Synchronization of foodborne disease seasonality Elena N. Naumova, - - PowerPoint PPT Presentation
Synchronization of foodborne disease seasonality Elena N. Naumova, Ryan Simpson Aishwarya Venkat, Bingjie Zhou Gerald J. and Dorothy R. Friedman School May 27 th , 2019 of Nutrition Science and Policy University of Vermont, Burlington, VT
Elena N. Naumova, Ryan Simpson Aishwarya Venkat, Bingjie Zhou
Gerald J. and Dorothy R. Friedman School
Boston, MA USA May 27th, 2019 University of Vermont, Burlington, VT Symposium on Networks in Food Systems & Nutrition
Food Systems are complex and dynamic with potential for contamination at almost every node
Source:
https://foodtechconnect.com/2010/0 7/29/exploring-relationships-in-the- food-system-map/
Source: https://www.businessinsider.com/biggest-food-chains-in-america-maps-2016-11 Source: http://fortune.com/2017/06/16/amazon-whole-foods-stores-locations/ Source: https://www.gopopro.com/starters/2015/4/14/mcdonalds Source:https://www.nass.usda.gov/Publications/AgCensus/2007/Online_Highligh ts/Ag_Atlas_Maps/Economics/Farm_Related_Income_and_Direct_Sales/07- M038.php
Food supply chains are stretching nationwide
Health alert issues for salads, wraps from Kroger, Trader’s Joe’s, Walgreens due to parasite concerns CNN (Aug. 01, 2018) Chicken Is the Number One Cause
Here’s How to Stay Safe Health (Feb. 11, 2019) These Are the Foods That Cause the Most Illnesses, the CDC Says Self (Jul. 30, 2018) Should You Throw Out Your Avocados? What to Know About the Recall Healthline (Mar. 26, 2019) Potential Hepatitis A Exposure from McDonalds Restaurant Worker - Again Food Poison Journal (Mar. 28, 2019) Chipotle to retrain all workers on food safety after Ohio episode USA Today (Aug. 16, 2018) The key role farm workers play in produce safety Food Safety News (Oct. 21, 2018) Farmers’ market vendors need training to improve food-safety practices Science Daily (Nov. 03, 2018) You May Be at Higher Risk of Eating Contaminated Food During the Government Shutdown TIME (Jan. 10, 2019) FDA restarts food inspections, mainly with furloughed workers CIDRAP (Jan. 15, 2019) Will the foodservice industry ever knock down it’s brick wall of denial? Food Safety News (Oct. 04, 2018) Economics of Food Safety Convenience Store Decisions (May. 11, 2018) The Tremendous Cost of Foodborne Illnesses, and What to Do About It QSR Magazine (Dec. 17, 2018) The Staggering Costs of Foodborne Illness Incidents CS News (Aug. 14 2018) Study: Foodborne Illness Outbreak Could Cost a Restaurant Millions Claims Journal (Apr. 26, 2018) USDA: US Foodborne Illnesses Cost More Than $15.6 Billion Annually Food Safety News (Oct. 08, 2014)
Source: https://consultqd.clevelandclinic.org/collaboration-with-cdc-uncovers-genetic-risk-for-foodborne-disease/
Salmonella Infection 72% variability is explained by
in the USA elderly seasonal and trend components
time in weeks n u m b e rSummer time: 1998 1999 2000 2001 2002
Chui K, Webb P, Russell RM, Naumova EN. Geographic variations and temporal trends of Salmonella-associated hospitalization in the US elderly, 1991-2004: A time series analysis of the impact of HACCP regulation. BMC Public Health. 2009. 9(1):447
Many infectious diseases exhibit a strong seasonal pattern distinct for a specific pathogen in a given population and locality. What is driving disease seasonality?
TIME IN DAYS
15 20 25 30 35 40 45 1 16 31 46 61 76 91 106 121 136 151 166 181 196 211 226 241 256 271 286 301 316 331 346 361
JAN FEB MAR APR MAY JUN JUL AUG SEP OCT NOV DEC
Time of Disease Incidence Maximum
Amplitude of 1st Seasonal Peak 55 65 75 85 95 105 115 JAN FEB MAR APR MAY JUN JUL AUG SEP OCT NOV DEC TIME IN DAYS Time of the 2nd Seasonal Peak
1 16 31 46 61 76 91 106 121 136 151 166 181 196 211 226 241 256 271 286 301 316 331 346 361
Amplitude of 2nd Seasonal Peak Lag Time of the 1st Seasonal Peak
Seasonality can be defined by:
1. Timing of maximum incidence (Peak Timing) 2. Magnitude of peak incidence (Amplitude) 3. Duration of peak incidence (Duration) 4. Rate of incidence change from nadir to peak (Acceleration) 5. Rate of incidence change from peak to nadir (Deceleration)
Incidence
Consistent and systematic characterization of a temporal disease pattern
Source: Naumova, E. N. (2006). Mystery of seasonality: getting the rhythm of nature. Journal of public health policy, 27(1), 2-12.
δ-Method has been applied to trend- adjusted Negative Binomial regression models to estimate peak timing, amplitude, and their measures of uncertainty.
Time (months)
Alarcon, T. F., Cruz, M. S., & Naumova, E. N. (2018). The shift in seasonality of legionellosis in the USA. Epidemiology and infection, 146(14), 1824-1833.
1: recurring or operating at the same time or periods 2: having the same period, phase, amplitude or any combination
1: firmly established; fixed, steadfast 2: not changing or fluctuating; unvarying
Synchronization of Peak Timing Amplitude Calibration
when peak timing estimates overlap (given their narrow 95% CI)
peak timing estimates
with amplitude
incidence by their nadir values in de- trended time series
amplitude estimates and variability
Naumova, Elena N., et al. "Seasonality in six enterically transmitted diseases and ambient temperature." Epidemiology & Infection 135.2 (2007): 281-292.
❑ How stable is peak timing for specific
infections?
❑ How well are peak timing and amplitude
synchronized for specific infections?
❑ How well are peak timing and amplitude
synchronized across locations?
Source: https://www.cdc.gov/foodnet/sites.html
Yersinia enterocolitica Vibrio Shigella Salmonella Listeria Escherichia Cyclospora Cryptosporidium Campylobacter
https://www.cdc.gov/foodnet/PDFs/2007_annual_report_508.pdf
Annualized phase- amplitude synchronization
➢ The upper panel displays a forest plot of annual peak timing estimates. ➢ The low panel displays a forest plot of annual amplitude estimates. ➢ Dashed red lines indicate the average peak timing and amplitude estimates for the full time series. ➢ The two original panels contributes to a scatterplot across the shared peak timing and amplitude axes (with 95% CI) to assess possible synchronization and trends
amplitude shift.
Synchronization between peak timing and amplitude over time
Year of Study Year of Study Amplitude Peak Timing (Month)
Cross-state analysis can inform national surveillance
➢ Cross-state synchronization could enable triangulation of disease incidence across states in the absence of sufficient surveillance information ➢ A more refined temporal and spatial data resolution offers a better ability to distinguish changes from noise or artifacts of surveillance system ➢ State participation is patchy (Pop in M) ➢ y ➢
Salmonella peak timing estimates show summer peak across states, yet earlier peak in states with shorter growing season
97 98 99 00 01 02 03 2004 –2017* CA 1.92 1.94 1.96 3.18 3.22 3.21 3.21 3.21 –3.69 CO 2.10 2.46 2.51 2.53 –3.15 CT 1.62 3.27 3.28 3.40 3.41 3.43 3.46 3.49 –3.59 GA 3.66 3.78 7.78 8.52 8.62 8.77 9.07 9.15 –10.43 MD 2.44 2.45 2.52 4.24 5.43 5.50 5.55 –6.05 MN 4.69 4.73 4.78 4.93 4.98 5.02 5.05 5.09 –5.58 NM 1.90 –2.09 NY 1.11 2.08 2.12 2.12 3.34 3.98 4.15 –4.34 OR 3.24 3.28 3.32 3.43 3.47 3.51 3.55 3.57 –4.14 TN 2.88 2.91 2.95 5.84 5.91 –6.72
Phase–phase and phase– amplitude synchronization
➢ Synchronization & stability serve as tools for assessing drifts in peak timing and amplitude estimates. ➢ A deeper understanding of drivers for phase-amplitude synchronization may yield new insights into interactions within food systems. ➢ Comparisons across geographic locations for the same disease provides insight on possibility for multi-state outbreaks Peak Timing Amplitude
Peak Timing and Amplitude Synchronization for Salmonellosis in 10 U.S. States
Location Location
Protozoa Bacterial Infections
Synchronization of peak timing across pathogens
① Except Yersinia all notifiable infections peaked during the 3- month “summer months” period (June to September). ② Except Cyclospora seven infections peaked between mid- July to mid-August: STEC, Salmonella, & Vibrio have synchronized peak near end of July (7.6-7.9 month) Cryptosporidium, Shigella, & Listeria have synchronized peak in August (8.0-9.0 month)
Peak Timing Pathogen
Peak Timing and Amplitude synchronization of 9 Infections in the U.S.
Specific temporal effects:
Short-term vs long-term changes Transitory vs irreversible changes Instantaneous vs gradual change Delayed effects Time-distributed effects
Specific spatial effects:
Spatial heterogeneity Topology and boundary effects Characterization of cluster size
Dynamics of traveling waves: Stability of spatial clustering
Stability of a outbreak origin Stability of a spread pattern
Amplitude Pathogen
Weak synchronization: detected by cross-correlation Phase-phase synchronization: detected by correlation across peak timing Phase-amplitude synchronization: detected by correlation across peak timing and amplitudes
Pathogens Populations Places of exposure (PoE) Places of residency (PoR) Places of healthcare (PoH) Shared sources of exposure Shared practices of preparation, delivery, trade partners, and storage facilities The Kuramoto model in complex networks
Rodriges Fa et al, Physics Reports, January 2016; 610:1-98 DOI: 10.1016/j.physrep.2015.10.008
Ryan Simpson, MS PhD student Nutrition Data Sciences Ryan.Simpson@tufts.edu Aishwarya Venkat, MS PhD student Agriculture, Food & Environment Aishwarya.Venkat@tufts.edu Bingjie Zhou, MS Biochemical & Molecular Nutrition Bingjie.Zhou@tufts.edu Elena Naumova, PhD Professor and Chair, Nutrition Data Sciences Elena.Naumova@tufts.edu
❑ Naumova EN, Christodouleas J, Hunter PR, Sued Q. Temporal and spatial variability in cryptosporidiosis
recorded by the surveillance system in North West England in 1990 - 1999. Water and Health. 2005; 3(2):185-96.
❑ Naumova EN, MacNeill IB. Seasonality assessment for biosurveillance systems. In: Advances in Statistical
Methods for the Health Sciences: Applications to Cancer and AIDS Studies, Genome Sequence Analysis, and Survival Analysis. Edited by N. Balakrishnan, Jean-Louis Auget, M. Mesbah, Geert Molenberg. Birkhauser,
❑ Naumova EN. Mystery of seasonality: getting the rhythm of nature. Journal of Public Health Policy. 2006;
27(1):2-12.
❑ Naumova EN, Jagai J, Matyas B, DeMaria A, MacNeill IB, Griffiths JK. Seasonality in six enterically transmitted
diseases and ambient temperature. Epidemiology & Infections. 2007. 135(2):281-92.
❑ Lofgren E, Fefferman NH, Naumov YN, Gorski J, Naumova EN. Influenza seasonality: underlying causes and
modeling theories review. Journal of Virology. 2007; 81(11): 5429-36.
❑ Jagai JS, CastronovoDA, Monchak J, Naumova EN. Seasonality of cryptosporidiosis: a meta-analysis
❑ Castronovo DA, Chui KH, Naumova EN. Dynamic mapping: a visual-analytic methodology for exploring spatio-
temporal disease patterns. Environmental Health. 2009. Dec 30;8:61.
❑ Fefferman NH, Naumova EN. Innovation in Observation: A Vision for Early Outbreak Detection. Emerging
Health Threats. 2010, 3:e6. doi: 10.3134/ehtj.10.006
❑ Chui KHH, Jagai JS, Griffiths JK, Naumova EN. Hospitalizations due to non-specific gastrointestinal diseases: A
search for etiological clues. AJPH. 2011; (11): 2082-6.
❑ Chui KHH, Cohen SA, Naumova EN. Snowbirds and infection: New phenomena in pneumonia and influenza
hospitalizations from winter migration of older adults. BMC Public Health. 2011 Jun 7;11(1):444.
❑ Jagai JS, Griffiths JK, Kirshen P, Webb PM, Naumova EN. Seasonal Patterns of Gastrointestinal Illness and
Streamflowalong the Ohio River. Int. Journal of Environmental Research and Public Health. 2012 (on-line)
❑ Jagai JS, Sarkar R, CastronovoD, Kattula D, Ward H, Kang G, Naumova EN. Seasonality of Rotavirus in South
Asia: A Meta-Analysis Approach. PLoS ONE. May, 2012. (on-line)
❑ Cruz M, Alarcon T, Hartwick M, Venkat A, Ward H, Balaji B, Naumova EN. From hospitalization records to
surveillance: the use of a single-year age distribution to characterize patient profiles. PLoS ONE. 2017