Epidemic curve

Input data:

The input *.csv file should consist of two named columns: oid (object identifier, numeric) and edate (event date, DD/MM/YYYY). Additional columns can be included but these will be ignored. Typical input data would be as follows: oid, edate
1, 8/11/1967
2, 8/11/1967
3, 1/11/1967
4, 11/11/1967





Download PNG

Example data
Epidemic curve

Epidemic curve

Description

Plots an epidemic curve, that is, a frequency histogram of the count of incident events (e.g., disease cases) as a function of calendar time or number of days since outbreak start.

Usage

Upload a two-column CSV file listing:

Variable Details
oid Event identifier (integer, min 0, max ∞).
edate Event date (date, format dd/mm/yyyy).

Function settings are as follows:

Variable Details
CSV file The name of the CSV file listing event identifiers and event date (character string).
Days between horizontal axis ticks The number of days between horizontal axis tick marks (integer, default 7).
Bin width Days represented by each bar of the frequency histogram (integer, default 1).
Maximum value for primary vertical axis Upper limit of vertical axis of the plot (integer, default 40).
DPI for PNG Dots per inch for the plot PNG file (integer, default 2400).

Details

The temporal pattern of an epidemic can be described by drawing a histogram in which the number of cases of disease are plotted as a function of calendar date or time. This graph, called an epidemic curve, provides a visual representation of an outbreak's magnitude over time. Through review of the incident case pattern(s) evident in an epidemic curve, it is possible to hypothesise:

  • How an epidemic spread throughout a population.
  • At what point you are in an epidemic.
  • The likely date of exposure of the primary case.

Common source epidemics

In a common source epidemic subjects are exposed to a common noxious influence. A common point source epidemic is an epidemic in which a group of individuals are exposed to an infectious agent or a toxin from the same (point, i.e., single) source. An epidemic curve for a common point source epidemic is typically comprised of a rapid rise in case numbers over a single incubation period (in the case of an infectious disease) and a fall in case numbers as either the number of susceptible individuals becomes exhausted or the level of infectious agent or toxin in the population becomes depleted. A common intermittent source epidemic is an epidemic in which exposure to an infectious agent or toxin occurs over an extended period of time in episodes. An epidemic curve for a common intermittent source epidemic in a large population is comprised of a series of waves of elevated case numbers corresponding to the (intermittent) periods of exposure.

A common continuous source epidemic is an epidemic in which exposure to an infectious agent or toxin occurs over an extended period of time (over more than one incubation period in the case of infectious agents). An epidemic curve for a common continuous source epidemic is typically comprised of a rise in case numbers over a single incubation period (a rise that is typically more gradual than a common point source epidemic) and a decrease in case numbers as the number of susceptible individuals in the population decreases. Compared with common point source epidemics the decrease in case numbers as a function of time tends to be more gradual because exposure continues to occur in a smaller susceptible population.

Propagated epidemics

A propagated epidemic occurs when a case of disease serves as a source of infection for subsequent cases and those subsequent cases, in turn, serve as sources for later cases. In this situation the epidemic curve is characterised by waves (peaks in case numbers) that are separated by a period roughly equivalent to the incubation period. Because of this local transmission effect, incident case numbers per day typically don't tend to be as high as with common point source, common intermittent source epidemics or common continuous source epidemics (but this will depend on the pathogen involved).

References

Stephens N, Sault C, Firestone S, Lightfoot D, Bell C (2007) Large outbreaks of Salmonella Typhimurium phage type 135 infections associated with the consumption of products containing raw egg in Tasmania. Communicable Disease Intelligence Quarterly Report 31: 118 - 124.

Thaikruea L, Pataraarechachai J, Savanpunyalert P, Naluponjiragul U (1995) An unusual outbreak of food poisoning. Southeast Asian Journal of Tropical Medicine and Public Health 26: 78 - 85.

Wilesmith J, Stevenson M, King C, Morris R (2003) Spatio-temporal epidemiology of foot-and-mouth disease in two counties of Great Britain in 2001. Preventive Veterinary Medicine 61: 157 - 170. DOI: 10.1016/j.prevetmed.2003.08.002.

Growth phase of outbreak

Input data:

The input *.csv file should consist of two named columns: oid (object identifier, numeric) and edate (event date, DD/MM/YYYY). Additional columns can be included but these will be ignored. Typical input data would be as follows: oid, edate
1, 8/11/1967
2, 8/11/1967
3, 1/11/1967
4, 11/11/1967





Download PNG

Example data
Growth phase of an outbreak

Growth phase of an outbreak

Description

Plots an epidemic curve, that is, a frequency histogram of the count of incident events (e.g., disease cases) as a function of calendar time or number of days since outbreak start with a superimposed line indicating the growth phase of the epidemic.

Usage

Upload a two-column CSV file listing:

Variable Details
oid Event identifier (integer, min 0, max ∞).
edate Event date (date, format dd/mm/yyyy).

Function settings:

Variable Details
CSV file The name of the CSV file listing event identifiers and event date (character string).
Start of growth phase of epidemic Integer representing the day on which the outbreak started to grow (integer, default 1).
End of growth phase of epidemic Integer representing the day on which the outbreak stopped growing (integer, default 22).
Days between horizontal axis ticks The number of days between horizontal axis tick marks (integer, default 7).
Bin width Days represented by each bar of the frequency histogram (integer, default 1).
Maximum value for primary vertical axis Upper limit of vertical axis of the plot (integer, default 40).
DPI for PNG Dots per inch for the plot PNG file (integer, default 2400).

Details

This function returns predictions for the growth phase of an outbreak, using incident case counts. A log linear regression model is used for this purpose with the model taking the form:

log(y) = β0 + β1t

Where y is the predicted number of incident cases of disease per day, t is the number of days from the start of the outbreak and β1 the outbreak growth rate.

The function superimposes predictions from the model as a line over the number of detected cases per day, plotted as a frequency histogram. Uncertainty in model predictions are shown by the lower and upper 95% confidence interval, shown on the plot as dashed lines. The vertical dashed lines indicate the start of the outbreak and the end of the growth phase of the outbreak.

References

de Oliveira Otto M, Brito F, Tark J, Bakota E, Yamal J, Marko D, Sharma S, Brown M, Appana S, Rector A, Linder S, Kiger J, Tseng K, Morrison A, Boerwinkle E (2022) Case growth analysis to inform local response to COVID-19 epidemic in a diverse U.S community. Scientific Reports 12: 16217. DOI: 10.1038/s41598-022-20502-2.

Estimated dissemination ratio

Input data:

The input *.csv file should consist of two named columns: oid (object identifier, numeric) and edate (event date, DD/MM/YYYY). Additional columns can be included but these will be ignored. Typical input data would be as follows: oid, edate
1, 8/11/1967
2, 8/11/1967
3, 1/11/1967
4, 11/11/1967





Download PNG

Example data
Estimated dissemination ratio

Estimated dissemination ratio

Description

Plots an epidemic curve, that is, a frequency histogram of the count of incident events (e.g., disease cases) as a function of calendar time or number of days since outbreak start with a superimposed line indicating the estimated dissemination ratio.

Usage

Upload a two-column CSV file listing:

Variable Details
oid Event identifier (integer, min 0, max ∞).
edate Event date (date, format dd/mm/yyyy).

Function settings:

Variable Details
CSV file The name of the CSV file listing event identifiers and event date (character string).
EDR interval (days) The estimated dissemination ratio interval, in days (integer, default 4).
Span for loess smooth Amount of loess smoothing to apply to the computed EDR (numeric, min 0, max 1).
Include confidence intervals for EDR? Include confidence intervals for EDR? (binary, yes or no).
Confidence interval for EDR Magnitude of confidence interval for EDR plot (min 0, max = 1, default 0.95).
Days between horizontal axis ticks The number of days between horizontal axis tick marks (integer, default 7).
Bin width Days represented by each bar of the frequency histogram (integer, default 1).
Maximum value for primary vertical axis Upper limit of vertical axis of the plot (integer, default 40).
DPI for PNG Dots per inch for the plot PNG file (integer, default 2400).

Details

In infectious disease outbreaks the n-day estimated dissemination ratio (EDR) at day n equals the total number of incident disease cases between day n and day [i - (n - 1)] (inclusive) divided by the total number of incident disease cases between day (i - n) and day (i - 2n) + 1 (inclusive). EDR values are often calculated for each day of an outbreak and presented as a time series analysis. If the EDR is consistently less than one, the epidemic is said to be `under control'.

A simulation approach is used to calculate confidence intervals for each daily EDR estimate. The numerator and denominator of the EDR estimate for each day is taken in turn and a random number drawn from a Poisson distribution, using the calculated numerator and denominator value as the mean. EDR is then calculated for these simulated values and the process repeated 99 times. Confidence intervals are then derived from the vector of simulated values for each day.

Advantages

  • An EDR plot provides the same information as an effective reproduction number plot using event counts only.
  • During an ongoing outbreak EDR estimates are unbiased up until the last day of analysis.

Disadvantages

  • EDR estimates can only be calculated when you're 2n days into an outbreak.

This function uses the epi.edr function in the contributed epiR package (Stevenson et al., 2025) in R (R Development Core Team 2025).

References

Miller W (1976) A state-transition model of epidemic foot-and-mouth disease. In: Proceedings of an International Symposium: New Techniques in Veterinary Epidemiology and Economics, University of Reading, Reading, pp. 56 - 72.

Morris R, Sanson R, Stern M, Stevenson M, Wilesmith J (2002) Decision-support tools for foot-and-mouth disease control. Revue Scientifique et Technique de l'Office International des Epizooties 21, 557 - 567.

Perez-Reche FJ, Taylor N, McGuigan C, Conaglen P, Forbes K, Strachan N, Honhold N (2021) Estimated Dissemination Ratio --- A practical alternative to the reproduction number for infectious diseases. Frontiers in Public Health 9. DOI: 10.3389/fpubh.2021.675065.

Stevenson MA, Sergeant E, Nunes T, Heuer C, Marshall J, Sanchez J, Thornton R, Reiczigel J, Robison-Cox J, Sebastiani P, Solymos P, Yoshida K, Jones G, Pirikahu S, Firestone S, Kyle R, Popp J, Jay M, Reynard C, Cheung A, Singanallur N, Szabo A, Rabiee A (2025). epiR: Tools for the Analysis of Epidemiological Data. DOI: 10.32614/CRAN.package.epiR. URL: https://cran.r-project.org/web/packages/epiR/index.html.

R Core Team (2025) R: A language and environment for statistical computing. URL: http://www.R-project.org/.

Effective reproduction numbers

Input data:

The input *.csv file should consist of two named columns: oid (object identifier, numeric) and edate (event date, DD/MM/YYYY). Additional columns can be included but these will be ignored. Typical input data would be as follows: oid, edate
1, 8/11/1967
2, 8/11/1967
3, 1/11/1967
4, 11/11/1967





Download PNG

Example data
Effective reproduction numbers

Effective reproduction numbers

Description

Plots an epidemic curve, that is, a frequency histogram of the count of incident events (e.g., disease cases) as a function of calendar time or number of days since outbreak start with a superimposed error bar plot showing effective reproductive numbers per unit of time.

Usage

Upload a two-column CSV file listing:

Variable Details
oid Event identifier (integer, min 0, max ∞).
edate Event date (date, format dd/mm/yyyy).

Function settings:

Variable Details
CSV file The name of the CSV file listing event identifiers and event date (character string).
Mean serial interval (days) Mean serial interval in days (integer, min 1, max 100, default 5.77).
SD serial interval (days) Standard deviation of serial interval in days (integer, min 1, max 100, default 1.27).
Days between horizontal axis ticks The number of days between horizontal axis tick marks (integer, default 7).
Bin width Days represented by each bar of the frequency histogram (integer, default 1).
Maximum value for primary vertical axis Upper limit of vertical axis of the plot (integer, default 40).
DPI for PNG Dots per inch for the plot PNG file (integer, default 2400).

Details

This function provides estimates of the case reproduction number over time for an epidemic, for a disease with a specified serial interval defined as the period of time between the clinical signs onset times of the infector and infectee in a direct transmission pair. The methodology used follows that of Wallinga and Teunis (2004). Confidence intervals for case reproductive number are obtained by simulation.

For foot and mouth disease Hugh-Jones and Tinline (1976) report a median farm serial interval of 6 days (minimum 3 days, maximum 9 days). Assuming the farm level serial interval for FMD follows a gamma distribution, the corresponding shape and scale parameters for these data (calculated using quantile matching) are 20.7 and 0.278, respectively. The mean and standard deviation (set as default inputs for this function) are 5.77 and 1.27, respectively.

Advantages

  • During an outbreak response unbiased effective reproduction number plots can be developed before estimated dissemination ratio plots.
  • The concept of effective reproduction number tends to be better understood than estimated dissemination ratio by the general public.

Disadvantages

  • Collecting data during an outbreak to calculate serial intervals can be challenging. In addition, it is likely that serial intervals will vary dependent on disease and the characteristics of the population at risk.
  • During an ongoing outbreak effective reproduction number estimates are underestimated for the 7 to 10 days prior to the end of analysis period.

This function uses the wallinga_teunis function in the contributed EpiEstim package (Cori et al., 2021) in R (R Development Core Team 2025).

References

Aghaali M, Kolifarhood G, Nikbakht R, Saadati H, Hashemi Nazari S (2020) Estimation of the serial interval and basic reproduction number of COVID-19 in Qom, Iran, and three other countries: A data-driven analysis in the early phase of the outbreak. Transboundary and Emerging Diseases 67: 2860 - 2868. DOI: 10.1111/tbed.13656.

Cori A, Ferguson N, Fraser C, Cauchemez S (2013) A new framework and software to estimate time-varying reproduction numbers during epidemics. American Journal of Epidemiology 178: 1505 - 1512. DOI: 10.1093/aje/kwt133.

Cori A, Cauchemez S, Ferguson N, Fraser C, Dahlqwist E, Demarsh A, Jombart T, Kamvar ZN, Lessler J, Li S, Polonsky JA, Stockwin J, Thompson R, van Gaalen R (2021). EpiEstim: Estimate Time Varying Reproduction Numbers from Epidemic Curves. DOI: 10.32614/CRAN.package.EpiEstim. URL: https://CRAN.R-project.org/package=EpiEstim.

Hugh-Jones M, Tinline R (1976) Studies on the 1967 -- 1968 foot and mouth disease epidemic: incubation period and herd serial interval. Journal of Hygiene 77: 141 - 153. DOI: 10.1017/s0022172400024566.

R Core Team (2025) R: A language and environment for statistical computing. URL: http://www.R-project.org/.

Stockdale J, Susvitasari K, Tupper P, Sobkowiak B, Mulberry N, Goncalves da Silva A, Watt A, Sherry N, Minko C, Howden B, Lane C, Colijn C (2023) Genomic epidemiology offers high resolution estimates of serial intervals for COVID-19. Nature Communications 14: 4830. DOI: 10.1038/s41467-023-40544-y.

Susvitasari K, Tupper P, Stockdale J, Colijn C (2023) A method to estimate the serial interval distribution under partially-sampled data. Epidemics 45: 100733. DOI: 10.1016/j.epidem.2023.100733.

Wallinga J, Teunis P (2004) Different epidemic curves for severe acute respiratory syndrome reveal similar impacts of control measures. American Journal of Epidemiology 160: 509 - 516.

Wada plot

Input data:

The input *.csv file should consist of five named columns: oid (object identifier, numeric), detdate (detection date, DD/MM/YYYY), depsdate (depopulation start date, DD/MM/YYYY), depedate (depopulation end date, DD/MM/YYYY) and cleansdate (cleaning and disinfection start date, DD/MM/YYYY). Additional columns can be included but these will be ignored. Typical input data would be as follows: oid, detdate, depsdate, depedate, cleansdate
1, 22/08/2016, 24/08/2016, 27/08/2016, 30/08/2016
2, 5/09/2016, 11/09/2016, 17/09/2016, 21/09/2016
3, 28/08/2016, 2/09/2016, 6/09/2016, 13/09/2016
4, 2/09/201,6 8/09/2016, 14/09/2016, 21/09/2016





Download PNG

Example data
Wada plot

Wada plot

Description

Plots a stacked bar chart where each bar represents an identified infected premises, with the horizontal axis of the plot showing infected premises number and the vertical axis showing detection to depopulation interval (in days) and depopulation to end of cleaning and disinfection interval.

Usage

Upload a five-column CSV file listing:

Variable Details
oid Event identifier (integer, min 0, max ∞).
detdate Detection date (date, format dd/mm/yyyy).
depsdate Depopulation start date (date, format dd/mm/yyyy).
depedate Depopulation end date (date, format dd/mm/yyyy).
cleansdate Cleaning and disinfection start date (date, format dd/mm/yyyy).

Function settings:

Variable Details
CSV file The name of the CSV file listing event identifiers and event date (character string).
Days between horizontal axis ticks The number of days between horizontal axis tick marks (integer, default 7).
Maximum value for primary vertical axis Upper limit of vertical axis of the plot (integer, default 40).
DPI for PNG Dots per inch for the plot PNG file (integer, default 2400).

Details

Delays in: (a) detection to confirmation; and (b) confirmation to cleaning and disinfection intervals have a negative impact on infectious disease control efforts. A Wada plot provides a graphical means for monitoring these metrics during an outbreak response.

References

Bingham P, Wada M, van Andel M, McFadden A, Sanson R, Stevenson M (2020) Real-time standard analysis of disease investigation (SADI) --- a toolbox approach to inform disease outbreak response. Frontiers in Veterinary Science 7: 563140 - 563140. DOI: 10.3389/fvets.2020.563140.

Tracing windows

Input data:

Calculate start and end of back tracing interval and start and end of forward tracing interval.



Back trace ● Forward trace ●

Tracing windows

Tracing windows

Description

This function calculates start and end date of backward and forward trace windows given mode, minimum and maximum incubation periods, date of holding examination, age of lesions at the time of examination, examination date to end of cleaning and disinfection interval and length of back trace buffer period.

Usage

Variable Details
Minimum incubation period Minimum incubation period in days (integer, min 0, max 100, default 1).
Mode incubation period Mode incubation period in days (integer, min 0, max 100, default 4).
Maximum incubation period Maximum incubation period in days (integer, min 0, max 100, default 16).
Incubation period length Incubation period length method (min and max values entered or confidence interval).
Date of examination Date of on which animals on holding examined (date, default system date).
Lesion age at time of examination Age of lesions at time of examination (integer, min 0, max 100, default 7).
Exam date to end of cleaning and disinfection Exam date to end of cleaning and disinfection (integer, min 0, max 100, default 7).
Length of back trace buffer Length of back trace buffer (integer, min 0, max 100, default 14).
Level of confidence Magnitude of confidence interval (integer, min 0, max 1, default 0.95).

Details

Given an examination date, the back trace start date is:

BTstart = Exam date - Lesion age - Incubationmax - Buffer

The back trace end date is:

BTend = Exam date - Lesion age - Incubationmin

The forward trace start date is:

FTstart= Exam date - Lesion age - Incubationmax - Buffer

The forward trace end date is:

FTend= Exam date + Clean disinfection interval

Mode, minimum and maximum incubation periods for several infectious diseases of livestock are shown in the table below.

Disease Incubation period (days) References
  Min Mode Max  
African swine fever 3 5 to 7 21 Sanchez-Cordon et al., 2018
Avian influenza 1 2 to 5 14 WOAH 2025
Classical swine fever 2 4 to 7 14 Blome et al., 2017
Foot and mouth disease 2 3 to 5 14 to 21 Arzt et al., 2010

References

Arzt J, Pacheco J, Rodriguez L (2010) The early pathogenesis of foot-and-mouth disease in cattle after aerosol inoculation. Identification of the nasopharynx as the primary site of infection. Veterinary Pathology 47: 1048 - 1063. DOI: 10.1177/0300985810372509.

Blome S, Staubach C, Henke J, Carlson J, Beer M, (2017) Classical swine fever --- an updated review. Viruses 9. DOI: 10.3390/v9040086.

Sanchez-Cordon P, Montoya M, Reis A, Dixon L (2018) African swine fever: A re-emerging viral disease threatening the global pig industry. The Veterinary Journal 233: 41 - 48. DOI: 10.1016/j.tvjl.2017.12.025.

WOAH (2025) Terrestrial Animal Health Code, Chapter 10.4. World Organisation for Animal Health, Paris.

Geocode a single address

Input data:



Geocode a single address

Geocode a single address

Description

Returns the longitude and latitude (in decimal degrees) of a single address entered by the user as a text string. This function uses the Google geocoding API .

Details

References

Google Maps . Google Corporation, Mountain View, California. URL: http://maps.google.com

Geocode multiple addresses

Input data:

The input *.csv file should consist of two named columns: id (identifier, numeric), address (address details, character string). Typical input data would be as follows: id, address
1, 2 Wallaby Grove, Winmalee, New South Wales
2, 135 Donnybrook Rd, Mickleham VIC 3064
3, 390 St Kilda Rd, Melbourne Victoria
4, 400 Princes Highway, Werribee Victoria





Example data
Geocode multiple addresses

Geocode multiple addresses

Description

Returns the longitude and latitude (in decimal degrees) of addresses listed in a CSV file uploaded by the user. This function uses the Google geocoding API and is limited to geocoding 100 addresses per day.

Usage

Upload a two-column CSV file listing:

Variable Details
id Location identifier (integer, min 0, max ∞).
address Location address (character string).

Details

References

Google Maps . Google Corporation, Mountain View, California. URL: http://maps.google.com

Space time KML file

Input data:

This app returns either a space-time KML file or a space (location only) KML file, depending on the number of columns of data provided in the source CSV file. To create a space-time KML file suitable for display using Google Earth the input *.csv file should consist of four named columns: oid (object identifier, numeric), edate (date of event, DD/MM/YYYY), lon (longitude in decimal degrees, numeric), and lat (latitude in decimal degrees, numeric). Typical input data would be as follows: oid edate lon lat
1, 8/11/1967, -3.0332, 52.9052
2, 8/11/1967, -3.0198, 52.9035
3, 1/11/1967, -3.0985, 52.8983
4, 11/11/1967, -3.0628, 52.8986

To create a space (location only) KML file suitable for display using Google Earth the input *.csv file should consist of three named columns: oid (object identifier, numeric), lon (longitude in decimal degrees, numeric), and lat (latitude in decimal degrees, numeric). Typical input data would be as follows: oid lon lat
1, -3.0332, 52.9052
2, -3.0198, 52.9035
3, -3.0985, 52.8983
4, -3.0628, 52.8986


Download KML

Example data
Space time KML file

Space time KML file

Description

Returns a space-time KML file using event date, longitude and latitude details listed in a CSV file uploaded by the user.

Usage

Upload a four-column CSV file listing:

Variable Details
oid Event identifier (integer, min 0, max ∞).
edate Event date (date, format dd/mm/yyyy).
lon Longitude of event location (decimal degrees).
lat Latitude of event location (decimal degrees).

A three-column CSV file (without edate) will return a space KML file (instead of a space-time KML file).

Details

Early versions of this function used the plotKML package in R. Since removal of plotKML from CRAN the function has been re-written, eliminating dependence on third party packages.

References

Hengl T, Roudier P, Beaudette D, Pebesma E (2015) plotKML: Scientific Visualization of Spatio-Temporal Data. Journal of Statistical Software 63. DOI: 10.18637/jss.v063.i05.

R Core Team (2025) R: A language and environment for statistical computing. URL: http://www.R-project.org/.