https://orcid.org/0000-0002-0448-5338
Figures
Abstract
Objectives
Climate change and global warming are major threats for human health and impact the burden of infectious diseases. We investigated the effect of heat stress days (max. perceived temperature ≥32°C) on the incidence of Staphylococcus aureus bacteremia (SAB) and Escherichia coli bacteremia (ECB).
Methods
We performed a post-hoc analysis of a prospective multicenter cohort study with inclusion of all reported SAB and ECB episodes at six tertiary care centers in Germany from 01/2017–12/2019. The effect of the number of heat stress days on the incidence of bacteremia episodes was modelled by a negative binomial regression model with and without underlying seasonal trend.
Results
In the prospective multicenter cohort, we included 2870 episodes of SAB and 4421 episodes of ECB. For both entities, we found a significant seasonal variation over the year (ECB peak-to-trough ratio: 1.33, 95% CI: 1.23–1.45, p < 0.001); SAB peak-to-trough ratio: 1.19 (95% CI: 1.07–1.32, p < 0.001), especially in the subgroup of community-acquired ECB (1.47, 95%-CI: 1.32–1.64, p < 0.001). In the model with incorporation of an underlying seasonal trend, we discovered no overall significant association with the number of heat stress days for SAB and ECB. However, in the subgroup of patients with hospital-acquired SAB, we found a significant association after two days of heat (IRR 1.45, 95%-CI: 1.17–1.82, p = 0.001), that remained significant also in a sensitivity analysis focusing on summer days only (IRR 1.50, 95%-CI: 1.18–1.91, p = 0.001). However, after inclusion of an underlying seasonal trend, the incidence of bacteremia cases remained significantly associated with heat stress days only in the subgroup of patients with hospital-acquired SAB.
Citation: Mathé P, Graw K, Matzarakis A, Maros ME, Tobys D, Kohlmorgen B, et al. (2026) Impact of seasons and heat waves on the incidence of Staphylococcus aureus and Escherichia coli bacteremia – A prospective multicenter study using biometeorological data. PLoS One 21(7): e0352186. https://doi.org/10.1371/journal.pone.0352186
Editor: Amir Nutman, Tel Aviv University School of Medicine, ISRAEL
Received: March 9, 2026; Accepted: June 6, 2026; Published: July 14, 2026
Copyright: © 2026 Mathé et al. This is an open access article distributed under the terms of the Creative Commons Attribution License, which permits unrestricted use, distribution, and reproduction in any medium, provided the original author and source are credited.
Data Availability: The dataset of the bacteremia cohort including de-identified patient data is available on the discretion of the steering committee of the R-Net consortium (contact: info@dzif.de). There are ethical and legal restrictions for sharing the data set because the data is owned by a third-party organization (DZIF, contact: info@dzif.de). The meteorological data is publicly available at the Climate Data Center of the German meteorological service via www.dwd.de (35).
Funding: This work was supported by the German Centre for Infection Research (DZIF) (Grant. No. 805-4-8-08.811). Philipp Mathé was part of the Clinician Scientist program of the Medical Faculty of Medicine, University of Freiburg during the funding period 2024-2026.
Competing interests: The authors have declared that no competing interests exist.
Introduction
Seasonal influences have been known to be an important determinant of disease since the age of Hippocrates. Several studies in the last years have shown that external factors such as season, temperature or humidity may influence the rate of pneumonia, bloodstream and wound infections and the prevalence of antibiotic-resistant bacteria [1–7]. Significant seasonality has been shown for some pathogens, e.g., Streptococcus pneumoniae or Escherichia coli [8].
Staphylococcus aureus is a major pathogen of community-acquired and healthcare-associated infections including bacteremia [9]. However, data regarding seasonal influences are contradictory for S. aureus. Both, increases and decreases in the rate of S. aureus colonization and infection have been observed with increasing outdoor temperature [4,8,10,11]. Previous studies reported a higher MRSA colonization rate of human skin by decreasing latitude [12,13]. The geographic proximity to the equator is associated with higher average temperatures, as well as lower seasonal variation, which suggests a link of S. aureus infections to increased temperatures and humidity.
For E. coli, seasonality has been described with an increase in the number of infections during summer [4,8,14–17]. This increase was also found for hospital-acquired bacteremias in some studies [5,18], while others reported no significant differences [17]. The causes of this increase are incompletely understood, yet some studies discuss that improved growth conditions for Gram-negative bacilli, resulting in higher colonization levels, could play an important role [4,19].
Given the accelerating climate change, it seems prudent to examine heat separately from seasonality. Heat poses additional health risks by exceeding physiological limits and impairing body functions, while seasonality incorporates a broader range of factors, including general meteorological conditions and changes in human behavior [1]. The specific influence of extreme heat episodes remains unclear. Furthermore, there is a need for further investigation regarding the importance of the duration of heat episodes and the time interval between heat exposure and subsequent infection risk.
We set out to answer whether heat waves or consecutive heat stress days (as phases of constant heat stress) in a setting of temperate climate influence the incidence of two major invasive infectious disease entities, i.e., bacteremia due to S. aureus (SAB) and E. coli (ECB). Moreover, we aimed to elucidate whether there are differential influences on community-acquired or hospital-acquired SAB or ECB.
Methods
Patients and setting
We performed a post-hoc analysis of a prospectively evaluated multicenter cohort study including all reported Staphylococcus aureus and Escherichia coli bacteremia episodes at six tertiary care hospitals in Germany (Berlin, Cologne, Freiburg, Gießen, Lübeck, Tübingen) between January 2017 and December 2019. All blood cultures growing isolates of S. aureus or E. coli were reported on a daily basis by the microbiological laboratory. Only patients ≥ 18 years were included.
Ethical considerations
All IRB of the involved study sites approved the R-Net study protocol (Number 302/14 & 302/14_160765). The ethical standards set by the Helsinki Declaration of 1964, as revised in 2013, were followed. The need for consent was waived by the ethics committee.
Data acquisition of the clinical data
In our clinical dataset, the first positive blood culture for S. aureus or E. coli defined a case of bacteremia. Subsequently drawn positive blood cultures of the same patient in the following 30 days were subsidized as one case. Furthermore, every patient was further characterized for age, sex, the mode of acquisition (community-acquired vs. hospital-acquired), the kind of ward at onset (regular ward vs. intensive care unit), the presence of polymicrobial bacteremia and bacterial resistance. The data was retrieved via the electronic health record of each patient.
Definitions used for clinical data
The date of onset of bacteremia was defined as the date of the first positive blood culture for the respective pathogen. A case was considered community-acquired if the onset of the bacteremia was before or <48h after admission to the hospital, other cases were considered hospital-acquired. Polymicrobial infection was defined as blood cultures with growth of more than one relevant pathogen. The following resistance determinants were recorded, for S. aureus resistance to methicillin (MRSA), for E. coli resistance to third-generation cephalosporins (3GCREC). The nominal number of patient cases included all patients admitted to the hospital wards (pediatric/psychiatric/dermatological wards excluded) aggregated by year.
Data acquisition of the meteorological data
The meteorological data originated from measurement stations of the German Meteorological Service (DWD) next to the six tertiary university hospitals, slightly out of town or in the suburban area to diminish urban effects like the urban heat island (UHI) on the measurements. The six meteorological measurement stations with their DWD-Stations-IDs in parentheses were:
- Berlin-Tegel (430) for university hospital in Berlin,
- Freiburg (1443) at the aerodrome for university hospital in Freiburg im Breisgau,
- Gießen/Wettenberg (1639) for university hospital in Gießen,
- Köln/Bonn (2667) at the airport for university hospital in Köln,
- Lübeck-Blankensee (3086) for university hospital in Lübeck and
- Stuttgart-Echterdingen (4931) for university hospital in Tübingen.
For every station the perceived temperature was computed with the heat budget model of the DWD based on air temperature, humidity, wind velocity and the long and short-term radiation to predict the severity of the impact of thermal stress on the human body.
Definitions used for meteorological data
Heat days were defined as days with a maximum perceived temperature ≥ 32 °C. The number of heat stress days (0–3) was assigned to every date by the number of heat days in the three days period before the respective date.
Statistical analysis
The clinical data were merged with the meteorological data for consecutive three-day periods. We dropped the last week of the year due to an unlikely low number of reported cases, most likely due to an underreporting during Christmas holidays. Every heat day group was assigned to a consecutive three-day period with aggregation of the number of bacteremias, like shown here:
We used a generalized mixed-effects model with a negative binomial distribution to examine the aggregated three-day infection counts and to calculate incidence rate ratios (IRR). The analysis incorporated the following dependent variables: the number of heat days in the lagged time period (0, 1, 2, 3), year (2017, 2018, 2019), and a natural spline of the week with three degrees of freedom as fixed effects, along with the center as a random effect. As sensitivity analysis, we performed another generalized mixed-effects model without inclusion of a natural spline modelling seasonality, with included cases limited to those occurred in summer months (meteorological summer: 1st June – 31st August).
To estimate seasonality we calculated the aggregated case numbers of each pathogen by month corrected for the number of patient cases and calculated the peak-to-trough ratio (ratio of the peak monthly number of events to the lowest monthly number of events) including confidence intervals using Episheet (https://www.drugepi.org/dope/software#Episheet) [20].
All analyses were performed with the R statistics program (v.4.0.2, R Core Team 2024, Vienna Austria; RStudio IDE v. 2022.02.3−492, Boston, MA). Non-normally distributed data were displayed as median and interquartile range (IQR). Categorical variables were reported as proportions. P-values <0.05 were considered significant.
Results
Patient characteristics
During the study period, 2870 single episodes of S. aureus bacteremia (SAB) and 4421 single episodes of E. coli bacteremia (ECB) were detected in all centers, with a mean number of 159.4 cases (range 72−277) for S. aureus as well as 245.6 cases (range 146−529) for E. coli per year and center (Table 1). In nearly all centers we found an increasing number of detected bacteremias corrected by the number of patient cases between 2017–2019 (SAB average increase +14%, range: −3%- + 38%; ECB average increase +23%, range: −3%- + 52%). Patients were predominantly elderly men (65.5% for SAB; 53.6% for ECB) with community-acquired infection (54.1% for SAB;65.1% for ECB) and treated mainly on internal medicine wards (65.8% for SAB; 71.7% for ECB) as well as on non-ICU wards (70.9% for SAB; 77.2% for ECB). Polymicrobial bacteremia was rare for both pathogens, the proportion of infection by resistant pathogens was low (7.9% MRSA; 15.4% 3GCREC).
Meteorological characteristics and seasonal variation
Focusing on the meteorological features of the observed time period, 193/6594 days (2.9%) of the period were classified as heat days according to the heat definition used (Fig 1). Heat days occurred mainly between June and August of the three years. The two longest heat waves persisted for twelve days. The mean maximum perceived temperature on heat days was 34.5 °C.
A represents the number of S. aureus bacteremia per week aggregated for the years 2017–2019. B illustrates the respective numbers for E. coli bacteremia. The red beams represent weeks with heat days in all participating centers with an increasing intensity if heat days occurred in more than one center.
The peak-to-trough ratio for ECB was 1.334 (95% CI: 1.225–1.453, p < 0.001) with maximum of cases in July and minimum of cases in January. SAB showed a slightly lower peak-to-trough ratio of 1.192 (95% CI: 1.074–1.323, p < 0.001), peaking in August and falling to its lowest point in February (Fig 2). Overall, the peak-to-trough ratio was more pronounced for community-acquired bacteremia cases (ECB: 1.468, 95%-CI: 1.318–1.635, p < 0.001; SAB: 1.320 95%-CI: 1.136–1.534, p < 0.001) compared to hospital-acquired cases (ECB: 1.197, 95%-CI: 1.036–1.382, p = 0.014; SAB: 1.059 95%-CI: 1.000–1.243, p = 0.302).
The fitted curve for each pathogen was modelled by a sinusoidal regression. A shows the number of all S. aureus bacteremias, while B illustrates the number of all E. coli bacteremias. The second line of figures displays the case numbers stratified by mode of acquisition and pathogen (C: community-acquired cases of S. aureus bacteremia; D hospital-acquired cases of S. aureus bacteremia; E community-acquired cases of E. coli bacteremia; F hospital-acquired cases of E. coli bacteremia). The peak-to-trough ratio of the fitted values are shown in the left upper corner of every figure. The following table depicts the regression model estimates for the overall case numbers.
Negative binominal model of heat waves
In the model without an underlying seasonal trend, we found an increase in the incidence of SAB after previous two or three days of heat (IRR 1.20, p = 0.018; IRR 1.17, p = 0.068; Fig 3A). Concerning ECB, we detected a comparable IRR already after one day of heat exposure (IRR 1.15, p = 0.034; IRR 1.19 after two days, p = 0.007; IRR 1.24 after three days of heat, p = 0.002, Fig 3D). After inclusion of an underlying seasonal trend, the frequency of bacteremia cases remained significantly associated with heat stress days only in the subgroup of patients with hospital-acquired SAB (hospital-acquired SAB: 2d heat: IRR 1.45, p = 0.001, 3d heat: IRR 1.23, p = 0.13; community-acquired SAB: 2d heat: IRR 0.87, p = 0.25, 3d heat: IRR 0.96, p = 0.72, Fig 3B/C). However, with the inclusion of an underlying seasonal trend in the model for ECB a potential impact of heat days was no longer detectable overall (2d heat: IRR 1.02, p = 0.77, 3d heat: IRR 1.07, p = 0.36, Fig 3E) and after stratification by mode of acquisition (hospital-acquired ECB: 2d heat: IRR 1.11, p = 0.33, 3d heat: IRR 1.14, p = 0.26; community-acquired ECB: 2d heat: IRR 0.98, p = 0.82, 3d heat: IRR 1.03, p = 0.72, Fig 3F). In the sensitivity analysis including only summer months and not seasonal modeling component, we found no significant effect of the previous number of heat days on the incidence of SAB or ECB (S1 Fig A/C). Yet, the number of hospital-acquired SAB remained significantly associated with the number of previous heat stress days (2d heat: IRR 1.50, p = 0.001, 3d heat: IRR 1.14, p = 0.43, S4 Fig B)
A-C illustrate the model results of the negative binominal regression model for S. aureus bacteremias depending on the number of heat days in the previous three days. A does not include a seasonal component in the model, while B does include it. C shows the stratification of the analysis by mode of acquisition in the model with included seasonal component. D-F illustrate the model results of the negative binominal regression model for E. coli bacteremias depending on the number of heat days in the previous three days. D does not include a seasonal component in the model, while E does include it. F shows the stratification of the analysis by mode of acquisition in the model with included seasonal component.
Subgroup analyses
Given the observed impact of heat stress days on the incidence of hospital-acquired SAB, we analyzed whether the type of ward at SAB onset may influence this association. We found comparable point estimates after two to three heat days for the subgroups of SAB with onset in the ICU (2d heat: IRR 1.56, p = 0.07, 3d heat: IRR 1.36, p = 0.31) and onset on non-ICU wards (2d heat: IRR 1.43, p = 0.004; 3d heat: IRR 1.20, p = 0.22, S2 Fig), however, as fewer SAB cases with onset in the ICU were included, the confidence intervals were wider and IRRs did not significantly differ after two or three heat days in this subgroup. Furthermore, we found no significant differences in regard to resistance type for S. aureus (S3 Fig) and E. coli (S4 Fig).
Discussion
The main findings of our study were we found seasonal variations in the number of SAB and ECBs with peaks in August or July, respectively ECB exhibited a more consistent seasonal pattern with a notable decrease in cases during the colder months. In our setting of temperate climate, heat waves did not have an additional effect on top of seasonal variation on the overall number of SAB or ECB. However, we observed an association between excessive heat days and an increased incidence of hospital-acquired SAB. Finally, from a methodological point of view, our study demonstrates the importance of including a seasonality component in the respective models to prevent misinterpretations when analyzing the impact of heat waves.
Climate change and global warming are major threats for human health. With a global temperature increase of around 2°C by 2050 in even the intermediate future emission scenario presented by the Intergovernmental Panel on Climate Change (IPCC), the frequency of heat waves is highly likely to increase [21]. A growing number of research articles in the past years stressed the probable increase of infectious diseases driven by increasing temperatures, additional to preexisting seasonal changes [2].
Concerning E. coli, previous investigations suggested an increase of ECB by temperature and season [4,8,14–17]. In our study, we also found an increase in case numbers during summer months. Nevertheless, after including a general seasonal variable in our model, thereby mimicking underlying seasonal changes, we found no additional association between heat waves and the occurrence of ECBs. A seasonal variable includes season-related differences in behavior and exposition as well as changes of the general temperature and humidity level. Thus, our findings are in favor of an overall seasonal association with the occurrence of ECBs and argue against an additional impact of shorter temperature extremes. Previous studies discussed an increase of E. coli in the gut microbiome [22], as well as changes in human behavior during summer months, such as differences in diet and leisure patterns [23]. Further hypothetical mechanisms could be improved growth conditions in non-niche body regions, expression of (additional) virulence genes as well as impairment of innate and adaptive host immune defense mechanisms. Recently, a large population-based study from Israel described no changes in the incidence of ECB (including bacteremia due to multi-drug resistant E. coli) depending on temperature or season using the average temperature for all centers [24]. This difference could maybe be explained by the different climate setting with high temperatures throughout the year relative to a temperate setting like in Germany. While population-based data is of high quality in investigating large-scale changes, we would like to highlight the importance of linking local clinical data with high-resolution meteorological data.
For S. aureus, seasonal variation in the number of SAB was reported in a Belgian cohort study focusing on relative changes during the year [5]. Yet, other studies yielded conflicting results concerning SAB seasonality aggregating cases by months [4,12,25]. S. aureus skin and soft tissue infections (SSTIs) were reported to be more prevalent in summer months, while numbers of respiratory infections were described to be higher in colder months, which may be a consequence of S. aureus superinfection in lower respiratory tract infections [26]. With regard to colonization, few studies in healthy individuals found an increased colonization rate of S. aureus in skin and respiratory samples during summer, as well as higher MRSA colonization rates in ICU-patients with decreasing latitude, reflecting a possible role of higher temperatures in skin colonization, which is known to constitute a risk factor for subsequent SSTI [12,27,28].
We detected a seasonal variation in SAB cases in our multicenter study using high-resolution meteorological data. Upon adjusting for this seasonality component, hospital-acquired SAB cases were associated with preceding heat days, an association which was not observed for community-acquired SAB cases. In contrast to the intestinal habitat of E. coli, the primary niches of S. aureus are the anterior nares and the skin. The observed association with heat days could be explained by heat-related effects on the pathogen, like accelerated growth rates or altered expression of genes encoding virulence factors. Heat-sensitive expression of virulence genes such as hld, pmsα, pmsβ, and the agr-operon, alpha phenol-soluble modulins (αPSMs) as well as an increment in haemolysis and staphyloxanthin production has been described in S. aureus in vitro [29,30]. At the same time heat-related effects on the host could play a role, e.g., pronounced sweating leading to an increased skin permeability, altered host defense mechanisms, or disturbance of the protective skin microbiome of the host. Possible heat-induced changes in the skin microbiome composition could also persist from a community-setting into the hospital context, where healthcare-associated factors such as soaked dressings of indwelling catheters or surgical wounds yielding an additional increased risk of pathogen growth and subsequent infection, which could play a role. As the availability of air conditioning on most non-ICU wards in Germany is limited, direct heat effects are more likely to occur also in the hospital setting compared with other countries and settings where stricter temperature control is the norm. However, two recent small studies found no seasonal variation for catheter-associated bacteremia due to S. aureus, yet again with limited generalizability due to a monocentric study design and monthly aggregation of weather data [31,32].
The main strength of the current study is the use of a statistical model that accounts for effect variations by center and, most importantly, seasonal influences, allowing for the first time to differentiate between heat stress days and seasonal effects. Our study was conducted as a multicenter study, which likely increases the validity of our findings. Furthermore, we stratified our analysis by the mode of acquisition as the exposure to heat and other environmental factors is expected to be different for patients in the community compared to an intra-hospital setting, especially with regard to possible portals of entry. Finally, we employed high-resolution meteorological and temporal data integrating local weather data with 3-days resolution, which allowed us to detect short term changes compared to monthly aggregations.
Yet, there are also limitations that need to be taken into account. Due to the observational study design, we are only able to describe an association of hospital-acquired SAB cases with heat days and cannot infer causality. Our study was performed in the temperate climate of Germany, thus, the results cannot be generalized to other regions or climate zones, as influences of temperature and especially heat vary significantly between geographical settings. Although we included three consecutive years in a multicenter design yielding more than 2800 SAB and 4400 ECB episodes, our analysis may still be underpowered to detect a significant impact of heat days. Moreover, we chose to investigate potential consequences of heat phases of 1–3 days with a latency of 3 days. This latency was based on clinical reasoning, as SAB and ECB are considered entities with acute presentations and comparable latencies for other diseases [33-35]. However, longer periods of extreme heat may be required to yield more pronounced effects on host or pathogen physiology. Finally, we used data from close-by weather stations of the respective recruiting hospitals to model the meteorological exposure. Nevertheless, climatic conditions may vary between and even within hospitals. Our strategy to differentiate between ICU (with more efficient climate control/air conditioning units) is a first proxy, however, climate/temperature assessment at the patient/room level will allow for more accurate analyses in future studies.
Further studies are needed to substantiate seasonal trends in SAB and the observed association of hospital-acquired SAB with (previous) heat days. If our findings are confirmed and underlying mechanisms prove to be modifiable, heat protection measures within hospitals may become more important and may evolve into novel infection prevention strategies.
Conclusion
The current study underlines the relevance of seasonal influences and heat for human health and infections. Large multicenter studies, ideally from different healthcare settings and climate zones are needed to better map a potential impact of heat waves on the incidence of bacteremia and other invasive (vector- or non-vector-borne) infections. Deciphering the underlying mechanisms in host-pathogen interaction may contribute to mitigate climate-associated hazards – especially in countries, which are likely to be affected the hardest by climate change.
Supporting information
S1 Fig. Regression model for heat day effect on S. aureus and E. coli bacteremias according to mode of acquisition limited to occurrence in summer months.
A-D illustrate the model results of the negative binominal regression model for S. aureus and E. coli bacteremias limited to occurrence during summer months without a seasonal component depending on the number of heat days in the previous three days. A shows the model for all S. aureus bacteremias, while C does it for E. coli bacteremias. B displays the model results for S. aureus bacteremia stratified by mode of acquisition, while D does it for E. coli bacteremias.
https://doi.org/10.1371/journal.pone.0352186.s001
(DOCX)
S2 Fig. Regression model for heat day effect on hospital-acquired S. aureus bacteremias according to onset on ICU or regular ward.
Negative binominal model of heat day effect on hospital-acquired S. aureus bacteremias according to onset in Intensive Care Units (ICU) or regular wards. A illustrates the model results of the negative binominal regression model for S. aureus bacteremia depending on the number of heat days in the previous three days and onset of the infection on ICU or regular wards. B includes an additional seasonal component in the model.
https://doi.org/10.1371/journal.pone.0352186.s002
(DOCX)
S3 Fig. Regression model for heat day effect on S. aureus bacteremias according to resistance pattern.
Negative binominal model of heat day effect on S. aureus bacteremias according to resistance pattern. A illustrates the model results of the negative binominal regression model for S. aureus bacteremia depending on the number of heat days in the previous three days and resistance pattern against methicillin. B includes an additional seasonal component in the model.
https://doi.org/10.1371/journal.pone.0352186.s003
(DOCX)
S4 Fig. Regression model for heat day effect on E. coli bacteremias according to resistance pattern.
Negative binominal model of heat day effect on E. coli bacteremias according to resistance pattern. A illustrates the model results of the negative binominal regression model for E. coli bacteremia depending on the number of heat days in the previous three days and resistance to third-generation cephalosporins (3GCREC). B includes an additional seasonal component in the model.
https://doi.org/10.1371/journal.pone.0352186.s004
(DOCX)
S1 Table. Number of cases stratified by pathogen, year and center.
https://doi.org/10.1371/journal.pone.0352186.s005
(DOCX)
S2 Table. List of responsible ethics committees.
https://doi.org/10.1371/journal.pone.0352186.s006
(DOCX)
S1 File. Human participants research checklist.
https://doi.org/10.1371/journal.pone.0352186.s007
(PDF)
Acknowledgments
We thank all the involved patients and the contributing researcher from the R-Net Study group (Barisch Bader, Yannic Blum, Michael Buhl, Chiara Cattaneo, Ariane Gertraud Dinkelacker, Beryl Primerose Gladstone, Susanne Hauswaldt, Evelyn Kramme, Sara Christina Olawumi-Hurter, Luis Alberto Peña Diaz, Georg Pilarski, Susanna Proske, Norbert Thoma). Parts of the results have been presented at the 17th Congress for Infectious Diseases and Tropical Medicine in Leipzig 2023.
References
- 1. Ebi KL, Capon A, Berry P, Broderick C, de Dear R, Havenith G, et al. Hot weather and heat extremes: health risks. Lancet. 2021;398(10301):698–708. pmid:34419205
- 2. Mora C, McKenzie T, Gaw IM, Dean JM, von Hammerstein H, Knudson TA, et al. Over half of known human pathogenic diseases can be aggravated by climate change. Nat Clim Chang. 2022;12(9):869–75. pmid:35968032
- 3. Cilloniz C, Ewig S, Gabarrus A, Ferrer M, Puig de la Bella Casa J, Mensa J, et al. Seasonality of pathogens causing community-acquired pneumonia. Respirology. 2017;22(4):778–85. pmid:28093834
- 4. Eber MR, Shardell M, Schweizer ML, Laxminarayan R, Perencevich EN. Seasonal and temperature-associated increases in gram-negative bacterial bloodstream infections among hospitalized patients. PLoS One. 2011;6(9):e25298. pmid:21966489
- 5. Blot K, Hammami N, Blot S, Vogelaers D, Lambert M-L. Gram-negative central line-associated bloodstream infection incidence peak during the summer: a national seasonality cohort study. Sci Rep. 2022;12(1):5202. pmid:35338181
- 6. Anthony CA, Peterson RA, Polgreen LA, Sewell DK, Polgreen PM. The Seasonal Variability in Surgical Site Infections and the Association With Warmer Weather: A Population-Based Investigation. Infect Control Hosp Epidemiol. 2017;38(7):809–16. pmid:28506327
- 7. MacFadden DR, McGough SF, Fisman D, Santillana M, Brownstein JS. Antibiotic Resistance Increases with Local Temperature. Nat Clim Chang. 2018;8(6):510–4. pmid:30369964
- 8. Gradel KO, Nielsen SL, Pedersen C, Knudsen JD, Østergaard C, Arpi M, et al. Seasonal Variation of Escherichia coli, Staphylococcus aureus, and Streptococcus pneumoniae Bacteremia According to Acquisition and Patient Characteristics: A Population-Based Study. Infect Control Hosp Epidemiol. 2016;37(8):946–53. pmid:27142942
- 9. Diekema DJ, Hsueh P-R, Mendes RE, Pfaller MA, Rolston KV, Sader HS, et al. The Microbiology of Bloodstream Infection: 20-Year Trends from the SENTRY Antimicrobial Surveillance Program. Antimicrob Agents Chemother. 2019;63(7):e00355-19. pmid:31010862
- 10. Wang X, Towers S, Panchanathan S, Chowell G. A population based study of seasonality of skin and soft tissue infections: implications for the spread of CA-MRSA. PLoS One. 2013;8(4):e60872. pmid:23565281
- 11. Denny S, Rawson TM, Hart P, Satta G, Abdulaal A, Hughes S, et al. Bacteraemia variation during the COVID-19 pandemic; a multi-centre UK secondary care ecological analysis. BMC Infect Dis. 2021;21(1):556. pmid:34116643
- 12. Blanco N, Perencevich E, Li SS, Morgan DJ, Pineles L, Johnson JK, et al. Effect of meteorological factors and geographic location on methicillin-resistant Staphylococcus aureus and vancomycin-resistant enterococci colonization in the US. PLoS One. 2017;12(5):e0178254. pmid:28558010
- 13. Abdelnoor M, Vengen Ø, Johansen O, Sandven I, Abdelnoor A. Latitude of the study place and age of the patient are associated with incidence of mediastinitis and microbiology in open-heart surgery: a systematic review and meta-analysis. CLEP. 2016;151.
- 14. Caldeira SM, da Cunha AR, Akazawa RT, Moreira RG, Souza LR, Fortaleza CMCB. Weather parameters and nosocomial bloodstream infection: a case-referent study. Rev Saude Publica. 2015;49:19. pmid:25830871
- 15. Al-Hasan MN, Lahr BD, Eckel-Passow JE, Baddour LM. Seasonal variation in Escherichia coli bloodstream infection: a population-based study. Clin Microbiol Infect. 2009;15(10):947–50. pmid:19845704
- 16. Chazan B, Colodner R, Edelstein H, Raz R. Seasonal variation in Escherichia coli bloodstream infections in northern Israel. Clin Microbiol Infect. 2011;17(6):851–4. pmid:20673262
- 17. Deeny SR, van Kleef E, Bou-Antoun S, Hope RJ, Robotham JV. Seasonal changes in the incidence of Escherichia coli bloodstream infection: variation with region and place of onset. Clin Microbiol Infect. 2015;21(10):924–9. pmid:26141255
- 18. Schwab F, Gastmeier P, Hoffmann P, Meyer E. Summer, sun and sepsis-The influence of outside temperature on nosocomial bloodstream infections: A cohort study and review of the literature. PLoS One. 2020;15(6):e0234656. pmid:32559761
- 19. Alcorn K, Gerrard J, Macbeth D, Steele M. Seasonal variation in health care-associated bloodstream infection: increase in the incidence of gram-negative bacteremia in nonhospitalized patients during summer. Am J Infect Control. 2013;41(12):1205–8. pmid:23973420
- 20. Brookhart MA, Rothman KJ. Simple estimators of the intensity of seasonal occurrence. BMC Med Res Methodol. 2008;8:67. pmid:18945366
- 21.
Calvin K, Dasgupta D, Krinner G, Mukherji A, Thorne PW, Trisos C. Climate Change 2023: Synthesis Report. Geneva, Switzerland: Intergovernmental Panel on Climate Change (IPCC). 2023. https://doi.org/10.59327/IPCC/AR6-9789291691647
- 22. Soltys K, Stuchlikova M, Hlavaty T, Gaalova B, Budis J, Gazdarica J, et al. Seasonal changes of circulating 25-hydroxyvitamin D correlate with the lower gut microbiome composition in inflammatory bowel disease patients. Sci Rep. 2020;10(1):6024. pmid:32265456
- 23. Freeman JT, Anderson DJ, Sexton DJ. Seasonal peaks in Escherichia coli infections: possible explanations and implications. Clin Microbiol Infect. 2009;15(10):951–3. pmid:19845705
- 24. Feldman SF, Temkin E, Wulffhart L, Nutman A, Schechner V, Shitrit P, et al. Effect of temperature on Escherichia coli bloodstream infection in a nationwide population-based study of incidence and resistance. Antimicrob Resist Infect Control. 2022;11(1):144. pmid:36424647
- 25. Perencevich EN, McGregor JC, Shardell M, Furuno JP, Harris AD, Morris JG Jr, et al. Summer Peaks in the Incidences of Gram-Negative Bacterial Infection Among Hospitalized Patients. Infect Control Hosp Epidemiol. 2008;29(12):1124–31. pmid:19031546
- 26. Delorme T, Garcia A, Nasr P. A longitudinal analysis of methicillin-resistant and sensitive Staphylococcus aureus incidence in respect to specimen source, patient location, and temperature variation. Int J Infect Dis. 2017;54:50–7. pmid:27894984
- 27. Hassoun A, Huff MD, Weisman D, Chahal K, Asis E, Stalons D, et al. Seasonal variation of respiratory pathogen colonization in asymptomatic health care professionals: A single-center, cross-sectional, 2-season observational study. Am J Infect Control. 2015;43(8):865–70. pmid:26052103
- 28. Jiménez-Truque N, Saye EJ, Soper N, Saville BR, Thomsen I, Edwards KM, et al. Longitudinal Assessment of Colonization With Staphylococcus aureus in Healthy Collegiate Athletes. J Pediatric Infect Dis Soc. 2016;5(2):105–13. pmid:27199467
- 29. Venero ES, Galeano MB, Luqman A, Ricardi MM, Serral F, Porto DFD, et al. Fever-like temperature impacts on Staphylococcus aureus and Pseudomonas aeruginosa interaction, physiology, and virulence both in vitro and in vivo. bioRxiv. 2023. 2023.03.21.529514.
- 30. Bastock RA, Marino EC, Wiemels RE, Holzschu DL, Keogh RA, Zapf RL, et al. Staphylococcus aureus Responds to Physiologically Relevant Temperature Changes by Altering Its Global Transcript and Protein Profile. mSphere. 2021;6(2):e01303-20. pmid:33731473
- 31. Nakamura I, Takahashi H, Sakagami-Tsuchiya M, Machida M, Sato S, Watanabe Y, et al. The Seasonality of Peripheral Venous Catheter-Related Bloodstream Infections. Infect Dis Ther. 2021;10(1):495–506. pmid:33548036
- 32. Schreiber PW, Dunic M, Wolfensberger A, Clack L, Falk C, Sax H, et al. Seasonal differences in central line-associated bloodstream infection incidence rates in a Central European setting: Results from prospective surveillance. Am J Infect Control. 2019;47(8):1011–3. pmid:30904372
- 33. Santhanam N, Kim HE, Rügamer D, Bender A, Muthers S, Cho CG, et al. Machine learning-based forecasting of daily acute ischemic stroke admissions using weather data. NPJ Digit Med. 2025;8(1):225. pmid:40281217
- 34. Liu C, Yavar Z, Sun Q. Cardiovascular response to thermoregulatory challenges. Am J Physiol Heart Circ Physiol. 2015;309(11):H1793-812. pmid:26432837
- 35.
Wetter und Klima - Deutscher Wetterdienst - Leistungen - Klimadaten zum direkten Download. https://www.dwd.de/DE/leistungen/cdc/cdc_ueberblick-klimadaten.html