were less than ten individuals with anti-hantavirus antibodies (probable SEOV cases) reported outside the northeastern area in the nineties [ 5 ].…”
Section: Introductionmentioning
confidence: 99%
“…Puumala orthohantavirus (PUUV) is the hantavirus species most commonly detected in patients in metropolitan France, whereas Seoul orthohantavirus (SEOV) and Tula orthohantavirus (TULV) are detected sporadically. As in some other European countries [ 1 , 2 ], the spatial distribution of HFRS cases is not homogenous in metropolitan France, with detection of human cases being restricted to the northeastern area, including the highly endemic regions of Nord-Pas-de-Calais, Picardie, Franche-Comté [ 3 , 4 ]. There were less than ten individuals with anti-hantavirus antibodies (probable SEOV cases) reported outside the northeastern area in the nineties [ 5 ].…”
Section: Introductionmentioning
confidence: 99%
“…However, there are gaps: (1) most studies have concentrated on the effects of temperature, air pressure, rainfall, and humidity on HFRS 16,18 , with scant evidence concerning sunshine and wind's impact. But these six meteorological factors are coexisting, and the combined exposure may have complex interactions between positive and negative changes in these factors on HFRS; (2) previous work often neglected to account for autocorrelations among dependent variables 15,18,19 , leading to potential overestimations; (3) crucially, there is an absence of research probing into the dynamic impacts of climatic changes on HFRS-understanding if increases or decreases in climatic factors lead to differing effects and how potential factors respond to changes in the short run and how these responses evolve over time are vital for comprehensive insights into HFRS transmission control. To fill these gaps, we employed the nonlinear autoregressive distributed lag (NARDL) model 20 .…”
mentioning
confidence: 99%
“…Given that Shandong (Geographical distribution can be seen in Fig. S1) holds the distinction of being the riskiest among all HFRS-endemic provinces in China 4 , our study aims: (1) to clarify both long-and short-term asymmetric correlations between climatic variables and HFRS in Shandong using the NARDL model; (2) to ascertain if the NARDL model offers a more precise estimation of HFRS epidemic compared to the ARDL model.…”
mentioning
confidence: 99%