Link to Pubmed [PMID] – 40338871
Link to DOI – 10.1371/journal.pone.0322288
PLoS One 2025 ; 20(5): e0322288
The relationship between influenza transmission and climate has many public health implications, particularly on the occurrence of epidemics and disease severity. Environmental factors such as temperature, wind and humidity can influence transmission, particularly in this time of climate change. This study aims to use statistical modelling to decipher the impact of climate factors on influenza transmission in Niger. The reference center of respiratory disease (CERMES) collected samples from patients with acute respiratory illness in eight sentinel sites over a period of twelve years. Detection of respiratory virus was conducted on each sample using molecular approaches. Meteorological parameters were recorded on a weekly basis at the National Meteorological Station in Niamey. Climatic and virological data were plotted over the weeks of the years. A multivariate approach was used to identify clusters of weeks with homogeneous climatic conditions, independent of the season. The impact of the predictor variables was determined using generalized additive modelling (GAM). During this study, 9836 suspected influenza cases were PCR tested, of which 982 (9.98%) were confirmed positive for either influenza A or B. 631 (64.25%) of the influenza A/B positive cases were detected during the low temperature periods (December to February). Using clustering analysis, six distinct periods can be identified, with the most favorable conditions for influenza occurring in conjunction with dry, cold and windy weather patterns. Of greater importance, however, are the conditions that predominate in the weeks preceding the detection of clinical cases. The final GAM model accounts for 77% of the variability in the occurrence of influenza cases, indicating that the epidemic can be anticipated weeks before clinical detection in dispensaries using wind and minimum temperature as indicators. Clustering and GAM models can be considered as an efficient and simple approach to analyze the impact of climatic conditions on the transmission of infectious diseases.