Catching the Mardi Gras fever: Quantifying the impact of mass gathering tourism on local bacterial prevalence and community diversity in municipal wastewater

Catching the Mardi Gras fever: Quantifying the impact of mass gathering tourism on local bacterial prevalence and community diversity in municipal wastewater | Research Square window.SnipcartSettings = { analytics: { enabled: false } }; (function() { var accessVector = localStorage.getItem('access_vector') || ''; window.dataLayer = window.dataLayer || []; if (accessVector) { window.dataLayer.push({ user: { profile: { profileInfo: { snid: accessVector } } } }); } })(); (function(w,d,s,l,i){w[l]=w[l]||[];w[l].push({'gtm.start':new Date().getTime(),event:'gtm.js'});var f=d.getElementsByTagName(s)[0],j=d.createElement(s),dl=l!='dataLayer'?'&l='+l:'';j.async=true;j.src='https://www.googletagmanager.com/gtm.js?id='+i+dl;f.parentNode.insertBefore(j,f);})(window,document,'script','dataLayer','GTM-K279D39R'); Browse Preprints In Review Journals COVID-19 Preprints AJE Video Bytes Research Tools Research Promotion AJE Professional Editing AJE Rubriq About Preprint Platform In Review Editorial Policies Our Team Advisory Board Help Center Sign In Submit a Preprint Cite Share Download PDF Research Article Catching the Mardi Gras fever: Quantifying the impact of mass gathering tourism on local bacterial prevalence and community diversity in municipal wastewater Joe Berta, Lorri Rowe, Bob Garry This is a preprint; it has not been peer reviewed by a journal. https://doi.org/ 10.21203/rs.3.rs-8681977/v1 This work is licensed under a CC BY 4.0 License Status: Under Revision Version 1 posted 17 You are reading this latest preprint version Abstract We employed 16S metagenomic analysis to measure the impact of Mardi Gras tourism on the bacterial ecology found in New Orleans’ municipal wastewater. Throughout the peak of the 2023 Carnivale season, species turnover was significantly higher in New Orleans than it was in our control site. Alpha diversity metrics peaked 2-to-3 weeks after Mardi Gras Day, increasing between 65% and 1967% over Carnivale. We also found that human pathogens and microbiota had significantly stronger, more positive correlations with the rise in Mardi Gras tourism than did environmental control species. These changes in wastewater abundance for two species – S. enterica and E. coli – mirrored the concurrent clinical isolate data from the same region for Salmonella spp. and STEC. We also found that multiple alpha and beta diversity measures correlated strongly with increases in tourism during the peak of Carnivale season. Figures Figure 1 Figure 2 Figure 3 Figure 4 Figure 5 Introduction Despite over a century of application in public health, bacterial wastewater surveillance is often overlooked as a tool for tracking the prevalence of bacteria. 1–3 Monitoring of bacterial pathogens and their outbreaks still occurs in wastewater epidemiology, but studies are sporadic compared to that of viruses, with most bacterial surveillance happening at the clinical level or via a handful of indicator species. 4–6 Generally, these epidemiological wastewater studies are conducted using targeted, quantitative molecular methods, such as qPCR, or culture. 4,5,7,8 Meanwhile, untargeted sequencing and 16S metabarcoding have been employed only in a limited capacity for such purposes. While many are familiar with New Orleans’s Mardi Gras celebration, Mardi Gras Day is only the final climax of the two-month Carnivale season. There are around forty official parades throughout Carnivale season, culminating in almost daily parades for the final two weeks. 9 Studies have estimated that Orleans Parish – local population of 364 thousand – receives 1-1.4 million tourists during Carnivale. 10–12 Tourism is mostly centered on the final weeks of the Carnivale season, with New Orleans hotels maintaining an average 74.2% capacity rate (18,763 rooms) throughout the closing two weeks of the 2023 season, spiking to 84.5% (21,331 rooms) and 93.5% (23,656 rooms) capacity the last two weekends. 13 With the majority of parade-goers coming from outside the city of New Orleans (56.2%), this temporary doubling of the Orleans Parish population makes Mardi Gras as deserving of consideration as more well-studied mass gatherings, e.g. the Kumbh Mela, the Hajj, and various music festivals and sporting events. We test the ability of 16S metabarcoding to track individual pathogens, antibiotics resistance genes (ARGs), and human microbiota. Wastewater concentrations of E. coli , K. pneumoniae , P. aeruginosa , and enterococci, have all been strongly correlated with the concentration of ARGs in a wastewater treatment plant (WWTP), making 16S a promising option for ARG surveillance to indicate when targeted, follow-up methods are warranted. 14,15 Studies have found a robust correlation between the concentrations of several bacterial pathogens in wastewater and their incidence in clinical isolates: S. enterica , C. trachomatis, T. pallidum , and E. coli , etc. 4,16–22 Tying bacterial sewage concentrations to clinical case rates is necessary as, unlike viruses, many disease-causing bacteria are facultative or opportunistic pathogens, meaning they can often reproduce without infecting a human host. 14,23 We use the CDC’s BEAM dashboard to verify correspondence between wastewater abundance and clinical incidence for two species: E. coli and S. enterica . 24 Finally, we propose and test a protocol for the study of mass gatherings using wastewater surveillance: employing matched sampling of a control WWTP to negate the variation weather induced on the sewage bacterial communities. Since the study of mass gathering medicine has produced several effective public health measures to ameliorate the risk of infectious disease during mass gatherings, tracking the extent and duration of their impact on circulating diseases yields actionable data that could aid in lowering the disease burden at the sites of our most beloved festival, religious, and sporting traditions. 25–29 Online Materials and Methods Sample Collection “North Shore” samples were collected from the Mandeville Public Works WWTP in Mandeville, Louisiana, which has a catchment population of around thirteen thousand. “NOLA” or “New Orleans” samples were collected from Veolia East Bank WWTP located in New Orleans, Louisiana. The New Orleans East Bank has an approximate catchment population of 330 thousand. From January 12th to March 16th, 2023, one-liter, twenty-four-hour composite samples were collected from New Orleans and the North Shore each Thursday by autosamplers from influent wastewater. Wastewater Sample Processing Samples were frozen at -80°C until they could be sequenced. Samples were first thawed and homogenized. Large solids were removed via tabletop centrifuge at 500 x g for five minutes. Samples were concentrated via high-speed centrifugation. The prokaryotic fraction was selectively pelleted from the resulting supernatant at an RCF AVE of 12,000 x g for fifteen minutes. Samples were then stored at 4°C overnight. Prokaryotic DNA extraction was conducted according to the Zymo Quick-DNA/RNA Miniprep Plus Kit and pre-sequencing quality control was confirmed via Nanodrop OneC. Library Prep and 16S Sequencing Library preparation for 16S rRNA sequencing was conducted using the Illumina MiSeq “16S Metagenomic Sequencing Library Preparation” protocol. 30 Samples were sequenced on an Illumina MiSeq using a V2 Reagent Kit generating circa 118k reads per sample. Read Process and Analysis Initial sequencing read quality control was conducted using FastQC. Bases with a Phred below Q20, truncated reads and adapter sequences were trimmed using Cutadapt version 4.8. 31,32 16S Metagenomic analysis was conducted using Kraken 2. 33 For Kraken 2, demultiplexed, trimmed reads were input as FASTQ files into Kraken 2 version 2.1.3. The Standard 8GB Kraken reference database was used with default parameters except for the confidence threshold, which was raised to 0.01. Read mapping selectivity was optimized by removing from consideration all detected taxa that had fewer than 0.004% of the total number of reads mapped to them. Resultant taxonomic reports were then fed into Bracken v. 2.9. 34 Species-level taxonomic reports were generated using 250 bp-length reads along with default settings. Alpha and Beta Diversity Calculations All alpha diversity measures were calculated using the “Diversity” package within Scikit-Bio. 35 Samples’ species richness was calculated using a simple Observed Taxonomic Unit (OTU) count. Evenness was calculated using Pielou’s Evenness and Simpsons Evenness Index, which is calculated by dividing the Gini-Simpson index by the total number of taxa. 36–38 Overall diversity was estimated using Shannon Entropy – calculated using the Shannon-Weiner Index – and the Gini-Simpson index subtracted from 1. 37–39 The Bray-Curtis dissimilarity formula was used to score the beta diversity between timepoints. 40 Significance Testing Student’s t-test, one-way ANOVAs and Pearson R calculations and significance testing was conducted using SciPi’s “stats” library. 41 Linear and multiple regressions were conducted using the OLS() function in the statsmodels.api library. 42 Historical Weather and Clinical Isolate Data All weather data was found via the National Centers for Environmental Information’s online Past Weather database and search function, found at https://www.ncei.noaa.gov/access/past-weather/ . Clinical isolate data was collected using the CDC’s BEAM dashboard found at https://www.cdc.gov/ncezid/dfwed/BEAM-dashboard.html . Results Controlling conflicting variation from weather Pearson correlation analysis showed that New Orleans and the North Shore both had a strong, positive correlation between their weekly temperatures (High: R = 0.845; Low: R = 0.969, Average: R = 0.952) and weekly precipitation (Pearson, R = 0.991) during the period of study. (see Supp. Figure 1 , Supp. Table 1 ) Paired t-tests determined that the sample means from each site for each component of weather were statistically similar. (High Temp: p = 2.08*10 − 3 ; Low Temp: p = 3.88*10 − 6 , Average Temp: p = 2.12*10 − 5 , Precipitation: 3.21*10 − 8 ) Dominant taxa turnover We plotted the abundance of the ten most dominant taxa for each time point at both sampling locations. (see Fig. 1 , Supp. Table 2) For weekly Bray-Curtis species turnover, New Orleans averaged 0.559, while the North Shore averaged 0.316. These weekly species turnover differences were found to be significantly different. (T-test: p = 1.46*10 − 3 ; t = 3.83) When comparing the weekly diversity to those seen in week one, the New Orleans site averaged a Bray-Curtis dissimilarity of 0.592, while the North Shore averaged 0.288. Again, these differences were determined to be statistically significant. (T-test: p = 2.55*10 − 4 ; t = 4.67) Total taxa turnover Bray-Curtis dissimilarity was also measured for all taxa. ( see Fig. 2 ) Weekly species turnover steadily increased over weeks 6-to-8 before gradually decreasing over weeks 8-to-10. Bray-Curtis dissimilarity between week four diversity and each subsequent week gradually increased and peaked at week nine, with a smaller peak at week seven. This steady rise in dissimilarity had a strong, significant, positive correlation with progression through Carnivale. (R = 0.895, p = 0.0160) Alpha Diversity Following week four, species richness slowly increased 65.7% until peaking at week eight – about ten days following Mardi Gras Day – and then began a gradual decline. (see Fig. 3 , Supp. Tables 3) All metrics for evenness and overall diversity reached peak values at week nine – over two weeks after Mardi Gras Day. Pielous’s evenness increased 84.3% from its week four minimum, while Simpson evenness increased 77.4% from its week six minimum. Shannon entropy and Simpson diversity index increased 1,967.8% and 195.4% from their week five minimums, respectively. When we focused on weeks five through nine, we found that changes in all alpha diversity measures had strong, positive correlations with progression through Carnivale, while only Simpson diversity index reached statistical significance. ( see Supp. Table 4 ) Pathogen tracker We calculated the periods of peak significance between a species’ change in wastewater concentration and progression through Carnivale season using linear regression. The means of these values for our pathogen, microbiome, and environmental control groups varied significantly from one another. (one-way ANOVA: F-statistic = 7.23, p = 6.34*10 − 3 ) (see Fig. 4 , Table 1 , Supp. Table 5) Our six pathogens and six microbiota were most alike on average. (t-test: t-statistic = 0.888, p-value = 0.395) However, the peak significance values for the environmental bacteria, our negative control, differed significantly from each. (Pathogens vs environmental: t-statistic=-2.42, p-value = 0.0361; Microbiota vs environmental: t-statistic = 4.60, p-value = 9.77*10 − 4 ) To isolate and remove even more variation from weather, we ran multiple regression analysis between each species’ wastewater variation using the temperature and precipitation as covariates. We also standardized the period of interest to weeks 4–10. (see Table 2) Here too, the microbiota group varied the most from the environmental control group, with the differences in the mean significances between their wastewater concentrations and Carnivale progression for both groups nearly being significant itself, while the difference between the pathogens and the environmental controls were not significant. (Microbiota: p = 0.061; Pathogens: 0.304) However, the difference in the t statistics for both experimental groups was significant as compared to the control group, (Microbiota: p = 0.034, Pathogens: p = 0.003) with the environmental group having a negative mean t statistic while the other two groups were strongly positive on average. CDC BEAM Monthly Clinical Isolate Data We collected clinical isolate data from the CDC’s BEAM (Bacteria, Enteric, Ameba, and Mycotic) Dashboard to confirm whether rises in wastewater concentrations coincided with rises in symptomatic cases. (see Fig. 3 ) Two taxa were able to be compared tangentially: BEAM’s Salmonella spp. isolates to our wastewater S. enterica , which failed to increase significantly in wastewater concentration over the 2023 Carnivale season, and BEAM’s STEC to our wastewater E. coli , which showed the most significant increase of all the species concentrations that we tracked. All clinical isolate data underwent min-max normalization over a span of 0 to 1 so that states with vastly different population sizes could be accurately compared. In 2023, Louisiana’s Salmonella spp. case isolates increased from 0 in February to 0.42 in March, a statistically significant increase when the surrounding states only rose an average of 0.11 over the same period. (Student’s t-test: p = 0.012) Louisiana saw an even more significant increase in STEC isolates, doubling from February to March. This represents a 0.5 increase when surrounding states decreased by 0.22. (Student’s t-test: p = 0.009) To put these numbers into their proper context, we compared the 2023 isolate data to those of the 2021 season, in which all New Orleans’ Carnivale events were cancelled due to the COVID-19 pandemic. Louisiana’s Salmonella spp. isolates increased from a value of 0 in February to 0.7 in March. This Salmonella spp. increase is nearly as significant as it was in 2023, as the surrounding states only rose to an average of 0.16 in 2021. (Student’s t-test: p = 0.019) The 2021 STEC numbers, however, showed no increase in cases for Louisiana while the surrounding states also remained mostly static, decreasing by a value of less than 0.01 between February and March. (Student’s t-test: p = 0.996) Discussion It is documented that ambient temperature and precipitation can influence prevalence of both bacterial concentrations and microbial resistance genes in wastewater and other surface waters. 43–45 To control for variation in the New Orleans sewerage bacterial concentrations and community diversity due to influence from weather, we attempted an experimental design for the study of mass gatherings by taking matched samples from New Orleans and the North Shore and using the difference between the two values as the weather-corrected measure of New Orleans’ sewage bacteria. The justification for the use of bacterial concentrations on the North Shore as a control group is grounded in both the North Shore and New Orleans sampling locations sharing nearly identical temperatures and rainfall – being only thirty-five miles apart – but experiencing vastly differing tourism rates. Travelling between the two cities involves an hour-long drive across the world’s longest contiguous bridge, the Lake Pontchartrain Causeway, which is also a toll road. Due to this and the relative scarcity of hotels on the North Shore, we reason that tourism to the North Shore over Mardi Gras would be negligible in comparison to that in The Big Easy. To confirm that the weather in the two locations was sufficiently similar, we pulled out historical weather data for each sampling location and compared them. (see Supp. Figure 1 , Supp. Table 1 ) Indeed, New Orleans and the North Shore have extremely similar temperature and weekly precipitation. The various bacterial species’ concentrations measured had different degrees of correlation with the local weather during the 2023 New Orleans’ Carnivale season. Generally, our selected human pathogens’ abundances had extremely weak correlations with average weekly rainfall, with the absolute value of Pearson correlation coefficient for the various species and rainfall averaging a Pearson correlation coefficient of 0.120. There was a slightly stronger correlation between temperature and bacterial abundance, with the mean absolute value of correlation being R = 0.332. Our matched design allowed for us to reduce bacterial correlation with ambient temperature by 26.1%, down to a final mean Pearson correlation of R = 0.245. By comparing weekly species turnover we demonstrated that the rate of change in community composition for New Orleans was higher that that found in the North Shore, as we would expect for a community experiencing biotic invasion. (see Figs. 1 & 2 ) Furthermore, we found that the rate of change in the New Orleans’ community composition increased slowly over weeks 6-to-8 before peaking and gradually declined over weeks 8-to-10. This peak occurs a little over a week after Mardi Gras Day. Community diversity metrics strayed from baseline levels the most during weeks nine and seven. This seems to demonstrate that some species peaked in diversity during Mardi Gras, while others continued to slowly increase over the following 2-to-3 weeks. All diversity measures tested increased from week 5-to-9, i.e. two weeks prior to Mardi Gras Day to over two weeks post. (see Fig. 3 ) . This is expected if tourism is indeed importing new species to the New Orleans’ population, as new species means increased richness and even increased evenness initially if the imported bacteria exist in similar abundances in their own endemic populations. The time frame, weeks 5 through 9, appears to coincide with the peak of Carnivale festivities from weeks 4 to 7 when we factor in the apparent 2–3-week lag established elsewhere in this paper and others. 4,19,49 Of most interest to public health surrounding mass gatherings, all our alpha and beta diversity metrics were returning to baseline levels by week ten, just over three weeks following Mardi Gras Day. This may indicate that public health interventions need only concentrate on the weeks during and directly after the mass gathering event. However, we would need to test further on post-Mardi Gras to make this claim definitively and ensure this was not a temporary aberration. The pathogens selected are known human pathogens that have a documented predilection for acquiring ARGs, making them dually useful for both tracking of human disease and as sentinel species representing greater accumulation of ARGs. For example, if an abundance of E. coli – a common indicator species of ARG buildup – was to be found via 16S sequencing, it could signal that targeted PCR-based follow-up testing would be warranted to determine if there was a rise in particular pathogenic strains, e.g. ETEC, STEC, etc, or if particular antibiotics-resistance genes were present in those populations, e.g. AmpC, TetA, etc. 6 We looked at two other populations of species typical of municipal wastewater. As a negative control, we looked at six environmental bacterial species, i.e. bacteria that do not typically colonize humans and whose abundance in municipal wastewater should be unaffected by human migration. We performed t-tests between each species and progression through Carnivale. When we averaged the t statistics, which indicates both the magnitude and the direction of the relationship, our environmental controls had an average t statistic of -0.534, indicating on average a weak, negative relationship between the species’ concentration and progression though the heart of Carnivale season. Among the six selected environmental species, we only measured one period of significant, strongly correlated increase in wastewater concentration over Carnivale season: Vibrio fluvialis. (see Fig. 4 , Supp. Table 5) Perhaps not coincidentally, V. fluvalis does have a documented ability to opportunistically infect the human gut, despite typically preferring coastal waterways. 46 The second group we tracked were six common members of the human gut or urogenital tract microbiota, a category that makes up a major proportion of municipal waste bacteria. 47 These species are certainly imported en mass during large-scale movements of humans. However, these species are not generally known to commonly induce pathogeneses which would increase their transmission from person to person i.e. conditions like UTIs or diarrhea that require an incubation time to manifest. Our microbiota had a mean t statistic of 2.07. Here we see a statistically significant difference between the mean t statistic of our environmental negative controls. (p = 0.003) One species of human microbiota, F. prausnitzii , had a period of significant, strongly negative correlation between its wastewater concentration and the progression through Carnivale, the only microbiota species found to do so. Again, possibly not by coincidence, the prevalence of F. prausnitzii in the human gut has been found to decrease in abundance during periods of prolonged alcohol consumption. 48 Turning then to our selected pathogens, we measured significant increases in abundances of nearly all human pathogens tracked over the course of late Carnival. The mean t statistic over the heart of Carnivale for this group was 1.97. Here again, this was statistically different from that of the environmental controls. (p = 0.034) Such a gap between our two experimental groups and our environmental control suggests that the admixture of humans around mass gatherings has a significantly positive impact on the spread of common human colonizers and pathogens. This supposition is further supported by the observation that while a few of these pathogens peaked in abundance around weeks six or seven – the timepoints directly before and after of Mardi Gras Day – most peaked in abundance 2-to-3 weeks after, further mirroring the lag time seen in other studies of mass gatherings and contagious disease where the temporal dimension was analyzed. This is likely due to the incubation time between inoculation and shedding of bacteria. 19 This contrasts our microbiome species, which typically peaked around week six. This may indicate that human microbiota are more directly impacted by importation into the community, as they typically occur at higher abundances in humans and do not need to rely on pathogenesis to spread and raise their wastewater profile. Other studies that have explored the temporal correlation between mass gatherings, pathogen concentration in public wastewater and incidence of clinical isolates have found, on average, a 1–4 week lag between the mass gathering event and a rise in wastewater concentration and 2–3 week lag between the event and increasing clinical cases, depending on the length of incubation period for that particular species. 4,19,49 Mardi Gras and the crescendo of Carnivale was not until the third week of February and most pathogen wastewater concentrations did not peak until early in the second week of March. (see Fig. 5 ) Therefore, we would expect to see a large spike in clinical isolates from March relative to February for any species which underwent significant wastewater spikes in the beginning of that month. Compared to every state surrounding it, Louisiana had significant increases in clinical isolates of both Salmonella spp. (compared to wastewater: S. enterica ) and STEC (compared to wastewater: E. coli ). (see Fig. 4 ) However, S. enterica’s increases in wastewater concentration failed to significantly correlate to progression through Carnivale season, while E. coli showed the strongest wastewater correlations of all pathogens tracked. To shed light on this apparent contradiction, we looked at the same clinical isolate metrics from just two years prior, when the COVID-19 outbreak cancelled Carnivale festivities in New Orleans for the first time since the late 1970s. Here we saw clinical isolates of Salmonella spp. increase in early Spring without Carnivale tourists just as significantly as they had when they were present, suggesting that the increases in clinical cases, and very well the moderate increases in wastewater, are likely due to a different phenomenon, such as seasonality. However, in the year without Mardi Gras, Louisiana’s STEC clinical cases were indistinguishable from those of the surrounding states, while in the 2023 Carnivale season Louisiana was significant in both the number of March STEC isolates and the increase in isolates from February to March when compared to all surrounding states. Being as the E. coli wastewater correlation was also robust, it is hard to argue that Mardi Gras tourism, the spike in wastewater E. coli concentrations, and that of STEC clinical isolates are not likely related. Declarations Funding declaration: This work was supported by the National Institute of Health grant 3U01AI151812-04S1. Clinical declaration: Clinical trial number: not applicable Ethical declaration: Not applicable Consent for publication: Not applicable Data availability: All raw sequencing reads datasets were deposited in GenBank SRA under the BioProject PRJNA1363154. Competing interests: The authors declare that they have no competing interests. Author Contribution J.B. performed all wet work (besides library prep and sequencing), did all data analysis and figures, and wrote the manuscript.L.R. conceptualized and performed all library prep and sequencing.B.G. acquired funding, supervised, and assisted J.B. in conceptualizing the project. Acknowledgement Theresa Sokol and Sean Simonson of The Louisiana Department of Health for helping procure samples. Data Availability All raw sequencing reads datasets were deposited in GenBank SRA under the BioProject PRJNA1363154. References Budd W. Typhoid Fever: its Nature, Mode of Spreading, and Prevention | Nature. 1874. Accessed November 6, 2025. https://www.nature.com/articles/009280a0 Moorhead R. William Budd and Typhoid Fever. J R Soc Med . 2002;95(11):561-564. doi:10.1177/014107680209501115 Wilson WJ. Isolation of Enteric Bacilli from Sewage and Water and its Bearing on Epidemiology. Br Med J . 1933;2(3794):560-562. doi:10.1136/bmj.2.3794.560 Yan T, O’Brien P, Shelton JM, Whelen AC, Pagaling E. Municipal Wastewater as a Microbial Surveillance Platform for Enteric Diseases: A Case Study for Salmonella and Salmonellosis. Environ Sci Technol . 2018;52(8):4869-4877. doi:10.1021/acs.est.8b00163 Shannon KE, Lee DY, Trevors JT, Beaudette LA. Application of real-time quantitative PCR for the detection of selected bacterial pathogens during municipal wastewater treatment. Sci Total Environ . 2007;382(1):121-129. doi:10.1016/j.scitotenv.2007.02.039 Troja F, Indio V, Savini F, et al. Monitoring and preventing foodborne outbreaks: are we missing wastewater as a key data source? Ital J Food Saf . 2024;13(4). doi:10.4081/ijfs.2024.12725 Schets FM, de Heer L, de Roda Husman AM. Coxiella burnetii in sewage water at sewage water treatment plants in a Q fever epidemic area. Int J Hyg Environ Health . 2013;216(6):698-702. doi:10.1016/j.ijheh.2012.12.010 M’ikanatha NM, Goldblum ZS, Cesari N, et al. Outbreak-associated Salmonella Baildon found in wastewater demonstrates how sewage monitoring can supplement traditional disease surveillance. J Clin Microbiol . 2024;62(10):e00825-24. doi:10.1128/jcm.00825-24 Mardi Gras Parade Krewes | Mardi Gras New Orleans. Accessed November 7, 2025. https://www.neworleans.com/events/holidays-seasonal/mardi-gras/krewes/ U.S. Census Bureau QuickFacts. Accessed November 7, 2025. https://www.census.gov/quickfacts/fact/table/neworleanscitylouisiana/PST045219 Gotham KF. Tourism from Above and Below: Globalization, Localization and New Orleans’s Mardi Gras. Int J Urban Reg Res . 2005;29(2):309-326. doi:10.1111/j.1468-2427.2005.00586.x Kiernan JS. 2020 Mardi Gras Facts – Booze, Floats, Money & More. WalletHub. Accessed November 7, 2025. https://wallethub.com/blog/mardi-gras-facts/31999 Weiss T. Economic Impact and Net Fiscal Benefit of New Orleans’ 2023 Mardi Gras Season. January 2024. Accessed November 7, 2025. https://tulane.app.box.com/s/m1t5mjrl8n2xbu2urkg0uo6dq9jukg1v Tiwari A, Kurittu P, Al-Mustapha AI, et al. Wastewater surveillance of antibiotic-resistant bacterial pathogens: A systematic review. Front Microbiol . 2022;13. doi:10.3389/fmicb.2022.977106 Alexander J, Hembach N, Schwartz T. Evaluation of antibiotic resistance dissemination by wastewater treatment plant effluents with different catchment areas in Germany. Sci Rep . 2020;10(1):8952. doi:10.1038/s41598-020-65635-4 Li X, Zhang S, Sherchan S, et al. Correlation between SARS-CoV-2 RNA concentration in wastewater and COVID-19 cases in community: A systematic review and meta-analysis. J Hazard Mater . 2023;441:129848. doi:10.1016/j.jhazmat.2022.129848 Huang Y, Zhou N, Zhang S, et al. Norovirus detection in wastewater and its correlation with human gastroenteritis: a systematic review and meta-analysis. Environ Sci Pollut Res . 2022;29(16):22829-22842. doi:10.1007/s11356-021-18202-x Li D, Gu AZ, Zeng S ‐Y., Yang W, He M, Shi H ‐C. Monitoring and evaluation of infectious rotaviruses in various wastewater effluents and receiving waters revealed correlation and seasonal pattern of occurrences. J Appl Microbiol . 2011;110(5):1129-1137. doi:10.1111/j.1365-2672.2011.04954.x Collender PA, Morris C, Glenn-Finer R, et al. Mass Gatherings and Diarrheal Disease Transmission Among Rural Communities in Coastal Ecuador. Am J Epidemiol . 2019;188(8):1475-1483. doi:10.1093/aje/kwz102 Zhao L, Guzman HP, Xagoraraki I. Tracking Chlamydia and Syphilis in the Detroit Metro Area by Molecular Analysis of Environmental Samples. Environ Sci Technol . 2024;58(40):17606-17616. doi:10.1021/acs.est.4c05869 Yang CM, Lin MF, Liao PC, et al. Comparison of antimicrobial resistance patterns between clinical and sewage isolates in a regional hospital in Taiwan. Lett Appl Microbiol . 2009;48(5):560-565. doi:10.1111/j.1472-765X.2009.02572.x Urase T, Okazaki M, Tsutsui H. Prevalence of ESBL-producing Escherichia coli and carbapenem-resistant Enterobacteriaceae in treated wastewater: a comparison with nosocomial infection surveillance. J Water Health . 2020;18(6):899-910. doi:10.2166/wh.2020.014 Alberts B, Johnson A, Lewis J, Raff M, Roberts K, Walter P. Introduction to Pathogens. In: Molecular Biology of the Cell. 4th Edition . Garland Science; 2002. Accessed November 7, 2025. https://www.ncbi.nlm.nih.gov/books/NBK26917/ BEAM Dashboard | CDC. April 18, 2025. Accessed November 7, 2025. https://www.cdc.gov/ncezid/dfwed/BEAM-dashboard.html Hutton A, Ranse J, Munn MB. Developing Public Health Initiatives through Understanding Motivations of the Audience at Mass-Gathering Events. Prehospital Disaster Med . 2018;33(2):191-196. doi:10.1017/S1049023X18000067 Alqahtani AS, Heywood AE, Rashid H. Preparing Australian pilgrims for the Hajj 2018. J Travel Med . 2018;25(1):tay068. doi:10.1093/jtm/tay068 Hassan IN, Abuassa N, Ibrahim M, Ibrahim MNH. Health and safety management at the glastonbury festival: A mass gathering medicine perspective. Mass Gather Med . 2024;2:100010. doi:10.1016/j.mgmed.2024.100010 Yi H, Zheng’an Y, Fan W, et al. Public Health Preparedness for the World’s Largest Mass Gathering: 2010 World Exposition in Shanghai, China. Prehospital Disaster Med . 2012;27(6):589-594. doi:10.1017/S1049023X12001252 Polkinghorne BG, Massey PD, Durrheim DN, Byrnes T, MacIntyre CR. Prevention and surveillance of public health risks during extended mass gatherings in rural areas: The experience of the Tamworth Country Music Festival, Australia. Public Health . 2013;127(1):32-38. doi:10.1016/j.puhe.2012.09.014 S Metagenomic Sequencing Library Preparation: Preparing 16S Ribosomal RNA Gene Amplicons for the Illumina MiSeq System. Illumina . Babraham Bioinformatics - FastQC A Quality Control tool for High Throughput Sequence Data. Accessed November 7, 2025. https://www.bioinformatics.babraham.ac.uk/projects/fastqc/ Martin M. Cutadapt removes adapter sequences from high-throughput sequencing reads. EMBnet.journal . 2011;17(1):10-12. doi:10.14806/ej.17.1.200 Wood DE, Lu J, Langmead B. Improved metagenomic analysis with Kraken 2. Genome Biol . 2019;20(1):257. doi:10.1186/s13059-019-1891-0 Lu J, Breitwieser FP, Thielen P, Salzberg SL. Bracken: estimating species abundance in metagenomics data. PeerJ Comput Sci . 2017;3:e104. doi:10.7717/peerj-cs.104 Rideout JR, Caporaso G, Bolyen E, et al. scikit-bio/scikit-bio: scikit-bio 0.6.2. Published online July 7, 2024. doi:10.5281/zenodo.12682832 Pielou EC. Ecological Diversity . New York : Wiley; 1975. Accessed November 7, 2025. http://archive.org/details/ecologicaldivers0000piel Simpson EH. Measurement of Diversity. Nature . 1949;163(4148):688-688. doi:10.1038/163688a0 Rao CR. Gini-Simpson Index of Diversity: A Characterization, Generalization and Applications . University of Pittsburgh; 1981. Shannon CE. A Mathematical Theory of Communication. Bray JR, Curtis JT. An Ordination of the Upland Forest Communities of Southern Wisconsin. Ecol Monogr . 1957;27(4):325-349. doi:10.2307/1942268 Virtanen P, Gommers R, Oliphant TE, et al. SciPy 1.0: fundamental algorithms for scientific computing in Python. Nat Methods . 2020;17(3):261-272. doi:10.1038/s41592-019-0686-2 Seabold S, Perktold J. Statsmodels: Econometric and Statistical Modeling with Python. SciPy 2010 . Published online May 1, 2010. doi:10.25080/Majora-92bf1922-011 Kaleli HA, Islam MR. Effect of temperature on the growth of wastewater bacteria. Toxicol Environ Chem . 1997;59(1-4):111-123. doi:10.1080/02772249709358429 Ching C, Sutradhar I, Zaman MH. Understanding the impacts of temperature and precipitation on antimicrobial resistance in wastewater: theory, modeling, observation, and limitations. mSphere . 2025;10(3):e00947-24. doi:10.1128/msphere.00947-24 Paul MJ, Coffey R, Stamp J, Johnson T. A REVIEW OF WATER QUALITY RESPONSES TO AIR TEMPERATURE AND PRECIPITATION CHANGES 1: FLOW, WATER TEMPERATURE, SALTWATER INTRUSION. J Am Water Resour Assoc . 2019;55(4):824-843. doi:10.1111/1752-1688.12710 Ramamurthy T, Chowdhury G, Pazhani GP, Shinoda S. Vibrio fluvialis: an emerging human pathogen. Front Microbiol . 2014;5:91. doi:10.3389/fmicb.2014.00091 LaMartina EL, Mohaimani AA, Newton RJ. Urban wastewater bacterial communities assemble into seasonal steady states. Microbiome . 2021;9:116. doi:10.1186/s40168-021-01038-5 Gao B, Emami A, Zhou R, et al. Functional Microbial Responses to Alcohol Abstinence in Patients With Alcohol Use Disorder. Front Physiol . 2020;11:370. doi:10.3389/fphys.2020.00370 Brighton K, Fisch S, Wu H, Vigil K, Aw TG. Targeted community wastewater surveillance for SARS-CoV-2 and Mpox virus during a festival mass-gathering event. Sci Total Environ . 2024;906:167443. doi:10.1016/j.scitotenv.2023.167443 Table 1 Table 1 : Summary of correlation and significance of the relationship between individual species abundances and progression through Carnivale Measure Pathogens Microbiota Environmental Peak Periods Mean Significance value (p-value) 0.0513 0.0323 0.107 Significance Standard Deviation (s p ) 0.0466 0.0239 0.0317 Significance of Group vs Environmental Mean: p-values 0.0361 9.77*10 -4 0 Mean Correlation value (R) 0.528 0.529 0.282 Correlation Standard Deviation (s R ) 0.682 0.664 0.847 Significance of Group vs Environmental Mean: Pearson Rs 0.591 0.586 0 # Periods of Sig., Strong Corr. w/ Mardi Gras 8 4 1 # Taxa with Sig., Strong, Positive Corr. w/ MG 4 3 1 Weeks 4-to-9 Mean Significance 0.370 0.235 0.580 Mean T Test Statistic 1.968 2.069 -0.534 Significance of Group vs Environmental Mean: p-values 0.304 0.061 0.0 Significance of Group vs Environmental Mean: t statistics 0.034 0.003 0.0 Table 1: (Peak Periods) Average peak Pearson correlations and linear regression significances of selected species’ wastewater concentration with progression through Carnivale. (Weeks 4-to-9) Average Pearson correlations and linear regression significances of each experimental group over weeks 4-to-9, the peak of the variation due to tourism. Additional Declarations No competing interests reported. Supplementary Files supplementaryfiles.docx Cite Share Download PDF Status: Under Revision Version 1 posted Editorial decision: Revision requested 10 Feb, 2026 Reviews received at journal 09 Feb, 2026 Reviewers agreed at journal 03 Feb, 2026 Reviewers agreed at journal 03 Feb, 2026 Reviewers agreed at journal 03 Feb, 2026 Reviews received at journal 02 Feb, 2026 Reviewers agreed at journal 31 Jan, 2026 Reviews received at journal 31 Jan, 2026 Reviewers agreed at journal 31 Jan, 2026 Reviewers agreed at journal 29 Jan, 2026 Reviewers agreed at journal 29 Jan, 2026 Reviewers agreed at journal 29 Jan, 2026 Reviewers invited by journal 29 Jan, 2026 Editor invited by journal 27 Jan, 2026 Editor assigned by journal 24 Jan, 2026 Submission checks completed at journal 24 Jan, 2026 First submitted to journal 23 Jan, 2026 You are reading this latest preprint version Research Square lets you share your work early, gain feedback from the community, and start making changes to your manuscript prior to peer review in a journal. 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Also discoverable on Platform About Our Team In Review Editorial Policies Advisory Board Help Center Resources Author Services Accessibility API Access RSS feed Manage Cookie Preferences © Research Square 2026 | ISSN 2693-5015 (online) Privacy Policy Terms of Service Do Not Sell My Personal Information {"props":{"pageProps":{"initialData":{"identity":"rs-8681977","acceptedTermsAndConditions":true,"allowDirectSubmit":false,"archivedVersions":[],"articleType":"Research Article","associatedPublications":[],"authors":[{"id":583666734,"identity":"669535a1-1f4b-4c16-85df-1096d051d050","order_by":0,"name":"Joe Berta","email":"data:image/png;base64,iVBORw0KGgoAAAANSUhEUgAAAZAAAAAyAQMAAABI0h/eAAAABlBMVEX///8AAABVwtN+AAAACXBIWXMAAA7EAAAOxAGVKw4bAAAAwUlEQVRIiWNgGAWjYDACCTBpw8DADuEzNhCl5QBDmgQDM4laDpOghX9287HPH2rO1xkcZn724AODjeyGA4QsuXMsecaBY7clDA6zmRvOYEgzJqjFQCLHmOEAG0gLg5k0D8PhRCK05H9mOPDvHFAL+zfpPwz/idGSw8xwsO0AUAuPmTQwJAhrAfrFmOFsX7LkzMM8ZZI9BsnGMwlpAYbYY4aKb3b8fMfbt0n8qLCT7SOkBd2dpCkfBaNgFIyCUYADAADuTUI2wL6THwAAAABJRU5ErkJggg==","orcid":"","institution":"Tulane University","correspondingAuthor":true,"prefix":"","firstName":"Joe","middleName":"","lastName":"Berta","suffix":""},{"id":583666735,"identity":"a98f3bdf-f8c3-46f9-9432-ab1dbf2d3f47","order_by":1,"name":"Lorri Rowe","email":"","orcid":"","institution":"Tulane University","correspondingAuthor":false,"prefix":"","firstName":"Lorri","middleName":"","lastName":"Rowe","suffix":""},{"id":583666736,"identity":"70a04780-6758-4d60-85c0-85c4efecf8a6","order_by":2,"name":"Bob Garry","email":"","orcid":"","institution":"Tulane University","correspondingAuthor":false,"prefix":"","firstName":"Bob","middleName":"","lastName":"Garry","suffix":""}],"badges":[],"createdAt":"2026-01-23 18:53:44","currentVersionCode":1,"declarations":"","doi":"10.21203/rs.3.rs-8681977/v1","doiUrl":"https://doi.org/10.21203/rs.3.rs-8681977/v1","draftVersion":[],"editorialEvents":[],"editorialNote":"","failedWorkflow":false,"files":[{"id":101639649,"identity":"f8a98937-104e-477f-a065-f930ea4d59d7","added_by":"auto","created_at":"2026-02-02 07:21:05","extension":"jpg","order_by":1,"title":"Figure 1","display":"","copyAsset":false,"role":"figure","size":184458,"visible":true,"origin":"","legend":"\u003cp\u003eChange in dominant taxa throughout Carnivale.\u003c/p\u003e\n\u003cp\u003eRelative abundance of dominant taxa per week according to each pipeline bars representing the ten \u0026nbsp;\u0026nbsp;\u0026nbsp;\u0026nbsp;most dominant bacterial species at each timepoint throughout for (top) New Orleans and (bottom) the North Shore. Combined abundance of all other taxa \u0026nbsp;\u0026nbsp;\u0026nbsp;\u0026nbsp;represented in grey.\u003c/p\u003e","description":"","filename":"1.jpg","url":"https://assets-eu.researchsquare.com/files/rs-8681977/v1/02bd30b06b81ae6124c115e9.jpg"},{"id":101752610,"identity":"40778e12-e6be-45ce-8f2f-ca421983f7a3","added_by":"auto","created_at":"2026-02-03 10:28:29","extension":"jpg","order_by":2,"title":"Figure 2","display":"","copyAsset":false,"role":"figure","size":159241,"visible":true,"origin":"","legend":"\u003cp\u003eChange in Beta Diversity\u003c/p\u003e\n\u003cp\u003e(top left) Bray-Curtis dissimilarity compared to week 4 diversity measures. (top right) Weekly \u0026nbsp;\u0026nbsp;\u0026nbsp;\u0026nbsp;rate of change in diversity using Bray-Curtis dissimilarity. (bottom row) \u0026nbsp;heatmaps for Bray-Curtis dissimilarity for (left) uncorrected New Orleans, \u0026nbsp;(center) uncorrected North Shore, and (right) corrected New Orleans.\u003c/p\u003e","description":"","filename":"2.jpg","url":"https://assets-eu.researchsquare.com/files/rs-8681977/v1/62abf175c8c982b37a66db1e.jpg"},{"id":101639653,"identity":"69128b47-dc6f-4168-84e4-3cad63269970","added_by":"auto","created_at":"2026-02-02 07:21:05","extension":"jpg","order_by":3,"title":"Figure 3","display":"","copyAsset":false,"role":"figure","size":262858,"visible":true,"origin":"","legend":"\u003cp\u003eChange in Alpha Diversity\u003c/p\u003e\n\u003cp\u003e(top row) change throughout Carnival for New Orleans’s alpha diversity metrics corrected for weather variation. (bottom row) uncorrected alpha diversity values for New Orleans and the North Shore. (left column) species richness i.e. OUT counts, (center column) Pielou and Simpson’s Evenness, and (right column) Shannon’s Entropy and Simpson’s Diversity index. Week one was omitted as the diversity measures of week one North Shore were substantially low compared to others, enough to be easily ruled an outlier.\u003c/p\u003e","description":"","filename":"3.jpg","url":"https://assets-eu.researchsquare.com/files/rs-8681977/v1/51902c42656ad3746153fc1a.jpg"},{"id":101639655,"identity":"3fb9c854-c9d5-4a3d-8df0-2b2d83996c93","added_by":"auto","created_at":"2026-02-02 07:21:05","extension":"jpg","order_by":4,"title":"Figure 4","display":"","copyAsset":false,"role":"figure","size":176096,"visible":true,"origin":"","legend":"\u003cp\u003eChanges in abundance of individual species over Carnivale.\u003c/p\u003e\n\u003cp\u003eAbundance over Carnivale season of select (top) pathogens of interest, (center) dominant human gut microbiota and (bottom) environmental bacteria common in water and soil. Periods of the most significant increase in concentration that have a strong, positive correlation with the progression through Carnivale are indicated with upwards triangles at their start and downwards triangles at their conclusion.\u003c/p\u003e","description":"","filename":"4.jpg","url":"https://assets-eu.researchsquare.com/files/rs-8681977/v1/d0da7a8ee16908a0165d0e82.jpg"},{"id":101753381,"identity":"087a22ba-3a06-492e-bbeb-ece3e987c602","added_by":"auto","created_at":"2026-02-03 10:39:54","extension":"jpg","order_by":5,"title":"Figure 5","display":"","copyAsset":false,"role":"figure","size":160791,"visible":true,"origin":"","legend":"\u003cp\u003e\u003cem\u003eSalmonella sp\u003c/em\u003e. and STEC clinical isolates for selected states\u003c/p\u003e\n\u003cp\u003eMin-max normalization of the number of clinical isolates per month collected in Louisiana and surrounding states for: (top left) all Salmonella species in 2023, (top right) STEC in 2023, (bottom left) all Salmonella species in 2021, and (bottom right) STEC in 2021. The average line is the average number of clinical isolates for all selected states surrounding Louisiana\u003c/p\u003e","description":"","filename":"5.jpg","url":"https://assets-eu.researchsquare.com/files/rs-8681977/v1/2095067cb9d496751249f2c5.jpg"},{"id":101755672,"identity":"7d7acab0-a073-48f8-bf6e-959e12362013","added_by":"auto","created_at":"2026-02-03 10:53:34","extension":"pdf","order_by":0,"title":"","display":"","copyAsset":false,"role":"manuscript-pdf","size":1680804,"visible":true,"origin":"","legend":"","description":"","filename":"manuscript.pdf","url":"https://assets-eu.researchsquare.com/files/rs-8681977/v1/a6401469-6da8-4531-b7cf-b07dc64088f1.pdf"},{"id":101639650,"identity":"2d0d990c-94f2-4e66-b462-b3ea88a4a9e1","added_by":"auto","created_at":"2026-02-02 07:21:05","extension":"docx","order_by":1,"title":"","display":"","copyAsset":false,"role":"supplement","size":191410,"visible":true,"origin":"","legend":"","description":"","filename":"supplementaryfiles.docx","url":"https://assets-eu.researchsquare.com/files/rs-8681977/v1/7283eba38f21d3730789620f.docx"}],"financialInterests":"No competing interests reported.","formattedTitle":"Catching the Mardi Gras fever: Quantifying the impact of mass gathering tourism on local bacterial prevalence and community diversity in municipal wastewater","fulltext":[{"header":"Introduction","content":"\u003cp\u003eDespite over a century of application in public health, bacterial wastewater surveillance is often overlooked as a tool for tracking the prevalence of bacteria.\u003csup\u003e1\u0026ndash;3\u003c/sup\u003e Monitoring of bacterial pathogens and their outbreaks still occurs in wastewater epidemiology, but studies are sporadic compared to that of viruses, with most bacterial surveillance happening at the clinical level or via a handful of indicator species.\u003csup\u003e4\u0026ndash;6\u003c/sup\u003e Generally, these epidemiological wastewater studies are conducted using targeted, quantitative molecular methods, such as qPCR, or culture.\u003csup\u003e4,5,7,8\u003c/sup\u003e Meanwhile, untargeted sequencing and 16S metabarcoding have been employed only in a limited capacity for such purposes.\u003c/p\u003e \u003cp\u003eWhile many are familiar with New Orleans\u0026rsquo;s Mardi Gras celebration, Mardi Gras Day is only the final climax of the two-month Carnivale season. There are around forty official parades throughout Carnivale season, culminating in almost daily parades for the final two weeks.\u003csup\u003e9\u003c/sup\u003e Studies have estimated that Orleans Parish \u0026ndash; local population of 364 thousand \u0026ndash; receives 1-1.4\u0026nbsp;million tourists during Carnivale.\u003csup\u003e10\u0026ndash;12\u003c/sup\u003e Tourism is mostly centered on the final weeks of the Carnivale season, with New Orleans hotels maintaining an average 74.2% capacity rate (18,763 rooms) throughout the closing two weeks of the 2023 season, spiking to 84.5% (21,331 rooms) and 93.5% (23,656 rooms) capacity the last two weekends.\u003csup\u003e13\u003c/sup\u003e With the majority of parade-goers coming from outside the city of New Orleans (56.2%), this temporary doubling of the Orleans Parish population makes Mardi Gras as deserving of consideration as more well-studied mass gatherings, e.g. the Kumbh Mela, the Hajj, and various music festivals and sporting events.\u003c/p\u003e \u003cp\u003eWe test the ability of 16S metabarcoding to track individual pathogens, antibiotics resistance genes (ARGs), and human microbiota. Wastewater concentrations of \u003cem\u003eE. coli\u003c/em\u003e, \u003cem\u003eK. pneumoniae\u003c/em\u003e, \u003cem\u003eP. aeruginosa\u003c/em\u003e, and enterococci, have all been strongly correlated with the concentration of ARGs in a wastewater treatment plant (WWTP), making 16S a promising option for ARG surveillance to indicate when targeted, follow-up methods are warranted.\u003csup\u003e14,15\u003c/sup\u003e\u003c/p\u003e \u003cp\u003eStudies have found a robust correlation between the concentrations of several bacterial pathogens in wastewater and their incidence in clinical isolates: \u003cem\u003eS. enterica\u003c/em\u003e, \u003cem\u003eC. trachomatis, T. pallidum\u003c/em\u003e, and \u003cem\u003eE. coli\u003c/em\u003e, etc.\u003csup\u003e4,16\u0026ndash;22\u003c/sup\u003e Tying bacterial sewage concentrations to clinical case rates is necessary as, unlike viruses, many disease-causing bacteria are facultative or opportunistic pathogens, meaning they can often reproduce without infecting a human host.\u003csup\u003e14,23\u003c/sup\u003e We use the CDC\u0026rsquo;s BEAM dashboard to verify correspondence between wastewater abundance and clinical incidence for two species: \u003cem\u003eE. coli\u003c/em\u003e and \u003cem\u003eS. enterica\u003c/em\u003e.\u003csup\u003e24\u003c/sup\u003e\u003c/p\u003e \u003cp\u003eFinally, we propose and test a protocol for the study of mass gatherings using wastewater surveillance: employing matched sampling of a control WWTP to negate the variation weather induced on the sewage bacterial communities. Since the study of mass gathering medicine has produced several effective public health measures to ameliorate the risk of infectious disease during mass gatherings, tracking the extent and duration of their impact on circulating diseases yields actionable data that could aid in lowering the disease burden at the sites of our most beloved festival, religious, and sporting traditions. \u003csup\u003e25\u0026ndash;29\u003c/sup\u003e\u003c/p\u003e"},{"header":"Online Materials and Methods","content":"\u003cdiv id=\"Sec3\" class=\"Section2\"\u003e \u003ch2\u003eSample Collection\u003c/h2\u003e \u003cp\u003e\u0026ldquo;North Shore\u0026rdquo; samples were collected from the Mandeville Public Works WWTP in Mandeville, Louisiana, which has a catchment population of around thirteen thousand. \u0026ldquo;NOLA\u0026rdquo; or \u0026ldquo;New Orleans\u0026rdquo; samples were collected from Veolia East Bank WWTP located in New Orleans, Louisiana. The New Orleans East Bank has an approximate catchment population of 330 thousand. From January 12th to March 16th, 2023, one-liter, twenty-four-hour composite samples were collected from New Orleans and the North Shore each Thursday by autosamplers from influent wastewater.\u003c/p\u003e \u003c/div\u003e\n\u003ch3\u003eWastewater Sample Processing\u003c/h3\u003e\n\u003cp\u003eSamples were frozen at -80\u0026deg;C until they could be sequenced. Samples were first thawed and homogenized. Large solids were removed via tabletop centrifuge at 500 x g for five minutes. Samples were concentrated via high-speed centrifugation. The prokaryotic fraction was selectively pelleted from the resulting supernatant at an RCF\u003csub\u003eAVE\u003c/sub\u003e of 12,000 x g for fifteen minutes. Samples were then stored at 4\u0026deg;C overnight. Prokaryotic DNA extraction was conducted according to the Zymo Quick-DNA/RNA Miniprep Plus Kit and pre-sequencing quality control was confirmed via Nanodrop OneC.\u003c/p\u003e\n\u003ch3\u003eLibrary Prep and 16S Sequencing\u003c/h3\u003e\n\u003cp\u003eLibrary preparation for 16S rRNA sequencing was conducted using the Illumina MiSeq \u0026ldquo;16S Metagenomic Sequencing Library Preparation\u0026rdquo; protocol.\u003csup\u003e30\u003c/sup\u003e Samples were sequenced on an Illumina MiSeq using a V2 Reagent Kit generating circa 118k reads per sample.\u003c/p\u003e\n\u003ch3\u003eRead Process and Analysis\u003c/h3\u003e\n\u003cp\u003eInitial sequencing read quality control was conducted using FastQC. Bases with a Phred below Q20, truncated reads and adapter sequences were trimmed using Cutadapt version 4.8.\u003csup\u003e31,32\u003c/sup\u003e 16S Metagenomic analysis was conducted using Kraken 2.\u003csup\u003e33\u003c/sup\u003e For Kraken 2, demultiplexed, trimmed reads were input as FASTQ files into Kraken 2 version 2.1.3. The Standard 8GB Kraken reference database was used with default parameters except for the confidence threshold, which was raised to 0.01. Read mapping selectivity was optimized by removing from consideration all detected taxa that had fewer than 0.004% of the total number of reads mapped to them. Resultant taxonomic reports were then fed into Bracken v. 2.9.\u003csup\u003e34\u003c/sup\u003e Species-level taxonomic reports were generated using 250 bp-length reads along with default settings.\u003c/p\u003e\n\u003ch3\u003eAlpha and Beta Diversity Calculations\u003c/h3\u003e\n\u003cp\u003eAll alpha diversity measures were calculated using the \u0026ldquo;Diversity\u0026rdquo; package within Scikit-Bio.\u003csup\u003e35\u003c/sup\u003e Samples\u0026rsquo; species richness was calculated using a simple Observed Taxonomic Unit (OTU) count. Evenness was calculated using Pielou\u0026rsquo;s Evenness and Simpsons Evenness Index, which is calculated by dividing the Gini-Simpson index by the total number of taxa.\u003csup\u003e36\u0026ndash;38\u003c/sup\u003e Overall diversity was estimated using Shannon Entropy \u0026ndash; calculated using the Shannon-Weiner Index \u0026ndash; and the Gini-Simpson index subtracted from 1.\u003csup\u003e37\u0026ndash;39\u003c/sup\u003e The Bray-Curtis dissimilarity formula was used to score the beta diversity between timepoints.\u003csup\u003e40\u003c/sup\u003e\u003c/p\u003e \u003cdiv id=\"Sec8\" class=\"Section2\"\u003e \u003ch2\u003eSignificance Testing\u003c/h2\u003e \u003cp\u003eStudent\u0026rsquo;s t-test, one-way ANOVAs and Pearson R calculations and significance testing was conducted using SciPi\u0026rsquo;s \u0026ldquo;stats\u0026rdquo; library.\u003csup\u003e41\u003c/sup\u003e Linear and multiple regressions were conducted using the OLS() function in the statsmodels.api library.\u003csup\u003e42\u003c/sup\u003e\u003c/p\u003e \u003c/div\u003e\n\u003ch3\u003eHistorical Weather and Clinical Isolate Data\u003c/h3\u003e\n\u003cp\u003eAll weather data was found via the National Centers for Environmental Information\u0026rsquo;s online Past Weather database and search function, found at \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttps://www.ncei.noaa.gov/access/past-weather/\u003c/span\u003e\u003cspan address=\"https://www.ncei.noaa.gov/access/past-weather/\" targettype=\"URL\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e. Clinical isolate data was collected using the CDC\u0026rsquo;s BEAM dashboard found at \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttps://www.cdc.gov/ncezid/dfwed/BEAM-dashboard.html\u003c/span\u003e\u003cspan address=\"https://www.cdc.gov/ncezid/dfwed/BEAM-dashboard.html\" targettype=\"URL\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e.\u003c/p\u003e"},{"header":"Results","content":"\u003cdiv id=\"Sec11\" class=\"Section2\"\u003e \u003ch2\u003eControlling conflicting variation from weather\u003c/h2\u003e \u003cp\u003ePearson correlation analysis showed that New Orleans and the North Shore both had a strong, positive correlation between their weekly temperatures (High: R\u0026thinsp;=\u0026thinsp;0.845; Low: R\u0026thinsp;=\u0026thinsp;0.969, Average: R\u0026thinsp;=\u0026thinsp;0.952) and weekly precipitation (Pearson, R\u0026thinsp;=\u0026thinsp;0.991) during the period of study. \u003cb\u003e(see Supp.\u003c/b\u003e Figure\u0026nbsp;\u003cspan refid=\"Fig2\" class=\"InternalRef\"\u003e1\u003c/span\u003e, \u003cb\u003eSupp.\u003c/b\u003e Table\u0026nbsp;\u003cspan refid=\"Tab1\" class=\"InternalRef\"\u003e1\u003c/span\u003e\u003cb\u003e)\u003c/b\u003e Paired t-tests determined that the sample means from each site for each component of weather were statistically similar. (High Temp: p\u0026thinsp;=\u0026thinsp;2.08*10\u003csup\u003e\u0026minus;\u0026thinsp;3\u003c/sup\u003e; Low Temp: p\u0026thinsp;=\u0026thinsp;3.88*10\u003csup\u003e\u0026minus;\u0026thinsp;6\u003c/sup\u003e, Average Temp: p\u0026thinsp;=\u0026thinsp;2.12*10\u003csup\u003e\u0026minus;\u0026thinsp;5\u003c/sup\u003e, Precipitation: 3.21*10\u003csup\u003e\u0026minus;\u0026thinsp;8\u003c/sup\u003e)\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec12\" class=\"Section2\"\u003e \u003ch2\u003eDominant taxa turnover\u003c/h2\u003e \u003cp\u003eWe plotted the abundance of the ten most dominant taxa for each time point at both sampling locations. \u003cb\u003e(see\u003c/b\u003e Fig.\u0026nbsp;\u003cspan refid=\"Fig2\" class=\"InternalRef\"\u003e1\u003c/span\u003e, \u003cb\u003eSupp. Table\u0026nbsp;2)\u003c/b\u003e For weekly Bray-Curtis species turnover, New Orleans averaged 0.559, while the North Shore averaged 0.316. These weekly species turnover differences were found to be significantly different. (T-test: p\u0026thinsp;=\u0026thinsp;1.46*10\u003csup\u003e\u0026minus;\u0026thinsp;3\u003c/sup\u003e; t\u0026thinsp;=\u0026thinsp;3.83) When comparing the weekly diversity to those seen in week one, the New Orleans site averaged a Bray-Curtis dissimilarity of 0.592, while the North Shore averaged 0.288. Again, these differences were determined to be statistically significant. (T-test: p\u0026thinsp;=\u0026thinsp;2.55*10\u003csup\u003e\u0026minus;\u0026thinsp;4\u003c/sup\u003e; t\u0026thinsp;=\u0026thinsp;4.67)\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec13\" class=\"Section2\"\u003e \u003ch2\u003eTotal taxa turnover\u003c/h2\u003e \u003cp\u003eBray-Curtis dissimilarity was also measured for all taxa. (\u003cb\u003esee\u003c/b\u003e Fig.\u0026nbsp;\u003cspan refid=\"Fig8\" class=\"InternalRef\"\u003e2\u003c/span\u003e) Weekly species turnover steadily increased over weeks 6-to-8 before gradually decreasing over weeks 8-to-10. Bray-Curtis dissimilarity between week four diversity and each subsequent week gradually increased and peaked at week nine, with a smaller peak at week seven. This steady rise in dissimilarity had a strong, significant, positive correlation with progression through Carnivale. (R\u0026thinsp;=\u0026thinsp;0.895, p\u0026thinsp;=\u0026thinsp;0.0160)\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec14\" class=\"Section2\"\u003e \u003ch2\u003eAlpha Diversity\u003c/h2\u003e \u003cp\u003eFollowing week four, species richness slowly increased 65.7% until peaking at week eight \u0026ndash; about ten days following Mardi Gras Day \u0026ndash; and then began a gradual decline. \u003cb\u003e(see\u003c/b\u003e Fig.\u0026nbsp;\u003cspan refid=\"Fig10\" class=\"InternalRef\"\u003e3\u003c/span\u003e, \u003cb\u003eSupp. Tables\u0026nbsp;3)\u003c/b\u003e All metrics for evenness and overall diversity reached peak values at week nine \u0026ndash; over two weeks after Mardi Gras Day. Pielous\u0026rsquo;s evenness increased 84.3% from its week four minimum, while Simpson evenness increased 77.4% from its week six minimum. Shannon entropy and Simpson diversity index increased 1,967.8% and 195.4% from their week five minimums, respectively. When we focused on weeks five through nine, we found that changes in all alpha diversity measures had strong, positive correlations with progression through Carnivale, while only Simpson diversity index reached statistical significance. (\u003cb\u003esee Supp. Table\u0026nbsp;4\u003c/b\u003e)\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec15\" class=\"Section2\"\u003e \u003ch2\u003ePathogen tracker\u003c/h2\u003e \u003cp\u003eWe calculated the periods of peak significance between a species\u0026rsquo; change in wastewater concentration and progression through Carnivale season using linear regression. The means of these values for our pathogen, microbiome, and environmental control groups varied significantly from one another. (one-way ANOVA: F-statistic\u0026thinsp;=\u0026thinsp;7.23, p\u0026thinsp;=\u0026thinsp;6.34*10\u003csup\u003e\u0026minus;\u0026thinsp;3\u003c/sup\u003e) \u003cb\u003e(see\u003c/b\u003e Fig.\u0026nbsp;\u003cspan refid=\"Fig7\" class=\"InternalRef\"\u003e4\u003c/span\u003e, Table\u0026nbsp;\u003cspan refid=\"Tab1\" class=\"InternalRef\"\u003e1\u003c/span\u003e, \u003cb\u003eSupp. Table\u0026nbsp;5)\u003c/b\u003e Our six pathogens and six microbiota were most alike on average. (t-test: t-statistic\u0026thinsp;=\u0026thinsp;0.888, p-value\u0026thinsp;=\u0026thinsp;0.395) However, the peak significance values for the environmental bacteria, our negative control, differed significantly from each. (Pathogens vs environmental: t-statistic=-2.42, p-value\u0026thinsp;=\u0026thinsp;0.0361; Microbiota vs environmental: t-statistic\u0026thinsp;=\u0026thinsp;4.60, p-value\u0026thinsp;=\u0026thinsp;9.77*10\u003csup\u003e\u0026minus;\u0026thinsp;4\u003c/sup\u003e)\u003c/p\u003e \u003cp\u003eTo isolate and remove even more variation from weather, we ran multiple regression analysis between each species\u0026rsquo; wastewater variation using the temperature and precipitation as covariates. We also standardized the period of interest to weeks 4\u0026ndash;10. \u003cb\u003e(see Table\u0026nbsp;2)\u003c/b\u003e Here too, the microbiota group varied the most from the environmental control group, with the differences in the mean significances between their wastewater concentrations and Carnivale progression for both groups nearly being significant itself, while the difference between the pathogens and the environmental controls were not significant. (Microbiota: p\u0026thinsp;=\u0026thinsp;0.061; Pathogens: 0.304) However, the difference in the t statistics for both experimental groups was significant as compared to the control group, (Microbiota: p\u0026thinsp;=\u0026thinsp;0.034, Pathogens: p\u0026thinsp;=\u0026thinsp;0.003) with the environmental group having a negative mean t statistic while the other two groups were strongly positive on average.\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec16\" class=\"Section2\"\u003e \u003ch2\u003eCDC BEAM Monthly Clinical Isolate Data\u003c/h2\u003e \u003cp\u003eWe collected clinical isolate data from the CDC\u0026rsquo;s BEAM (Bacteria, Enteric, Ameba, and Mycotic) Dashboard to confirm whether rises in wastewater concentrations coincided with rises in symptomatic cases. \u003cb\u003e(see\u003c/b\u003e Fig.\u0026nbsp;\u003cspan refid=\"Fig10\" class=\"InternalRef\"\u003e3\u003c/span\u003e\u003cb\u003e)\u003c/b\u003e Two taxa were able to be compared tangentially: BEAM\u0026rsquo;s \u003cem\u003eSalmonella spp.\u003c/em\u003e isolates to our wastewater \u003cem\u003eS. enterica\u003c/em\u003e, which failed to increase significantly in wastewater concentration over the 2023 Carnivale season, and BEAM\u0026rsquo;s STEC to our wastewater \u003cem\u003eE. coli\u003c/em\u003e, which showed the most significant increase of all the species concentrations that we tracked. All clinical isolate data underwent min-max normalization over a span of 0 to 1 so that states with vastly different population sizes could be accurately compared.\u003c/p\u003e \u003cp\u003eIn 2023, Louisiana\u0026rsquo;s \u003cem\u003eSalmonella spp.\u003c/em\u003e case isolates increased from 0 in February to 0.42 in March, a statistically significant increase when the surrounding states only rose an average of 0.11 over the same period. (Student\u0026rsquo;s t-test: p\u0026thinsp;=\u0026thinsp;0.012) Louisiana saw an even more significant increase in STEC isolates, doubling from February to March. This represents a 0.5 increase when surrounding states decreased by 0.22. (Student\u0026rsquo;s t-test: p\u0026thinsp;=\u0026thinsp;0.009)\u003c/p\u003e \u003cp\u003eTo put these numbers into their proper context, we compared the 2023 isolate data to those of the 2021 season, in which all New Orleans\u0026rsquo; Carnivale events were cancelled due to the COVID-19 pandemic. Louisiana\u0026rsquo;s \u003cem\u003eSalmonella spp.\u003c/em\u003e isolates increased from a value of 0 in February to 0.7 in March. This \u003cem\u003eSalmonella spp.\u003c/em\u003e increase is nearly as significant as it was in 2023, as the surrounding states only rose to an average of 0.16 in 2021. (Student\u0026rsquo;s t-test: p\u0026thinsp;=\u0026thinsp;0.019) The 2021 STEC numbers, however, showed no increase in cases for Louisiana while the surrounding states also remained mostly static, decreasing by a value of less than 0.01 between February and March. (Student\u0026rsquo;s t-test: p\u0026thinsp;=\u0026thinsp;0.996)\u003c/p\u003e \u003c/div\u003e"},{"header":"Discussion","content":"\u003cp\u003eIt is documented that ambient temperature and precipitation can influence prevalence of both bacterial concentrations and microbial resistance genes in wastewater and other surface waters.\u003csup\u003e43\u0026ndash;45\u003c/sup\u003e To control for variation in the New Orleans sewerage bacterial concentrations and community diversity due to influence from weather, we attempted an experimental design for the study of mass gatherings by taking matched samples from New Orleans and the North Shore and using the difference between the two values as the weather-corrected measure of New Orleans\u0026rsquo; sewage bacteria.\u003c/p\u003e \u003cp\u003eThe justification for the use of bacterial concentrations on the North Shore as a control group is grounded in both the North Shore and New Orleans sampling locations sharing nearly identical temperatures and rainfall \u0026ndash; being only thirty-five miles apart \u0026ndash; but experiencing vastly differing tourism rates. Travelling between the two cities involves an hour-long drive across the world\u0026rsquo;s longest contiguous bridge, the Lake Pontchartrain Causeway, which is also a toll road. Due to this and the relative scarcity of hotels on the North Shore, we reason that tourism to the North Shore over Mardi Gras would be negligible in comparison to that in The Big Easy. To confirm that the weather in the two locations was sufficiently similar, we pulled out historical weather data for each sampling location and compared them. \u003cb\u003e(see Supp.\u003c/b\u003e Figure\u0026nbsp;\u003cspan refid=\"Fig2\" class=\"InternalRef\"\u003e1\u003c/span\u003e, \u003cb\u003eSupp.\u003c/b\u003e Table\u0026nbsp;\u003cspan refid=\"Tab1\" class=\"InternalRef\"\u003e1\u003c/span\u003e\u003cb\u003e)\u003c/b\u003e\u003c/p\u003e \u003cp\u003eIndeed, New Orleans and the North Shore have extremely similar temperature and weekly precipitation. The various bacterial species\u0026rsquo; concentrations measured had different degrees of correlation with the local weather during the 2023 New Orleans\u0026rsquo; Carnivale season. Generally, our selected human pathogens\u0026rsquo; abundances had extremely weak correlations with average weekly rainfall, with the absolute value of Pearson correlation coefficient for the various species and rainfall averaging a Pearson correlation coefficient of 0.120. There was a slightly stronger correlation between temperature and bacterial abundance, with the mean absolute value of correlation being R\u0026thinsp;=\u0026thinsp;0.332. Our matched design allowed for us to reduce bacterial correlation with ambient temperature by 26.1%, down to a final mean Pearson correlation of R\u0026thinsp;=\u0026thinsp;0.245.\u003c/p\u003e \u003cp\u003eBy comparing weekly species turnover we demonstrated that the rate of change in community composition for New Orleans was higher that that found in the North Shore, as we would expect for a community experiencing biotic invasion. \u003cb\u003e(see\u003c/b\u003e Figs.\u0026nbsp;\u003cspan refid=\"Fig2\" class=\"InternalRef\"\u003e1\u003c/span\u003e\u0026amp;\u003cspan refid=\"Fig8\" class=\"InternalRef\"\u003e2\u003c/span\u003e\u003cb\u003e)\u003c/b\u003e Furthermore, we found that the rate of change in the New Orleans\u0026rsquo; community composition increased slowly over weeks 6-to-8 before peaking and gradually declined over weeks 8-to-10. This peak occurs a little over a week after Mardi Gras Day. Community diversity metrics strayed from baseline levels the most during weeks nine and seven. This seems to demonstrate that some species peaked in diversity during Mardi Gras, while others continued to slowly increase over the following 2-to-3 weeks.\u003c/p\u003e \u003cp\u003eAll diversity measures tested increased from week 5-to-9, i.e. two weeks prior to Mardi Gras Day to over two weeks post. \u003cb\u003e(see\u003c/b\u003e Fig.\u0026nbsp;\u003cspan refid=\"Fig10\" class=\"InternalRef\"\u003e3\u003c/span\u003e\u003cb\u003e)\u003c/b\u003e. This is expected if tourism is indeed importing new species to the New Orleans\u0026rsquo; population, as new species means increased richness and even increased evenness initially if the imported bacteria exist in similar abundances in their own endemic populations. The time frame, weeks 5 through 9, appears to coincide with the peak of Carnivale festivities from weeks 4 to 7 when we factor in the apparent 2\u0026ndash;3-week lag established elsewhere in this paper and others.\u003csup\u003e4,19,49\u003c/sup\u003e\u003c/p\u003e \u003cp\u003eOf most interest to public health surrounding mass gatherings, all our alpha and beta diversity metrics were returning to baseline levels by week ten, just over three weeks following Mardi Gras Day. This may indicate that public health interventions need only concentrate on the weeks during and directly after the mass gathering event. However, we would need to test further on post-Mardi Gras to make this claim definitively and ensure this was not a temporary aberration.\u003c/p\u003e \u003cp\u003eThe pathogens selected are known human pathogens that have a documented predilection for acquiring ARGs, making them dually useful for both tracking of human disease and as sentinel species representing greater accumulation of ARGs. For example, if an abundance of E. coli \u0026ndash; a common indicator species of ARG buildup \u0026ndash; was to be found via 16S sequencing, it could signal that targeted PCR-based follow-up testing would be warranted to determine if there was a rise in particular pathogenic strains, e.g. ETEC, STEC, etc, or if particular antibiotics-resistance genes were present in those populations, e.g. AmpC, TetA, etc.\u003csup\u003e6\u003c/sup\u003e\u003c/p\u003e \u003cp\u003eWe looked at two other populations of species typical of municipal wastewater. As a negative control, we looked at six environmental bacterial species, i.e. bacteria that do not typically colonize humans and whose abundance in municipal wastewater should be unaffected by human migration. We performed t-tests between each species and progression through Carnivale. When we averaged the t statistics, which indicates both the magnitude and the direction of the relationship, our environmental controls had an average t statistic of -0.534, indicating on average a weak, negative relationship between the species\u0026rsquo; concentration and progression though the heart of Carnivale season.\u003c/p\u003e \u003cp\u003eAmong the six selected environmental species, we only measured one period of significant, strongly correlated increase in wastewater concentration over Carnivale season: \u003cem\u003eVibrio fluvialis.\u003c/em\u003e \u003cb\u003e(see\u003c/b\u003e Fig.\u0026nbsp;\u003cspan refid=\"Fig7\" class=\"InternalRef\"\u003e4\u003c/span\u003e, \u003cb\u003eSupp. Table\u0026nbsp;5)\u003c/b\u003e Perhaps not coincidentally, \u003cem\u003eV. fluvalis\u003c/em\u003e does have a documented ability to opportunistically infect the human gut, despite typically preferring coastal waterways.\u003csup\u003e46\u003c/sup\u003e\u003c/p\u003e \u003cp\u003eThe second group we tracked were six common members of the human gut or urogenital tract microbiota, a category that makes up a major proportion of municipal waste bacteria.\u003csup\u003e47\u003c/sup\u003e These species are certainly imported en mass during large-scale movements of humans. However, these species are not generally known to commonly induce pathogeneses which would increase their transmission from person to person i.e. conditions like UTIs or diarrhea that require an incubation time to manifest. Our microbiota had a mean t statistic of 2.07. Here we see a statistically significant difference between the mean t statistic of our environmental negative controls. (p\u0026thinsp;=\u0026thinsp;0.003)\u003c/p\u003e \u003cp\u003eOne species of human microbiota, \u003cem\u003eF. prausnitzii\u003c/em\u003e, had a period of significant, strongly negative correlation between its wastewater concentration and the progression through Carnivale, the only microbiota species found to do so. Again, possibly not by coincidence, the prevalence of \u003cem\u003eF. prausnitzii\u003c/em\u003e in the human gut has been found to decrease in abundance during periods of prolonged alcohol consumption.\u003csup\u003e48\u003c/sup\u003e\u003c/p\u003e \u003cp\u003eTurning then to our selected pathogens, we measured significant increases in abundances of nearly all human pathogens tracked over the course of late Carnival. The mean t statistic over the heart of Carnivale for this group was 1.97. Here again, this was statistically different from that of the environmental controls. (p\u0026thinsp;=\u0026thinsp;0.034)\u003c/p\u003e \u003cp\u003eSuch a gap between our two experimental groups and our environmental control suggests that the admixture of humans around mass gatherings has a significantly positive impact on the spread of common human colonizers and pathogens. This supposition is further supported by the observation that while a few of these pathogens peaked in abundance around weeks six or seven \u0026ndash; the timepoints directly before and after of Mardi Gras Day \u0026ndash; most peaked in abundance 2-to-3 weeks after, further mirroring the lag time seen in other studies of mass gatherings and contagious disease where the temporal dimension was analyzed. This is likely due to the incubation time between inoculation and shedding of bacteria.\u003csup\u003e19\u003c/sup\u003e This contrasts our microbiome species, which typically peaked around week six. This may indicate that human microbiota are more directly impacted by importation into the community, as they typically occur at higher abundances in humans and do not need to rely on pathogenesis to spread and raise their wastewater profile.\u003c/p\u003e \u003cp\u003eOther studies that have explored the temporal correlation between mass gatherings, pathogen concentration in public wastewater and incidence of clinical isolates have found, on average, a 1\u0026ndash;4 week lag between the mass gathering event and a rise in wastewater concentration and 2\u0026ndash;3 week lag between the event and increasing clinical cases, depending on the length of incubation period for that particular species.\u003csup\u003e4,19,49\u003c/sup\u003e Mardi Gras and the crescendo of Carnivale was not until the third week of February and most pathogen wastewater concentrations did not peak until early in the second week of March. \u003cb\u003e(see\u003c/b\u003e Fig.\u0026nbsp;\u003cspan refid=\"Fig9\" class=\"InternalRef\"\u003e5\u003c/span\u003e\u003cb\u003e)\u003c/b\u003e Therefore, we would expect to see a large spike in clinical isolates from March relative to February for any species which underwent significant wastewater spikes in the beginning of that month.\u003c/p\u003e \u003cp\u003eCompared to every state surrounding it, Louisiana had significant increases in clinical isolates of both \u003cem\u003eSalmonella spp.\u003c/em\u003e (compared to wastewater: \u003cem\u003eS. enterica\u003c/em\u003e) and STEC (compared to wastewater: \u003cem\u003eE. coli\u003c/em\u003e). \u003cb\u003e(see\u003c/b\u003e Fig.\u0026nbsp;\u003cspan refid=\"Fig7\" class=\"InternalRef\"\u003e4\u003c/span\u003e\u003cb\u003e)\u003c/b\u003e However, \u003cem\u003eS. enterica\u0026rsquo;s\u003c/em\u003e increases in wastewater concentration failed to significantly correlate to progression through Carnivale season, while \u003cem\u003eE. coli\u003c/em\u003e showed the strongest wastewater correlations of all pathogens tracked. To shed light on this apparent contradiction, we looked at the same clinical isolate metrics from just two years prior, when the COVID-19 outbreak cancelled Carnivale festivities in New Orleans for the first time since the late 1970s.\u003c/p\u003e \u003cp\u003eHere we saw clinical isolates of \u003cem\u003eSalmonella spp.\u003c/em\u003e increase in early Spring without Carnivale tourists just as significantly as they had when they were present, suggesting that the increases in clinical cases, and very well the moderate increases in wastewater, are likely due to a different phenomenon, such as seasonality. However, in the year without Mardi Gras, Louisiana\u0026rsquo;s STEC clinical cases were indistinguishable from those of the surrounding states, while in the 2023 Carnivale season Louisiana was significant in both the number of March STEC isolates and the increase in isolates from February to March when compared to all surrounding states. Being as the \u003cem\u003eE. coli\u003c/em\u003e wastewater correlation was also robust, it is hard to argue that Mardi Gras tourism, the spike in wastewater \u003cem\u003eE. coli\u003c/em\u003e concentrations, and that of STEC clinical isolates are not likely related.\u003c/p\u003e "},{"header":"Declarations","content":"\u003ch2\u003eFunding\u003c/h2\u003e \u003cp\u003edeclaration:\u003c/p\u003e \u003cp\u003eThis work was supported by the National Institute of Health grant 3U01AI151812-04S1.\u003c/p\u003e \u003cp\u003eClinical declaration:\u003c/p\u003e \u003cp\u003eClinical trial number: not applicable\u003c/p\u003e \u003cp\u003eEthical declaration:\u003c/p\u003e \u003cp\u003eNot applicable\u003c/p\u003e \u003cp\u003eConsent for publication:\u003c/p\u003e \u003cp\u003eNot applicable\u003c/p\u003e \u003cp\u003eData availability:\u003c/p\u003e \u003cp\u003eAll raw sequencing reads datasets were deposited in GenBank SRA under the BioProject PRJNA1363154.\u003c/p\u003e \u003cp\u003eCompeting interests:\u003c/p\u003e \u003cp\u003eThe authors declare that they have no competing interests.\u003c/p\u003e\u003ch2\u003eAuthor Contribution\u003c/h2\u003e\u003cp\u003eJ.B. performed all wet work (besides library prep and sequencing), did all data analysis and figures, and wrote the manuscript.L.R. conceptualized and performed all library prep and sequencing.B.G. acquired funding, supervised, and assisted J.B. in conceptualizing the project.\u003c/p\u003e\u003ch2\u003eAcknowledgement\u003c/h2\u003e\u003cp\u003eTheresa Sokol and Sean Simonson of The Louisiana Department of Health for helping procure samples.\u003c/p\u003e\u003ch2\u003eData Availability\u003c/h2\u003e\u003cp\u003eAll raw sequencing reads datasets were deposited in GenBank SRA under the BioProject PRJNA1363154.\u003c/p\u003e"},{"header":"References","content":"\u003col\u003e\n\u003cli\u003eBudd W. Typhoid Fever: its Nature, Mode of Spreading, and Prevention | Nature. 1874. Accessed November 6, 2025. https://www.nature.com/articles/009280a0\u003c/li\u003e\n\u003cli\u003eMoorhead R. William Budd and Typhoid Fever. \u003cem\u003eJ R Soc Med\u003c/em\u003e. 2002;95(11):561-564. doi:10.1177/014107680209501115\u003c/li\u003e\n\u003cli\u003eWilson WJ. Isolation of Enteric Bacilli from Sewage and Water and its Bearing on Epidemiology. \u003cem\u003eBr Med J\u003c/em\u003e. 1933;2(3794):560-562. doi:10.1136/bmj.2.3794.560\u003c/li\u003e\n\u003cli\u003eYan T, O\u0026rsquo;Brien P, Shelton JM, Whelen AC, Pagaling E. Municipal Wastewater as a Microbial Surveillance Platform for Enteric Diseases: A Case Study for Salmonella and Salmonellosis. \u003cem\u003eEnviron Sci Technol\u003c/em\u003e. 2018;52(8):4869-4877. doi:10.1021/acs.est.8b00163\u003c/li\u003e\n\u003cli\u003eShannon KE, Lee DY, Trevors JT, Beaudette LA. Application of real-time quantitative PCR for the detection of selected bacterial pathogens during municipal wastewater treatment. \u003cem\u003eSci Total Environ\u003c/em\u003e. 2007;382(1):121-129. doi:10.1016/j.scitotenv.2007.02.039\u003c/li\u003e\n\u003cli\u003eTroja F, Indio V, Savini F, et al. Monitoring and preventing foodborne outbreaks: are we missing wastewater as a key data source? \u003cem\u003eItal J Food Saf\u003c/em\u003e. 2024;13(4). doi:10.4081/ijfs.2024.12725\u003c/li\u003e\n\u003cli\u003eSchets FM, de Heer L, de Roda Husman AM. \u003cem\u003eCoxiella burnetii\u003c/em\u003e in sewage water at sewage water treatment plants in a Q fever epidemic area. \u003cem\u003eInt J Hyg Environ Health\u003c/em\u003e. 2013;216(6):698-702. doi:10.1016/j.ijheh.2012.12.010\u003c/li\u003e\n\u003cli\u003eM\u0026rsquo;ikanatha NM, Goldblum ZS, Cesari N, et al. Outbreak-associated Salmonella Baildon found in wastewater demonstrates how sewage monitoring can supplement traditional disease surveillance. \u003cem\u003eJ Clin Microbiol\u003c/em\u003e. 2024;62(10):e00825-24. doi:10.1128/jcm.00825-24\u003c/li\u003e\n\u003cli\u003eMardi Gras Parade Krewes | Mardi Gras New Orleans. Accessed November 7, 2025. https://www.neworleans.com/events/holidays-seasonal/mardi-gras/krewes/\u003c/li\u003e\n\u003cli\u003eU.S. Census Bureau QuickFacts. Accessed November 7, 2025. https://www.census.gov/quickfacts/fact/table/neworleanscitylouisiana/PST045219\u003c/li\u003e\n\u003cli\u003eGotham KF. Tourism from Above and Below: Globalization, Localization and New Orleans\u0026rsquo;s Mardi Gras. \u003cem\u003eInt J Urban Reg Res\u003c/em\u003e. 2005;29(2):309-326. doi:10.1111/j.1468-2427.2005.00586.x\u003c/li\u003e\n\u003cli\u003eKiernan JS. 2020 Mardi Gras Facts \u0026ndash; Booze, Floats, Money \u0026amp; More. WalletHub. Accessed November 7, 2025. https://wallethub.com/blog/mardi-gras-facts/31999\u003c/li\u003e\n\u003cli\u003eWeiss T. Economic Impact and Net Fiscal Benefit of New Orleans\u0026rsquo; 2023 Mardi Gras Season. January 2024. Accessed November 7, 2025. https://tulane.app.box.com/s/m1t5mjrl8n2xbu2urkg0uo6dq9jukg1v\u003c/li\u003e\n\u003cli\u003eTiwari A, Kurittu P, Al-Mustapha AI, et al. Wastewater surveillance of antibiotic-resistant bacterial pathogens: A systematic review. \u003cem\u003eFront Microbiol\u003c/em\u003e. 2022;13. doi:10.3389/fmicb.2022.977106\u003c/li\u003e\n\u003cli\u003eAlexander J, Hembach N, Schwartz T. Evaluation of antibiotic resistance dissemination by wastewater treatment plant effluents with different catchment areas in Germany. \u003cem\u003eSci Rep\u003c/em\u003e. 2020;10(1):8952. doi:10.1038/s41598-020-65635-4\u003c/li\u003e\n\u003cli\u003eLi X, Zhang S, Sherchan S, et al. Correlation between SARS-CoV-2 RNA concentration in wastewater and COVID-19 cases in community: A systematic review and meta-analysis. \u003cem\u003eJ Hazard Mater\u003c/em\u003e. 2023;441:129848. doi:10.1016/j.jhazmat.2022.129848\u003c/li\u003e\n\u003cli\u003eHuang Y, Zhou N, Zhang S, et al. Norovirus detection in wastewater and its correlation with human gastroenteritis: a systematic review and meta-analysis. \u003cem\u003eEnviron Sci Pollut Res\u003c/em\u003e. 2022;29(16):22829-22842. doi:10.1007/s11356-021-18202-x\u003c/li\u003e\n\u003cli\u003eLi D, Gu AZ, Zeng S ‐Y., Yang W, He M, Shi H ‐C. Monitoring and evaluation of infectious rotaviruses in various wastewater effluents and receiving waters revealed correlation and seasonal pattern of occurrences. \u003cem\u003eJ Appl Microbiol\u003c/em\u003e. 2011;110(5):1129-1137. doi:10.1111/j.1365-2672.2011.04954.x\u003c/li\u003e\n\u003cli\u003eCollender PA, Morris C, Glenn-Finer R, et al. Mass Gatherings and Diarrheal Disease Transmission Among Rural Communities in Coastal Ecuador. \u003cem\u003eAm J Epidemiol\u003c/em\u003e. 2019;188(8):1475-1483. doi:10.1093/aje/kwz102\u003c/li\u003e\n\u003cli\u003eZhao L, Guzman HP, Xagoraraki I. Tracking Chlamydia and Syphilis in the Detroit Metro Area by Molecular Analysis of Environmental Samples. \u003cem\u003eEnviron Sci Technol\u003c/em\u003e. 2024;58(40):17606-17616. doi:10.1021/acs.est.4c05869\u003c/li\u003e\n\u003cli\u003eYang CM, Lin MF, Liao PC, et al. Comparison of antimicrobial resistance patterns between clinical and sewage isolates in a regional hospital in Taiwan. \u003cem\u003eLett Appl Microbiol\u003c/em\u003e. 2009;48(5):560-565. doi:10.1111/j.1472-765X.2009.02572.x\u003c/li\u003e\n\u003cli\u003eUrase T, Okazaki M, Tsutsui H. Prevalence of ESBL-producing Escherichia coli and carbapenem-resistant Enterobacteriaceae in treated wastewater: a comparison with nosocomial infection surveillance. \u003cem\u003eJ Water Health\u003c/em\u003e. 2020;18(6):899-910. doi:10.2166/wh.2020.014\u003c/li\u003e\n\u003cli\u003eAlberts B, Johnson A, Lewis J, Raff M, Roberts K, Walter P. Introduction to Pathogens. In: \u003cem\u003eMolecular Biology of the Cell. 4th Edition\u003c/em\u003e. Garland Science; 2002. Accessed November 7, 2025. https://www.ncbi.nlm.nih.gov/books/NBK26917/\u003c/li\u003e\n\u003cli\u003eBEAM Dashboard | CDC. April 18, 2025. Accessed November 7, 2025. https://www.cdc.gov/ncezid/dfwed/BEAM-dashboard.html\u003c/li\u003e\n\u003cli\u003eHutton A, Ranse J, Munn MB. Developing Public Health Initiatives through Understanding Motivations of the Audience at Mass-Gathering Events. \u003cem\u003ePrehospital Disaster Med\u003c/em\u003e. 2018;33(2):191-196. doi:10.1017/S1049023X18000067\u003c/li\u003e\n\u003cli\u003eAlqahtani AS, Heywood AE, Rashid H. Preparing Australian pilgrims for the Hajj 2018. \u003cem\u003eJ Travel Med\u003c/em\u003e. 2018;25(1):tay068. doi:10.1093/jtm/tay068\u003c/li\u003e\n\u003cli\u003eHassan IN, Abuassa N, Ibrahim M, Ibrahim MNH. Health and safety management at the glastonbury festival: A mass gathering medicine perspective. \u003cem\u003eMass Gather Med\u003c/em\u003e. 2024;2:100010. doi:10.1016/j.mgmed.2024.100010\u003c/li\u003e\n\u003cli\u003eYi H, Zheng\u0026rsquo;an Y, Fan W, et al. Public Health Preparedness for the World\u0026rsquo;s Largest Mass Gathering: 2010 World Exposition in Shanghai, China. \u003cem\u003ePrehospital Disaster Med\u003c/em\u003e. 2012;27(6):589-594. doi:10.1017/S1049023X12001252\u003c/li\u003e\n\u003cli\u003ePolkinghorne BG, Massey PD, Durrheim DN, Byrnes T, MacIntyre CR. Prevention and surveillance of public health risks during extended mass gatherings in rural areas: The experience of the Tamworth Country Music Festival, Australia. \u003cem\u003ePublic Health\u003c/em\u003e. 2013;127(1):32-38. doi:10.1016/j.puhe.2012.09.014\u003c/li\u003e\n\u003cli\u003eS Metagenomic Sequencing Library Preparation: Preparing 16S Ribosomal RNA Gene Amplicons for the Illumina MiSeq System. \u003cem\u003eIllumina\u003c/em\u003e.\u003c/li\u003e\n\u003cli\u003eBabraham Bioinformatics - FastQC A Quality Control tool for High Throughput Sequence Data. Accessed November 7, 2025. https://www.bioinformatics.babraham.ac.uk/projects/fastqc/\u003c/li\u003e\n\u003cli\u003eMartin M. Cutadapt removes adapter sequences from high-throughput sequencing reads. \u003cem\u003eEMBnet.journal\u003c/em\u003e. 2011;17(1):10-12. doi:10.14806/ej.17.1.200\u003c/li\u003e\n\u003cli\u003eWood DE, Lu J, Langmead B. Improved metagenomic analysis with Kraken 2. \u003cem\u003eGenome Biol\u003c/em\u003e. 2019;20(1):257. doi:10.1186/s13059-019-1891-0\u003c/li\u003e\n\u003cli\u003eLu J, Breitwieser FP, Thielen P, Salzberg SL. Bracken: estimating species abundance in metagenomics data. \u003cem\u003ePeerJ Comput Sci\u003c/em\u003e. 2017;3:e104. doi:10.7717/peerj-cs.104\u003c/li\u003e\n\u003cli\u003eRideout JR, Caporaso G, Bolyen E, et al. scikit-bio/scikit-bio: scikit-bio 0.6.2. Published online July 7, 2024. doi:10.5281/zenodo.12682832\u003c/li\u003e\n\u003cli\u003ePielou EC. \u003cem\u003eEcological Diversity\u003c/em\u003e. New York : Wiley; 1975. Accessed November 7, 2025. http://archive.org/details/ecologicaldivers0000piel\u003c/li\u003e\n\u003cli\u003eSimpson EH. Measurement of Diversity. \u003cem\u003eNature\u003c/em\u003e. 1949;163(4148):688-688. doi:10.1038/163688a0\u003c/li\u003e\n\u003cli\u003eRao CR. \u003cem\u003eGini-Simpson Index of Diversity: A Characterization, Generalization and Applications\u003c/em\u003e. University of Pittsburgh; 1981.\u003c/li\u003e\n\u003cli\u003eShannon CE. A Mathematical Theory of Communication.\u003c/li\u003e\n\u003cli\u003eBray JR, Curtis JT. An Ordination of the Upland Forest Communities of Southern Wisconsin. \u003cem\u003eEcol Monogr\u003c/em\u003e. 1957;27(4):325-349. doi:10.2307/1942268\u003c/li\u003e\n\u003cli\u003eVirtanen P, Gommers R, Oliphant TE, et al. SciPy 1.0: fundamental algorithms for scientific computing in Python. \u003cem\u003eNat Methods\u003c/em\u003e. 2020;17(3):261-272. doi:10.1038/s41592-019-0686-2\u003c/li\u003e\n\u003cli\u003eSeabold S, Perktold J. Statsmodels: Econometric and Statistical Modeling with Python. \u003cem\u003eSciPy 2010\u003c/em\u003e. Published online May 1, 2010. doi:10.25080/Majora-92bf1922-011\u003c/li\u003e\n\u003cli\u003eKaleli HA, Islam MR. Effect of temperature on the growth of wastewater bacteria. \u003cem\u003eToxicol Environ Chem\u003c/em\u003e. 1997;59(1-4):111-123. doi:10.1080/02772249709358429\u003c/li\u003e\n\u003cli\u003eChing C, Sutradhar I, Zaman MH. Understanding the impacts of temperature and precipitation on antimicrobial resistance in wastewater: theory, modeling, observation, and limitations. \u003cem\u003emSphere\u003c/em\u003e. 2025;10(3):e00947-24. doi:10.1128/msphere.00947-24\u003c/li\u003e\n\u003cli\u003ePaul MJ, Coffey R, Stamp J, Johnson T. A REVIEW OF WATER QUALITY RESPONSES TO AIR TEMPERATURE AND PRECIPITATION CHANGES 1: FLOW, WATER TEMPERATURE, SALTWATER INTRUSION. \u003cem\u003eJ Am Water Resour Assoc\u003c/em\u003e. 2019;55(4):824-843. doi:10.1111/1752-1688.12710\u003c/li\u003e\n\u003cli\u003eRamamurthy T, Chowdhury G, Pazhani GP, Shinoda S. Vibrio fluvialis: an emerging human pathogen. \u003cem\u003eFront Microbiol\u003c/em\u003e. 2014;5:91. doi:10.3389/fmicb.2014.00091\u003c/li\u003e\n\u003cli\u003eLaMartina EL, Mohaimani AA, Newton RJ. Urban wastewater bacterial communities assemble into seasonal steady states. \u003cem\u003eMicrobiome\u003c/em\u003e. 2021;9:116. doi:10.1186/s40168-021-01038-5\u003c/li\u003e\n\u003cli\u003eGao B, Emami A, Zhou R, et al. Functional Microbial Responses to Alcohol Abstinence in Patients With Alcohol Use Disorder. \u003cem\u003eFront Physiol\u003c/em\u003e. 2020;11:370. doi:10.3389/fphys.2020.00370\u003c/li\u003e\n\u003cli\u003eBrighton K, Fisch S, Wu H, Vigil K, Aw TG. Targeted community wastewater surveillance for SARS-CoV-2 and Mpox virus during a festival mass-gathering event. \u003cem\u003eSci Total Environ\u003c/em\u003e. 2024;906:167443. doi:10.1016/j.scitotenv.2023.167443\u003c/li\u003e\n\u003c/ol\u003e"},{"header":"Table 1","content":"\u003cp\u003e\u003cstrong\u003eTable 1\u003c/strong\u003e: Summary of correlation and significance of the relationship between individual species abundances and progression through Carnivale\u003c/p\u003e\n\u003ctable border=\"1\" cellspacing=\"0\" cellpadding=\"0\" align=\"\" width=\"732\"\u003e\n \u003ctbody\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 43.4426%;\"\u003e\n \u003cp\u003eMeasure\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 19.6721%;\"\u003e\n \u003cp\u003ePathogens\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 19.6721%;\"\u003e\n \u003cp\u003eMicrobiota\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 17.2131%;\"\u003e\n \u003cp\u003eEnvironmental\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 43.4426%;\"\u003e\n \u003cp\u003e\u003cu\u003ePeak Periods\u003c/u\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 19.6721%;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 19.6721%;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 17.2131%;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 43.4426%;\"\u003e\n \u003cp\u003e\u003cem\u003eMean Significance value (p-value)\u003c/em\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 19.6721%;\"\u003e\n \u003cp\u003e0.0513\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 19.6721%;\"\u003e\n \u003cp\u003e0.0323\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 17.2131%;\"\u003e\n \u003cp\u003e0.107\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 43.4426%;\"\u003e\n \u003cp\u003e\u003cem\u003eSignificance Standard Deviation (s\u003csub\u003ep\u003c/sub\u003e)\u003c/em\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 19.6721%;\"\u003e\n \u003cp\u003e0.0466\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 19.6721%;\"\u003e\n \u003cp\u003e0.0239\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 17.2131%;\"\u003e\n \u003cp\u003e0.0317\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 43.4426%;\"\u003e\n \u003cp\u003eSignificance of Group vs Environmental Mean: p-values\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 19.6721%;\"\u003e\n \u003cp\u003e0.0361\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 19.6721%;\"\u003e\n \u003cp\u003e9.77*10\u003csup\u003e-4\u003c/sup\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 17.2131%;\"\u003e\n \u003cp\u003e0\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 43.4426%;\"\u003e\n \u003cp\u003e\u003cem\u003eMean Correlation value (R)\u003c/em\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 19.6721%;\"\u003e\n \u003cp\u003e0.528\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 19.6721%;\"\u003e\n \u003cp\u003e0.529\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 17.2131%;\"\u003e\n \u003cp\u003e0.282\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 43.4426%;\"\u003e\n \u003cp\u003e\u003cem\u003eCorrelation Standard Deviation (s\u003csub\u003eR\u003c/sub\u003e)\u003c/em\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 19.6721%;\"\u003e\n \u003cp\u003e0.682\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 19.6721%;\"\u003e\n \u003cp\u003e0.664\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 17.2131%;\"\u003e\n \u003cp\u003e0.847\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 43.4426%;\"\u003e\n \u003cp\u003eSignificance of Group vs Environmental Mean: Pearson Rs\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 19.6721%;\"\u003e\n \u003cp\u003e0.591\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 19.6721%;\"\u003e\n \u003cp\u003e0.586\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 17.2131%;\"\u003e\n \u003cp\u003e0\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 43.4426%;\"\u003e\n \u003cp\u003e\u003cem\u003e# Periods of Sig., Strong Corr. w/ Mardi Gras\u003c/em\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 19.6721%;\"\u003e\n \u003cp\u003e8\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 19.6721%;\"\u003e\n \u003cp\u003e4\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 17.2131%;\"\u003e\n \u003cp\u003e1\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 43.4426%;\"\u003e\n \u003cp\u003e\u003cem\u003e# Taxa with Sig., Strong, Positive Corr. w/ MG\u003c/em\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 19.6721%;\"\u003e\n \u003cp\u003e4\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 19.6721%;\"\u003e\n \u003cp\u003e3\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 17.2131%;\"\u003e\n \u003cp\u003e1\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 43.4426%;\"\u003e\n \u003cp\u003e\u003cu\u003eWeeks 4-to-9\u003c/u\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 19.6721%;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 19.6721%;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 17.2131%;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 43.4426%;\"\u003e\n \u003cp\u003e\u003cem\u003eMean Significance\u003c/em\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 19.6721%;\"\u003e\n \u003cp\u003e0.370\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 19.6721%;\"\u003e\n \u003cp\u003e0.235\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 17.2131%;\"\u003e\n \u003cp\u003e0.580\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 43.4426%;\"\u003e\n \u003cp\u003e\u003cem\u003eMean T Test Statistic\u003c/em\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 19.6721%;\"\u003e\n \u003cp\u003e1.968\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 19.6721%;\"\u003e\n \u003cp\u003e2.069\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 17.2131%;\"\u003e\n \u003cp\u003e-0.534\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 43.4426%;\"\u003e\n \u003cp\u003e\u003cem\u003eSignificance of Group vs Environmental Mean: p-values\u003c/em\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 19.6721%;\"\u003e\n \u003cp\u003e0.304\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 19.6721%;\"\u003e\n \u003cp\u003e0.061\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 17.2131%;\"\u003e\n \u003cp\u003e0.0\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 43.4426%;\"\u003e\n \u003cp\u003e\u003cem\u003eSignificance of Group vs Environmental Mean: t statistics\u003c/em\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 19.6721%;\"\u003e\n \u003cp\u003e0.034\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 19.6721%;\"\u003e\n \u003cp\u003e0.003\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 17.2131%;\"\u003e\n \u003cp\u003e0.0\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003c/tbody\u003e\n\u003c/table\u003e\n\u003cp\u003e\u0026nbsp;\u003c/p\u003e\n\u003ctable cellpadding=\"0\" cellspacing=\"0\" width=\"100%\" class=\"fr-table-selection-hover\"\u003e\n \u003ctbody\u003e\n \u003ctr\u003e\n \u003ctd\u003e\n \u003cp\u003eTable 1: (Peak Periods) Average peak Pearson correlations and linear regression significances of selected species\u0026rsquo; wastewater concentration with progression through Carnivale. (Weeks 4-to-9) Average Pearson correlations and linear regression significances of each experimental group over weeks 4-to-9, the peak of the variation due to tourism.\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003c/tbody\u003e\n\u003c/table\u003e"}],"fulltextSource":"","fullText":"","funders":[],"hasAdminPriorityOnWorkflow":false,"hasManuscriptDocX":true,"hasOptedInToPreprint":true,"hasPassedJournalQc":"","hasAnyPriority":false,"hideJournal":false,"highlight":"","institution":"","isAcceptedByJournal":false,"isAuthorSuppliedPdf":false,"isDeskRejected":"","isHiddenFromSearch":false,"isInQc":false,"isInWorkflow":false,"isPdf":false,"isPdfUpToDate":true,"isWithdrawnOrRetracted":false,"journal":{"display":true,"email":"[email protected]","identity":"bmc-microbiology","isNatureJournal":false,"hasQc":true,"allowDirectSubmit":false,"externalIdentity":"mcro","sideBox":"Learn more about [BMC Microbiology](http://bmcmicrobiol.biomedcentral.com/)","snPcode":"","submissionUrl":"https://www.editorialmanager.com/mcro","title":"BMC Microbiology","twitterHandle":"#bmcmicrobiology","acdcEnabled":true,"dfaEnabled":false,"editorialSystem":"em","reportingPortfolio":"BMC Series","inReviewEnabled":true,"inReviewRevisionsEnabled":true},"keywords":"","lastPublishedDoi":"10.21203/rs.3.rs-8681977/v1","lastPublishedDoiUrl":"https://doi.org/10.21203/rs.3.rs-8681977/v1","license":{"name":"CC BY 4.0","url":"https://creativecommons.org/licenses/by/4.0/"},"manuscriptAbstract":"\u003cp\u003eWe employed 16S metagenomic analysis to measure the impact of Mardi Gras tourism on the bacterial ecology found in New Orleans\u0026rsquo; municipal wastewater. Throughout the peak of the 2023 Carnivale season, species turnover was significantly higher in New Orleans than it was in our control site. Alpha diversity metrics peaked 2-to-3 weeks after Mardi Gras Day, increasing between 65% and 1967% over Carnivale. We also found that human pathogens and microbiota had significantly stronger, more positive correlations with the rise in Mardi Gras tourism than did environmental control species. These changes in wastewater abundance for two species \u0026ndash; \u003cem\u003eS. enterica\u003c/em\u003e and \u003cem\u003eE. coli\u003c/em\u003e \u0026ndash; mirrored the concurrent clinical isolate data from the same region for \u003cem\u003eSalmonella spp.\u003c/em\u003e and STEC. We also found that multiple alpha and beta diversity measures correlated strongly with increases in tourism during the peak of Carnivale season.\u003c/p\u003e","manuscriptTitle":"Catching the Mardi Gras fever: Quantifying the impact of mass gathering tourism on local bacterial prevalence and community diversity in municipal wastewater","msid":"","msnumber":"","nonDraftVersions":[{"code":1,"date":"2026-02-02 07:21:00","doi":"10.21203/rs.3.rs-8681977/v1","editorialEvents":[{"type":"communityComments","content":0},{"type":"decision","content":"Revision requested","date":"2026-02-10T05:58:14+00:00","index":"","fulltext":""},{"type":"editorInvitedReview","content":"","date":"2026-02-09T11:14:17+00:00","index":"hide","fulltext":""},{"type":"reviewerAgreed","content":"253912087257343164449587455065713282872","date":"2026-02-03T13:40:21+00:00","index":"hide","fulltext":""},{"type":"reviewerAgreed","content":"103978288249771518431368399991037072527","date":"2026-02-03T10:26:44+00:00","index":"hide","fulltext":""},{"type":"reviewerAgreed","content":"228795917586817695775762514278690342043","date":"2026-02-03T08:09:50+00:00","index":"hide","fulltext":""},{"type":"editorInvitedReview","content":"","date":"2026-02-02T20:02:36+00:00","index":"hide","fulltext":""},{"type":"reviewerAgreed","content":"207075805273110023665262770197259909671","date":"2026-01-31T15:51:02+00:00","index":"hide","fulltext":""},{"type":"editorInvitedReview","content":"","date":"2026-01-31T14:32:36+00:00","index":"hide","fulltext":""},{"type":"reviewerAgreed","content":"29596629239836957844842661129915068250","date":"2026-01-31T12:48:27+00:00","index":"hide","fulltext":""},{"type":"reviewerAgreed","content":"35758453682588244926089265344178632087","date":"2026-01-29T22:34:32+00:00","index":"hide","fulltext":""},{"type":"reviewerAgreed","content":"308335376149617540050160163441959595582","date":"2026-01-29T08:43:53+00:00","index":"hide","fulltext":""},{"type":"reviewerAgreed","content":"250618178860318067216292600742545162776","date":"2026-01-29T07:32:57+00:00","index":"hide","fulltext":""},{"type":"reviewersInvited","content":"","date":"2026-01-29T06:25:37+00:00","index":"","fulltext":""},{"type":"editorInvited","content":"","date":"2026-01-27T05:55:31+00:00","index":"","fulltext":""},{"type":"editorAssigned","content":"","date":"2026-01-24T13:55:29+00:00","index":"","fulltext":""},{"type":"checksComplete","content":"","date":"2026-01-24T13:53:53+00:00","index":"","fulltext":""},{"type":"submitted","content":"BMC Microbiology","date":"2026-01-23T18:31:24+00:00","index":"","fulltext":""}],"status":"published","journal":{"display":true,"email":"[email protected]","identity":"bmc-microbiology","isNatureJournal":false,"hasQc":true,"allowDirectSubmit":false,"externalIdentity":"mcro","sideBox":"Learn more about [BMC Microbiology](http://bmcmicrobiol.biomedcentral.com/)","snPcode":"","submissionUrl":"https://www.editorialmanager.com/mcro","title":"BMC Microbiology","twitterHandle":"#bmcmicrobiology","acdcEnabled":true,"dfaEnabled":false,"editorialSystem":"em","reportingPortfolio":"BMC Series","inReviewEnabled":true,"inReviewRevisionsEnabled":true}}],"origin":"","ownerIdentity":"eceec7cb-6c7d-47c7-b5bb-1a703380d5ac","owner":[],"postedDate":"February 2nd, 2026","published":true,"recentEditorialEvents":[],"rejectedJournal":[],"revision":"","amendment":"","status":"in-revision","subjectAreas":[],"tags":[],"updatedAt":"2026-02-10T06:10:18+00:00","versionOfRecord":[],"versionCreatedAt":"2026-02-02 07:21:00","video":"","vorDoi":"","vorDoiUrl":"","workflowStages":[]},"version":"v1","identity":"rs-8681977","journalConfig":"researchsquare"},"__N_SSP":true},"page":"/article/[identity]/[[...version]]","query":{"redirect":"/article/rs-8681977","identity":"rs-8681977","version":["v1"]},"buildId":"XKTyCvWXoU3ODBz1xrDgd","isFallback":false,"isExperimentalCompile":false,"dynamicIds":[84888],"gssp":true,"scriptLoader":[]}